<h3>Executive Summary</h3>Ben Plummer, CEO of Invisible Technologies, asserts that markets and businesses are on the brink of a monumental transformation, unrivaled since the beginning of the Information Age. He says three pivotal forces - advancements in artificial intelligence (AI), the evolution of global talent markets, and changing competitive dynamics - are redefining the business landscape.According to Plummer, a rising cost of capital is shifting focus from growth-at-all-costs to operational efficiency and profitability. This recalibration is crucial as businesses aim for growth amidst a moderating macroeconomic climate and increased competition, demanding precision in strategic decision-making with little room for error.In response to the shifting talent landscape, the most dynamic companies are navigating this new environment by embracing the convergence of global talent markets and advancements in AI. This strategic realignment enables them to outpace competitors with superior solutions and cut fixed costs significantly.However, the swift evolution of AI presents a formidable challenge; most companies find themselves unprepared, lacking the talent or understanding necessary to harness this technology effectively. Organizations that delay or resist incorporating AI into their strategic frameworks are at a significant disadvantage, risking irrelevance as they persist with antiquated practices in an age where AI's capabilities are expanding soundlessly.Plummer's insights underscore the necessity for re-engineering the enterprise, urging businesses to fundamentally reassess their strategies, structures, and operations amidst these converging trends. The future of business, as depicted by Plummer, is one where AI and human collaboration enable strategic agility and operational efficiency. This necessitates a profound transformation in the approach to business leadership and management.By acting now, businesses can secure a competitive advantage in a swiftly changing global marketplace and unlock unprecedented opportunities for growth and innovation. Those who embrace this transformation stand to gain significantly, while those who delay may be irreversibly left behind.<hr>“Businesses and the markets in which they operate are about to undergo a drastic transformation, the scale of which hasn’t been seen since the beginning of the Information Age," says Ben Plummer, Chief Executive Officer of Invisible Technologies.The CEO argues that three major trends are converging to redefine how companies are built and structured and revolutionize the very nature of work itself.“There is a lot of talk about how AI will transform business organizations over the next few years, but even many of the wildest forecasts often underestimate just how much things will change,” he explains.“That’s because the rise of AI isn’t unfolding in a vacuum. It’s occurring alongside significant shifts in global workforce dynamics and under the complicating backdrop of fluctuating economic conditions. These changes are not just influencing companies' strategies from growth at all costs to a focus on efficiency; they're actually constraining their ability to adapt and respond effectively,” Plummer notes.“While economic conditions may ebb and flow, imposing necessary adjustments, the advancement of AI and the transformation of the workforce represent enduring trends reshaping businesses and workplaces.”“From startups to multinational enterprises, business leaders who seize this moment to fundamentally reinvent their methods of value creation will unlock unprecedented opportunities, often in places least expected. Those who persist with outdated approaches risk not just falling behind, but facing irrelevance in a rapidly evolving market.”<h2>Moving from Growth At All Costs to Efficiency and Profitability</h2>The first trend Plummer notes is the impact that capital market trends are having on the broader economic landscape, with the past 15 years characterized by cheap and freely available capital, which companies could draw on to expand.“Companies operated under unprecedented conditions, many frequently remaining unprofitable for over a decade as they pursued aggressive growth and market dominance,” Plummer says. “They took the view that, if the product was good, the returns would eventually come.”This mentality allowed many of today’s leading companies to flourish. One such example is Tesla, now one of the world's most valuable companies, which recorded annual losses from its founding in 2003 until achieving profitability in 2020.The innovators of the late 2010s and early 2020s, on their path to IPOs, were also allowed a level of capital inefficiency that wasn’t necessarily given to their forebears. Google’s journey to a market capitalization of over $1.7 trillion by March 2024, from just $26.1 million raised over three funding rounds, showcases remarkable capital efficiency and strategic foresight.On the other hand, Uber’s narrative before its 2019 IPO paints a starkly different picture. Despite raising $24.2 billion, Uber’s share price had declined by almost a third by year's end as it burned through capital on aggressive expansions and investments like autonomous vehicles and food delivery.Plummer argues the era of prioritizing growth over profitability has ended, influenced by economic uncertainty, higher interest rates, and competitive pressures. Today, investors value cash flow and sustainability, reflecting a strategic pivot in the business landscape.“It’s no longer a case of growth at all costs for many investors,” he says. “There is much greater emphasis on efficiency and profitability.”However, efficiency is not the only critical factor; agility has become equally vital. The ability to pivot rapidly, adapt to market changes, and innovate with speed is now a cornerstone of modern business strategy. This applies to both startups and established enterprises, which now must navigate rapidly changing market conditions, technological advancements, and shifting consumer expectations with unprecedented speed.“More than just competing, if companies want to win, they increasingly need to prove that they’re not just lean and capital efficient, but also capable of agile adaptation and innovation,” Plummer adds. This need for agility and the demand for efficiency creates a dual challenge for many organizations.“This isn’t a muscle that’s well developed in many organizations,” Plummer notes, highlighting a gap that complicates navigating the multitude of existing challenges businesses face today.<h3>Global Labor Market Disruption</h3>As capital becomes more challenging to access, Plummer highlights a concurrent global labor market disruption driven by technological advancements, the COVID-19 pandemic, and evolving employment expectations.This goes beyond mere remote work; it is fundamentally altering how businesses access global talent, accelerated by the pandemic's push for digital work practices and a collective reevaluation of work-life priorities.“The pandemic mandated a digital-first strategy for work across the board,” Plummer says. “This significant, though often superficial, shift towards digital, coupled with the rush to return employees to offices post-pandemic, missed the deeper, lasting opportunity towards a more flexible, fractional work model.”The gig economy’s surge and the move towards contract and freelance work reflect this move. Platforms like Freelancer and Fiverr have dramatically changed traditional employment models. Between 2020 and 2022 alone, the number of freelancers in the United States grew 69.1% from 38.2 million to 64.6 million, making up 38% of the US workforce. This highlights a significant move towards the fractionalization of talent, emphasizing the value of autonomy and flexibility for professionals.“There is a whole class of skilled professionals leaving their employers to sell their skills and work on their terms,” he says.“In doing so, they’re often leaving their employers short of the best in-house talent, but they’re giving themselves autonomy, flexibility, and work/life balance while performing work that aligns with personal interests and values.”This evolution signifies a change in workforce engagement’s very architecture, moving towards a more agile project-oriented business model.“Today’s dynamic workplaces are not defined by physical spaces but by the talent itself,” Plummer notes. “They harness digital tools to seamlessly integrate remote employees, skilled and specialist freelancers, and AI into high-performing teams.”Plummer underscores the emergence of a new global talent class as a pivotal advantage for forward-looking companies. This evolution allows businesses to tap into a global marketplace of on-call labor, enabling access to specific skills on demand and circumventing the limitations of a permanent workforce.This strategy benefits companies, offers access to top talent, cost-effectiveness, agility, flexibility, speed, and scalability, and benefits individuals by providing flexibility, variety, autonomy, and competitive pay.Plummer emphasizes, “This approach significantly cuts fixed labor costs and improves operational outcomes. Companies can now dynamically adjust their labor costs, scaling operations up or down as the market dictates.”“Companies are now generating higher financial returns with a workforce that is both smaller and more technologically adept,” Plummer notes. “The productivity of a single modern worker can match that of multiple workers from previous decades.”<h3>The Limits of 20th-Century Solutions for 21st-Century Problems</h3>Plummer observes that, despite these profound changes reshaping the workplace, most enterprise leaders still tend to be run by outmoded management models. He cites a 2020 HBR article that noted that, despite the ‘cult of leadership’ becoming prominent in business, new management practices are virtually non-existent.“These challenges have become more acute in recent years,” Plummer explains, “as we see business leaders still implementing practices that pre-date the internet, or even computers, in a digital native economy.”“When you zoom out, there hasn’t really been a major evolution in the practices around how businesses operate since World War II,” he adds. “The foundational management practices many businesses operate on today represent a bygone era where work was predominantly manual and centralized in factories or similar settings.”“Even the trends that we saw in the 1990s, such as outsourcing and downsizing, didn’t really change how enterprises were organized; they just made them slightly leaner, often at the expense of agility and quality.”“So what we’re really seeing is these new conditions emerging to shake up companies, while at the same time managers are trying to solve the challenge by using ‘command and control’ theories that were developed in the industrial age.”As evidence of this, he notes that labor productivity actually surged during the pandemic after years of sluggish or even negative growth. People tended to achieve more when working from home, often in fewer hours. Yet, the first instinct of most businesses when the world returned to normalcy was to recall employees into the office.“It’s like many managers missed all the important learnings that should have been obvious from the past four years,” he observes.“As a result, there is a growing disconnect between what many managers see as the ideal and the reality of what people now expect. Those that realize this are in a strong position to capitalize on it.”<h3>The AI Mindset Shift</h3>When introduced against this backdrop, the most talked-about trend - AI - takes on a whole new relevance.Plummer observes that Large Language Models (LLMs) that operate within apps like ChatGPT are already augmenting some workers’ skills, helping make them more productive and efficient. However, he also observes that many enterprises have not taken advantage of AI to anything like its full extent.“To date, there has been a significant discrepancy between consumer AI adoption and enterprise AI adoption, with many enterprises still scrambling to work out how AI will benefit them,” he says.“They often know they’re going to have to do something, but they don’t know what, or - if they have an idea on what they want to do - they don’t even know where to get started,” Plummer observes.He suggests the challenge extends beyond mere familiarity with the technology. “Many companies struggle because they’re not just unfamiliar with AI; it’s that the pace and complexity of AI evolution make it hard to grasp and integrate effectively,” he clarifies.“Furthermore, there's a common misconception about AI being primarily a tool for generating responses to queries, as that’s the extent of their personal use. This narrow view overlooks AI's broader potential to transform business operations,” Plummer points out.Plummer argues for a deeper, more strategic approach: recognizing AI as more than just an instant fix. It demands a comprehensive reevaluation of how businesses operate and envision their future.<h3>“Adopting AI successfully means weaving it thoughtfully into the fabric of how a company functions. This isn’t about replacing old tools with new; it's about reimagining the very way we do business.”</h3><h3></h3><h3>“Re-Engineering” the Enterprise</h3>Plummer argues there is far more to succeeding in this new paradigm than simply capitalizing on today’s globalized and decentralized labor market or implementing AI to make some processes more efficient. Instead, what’s required is a ‘re-engineering of the enterprise’ from the ground up.“Companies need to move away from thinking about capability acquisition, hiring, procurement, and software development as separate issues and start thinking about them in an integrated way,” he says.“This means getting to the core of what they actually do, what differentiates them from their competitors, and how they create value.”As part of this process, Plummer says every enterprise should meticulously evaluate the elements that drive their outcomes. That includes assessing whether tasks are optimally performed by humans or AI and considering the balance between in-house employees and outsourced talent. When they do, he believes they’ll conclude most tasks are best performed by a combination of the two.“AI is not automation in the traditional sense; it’s a dynamic tool that evolves over time and, in doing so, enhances human capabilities. That means organizations won’t always be able to slot AI neatly into how they work now. Instead, they’ll have to rethink and re-engineer their workflows.”“We’re likely to see that the future workplace is not about AI replacing humans but more about AI and humans working together to create hybrid solutions that better deliver on a company’s value proposition.”

Re-engineering the Enterprise: How Seismic Changes Are Shaping the Business of Tomorrow

Los Angeles, California&nbsp;/ March 15, 2024 / Invisible Technologies, the operations innovation company that combines AI and automation with a skilled human workforce, is pleased to announce that they will present as “Breakthrough Partner 2024” at the Abundance Summit, held March 17 -21, 2024.&nbsp; The Abundance Summit provides a platform to share insights and explore the collaboration between AI, automation, and human partnership with an equally passionate audience about harnessing technology for exponential growth and positive global change. “We are proud to announce that Invisible Technologies will be this year's breakthrough partner. Renowned for their pioneering work in AI and collaboration across various industries, attendees can anticipate invaluable insights into the vast potential of AI and the imperative operational innovation necessary to navigate the ongoing AI revolution,” said Peter Diamandis, founder of Abundance360. Francis Pedraza, founder and chair of the board, and Benjamin Plummer, CEO of Invisible Technologies Inc., are slated to present a fireside chat, "From Confusion To Revolution: A Practical Approach To Harnessing The Power Of AI," moderated by the event's founder, Peter Diamandis, on March 19, 2024.&nbsp; Also present at the Summit will be such media luminaries as Geoffrey Hinton, the godfather of AI; Eric Schmidt, former CEO and Chairman of Google; Anousheh Ansari, CEO of XPRIZE and Emad Mostaque, founder and CEO of Stability AI.&nbsp; “We are thrilled to announce our participation in the Abundance Summit," stated Benjamin Plummer, CEO of Invisible Technologies. “Our involvement in the summit, especially the fireside chat led by Peter Diamandis, presents a unique opportunity to engage with over 500 CEOs and entrepreneurs, sparking meaningful conversations." To register for the event, <a target="_blank" rel="" href="https://www.abundance360.com/a360-invite">click here</a>, and to schedule a one-on-one meeting with Invisible Technologies, <a target="_blank" rel="" href="https://www.invisible.co/get-started/">click here</a>. The presentation will be webcast live and available for replay at <a target="_blank" rel="" href="https://www.abundance360.com/summit">https://www.abundance360.com/summit</a>.&nbsp; About Abundance360At its core, Abundance360 is a year-round program for entrepreneurs, investors, and executives who want to create positive change. It offers them a unique opportunity to unlock their potential and access the latest technologies, tools, and connections needed to succeed in today's world. In addition to the Summit, Workshops, and Masterminds, members benefit from the support of a close-knit community of like-minded individuals who share the goal of creating a better future for humanity. For more information, visit <a target="_blank" rel="" href="http://www.abundance360.com">www.abundance360.com</a>&nbsp; About Invisible Technologies&nbsp;Invisible is the operations innovation company that combines AI and automation with a skilled human workforce to unlock strategic execution bottlenecks. With Invisible, teams can quickly innovate, scale operations, or bring new products to market. Companies like DoorDash trust Invisible for process innovation while leading AI firms like Cohere and AI21 enhance their model performance with us. Dedicated to optimizing work through technology and human insight, we’re driving enterprise AI adoption and are a trusted partner for AI innovators worldwide. For more information, please visit <a target="_blank" rel="" href="http://www.Invisible.co">www.Invisible.co</a>.&nbsp; On behalf of the Company&nbsp; Eric Holder, Director of Communications, Invisible Technologies Inc., <a target="_blank" rel="" href="mailto:press@invisible.com">press@invisible.com</a>&nbsp; ###

Invisible Technologies Named Official Breakthrough Partner for Abundance Summit 2024

Mention the transformative power of AI, and many people’s minds turn to futuristic scenarios where robots eventually run the world free from human influence. However, Nate Castro, GM of Financial Services at Invisible Technologies, says that for FinServ enterprises, the reality is more mundane and much more immediate.&nbsp;“AI will transform financial services very quickly, but not in the way many people expect,” he explains. “And the real potential for impact is likely in the back office.”“While it may not be as sexy as many people think, those enterprises that get it right could transform everything about how they operate, turning what’s currently treated as a line item into a strategic driver for business success.”&nbsp;“But those who don’t and let others get the jump could soon find it difficult to compete at all.”<h2>The Commonalities of the Global Financial Services Sector</h2>Castro says it’s difficult to speak of a common financial services sector, given its sheer size. In 2023, the industry was worth $28.1 trillion or roughly 20% - 25% of the entire global economy. Within this, there is a range of businesses whose functions, customers, jurisdictions, and their missions seem to share very little in common.“It’s difficult to compare a hedge fund’s priorities with those of a retail bank or an insurer’s priorities with an asset manager’s,” Castro notes. “They exist for different purposes, perform different functions, have different risk tolerances, and serve different audiences.”However, Castro observes that, while there might be vast differences in form and function, most FinServ enterprises also share some common denominators. Most notably, they all tend to operate in a strongly regulated and ‘zero error’ environment, where decisions need to be made based on a high level of due diligence.&nbsp;&nbsp;&nbsp;&nbsp;“Whether it’s a retail bank assessing a home loan application, an insurer analyzing risk factors, or a hedge fund researching business fundamentals, financial services businesses need to make careful decisions that weigh up risk based on evidence, or data.”<h3>“That means, by their very nature, they’re exceptionally process-driven,” he observes. “And that could give them a powerful advantage in the age of AI.”</h3><h3>The Low-Hanging Fruit for FinServ Companies</h3>But while businesses in the financial services sector may be process-driven, that doesn’t mean their processes are efficient.&nbsp;In fact, a <a target="_blank" rel="" href="https://www2.deloitte.com/xe/en/insights/industry/financial-services/financial-services-industry-outlooks/banking-industry-outlook.html">2024 Deloitte Study into retail banks</a> found that many were being hampered by reliance on legacy systems, manual processes and operational silos. These were serving as a drag on many businesses, leaving them vulnerable to competition from digital competitors.&nbsp;It is a similar scenario in other financial services fields. One comprehensive study found that<a target="_blank" rel="" href="https://www.forbes.com/sites/forbescommunicationscouncil/2022/12/23/four-operational-challenges-in-the-insurance-industry-and-how-to-solve-them-using-rpa/?sh=180555a21745"> insurers</a> were plagued by inaccessible data, manual entry, and poorly conceived attempts at automation (many of which neglected the human elements). There has also been a <a target="_blank" rel="" href="https://www.linkedin.com/pulse/2025-investment-banking-ma-could-lot-like-tinder-michael-hutchens/">lack of process innovation in investment banking firms</a>, with analysts still building their financial models using spreadsheets.Meanwhile, another <a target="_blank" rel="" href="https://www2.deloitte.com/us/en/pages/regulatory/articles/cost-of-compliance-regulatory-productivity.html">Deloitte study</a> found that financial services organizations’ compliance costs had been spiraling since the Global Financial Crisis of 2008, adding further processes and reducing capacity for innovation.&nbsp;Castro says that transforming these processes by taking them apart and working out which tasks are best performed by humans, and which could be completed more efficiently by AI, is an obvious and easy way for financial services companies to save serious money.“The world has changed a lot in the past 20 years but the processes used in many financial services enterprises haven’t really,” Castro observed. “Even though we’ve seen practices such as outsourcing take hold, they haven’t yet fundamentally changed the processes themselves. They’ve just tended to look at ways of performing the process more cheaply.”“When there is a lack of innovation in any function, it means organizations tend to see it as a fixed cost that needs to be managed,” he continues. “However, given advances in AI, this no longer should be true with many of their processes—if it ever was.”<h3>Turning the Back Office into a Strategic Driver</h3>Castro argues that when it comes to transforming the way back offices operate, simply making their processes more efficient is the tip of the iceberg. What excites him more is the potential for financial services organizations to turn their back offices into strategic business drivers.&nbsp;“By their very nature, financial services enterprises generate an enormous amount of proprietary data. Until recently, they haven’t really been able to do much with it because it has been so disparate and unwieldy and the juice hasn’t been worth the squeeze.” Castro notes that recent developments in AI have changed this so that enterprises can actually tap into it relatively easily. In doing so, they could give themselves actionable insights to drive strategic decisions, optimize operational efficiency, and create new value propositions.&nbsp;“If harnessed properly, the data generated by the back office could be used to transform everything from customer service and marketing through to risk management, product development, and strategic decision-making,” he says.For an example of how this could be done, Castro points to JP Morgan, which has integrated AI into many parts of its operations, including developing a <a target="_blank" rel="" href="https://www.cnbc.com/2023/05/25/jpmorgan-develops-ai-investment-advisor.html">customer-facing investment advisor known as IndexGPT</a>. It has also deployed AI across functions ranging from risk management and fraud prevention through to customer service.&nbsp;JP Morgan’s embrace of AI has reportedly involved more than 900 data scientists, 600 machine learning engineers, and a $2 billion investment in cloud-based data centers to support AI deployment. In late 2023, <a target="_blank" rel="" href="https://www.bloomberg.com/news/articles/2023-11-29/jpmorgan-says-ai-technology-is-starting-to-generate-revenue">the bank announced its AI tools were already generating revenue</a>.&nbsp;In some jurisdictions, however, FinServ enterprises are even further down the AI path. In the Republic of Korea, the leading financial services organization by valuation is Kakao Bank, an AI-rich spin-off from social media platform Kako that offers users short-term deposits, loans, credit cards, stock trading, and overseas transfers.In China, Ant Group-owned Alipay uses AI for almost all its operations, from detecting fraud to customer service and personalization. Founded in 2014, it now boasts 1.3 billion users around the globe and, by some measures, is the second-largest financial services corporation in the world, behind only Visa.“This same thing will happen in other markets too,” Castro notes. “FinServ enterprises really have two options: they can use their proprietary data to be the ones driving disruption or they can become the victims of it.”<h3>Getting From Today to an AI-First Future</h3>Castro says the most challenging step for many financial services enterprises on this path is simply getting started.“Many FinServ enterprise leaders think that, when introducing AI, you’re looking at overhauling the whole organization overnight.”&nbsp;The reality is that you can often get enormous gains by starting small and focusing on just one or two processes, optimizing them, and using the insights to inform other parts of the business”In fact, an HBR article revealed that many discrete AI projects cost between just one and five million dollars. However, the impact of embarking on something of this size could have broad implications for the business as a whole.Castro, also notes that one thing some organizations overlook is that AI is not a standalone solution, but one that must be complemented and maintained by humans.&nbsp;&nbsp;“When combined with humans, AI has the power to radically transform the way financial services businesses operate. What’s more, it can do it quickly, efficiently, and relatively cost-effectively - providing new ways of doing things almost immediately,” he concludes.

How AI Will Turn the Back Office into a Strategic Driver for FinServ

When it comes to AI, logistics businesses should take their lead from digital marketplaces such as Amazon and DoorDash, says VP of Engineering, Invisible Technologies, Adam Haney.&nbsp;&nbsp;“Both Amazon and DoorDash are using AI to transform the way they do everything from recommending products through to last-mile delivery,” he explains. “By harnessing the power of AI they have streamlined their operations, improved their services, and expanded their operations.”“Many logistics businesses can do the same - and for far less than they probably think.” <h2>How E-Commerce Showed the Way Forward</h2>Haney notes that, while DoorDash is the United States’ most popular food app with around 65 percent market share, the core of what it does comes down to logistics.“DoorDash used AI to help solve the challenge of effectively connecting individual merchants and customers with its deliverers, in an environment where efficiency, productivity, and cost are everything.”&nbsp;&nbsp;This included optimizing routes, forecasting delivery times, and allocating orders to the most suitable driver (known as Dashers). It also implemented dynamic delivery pricing based on factors such as demand, weather conditions, driver availability, and other variables.&nbsp;Meanwhile, Amazon began implementing AI almost 25 years ago in its personalized recommendations. Since then it has integrated AI into every step of the buying and delivery process, including inventory management, stock picking and packing, and shipping and delivery. It also uses AI to optimize drivers’ delivery routes. <h3>Complex, Data-Heavy, and Disparate</h3>While both DoorDash and Amazon may be digital giants, Haney argues that smaller logistics companies also have many of the ingredients at hand to emulate these practices, including the most important one - lots of data.&nbsp;“<a target="_blank" rel="" href="https://www.invisible.co/blog/finserv-needs-outside-help-for-ai/">Data is the fuel that powers AI</a>, and even the smallest logistics companies generate significant volumes of it,” Haney explains.“A typical logistics operation is likely to have mountains of customer and order data, as well as tracking information and delivery schedules. Then there will be bills of lading, bills of materials, and other documents to understand the content of different containers or vehicles or vessels.”&nbsp;&nbsp;The main challenge, according to Haney, is that this data is unstructured, or from disparate - often outdated - sources.“Some logistics companies are still using technology like the fax machine to process orders, " he says. “Others try to get around this by using a protocol known as EDI (electronic data interchange) but it is often criticized for being complex, inflexible, and expensive.”&nbsp; <h2>How Data Can Propel Logistics Businesses Further</h2>To make their data effective enough to use for AI, logistics companies need to collect, ‘clean’ and ‘annotate’ it. That means labeling or tagging it in a way that makes it understandable to AI algorithms. However, this isn’t out of reach for most logistics companies, according to Jean-Paul Biondi, Invisible’s VP of Operations.“This is the same issue a major retailer client faced,” Biondi explains. “With the rapid rise of online shopping in the COVID pandemic, digital ordering took off massively. To keep up with demand, it suddenly found it needed to catalog hundreds of thousands of items to make them available to shoppers.”&nbsp;“The challenge was that each of these items needed to be described in a way that was accurate and searchable, but also resonated with their audience. That might sound straightforward but telling for instance, whether one bottle being displayed is mascara and one is eyeliner, can be a lot more difficult than it sounds.”&nbsp;Biondi says that Invisible used a team of humans to complement AI by going through each image and making sure that its contents were described properly.&nbsp;“This allowed us to cut the average onboarding time significantly,” he says. <h2>Making AI Cost-Effective</h2>After data is annotated, it can be fed into an AI/ML model, which then also needs to be<a target="_blank" rel="" href="https://www.invisible.co/blog/finserv-needs-outside-help-for-ai/"> trained, evaluated, and fine-tuned before it can be evaluated and deployed</a> - something that can take months and requires the input of SMEs, as well as ongoing maintenance.But, even then, Biondi says logistics companies shouldn’t be deterred.&nbsp;“AI isn’t necessarily that expensive, especially in light of the efficiencies it can bring to an organization and the savings that can be made in a very short space of time,” he explains.“For logistics companies, AI projects can also be small, manageable and discrete - revolving around the ‘low hanging fruit’ of inventory management, demand forecasting or route optimization.”<h3>“Once it has been embedded in that process, it can then be rolled out more comprehensively,” he says. “So that it really optimizes all operations and provides a genuine competitive edge.”&nbsp;</h3>Finally, Biondi points out that logistics firms don’t need to go to the expense of building in-house capacity to start capitalizing on AI. Instead, they can outsource integrating AI to an experienced third-party provider.“Partnering with a firm that specializes in AI can provide smaller logistics companies with easy access to advanced technologies,” Biondi concludes.&nbsp;“This way, they can enjoy the benefits of AI-driven logistics solutions without the challenges of building and maintaining these systems in-house.”

How AI Will Continue to Transform Logistics

<h1>“People today are far more productive than 50 years ago, but this doesn't mean there’s less work to do.”</h1> AI will unleash a new wave of competition that will disrupt industries and redefine the nature of work itself. To survive it, businesses must know what sets them apart from existing competitors, as well as the likely new entrants to their fields.“AI is not just a technological upgrade; it’s a transformative force that will reshape the competitive dynamics of all industries,” says Joseph Chittenden-Veal, Chief Financial Officer at Invisible Technologies.&nbsp;“The businesses that come through it will be the ones that understand where they really add unique value to their customers or clients; they can then work out how AI can be applied to everything else.”<hr><h3>“Far from taking jobs, the reality is that new technology is more likely to usher in a new era of prosperity - driving economic growth and creating new opportunities.”</h3><hr><h2 style="text-align: start">AI Leveled the Playing Field, but Only Temporarily</h2>The rise of generative AI has democratized access to technology and seemingly leveled the playing field. No longer does acquiring the most powerful technology require significant resources. Instead, some of the most advanced AI models in the world today are available to anyone for free.&nbsp;“Some large language models (LLMs) like ChatGPT almost bypassed major enterprises and went straight into the hands of consumers,” says Benjamin Plummer, Chief Executive Officer of Invisible Technologies.“Suddenly, large enterprises were playing catch-up, rather than the other way around.”While businesses hoping to take advantage of these consumer-facing models will still need to modify them for their own purposes, it’s a task that can now cost millions - rather than tens or even hundreds of millions - of dollars, Chittenden-Veal notes.&nbsp;“Enterprises moving to AI can start with a discrete project, test it, and then start ramping up from there,” he says.<h3 style="text-align: start">Sectors Already Disrupted by AI</h3>At the same time, however, there are already instances around the world where fully - or close to fully - AI-powered organizations have rewritten the rules of engagement in entire sectors.&nbsp;In 2017, Kakao, owner of South Korea’s leading social media platform, Kakao Talk, released an AI-driven and <a target="_blank" rel="" class="MuiTypography-root MuiTypography-inherit MuiLink-root MuiLink-underlineHover css-1bc40bi" href="https://www.ey.com/en_gl/banking-capital-markets/how-social-media-firm-created-south-korea-s-fastest-growing-bank">digital-only financial services provider</a> known as Kakao Bank, which offers users short-term deposits, loans, credit cards, stock trading, and overseas transfers.&nbsp;The bank uses data gathered from Kakao Talk and other sources to provide an exceptionally personalized banking experience - one that integrates with shopping recommendations and gamified financial tools, such as savings apps.&nbsp;Despite only launching in 2016, Kakao Bank now boasts more than 20 million customers from a population of just over 50 million. Valued at <a target="_blank" rel="" class="MuiTypography-root MuiTypography-inherit MuiLink-root MuiLink-underlineHover css-1bc40bi" href="https://companiesmarketcap.com/kakaobank/marketcap/">10.08 trillion Korean Wan</a> (US$7.62 billion), it is also South Korea’s largest financial services firm by market capitalization, as well as its <a target="_blank" rel="" class="MuiTypography-root MuiTypography-inherit MuiLink-root MuiLink-underlineHover css-1bc40bi" href="https://asianbankingandfinance.net/retail-banking/news/south-koreas-kakaobank-most-recommended-retail-bank-in-apac-survey">most recommended</a>.&nbsp;According to a report from strategy adviser <a target="_blank" rel="" class="MuiTypography-root MuiTypography-inherit MuiLink-root MuiLink-underlineHover css-1bc40bi" href="https://www.oliverwyman.com/content/dam/oliver-wyman/v2/publications/2022/sept/ai-revolution-in-banking-report.pdf">Oliver Wyman</a>, Chinese-based Ant Group has performed even better. The company, which owns Alipay, uses AI for almost all its operations, from detecting fraud to customer service and personalization. Founded in 2014, has accumulated 1.3 billion users around the globe and is now the second-largest financial services corporation in the world, behind only Visa.&nbsp;“In the new banking environment, it is not track record or history that helps an organization stand apart; rather, it is the ability to operate with the kind of agility characteristic of software companies,” the report observes.&nbsp;<h3 style="text-align: start">Every Sector is Vulnerable to Disruption&nbsp;</h3>With AI becoming both more accessible and more pervasive, Chittenden-Veal says businesses in every country and every sector should soon expect similar levels of disruption.&nbsp;“Because the barriers to entry are now low, every enterprise is going to be using some form of AI within the next few years,” he notes. “There will be an incredible amount of competition.”“This should sharpen enterprise leaders’ minds to focus on what truly makes them differentiated. For the rest of their operations, they should already be looking at how AI can help them deliver more efficiently,” he explains.&nbsp;Chittenden-Veal notes, for instance, that a legal firm might determine its real strength lies in devising innovative company structures or strategies. Meanwhile, the remainder of its work might be relatively mundane and equally well performed by any competent legal provider.“This is the work that is better given to AI so that the lawyers are free to focus on where they add real value and can deliver the best and most efficient service to their clients.”<h3 style="text-align: start">Unlocking Hidden Value</h3>If large companies can understand their true value proposition and how they are differentiated from competitors, Invisible Technologies’ SVP of Growth, Jay Kumar, believes they could actually stand to gain more from AI disruption than smaller or challenger businesses.&nbsp;“There is an incredible amount of knowledge embedded within most large organizations that newcomers to their industry can’t compete with. Now, AI has the capacity to unlock it,” he says.&nbsp;“As organizations grow, it has traditionally become harder to manage and leverage institutional knowledge effectively. There are genuine coordination costs that happen as you get to scale,” he says.&nbsp;“Imagine how powerful it will be for those companies that use AI to change this and break down the silos so that the organization genuinely shares knowledge and insights to work towards a common goal.”“It will be like cutting the anchor from the ocean floor.”<h3 style="text-align: start">Orchestration is the Key&nbsp;</h3>Jay Kumar says, however, that unlocking the hidden value within a larger enterprise won’t mean that AI will be given free reign over everything. Employees will continue to remain a vital part of the equation.&nbsp;&nbsp;&nbsp;“What will become more important is not the AI itself but the orchestration of AIs and humans to deliver on the value proposition,” Kumar says. “Most enterprises will likely need a host of AI models performing different tasks, as well as a human workforce.”“It's about creating a harmony where AI handles the procedural, and human talent concentrates on the creative, the strategic— essentially the irreplaceably human — aspects of work," he concludes.“The result will be a compelling offering for any customer or client.”

How AI Will Unleash a New Wave of Competition

Many enterprise leaders recognize generative AI's transformative potential. Yet, some hesitate to adopt it, fearing AI errors could harm their reputation or operations.&nbsp;But Invisible’s CFO Joseph Chittenden-Veal says the risk of doing nothing is potentially far higher than the risk of getting something wrong and that it is possible to implement AI into a business in a controlled and systemic way.&nbsp;“By beginning with a focused application of AI in areas where your organization faces challenges, you can manage the integration effectively and provide immediate benefits, reducing the likelihood of errors,“ he explains.&nbsp;With that in mind, here’s our guide to avoiding AI errors when implementing large language models (LLMs) or other generative AI in your organization.&nbsp;<h2>Why AI Makes Errors</h2>“When you’re working with an LLM, two plus two doesn’t always equal four,” says Invisible’s CTO Scott Downes. “AI is not deterministic. Nor is it supposed to be.”“To avoid AI errors, you first need to understand why it makes them in the first place, and the essence of this comes down to the fact that it operates in a fundamentally different way from traditional software.”Instead, Downes notes that generative AI operates on principles of statistical inference and pattern recognition, which means it makes decisions based on the likelihood of something being true rather than absolute certainties. This approach allows AI to handle a vast array of complex, nuanced tasks that other software simply can’t.&nbsp;"However, this strength also brings its challenges,” Downes says. “Our experience has shown that when AI systems are trained on narrow or skewed datasets, the margin for error can increase significantly.”&nbsp;&nbsp;“It's not just about the volume of data but its quality and diversity."<h2>The Importance of Diverse Data&nbsp;</h2>Much of AI’s propensity for error can stem from its training, beginning with what it is actually trained on, Downes says.&nbsp;“A model can only ever be as good as the data on which it is trained,” he explains. “If that data is limited, biased, or doesn’t represent real-world scenarios, AI’s decision-making will likely be flawed.”For example, if a model for screening job applicants is trained only on resumes historically selected and those that tend to belong to a specific demographic group, AI could begin to automatically favor applicants from that group.&nbsp;Alternatively, AI used for healthcare diagnosis could be less accurate in analyzing and diagnosing illnesses across ethnic groups, ages, or genders if trained predominantly on one demographic.&nbsp;&nbsp;“For this reason, data must be diverse enough to encapsulate the scenarios the model will encounter,” Downes says.&nbsp;<h3>Human Insight Is Essential</h3>Even still, training an LLM effectively and avoiding AI errors requires much more than just good data. It also requires using human subject matter experts (SMEs) to coach the model into providing accurate and contextually appropriate answers.<h3>“Like a human, AI can be wrong, and it can also be very convincing,” Downes explains. “Any organization implementing a large language model needs humans to analyze the model’s outputs and guide it towards generating more ‘correct’ responses.”</h3>This could mean, for instance, that a financial services organization looking to introduce a new AI model might have a team that includes regulatory experts to ensure compliance with financial laws, communication professionals to maximize the effectiveness of customer interactions, and legal advisors overseeing ethical and legal considerations.Meanwhile, data scientists and product managers would also play a key role in aligning the AI's functionality with business objectives and customer requirements.<h3>Ongoing Maintenance Another Key</h3>Because of AI’s fundamental differences compared with normal software, the training doesn’t end once a model has been deployed. Instead, outputs need to be continually monitored and refined so that they continue to match an organization’s objectives.“LLMs can end up with what’s known as ‘model drift’,” Chittenden-Veal explains. “This occurs when the model, initially well-tuned to current data, begins to falter as the underlying business dynamics or customer behaviors evolve.”In fact, one study reported a 5-to-20 percent drop off in accuracy due to model drift within the first six months of deployment. Chittenden-Veal says that organizations should always be mindful of this, as well as of what he describes as rare or unusual scenarios, known as ‘edge cases’.&nbsp;“AI is essentially a statistical machine, and because it operates via statistics, you’re going to get some edge cases the model can’t effectively handle, every now and then,” he explains.“You need humans in the loop to catch and handle these exceptions.”<h3>Bite-sized Chunks and Outsourcing&nbsp;</h3>If this sounds daunting, Chittenden-Veal says it shouldn’t necessarily be. While training data and capturing errors across a whole organization may be time-consuming and expensive, there are alternatives.&nbsp;For starters, he explains, implementing AI doesn’t have to be an all-or-nothing thing.“You need to start implementing AI now, but that doesn’t mean you have to do it across your whole organization,” he says.&nbsp;"My recommendation is to start with a small use case. Where you know you've got pain points right now, perhaps with your most high-value people.”&nbsp;“Integrating AI is complex. It’s not just about technology but aligning it with your business processes.” This means ensuring that the AI not only functions technically but also aligns seamlessly with the organization's operational goals and enhances existing workflows.Meanwhile, Scott Downes says that the complexity involved in implementing and maintaining AI systems requires expertise beyond the scope of most organizations. He says that partnering with an experienced AI provider is usually the most effective way to avoid errors.“The right provider will integrate human feedback with AI capabilities to achieve those most reliable and effective outcomes,” he concludes.

How to Prevent AI Errors

Digital marketplaces usually operate in an unpredictable and ever-changing environment.&nbsp; Consumer preferences can change in an instant, new technologies can render existing platforms obsolete, and new competitors can emerge to capture market share almost overnight.For these reasons, businesses need to be able to stay on the front foot, regularly testing new ideas.“Perhaps more than any other sector, marketplaces need to constantly innovate,” explains Jean-Paul Biondi, Vice President Operations - Partner at Invisible Technologies. “What works today may not work tomorrow and, if someone comes along with a better way to do things, you can find yourself going quickly from market leader to playing catch up.”Biondi says it’s therefore critical that marketplaces have an operations model that allows for rapid testing and adaptation of new ideas.&nbsp;“You need to be agile enough to grow - or scale - an idea quickly if it proves successful, or to pivot without significant losses if it doesn’t.”&nbsp;<h2>AI’s Role in Reducing Risk</h2>Attempting to execute this with a traditional workforce is often prohibitively expensive and inflexible. If the project fails, there is the financial fallout, as well as the reputational risk that can come from having to carry out mass redundancies.&nbsp;&nbsp;Even if the project succeeds, there will be the need to quickly attract, train, and retain new employees - something that can be a challenge in a tight labor market.&nbsp;This is one of the reasons a study found <a target="_blank" rel="" href="https://www.theceomagazine.com/business/management-leadership/scale-up-pilot-project/">fewer than 30% of pilots ever get scaled</a>, while another concluded that, of those that do, <a target="_blank" rel="" href="https://www.governing.com/assessments/why-so-many-clever-pilot-programs-flunk-out-at-scale">the failure rate is anywhere between 50% and 90%</a>.“AI has an obvious and important role to play for marketplaces,” Biondi explains. “It can reduce much of the risk that’s inherent in pilot programs.”&nbsp;Biondi says this includes automating routine tasks, analyzing large volumes of data quickly, and handling many of the operational complexities involved in a pilot more effectively and efficiently than humans.“If a pilot succeeds, it can also help companies ramp up much more quickly and cost-effectively than traditional methods,” he says.<h3>Why Marketplaces Have an Inherent Advantage</h3>When it comes to implementing and scaling pilots using AI, marketplaces are often far better placed than many other types of businesses.“By its very nature AI requires good data,” explains Adam Haney, Invisible’s VP of Engineering. “Marketplaces tend to have an abundance of this, generated from user interactions, reviews, transactions, listings, and so on.”“AI thrives on this kind of data because it can be used to extract meaningful insights, forecast trends, and even anticipate user needs.”For instance, AI can help automate marketplace operations such as inventory management, pricing strategies, and demand forecasting, all of which are likely to give a pilot project the greatest chance of success.&nbsp;<h3>Humans in the Loop</h3>But for all its advantages, AI still can’t be left to implement and scale a concept on its own. Human intervention is required to make sure AI is applied in a way that truly benefits the business, while also addressing unforeseen consequences of ethical considerations.Humans can also often be needed in a more detailed way, especially when it comes to making sure AI understands the data and applies its learnings properly.&nbsp;As an example of this, Adam Haney cites Invisible’s work helping a major retailer digitize its catalog for online expansion.“A large part of the project involved analyzing thousands of items for sale and describing them online,” he says. “While AI could do this up to a point, it wasn’t as adept as humans at identifying the nuances and differences between products.”“For example, it had difficulty analyzing an image and telling the difference between, say, eyeliner and mascara, or describing the style of a swimsuit. This means humans had to also look at each product, apply their own knowledge, and make sure they matched how the AI had described them.”Haney also mentions Invisible’s work helping a food delivery giant grow quickly during the pandemic - something that involved onboarding thousands of new restaurants around the world.&nbsp;“While AI could do this up to a point, we still needed humans to go through each menu and make sure everything was properly captured, including some handwritten notes.”&nbsp;<h3>“However, by combining humans with AI we were able to take what was a 72-hour process to onboard a restaurant and turn it into a six-to-seven hour one.”</h3><h3>How Workforces Will Change</h3>What this means is that, for digital marketplaces looking to scale, the future of the workforce is likely to look very different from the past. In fact, testing new ideas, scaling them up, and bringing them into ‘business as usual’ (BAU) may require very few new employees and much less risk than in the past.“What we’ve already shown is that we can help marketplaces spin up a team quickly to test out an idea for a short period,” Jean-Paul Biondi explains. “This lets them assess the viability of a project without the significant risks of hiring a bunch of people and overcommitting resources.'"In doing so, Biondi says that Invisible doesn’t just help reduce costs, it can also help marketplaces take what they have always regarded as fixed costs and turn them into variable ones.“This is where the real value in outsourcing both AI and human resources lies,” he concludes.

How AI Can Help Marketplaces Innovate and Scale Fast

AI has the power to transform digital platforms, allowing them to scale up and take on new markets far more efficiently and cost-effectively than ever before. However, unlocking its potential is impossible without humans in the loop, says Invisible Technologies’ Partner and Vice President of Operations, Jean-Paul Biondi.&nbsp;“AI indeed has the capacity to elevate online businesses to unprecedented levels, but it's not a solitary journey,” he explains. “The real power of AI unfolds when it's paired with human expertise and oversight.”<h2>The Challenge of Unstructured Data</h2>When digital platforms move into a new market or launch a new product, they typically need to take onboard a significant amount of data. This often includes product descriptions, vendor and pricing information, inventory data, compliance data, and customer reviews and feedback.&nbsp;“We often work with digital platforms, such as marketplaces, that are looking to scale rapidly,” Biondi says. “However, by their nature, these businesses are very decentralized, with thousands of products often coming from thousands of different providers or sources, and none of it standardized.”Biondi says that, while AI can be used to take on and analyze this data, AI algorithms typically rely on patterns they identify in their training.&nbsp;This means they’re typically great at dealing with data when it is presented in a consistent format, but can struggle where it is inconsistent - particularly where there are a lot of outliers or anomalies to which AI can’t apply what it has learned.As a real-life example of this, Biondi cites Invisible’s work with a well-known food delivery app that needed to scale quickly during COVID-19.&nbsp;“The platform found itself in a position where it needed to onboard tens of thousands of restaurants around the world in a very short space of time,” Biondi explains.&nbsp;“As you can imagine, restaurants around the world don’t always have a lot in common when it comes to how they display or sell their products. Their menus were all in different formats and there was no consistency; some even had handwritten annotations.”&nbsp;<h3>“There was no way AI could take on and do this work alone. The error rate would have been extraordinarily high.”</h3>According to Adam Haney, Invisible’s VP of Engineering, the solution lies in mapping out every single process involved in onboarding clients, and allocating some tasks to humans, while training AI to perform the rest.&nbsp;“By breaking down every single process into its components, we could make a call on which tasks could be better - and more efficiently - completed by AI, and which were better performed by humans. We could also analyze what level of human intervention was needed.”<h3>A Hybrid Solution</h3>Haney says it’s not only the nature of the data that can pose a problem for AI. Sometimes interpreting that data can challenge its limits too. This is another area in which human knowledge and intuition are still very much needed.“No matter how well it is trained initially, an AI Model might not, for example, understand the cultural nuances of a particular product or statement or be able to capture the subtleties and variations of something in the market,” Adam Haney says.This means that, even though it may process data accurately, it may fail to grasp the broader context or implications, which can lead to misinterpretations or overly literal interpretations, particularly in cases where human communication is involved.&nbsp;“Without significant human intervention, it’s likely to misinterpret product descriptions or customer interactions. It could also fail to understand the sentiment of customer reviews or to effectively localize content.”&nbsp;“This can have a real impact on customer engagement and satisfaction, especially where an app is being rolled out or used across cultures or regions,” he says.&nbsp;<h2>The Nature of Generative AI</h2>Another reason humans are still needed in the loop goes to the fundamental - and revolutionary - nature of AI.&nbsp;To date, most technologies have been ‘deterministic’. That means they operate like a calculator, whereby if you enter certain inputs it will give you an accurate answer. However, AI is that it is ‘probabilistic’, which means it provides ‘likely’ rather than accurate answers and incorporates some lament of randomness in its approach.“With a lot of AI models you can give exactly the same prompt but get very different answers on the other side,” Haney explains. “The biggest issues can come where there is insufficient data or no data at all, in which case it often produces its own based on what it is trained to believe is most likely.”&nbsp;“To some extent, this replicates human behavior but it also means, just like in a human-based workplace, you are going to have to have some degree of oversight,” he says.<h3>‘The Last Mile’ of Data</h3>Jean-Paul Biondi says that many online businesses would be familiar with the concept of the ‘last mile’ when it comes to deliveries, which still largely involves human-based interactions. However, in many ways, both the strengths and limitations of AI mean that the same concept can also be applied to data.In other words, while AI can efficiently process and analyze vast amounts of data in the same way logistics systems manage the bulk of the transportation journey, the final stages of data processing and interpretation also benefit from human intervention."Much like the last mile in delivery services requires a human touch to navigate the final complexities, AI in data handling encounters its own 'last mile' challenges,” he says.&nbsp;“AI can process the bulk of data efficiently, but the nuances, the context-specific interpretations at the end of this journey, often demand human insight. It's in this final stretch where the most crucial decisions are made, and where human oversight becomes indispensable."Haney also cautions marketplaces to view AI as a partner rather than a panacea.&nbsp;“While AI offers significant capabilities, many businesses, especially those not at the scale of large tech companies like Google, would benefit from partnering with a firm that can provide both AI solutions and the necessary human oversight,” Haney explains.&nbsp;“This approach is especially relevant for companies that may not have the extensive resources or specialized expertise to develop or manage AI solutions internally,” he concludes.&nbsp;

How to Unlock AI's Potential in High Growth Marketplaces

Today, AI21, a leader in generative AI for enterprise, and Invisible Technologies, an industry leader in AI training and support, have announced a strategic partnership to serve the enterprise with improved AI solutions.“We partnered with Invisible for their ability to provide high quality data at scale, essential for developing our Foundation and Task-Specific Models that deliver reliable outcomes,” said Ori Goshen, co-founder and co-CEO of AI21. “This partnership is yet another strategic step to help our enterprise clients customize AI models on more reliable data.”The partnership will leverage Invisible’s process orchestration platform and a global workforce of experts in Supervised Fine-Tuning (SFT) - a process that refines AI models for specific tasks using tailored datasets for enhanced model performance - and Reinforced Learning from Human Feedback (RLHF) - a method that employs human feedback to further improve the quality and efficacy of AI21 models. This approach ensures the creation of more custom-built and flexible models for clients worldwide.Invisible’s unique approach to AI training, which encompasses deep partnership with researchers, efficient management of complex processes, and a holistic focus on agent engagement and development, distinguishes them in the AI landscape. “Our strong belief in the synergistic power of technology and human expertise is at the core of our vision for the future of AI,” said Ben Plummer, CEO of Invisible. “Our collaboration with AI21 highlights this synergy and stands as a testament to our shared values of delivering the highest quality in AI.”This partnership underscores AI21 and Invisible Technologies’ joint mission to make machines a thought partner to humans. The collaboration between AI21’s advanced models and Invisible’s best in class training methods promises a productive future for AI-driven organizations worldwide.<h2>About AI21</h2>AI21 is a leader in generative AI and Large Language Models, with the mission to empower businesses with state-of-the-art LLMs and AI applications. Founded in 2017, AI21 has raised a total of $336 million. It was among the first companies to bring generative AI to the masses and, to date, offers consumer applications and enterprise solutions. With the developer platform AI21 Studio, businesses can build their own generative AI-driven applications and services with the world’s most advanced family of language models, Jurassic-2. AI21’s consumer product, Wordtune, is the first AI-based reading and writing assistant to understand context and meaning, enabling its millions of users to refine and generate text or read and summarize text within seconds. For more information, please visit <a target="_blank" rel="" href="http://www.AI21.com">www.AI21.com</a>.<h2>About Invisible Technologies</h2>Invisible is the operations innovation company that combines AI and automation with a skilled human workforce to unlock strategic execution bottlenecks. With Invisible, teams can quickly innovate, scale operations, or bring new products to market. Companies like DoorDash trust Invisible for process innovation, while leading AI firms like Cohere and AI21 enhance their model performance with us. Dedicated to optimizing work through technology and human insight, we’re driving enterprise AI adoption and are a trusted partner for AI innovators worldwide. For more information, please visit <a target="_blank" rel="" href="http://www.Invisible.co">www.invisible.co</a><h2 style="text-align: start">Contacts</h2>AI21 Leah Sheltry Clarity for AI21 Labs Email: <a target="_blank" rel="" href="mailto:ai21labs@clarity.global">ai21labs@clarity.global</a>Invisible Technologies Cameron Partridge Chief Marketing Officer Email: <a target="_blank" rel="" href="mailto:press@Invisible.co">press@invisible.co</a>

AI21 and Invisible Technologies Announce Strategic Partnership to Drive AI Adoption for Enterprise

The growing uptake of AI has led some to suggest it will destroy jobs and contribute to unemployment. However, Invisible Technologies Chief Financial Officer Joe Chittenden-Veal says the reverse is more likely.&nbsp;“Any concerns of widespread job losses are completely overblown,” he argues.&nbsp; “Far from taking jobs, the reality is that the new technology is more likely to usher in a new era of prosperity&nbsp; - driving economic growth, creating new opportunities, and delivering more fulfilling employment.”&nbsp;<h2>A New Wave of Growth</h2>Despite holding this view, Chittenden-Veal acknowledges some workers and sectors will experience short-term pain.“There will be labor displacement over the next few years,” he says. “Especially in sectors where there is a lot of administrative or routine work that can be better performed by AI. Intentional investment in workforce retraining will be crucial for minimizing the harm done by this displacement.”&nbsp;&nbsp;This is supported by <a target="_blank" rel="" href="https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html">a recent Goldman Sachs report</a>, which states that AI could potentially expose the equivalent of up to 300 million full-time jobs to automation.&nbsp;But this won’t necessarily lead to widespread layoffs, the report found, with two-thirds of all jobs likely to be only partly automated by AI.&nbsp;Moreover, the positive effects would more than mitigate any short-term job losses, with increased AI takeup likely to drive global GDP up by US$7 trillion or 7% and lift productivity growth by 1.5 percentage points over the next 10 years.<h3>Pushing the Productivity Frontier Forward</h3>Chittenden-Veal also points to the many historical precedents that support this view.“History is full of these examples - the mechanization of agriculture, the advent of the motor vehicle, developments in personal computing.”Most recently, Chittenden-Veal notes, the internet has transformed how people gather and disseminate information, making research and communication far more accessible than ever before.“People today are far more productive than 50 years ago,” he observes. “But this doesn't mean there’s less work to do. On the contrary, there’s more work to do because there’s more opportunity for value creation.”“AI is going to accelerate that growth and value creation but not destroy work that needs to be done.”&nbsp;“The amount of work in the world is infinite. There is no ceiling on the amount that can be done. It’s the productivity frontier we keep pushing forward.”&nbsp;<h3>Three Types of Employment AI Will Create</h3>With this in mind, Chittenden-Veal suggests AI is likely to create three main types of employment.&nbsp;The first and most obvious will be those jobs involved directly in building and training AI models. While this group may not account for all the jobs AI will replace, it will still likely be much broader and more diverse than many might imagine.“It won’t just be tech people training AI,” Chittenden-Veal says. “For instance, if a law firm is building a model to review and develop commercial contracts, it will be the lawyers that must train it.”The second group of jobs AI will create are hybrid roles where a skilled or semi-skilled operator works with a model to deliver more efficient, more personalized, or much better outcomes. For instance, in the example above, lawyers will still be needed to maintain and refine any model they develop to make sure it continues to meet the firm’s - and their clients’ - needs.&nbsp;“In this category, you might also have the AI-enabled teacher, who uses a model to deliver a personalized and engaging learning plan to all their students, but who is also needed to oversee that process and deliver help to the students when and where needed most.”The third category of employment, Chittenden-Veal notes, is likely to be the jobs that we simply can’t yet envisage. It’s worth noting that the Goldman Sachs study found that as many as 60% of roles currently performed didn’t exist 70 years ago. These are the roles that can’t possibly be known until the technology becomes more established.“Take, for example, the car,” Chittenden-Veal notes. “Its invention didn’t just create direct employment through manufacturers, service station owners, car dealers, and cab drivers; it fundamentally changed how we lived.”&nbsp;“This led to indirect growth in sectors such as outdoor recreation, retail, and tourism. It also reorganized cities and created new ones - spurring a housing and construction boom in the suburbs.”“Because people could live in bigger houses with more space, it even contributed to the growth of things like household appliance manufacturing because suddenly people had more space.”“These are the kinds of indirect impacts AI will have that we just can’t envisage right now.”&nbsp;&nbsp;<h3>Reimaging the Future of Work</h3>Jay Kumar, SVP of Growth at Invisible Technologies, says that corporations and individuals must also make a call on how they intend to capitalize on the efficiencies AI brings.“Essentially, we’re going to face this situation where humans get the best, most interesting work, and AI eventually does everything routine or mundane.”&nbsp;But even this will bring its own challenges, Kumar says because <a target="_blank" rel="" href="https://www.7pace.com/blog/how-much-work-can-you-really-get-done">studies</a> show professionals only have so many productive hours in a day, and most days are filled with busy work. “The question is whether we decide to go down the route of working fewer hours - such as the Four Day Work Week - or whether we continue the same work patterns.”“Beyond that, people and companies need to make a call on what AI should be allowed to do and what it shouldn’t. Human judgment will always need to be part of the equation here too.”

Why AI Will Be A Collaborator, Not a Competitor

When many people think of AI, they think of off-the-shelf large language models (LLMs) like ChatGPT that write marketing copy, engage customers, or generate images. But for enterprises, AI is likely to look very different, says Invisible Technologies CEO Benjamin Plummer.“Individual use cases inherently have a ‘human-in-the-loop’ overseeing and guiding the LLM very closely,” he notes. “However, to make AI work at enterprise scale, you need a completely different architecture and approach.”So, how does your business take AI from a side project to center stage? Here are four things all enterprise leaders need to know about adopting AI.<h2>1. You Need to Act Fast</h2>There is a lot of hype around large language models (LLMs) right now, Benjamin Plummer notes, and it’s easy for enterprise leaders to think that they should wait for this to die down before choosing which technology to back. However, he cautions those who do are likely to miss the boat.“This is a completely different way of thinking that you can’t expect to develop overnight,” Plummer explains. “You really have to build AI muscles within your organization today so you’re not caught out.”Plummer observes that AI is likely to reach well into every organization within the next few years, fundamentally reshaping both the way they operate and the markets they operate in. In the process, it should significantly reduce the barriers to entry into many fields, creating real opportunities for ‘AI-first’ businesses, many of whom will be newcomers to a field.This is supported by PWC Global research, which found that <a target="_blank" rel="" href="https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf">healthcare, automotive, and financial services were the three sectors most likely to face severe disruption</a>.Meanwhile, Invisible Technologies CFO, Joseph Chittenden-Veal, says that to survive in this post-AI world, enterprises in these sectors and others need to quickly work out how they’re sufficiently differentiated. Otherwise, they are likely to find themselves at the mercy of lower-cost competitors who harness AI more effectively.“In this environment, those businesses that thrive will be the ones who work out how they add value and focus only on these core competencies,” he explains. “They can then use AI to carry out the rest of what they do.”<h2>2. AI Is Not a Silver Bullet</h2>That said, Benjamin Plummer argues that executives should refrain from seeing AI as the savior to all their business problems.“Even those people who have started using generative AI for more complex tasks know that you can’t just input text at one end and expect magic to happen,” he explains. “Process still matters.”Plummer says enterprises should see AI as part of a broader ecosystem that still involves humans. However, he cautions that enterprises are most likely to need several AI models performing different tasks within their business.“Like humans, these models will have different strengths and weaknesses,” he says. “It will be important to use the right one for the right job.”Plummer also says that thinking through the orchestration of these AI models and the way they interact with employees, customers, and clients will be just as important as the models themselves.“The challenge is combining these models, which are great at particular types of tasks, with humans who are great at other types of tasks.”<h3>“Enterprises will have to map out every challenge and every process to determine who can perform it best - human or machine. And, this is likely to be an ongoing process."</h3><h2>3. AI is More than Just ChatBots</h2>The individual takeup rate of Generative AI has been nothing short of extraordinary. By September 2022, <a target="_blank" rel="" href="https://www.salesforce.com/news/press-releases/2023/09/07/ai-usage-research">49% of people in the United States, India, Australia, and the United Kingdom reported using generative AI</a>.However, Joseph Chittenden-Veal believes the popularity of large language models (LLMs) such as OpenAI’s ChatGPT, Google Bard, and Meta’s Llama 2, may have already conditioned people to see AI only as a ‘text in/text out’ technology.“While text in/text out processes may be the ‘low hanging fruit’, really AI as a technology can be built around anything in and anything out,” he explains.“For instance, in the context of manufacturing, the input could be a slight surge in demand. The output could be a series of actions behind the scenes that involve a multitude of applications and datasets that ultimately produce more goods.”“When you understand AI this way, it can really be applied to anything,” he says. “Industries such as retail, healthcare, financial services, logistics, agriculture, and manufacturing could be revolutionized.”“As we roll out more and more generalized AI and build more AI applications and use cases in different verticals, the realm of things that can be automated and enhanced by technology is going to radically increase.”<h2>4. AI Can Be Implemented in Small Steps</h2>Despite AI’s transformative power, Joseph Chittenden-Veal says that another common misconception among enterprises is that they need to go ‘all in’ on AI right from the start, making it a costly and time-consuming process.The truth is that, unlike most other technologies, enterprises can start incrementally - implementing it in a contained way before expanding.“The great thing with AI is that adopting it doesn’t require an overhaul of your systems,” Chittenden-Veal explains. “Consider diagnostic imaging. Initially, AI helped enhance the <a target="_blank" rel="" href="https://www.rsna.org/news/2019/may/ai-based-applications-boost-image-quality">resolution of images</a> as a simple step in the pre-processing. Soon after, it began being used as a backup to catch missed anomalies after radiologists had their turn, adding another step after the existing workflow. As its precision improved, it has started leading on early diagnoses and even treatment plans, becoming a larger piece of the process. This expansion of impact is characteristic of great AI applications.”“It will be the same for enterprises,” he says. “Implementing AI will often be a matter of building on what’s already there. You can pick and choose where you want to implement it first.”“With the right partner, you can have very light touch integration points and get value in just weeks. This is especially true if you already have an existing bank of good data.”In fact, a 2021 Harvard Business Review article reported that <a target="_blank" rel="" href="https://hbr.org/2021/07/ai-doesnt-have-to-be-too-complicated-or-expensive-for-your-business">companies in specialist fields such as manufacturing, healthcare, and agriculture</a> would be better served by implementing more streamlined solutions underpinned by small datasets focused on discrete tasks. Many of these projects cost in the range of just $1 million to $5 million.The flip side, though is that, unlike software products, AI requires a high level of oversight and maintenance.“Enterprises will need to make sure they have the right machine learning platform and resources in place to keep watching and refining what the AI is doing, whether that’s in-house or outsourced,” Chittenden-Veal says.“My advice is to go fast and think big - don’t constrain your thinking around AI.”“But you can start in bite-sized chunks.”<h2>What Every Enterprise Should be Doing Right Now</h2>In the face of sweeping changes across industries, all businesses should assess their current processes and make sure they’re AI-ready. Don't wait for disruption; prepare for it proactively.If you need guidance on how to align your business with AI, <a target="_parent" rel="" href="/contact-us/">get in touch</a>.&nbsp;

What Most Enterprises Get Wrong About AI

AI has the potential to transform modern enterprises <a target="_blank" rel="" href="https://www.pewresearch.org/internet/2014/08/06/future-of-jobs/">more profoundly</a> than virtually any technology that has come before it. In the process, it will likely inspire a new generation of challenger businesses that reshape the industries in which they operate. But it will also create a new generation of losers.With that in mind, here are five things all enterprises should do to make sure their company doesn’t join the list of AI casualties.<h2>1. Change The Way They See Technology</h2>AI and large language models (LLMs) are fundamentally different from existing technology solutions, says Scott Downes, Chief Technology Officer at Invisible Technologies.“To date, technology has always been deterministic. Ask it what two plus two equals, and it gives you four,” Downes explains. “That’s not how AI works, nor what it’s intended for.”Instead of relying on predetermined rules or fixed algorithms, Downes notes that AI learns and adapts from vast datasets. This means it can comprehend context, nuance, and even human emotions, allowing it to provide responses that are contextually relevant.<h3>“AI isn’t just a one-time solution but an ongoing journey. It’s about constantly refining and improving the technology, understanding its limitations, and adapting to its evolving capabilities.”</h3>“This isn’t just a technological upgrade; it’s a shift in mindset,” Downes explains. “To be successful, you have to approach it in a fundamentally different way."<h2>2. Start With the Challenge, Not the Solution</h2>Ben Plummer, Chief Executive Officer of Invisible Technologies, says many tech providers still build a solution and then look for a problem to fit it to. This has conditioned enterprise leaders to see technology in terms of its functionalities - an approach that can lead to less-than-ideal results."The level of hype around new tech can often be high, but the level of real-world activity can be low,” he cautions.However, with the rise of AI, Plummer says businesses have the opportunity to change the way they apply tech to their businesses.“Because you’re not buying software out of the box, you can start with the problem and work backwards,” he says.“Instead of trying to keep up with every last development in the world of technology, enterprise leaders should prioritize identifying their specific challenges and needs. Then, they can work out how to address those challenges.”This approach, he says, will encourage a problem-solving mindset, where AI solutions are customized to fit the organization's unique requirements rather than attempting to fit existing technology solutions into the organization's processes.<h2>3. Keep Humans in the Loop</h2>A <a target="_blank" rel="" href="https://www.bbc.com/news/technology-65102150">recent Goldman Sachs report</a> found AI could replace as many as 300 million jobs and perform up to 25% of all work-based tasks carried out in the United States and Europe. For some occupations, such as administrative (46%) and legal (44%), the number of processes it could potentially replace was higher still.However, Plummer says that rather than being a threat to employment, AI will enable and empower people, removing the mundane and monotonous and lifting people up to do more creative and high-impact work.“AI is better at some things, but humans will always be better at others. This will be the catalyst for many people to be able to do more interesting and fulfilling work.”Plummer also says that humans will still be needed to oversee AI, both from a practical and ethical point of view.“The right solution always ends up being some blend of human judgment and intent with an AI system augmenting them.”By blending AI's strengths with human ingenuity, Plummer argues businesses can stay ahead in the tech race.<h2>4. Avoid Getting Tied Into One Set Path&nbsp;</h2>AI is evolving so rapidly and developing so many new capabilities that Downes believes it is a mistake for enterprises to tie themselves to just one solution or platform.“Those enterprises that chose to go it alone or who put all their eggs in one basket will be taking a huge risk,” he says.“The right approach is to be both flexible and ‘solution agnostic’. What’s important is that there is a layer of orchestration to seamlessly integrate humans and tech.”Downes says that properly integrating AI means analyzing and documenting every single action currently performed across an entire process, department or organization. Only then is it possible to determine whether each step is best performed by a human or AI.Beyond this, enterprises need to either build or engage a dedicated and experienced machine learning team that can process data and train their AI model. They also need to constantly monitor and refine AI’s outputs to make sure they continue to match its needs.“Rather than spending the millions of dollars required to build these capabilities in-house, most enterprises are far better off outsourcing to someone who specializes in the space,” Downes says.“By outsourcing, you can also get someone who understands and is experienced in the shortcomings of AI, such as data and privacy issues and model hallucinations, and knows how to train your model to potentially overcome them.”<h2>5. Think Bigger</h2>Finally, Plummer observes that most organizations have “blinkers on” when it comes to assessing the extent of what’s possible with AI. That’s because, even when contemplating the future of tech, they still tend to think in terms of what has already been achieved rather than what’s possible.Plummer encourages organizations to challenge their preconceived notions and think beyond the boundaries of what has traditionally been automated. By doing so, they can uncover new ways to leverage AI technology to enhance productivity, efficiency, and innovation across a broader spectrum of their operations.“Those that do will find they have a huge competitive advantage and their tech will be propelling them forward rather than holding them back.”

5 Ways to Avoid Becoming an AI-Induced Corporate Casualty

AI is likely to have a significant impact on financial services and many enterprises within the sector have been among the technology’s early adopters. But most still aren’t properly equipped to take full advantage of AI without outside help, according to Invisible Technologies’ Head of Business Development, Financial Services, Addison Chu.“We talk about AI-readiness or how equipped enterprises are to deploy resources into the AI space,” he says. “We see finserv businesses at all ends of the spectrum. But even the best ones won’t be able to integrate to an AI-first approach without strategic guidance and infrastructure support.<h2>Becoming an AI-First Enterprise&nbsp;</h2>A 2021 McKinsey survey revealed the financial services sector was one of the most knowledgeable and engaged of any sector when it came to AI. Already by then, one-third of global financial services enterprises declared they had incorporated AI into their operations.&nbsp;This year, a subsequent McKinsey study revealed that as many as 24% of financial services employees regularly used AI for work, placing them second only to employees in the ‘tech, media and telecom’ sector. Another 18% of financial services workers said they regularly used it outside of work.But for all their AI exposure, McKinsey also found that few financial services enterprises were deploying AI successfully.&nbsp; Many lacked a clear and comprehensive AI strategy, had “a weak core technology and data backbone”, and “an outmoded operating model and talent strategy”.McKinsey concluded that the financial services companies that were likely to survive and thrive over the next decade would be the ones that adopted an ‘AI-first’ approach to everything. This meant “adopting AI technologies as the foundation for new value propositions and distinctive customer experiences”.&nbsp;“Those that use AI already often apply it to siloed parts of their business,” Chu says. “They might use it for fraud detection, underwriting, or virtual assistants for customer service. But it doesn’t yet underpin everything they do.”<h2>The Missing Ingredients in Financial Services</h2>Chu argues that, despite their highly regulated operating environment, financial services businesses should really be looking to the examples of AI-first businesses outside of their sector to see what lessons they can draw.For instance, platforms such as TikTok, Pinterest, and Spotify have all successfully embedded AI and machine learning (ML) into the fabric of their recommendation platform, allowing them to provide exceptional levels of personalization. This, in turn, has helped them grow rapidly, disrupt existing players, and become dominant in their respective markets.&nbsp;Chu says financial services enterprises could conceivably use AI in the same way - reimagining the customer experience to provide deep personalization and a seamless experience, especially in personalized financial management.&nbsp;“This has the potential to truly democratize financial service products and reduce today’s ‘financial information asymmetry’,” he says.&nbsp;<h3>“They could also use it to integrate all aspects of their internal operations, ensuring the knowledge and information from one part of the business is immediately understandable, available, and actionable by other parts and team members.”</h3>But for that to happen, Chu says financial services businesses need some key ingredients in place. This includes having a data infrastructure in place that is ready to ingest a large language model (LLM). It also includes having a skilled machine learning team to clean that data and carry out the next steps.“For those finserv enterprises with an existing machine learning team, moving to AI really is the next step. But for those without these existing and established resources, it will be more difficult,” Chu explains.&nbsp;<h3>How to Train Your Data</h3>And yet, having a rich bank of good, clean data really is just the beginning, according to Chu. Enterprises next need to prepare their data for training, a process that begins with data annotation.This involves subject matter experts labeling or tagging the data so that it’s understandable to machine learning algorithms and can be used to teach them how to make accurate predictions or decisions.Only then is the data in a fit state to train an AI model.<blockquote>The Machine Learning/AI lifecycleTo become AI-first, financial services enterprises need to go through four key steps, according to Invisble Technologies' Addison Chu and Adam Haney. These will need to be carried out by the machine learning team or an outside provider.&nbsp;1. Data collection and cleaning. Organisations with a rich bank of clean data will have a headstart on competitors.&nbsp;2. Data annotation. It’s only when that data is tagged that large language models (LLMs) can understand it.&nbsp;3. Model training/fine tuning. These intensive processes require the input of subject matters experts (SMEs), who make sure the model’s outputs are fit for purpose and accurate.4. Model evaluation and deployment. Finally, the model&nbsp;must be rigorously assessed to make sure it aligns with the enterprise’s specific goals and needs.</blockquote>Adam Haney, VP of Engineering at Invisible Technologies, says that enterprises have two real options to carry this out. The first is to start from scratch, developing their own AI model in the same way as Pinterest, Spotify and TikTok - usually a time-consuming and costly process.&nbsp;The second and more cost-effective option is to take what essentially amounts to a shortcut by tapping into existing large language models (LLMs) such as OpenAI’s ChatGPT, or Meta’s LLaMA 2, and refining them for their own use.&nbsp;“You may not be able to realistically get to 100 like a TikTok without significant investment, but you can get to 60 or 70 much more easily.”To do this, the model must first be fine-tuned - a process in which a generalist model is taught the features and patterns specific to a task or domain. This involves SMEs instructing the LLM on the right response to questions involving the data.“In the context of FinServ, the data that AI needs to understand and respond to could include anything from understanding financial statements and regulatory documents that need to be filed. But it can also really apply to any kind of document-heavy workflow,” Haney says.&nbsp;Beyond fine-tuning, there also needs to be a period of reinforcement learning with human feedback (RLHF), during which the SMEs stack rank five different responses. In doing so, they teach the model which answers align best with human preferences.&nbsp;“Without this step, the model will provide answers that are sometimes inaccurate,” Haney says. “When it’s done, you can have the model understanding exactly what matters to a financial analyst or customer service agent, or anyone within the business.”“Invisible can help with the technical aspects of model selection and model evaluation. We can also capture the initial data sets needed to fine-tune those models, and ensure that, as they’re being fine-tuned, they still meet with company’s requirements,” Haney explains.<h3>The Threat of Doing Nothing</h3>As AI evolves and consumers’ habits change, Chu believes the pool of competitors for financial services enterprises will widen significantly.“It’s conceivable that some other platforms will begin to offer financial products, and they will have the advantage of coming from a different risk profile to traditional FinServ enterprises, especially when it comes to AI.”“Financial services is a ‘zero error environment’. Having AI make a mistake is not an option,” Chu says.&nbsp;“You can’t have a model ‘hallucinating’ or giving an incorrect answer because it could lead to a breach of regulations or privacy and serious reputational damage.”“The writing is on the wall that FinServ enterprises need to move to AI-first. But it’s also clear that they need to do it in a way that minimizes the risk of getting anything wrong.”

Why Most Financial Services Businesses Won’t Maximize AI Without Outside Help

The allure of turning AI into a competitive advantage is just too compelling to ignore for enterprises.&nbsp; That’s why many of them are either thinking about, or already deploying, generative AI in some form.However, as the <a target="_blank" rel="" href="https://invisible.co/blog/overcome-ai-talent-scarcity/">level of AI adoption</a> within these enterprises increases, executives are finding the implementation has more challenges than they anticipated. <h3>Overcoming the operational roadblocks that come with AI deployment is the difference between a blue ocean of opportunity and a minefield of problems and costs that impact the ability to deliver ROI.&nbsp;</h3>In this blog post, we unpack the enterprise AI journey, identify common challenges, and explain the importance of human involvement in preserving AI ROI. Let’s dive in.<h2>Snapshot of the AI Deployment Lifecycle&nbsp;</h2>Despite the prevalence of off-the-shelf AI tools, the AI requirements for every organization are different, ensuring that no approach to the implementation of AI looks exactly alike. This means that enterprises are now attempting to customize foundation models like GPT, Llama 2, and Claude for specific use cases.&nbsp;Despite this, we believe that enterprise-scale AI deployment can follow similar checkpoints. Let’s explore what steps organizations need to take in order to effectively adapt these foundation models to their unique operational needs.Below, we’ve mapped out each checkpoint and its scope, the business functions involved, and the roadblocks they might encounter.&nbsp;1. Identify Problem<ul><li>Scope: Defining the business challenge or opportunity where AI can be leveraged.</li><li>Potential Roadblocks: Lack of clarity or consensus on the problem.</li><li>Business Units Involved: Executive Leadership, Strategic Planning.</li><li>Inputs Required: Comprehensive understanding of business objectives, industry challenges, and potential AI capabilities.</li></ul>2. Collect and Prepare Data<ul><li>Scope: Gathering, cleaning, and organizing data relevant to the problem.</li><li>Potential Roadblocks: Insufficient data, data privacy concerns.</li><li>Business Units Involved: Information Management, Legal &amp; Compliance.</li><li>Inputs Required: Data sources, data cleaning tools, and data privacy guidelines.</li></ul>3. Select Model<ul><li>Scope: Choosing and fine-tuning a suitable AI model.</li><li>Potential Roadblocks: Selecting an inappropriate model, overfitting.</li><li>Business Units Involved: Research &amp; Development, Data Science.</li><li>Inputs Required: Problem definition, data, and machine learning frameworks.</li></ul>4. Integrate via API into Tech Stack<ul><li>Scope: Ensuring the model communicates seamlessly with existing systems.</li><li>Potential Roadblocks: Incompatibility issues, data integration challenges.</li><li>Business Units Involved: IT &amp; Systems, Software Engineering.</li><li>Inputs Required: API specifications, integration tools, and system architecture details.</li></ul>5. Test and Validate<ul><li>Scope: Assessing the model’s performance and reliability.</li><li>Potential Roadblocks: Unforeseen bugs, poor performance.</li><li>Business Units Involved: Quality Assurance, Data Science.</li><li>Inputs Required: Testing frameworks, validation criteria, and performance metrics.</li></ul>6. Deploy<ul><li>Scope: Launching the AI solution in a live environment.</li><li>Potential Roadblocks: Deployment failures, unexpected behavior.</li><li>Business Units Involved: Operations, IT &amp; Systems.</li><li>Inputs Required: Deployment plans, rollback strategies, and operational guidelines.</li></ul>7. Monitor and Evaluate<ul><li>Scope: Ongoing performance tracking and analysis.</li><li>Potential Roadblocks: Inadequate monitoring tools, performance degradation.</li><li>Business Units Involved: Operations, Data Science.</li><li>Inputs Required: Monitoring tools, evaluation metrics, and performance benchmarks.</li></ul>8. Iterate<ul><li>Scope: Making necessary improvements based on feedback and performance data.</li><li>Potential Roadblocks: Resistance to change, lack of actionable insights.</li><li>Business Units Involved: Research &amp; Development, Data Science.</li><li>Inputs Required: Feedback loops, iteration plans, and updated data.</li></ul>9. Scale<ul><li>Scope: Expanding the AI solution across various departments or functions.</li><li>Potential Roadblocks: Scalability issues, inadequate resources.</li><li>Business Units Involved: Operations, Executive Leadership.</li><li>Inputs Required: Scalability assessment, resource allocation, and expansion plans.</li></ul>10. Document, Train<ul><li>Scope: Educating stakeholders, documenting processes, and training users.</li><li>Potential Roadblocks: Resistance to adoption, inadequate training materials.</li><li>Business Units Involved: Human Resources, Training &amp; Development.</li><li>Inputs Required: Training materials, documentation tools, and communication plans.</li></ul>11. Compliance and Governance<ul><li>Scope: Ensuring regulatory compliance and establishing governance mechanisms.</li><li>Potential Roadblocks: Regulatory changes, compliance violations.</li><li>Business Units Involved: Legal &amp; Compliance, Governance.</li><li>Inputs Required: Regulatory guidelines, auditing tools, and governance frameworks.</li></ul>Given that each checkpoint can span weeks or months, executives should anticipate lengthy deployments. Some leaders are even sharing concerns that fully integrating AI into their workflows may take <a target="_blank" rel="" href="https://www.axios.com/2023/08/19/ai-corporate-barriers-cost-data">5 years or more</a>.&nbsp;<h2>The Roadblocks Slowing Enterprise AI Deployment Down</h2>In order for an AI model to be helpful for a business use case, generated outputs must be accurate, actionable, and compliant. To achieve these goals, some steps in the AI deployment lifecycle can be especially challenging for an enterprise to overcome because of their unique requirements.&nbsp;For many enterprises, what stands in the way of a successful deployment:<ol><li>Analysis paralysis: Model selection can grind momentum to a halt for two reasons: new models are frequently becoming available, and a litany of factors like cost, training weights, and model size each complicate decision-making. Without an experienced leader or expert partner, these factors can prevent an AI project from getting off the ground.</li><li>LLMs are prone to hallucinations: An AI model’s efficacy is tied directly to the quality and specificity of the data that it’s trained on. In most organizations, proprietary data is riddled with gaps, inaccuracies, and unseen biases. For successful deployment, enterprise data must be intricately enriched and prepared, or AI outputs risk being ineffective at best, and harmful at worst.</li><li>Integration challenges: Transitioning from legacy systems to AI-augmented infrastructures is like changing the wheels of a moving car. Without extensive (and expensive) overhauls, most of the existing enterprise tech stack will be incompatible with emerging AI technology, making an in-house end-to-end AI solution difficult.</li><li>99.5% accuracy doesn’t cut it: Especially in highly regulated industries like financial services and healthcare, almost perfect isn’t enough. While AI models appear to be increasingly accurate, the margin for error and the high stakes within regulated environments require 100% quality assurance rooted in human judgment.</li></ol><h3>How Keeping Humans in the Loop Maintains AI Momentum</h3>Amid these challenges, human expertise emerges as the linchpin for successful AI deployment. People who monitor the outputs generated by AI can be the failsafe to catch errors that put business-critical processes at risk.To mitigate hallucinations from LLMs, a team of experts can provide several essential inputs: thoughtfully prepared data, reinforcement learning and fine-tuning processes (RLHF, SFT, and model evaluations), and QA - all of which ensure outputs are as accurate and useful as they can be.When disparate technologies do not work together in synergy, people fill in the gaps. If a business process is well-structured, the handoff from AI to the enterprise system to the end user isn’t slowed down by too much friction.And while AI continues to make strides in accuracy, a layer of human judgment is essential for compliance as a last line of defense. Through an agile human-AI collaboration, enterprises can navigate a strict regulatory landscape, harnessing the power of AI while ensuring uncompromised compliance and quality.<h3>Why Enterprises Should Turn to a Strategic Partner</h3>Most enterprises don’t have internal teams available to perform each step of AI deployment and overcome these roadblocks on their own. Instead, they need a strategic partner who has extensive experience with AI to maximize the ROI of their deployment.&nbsp;At Invisible, we use our experience in training foundation models for leading AI development firms to help enterprises deploy AI effectively. We take the following steps to remove operational burdens created by the AI deployment lifecycle:<ol><li>Apply process expertise. We break down complex business problems into their smallest parts and then transform them into structured processes performed by AI, automation, and our global team.&nbsp;</li><li>Leverage relationships with the leading AI development firms. These partnerships give us unique access and insight into the latest models, allowing us to quickly determine the most suitable model for our clients’ needs.&nbsp;</li><li>Our process orchestration engine eliminates integration challenges. It’s no-code and built for flexibility, ensuring we can integrate with just about any contemporary and legacy system.&nbsp;&nbsp;</li></ol>These three capabilities yield AI-enabled outcomes for enterprises. Interested in learning more? <a target="_blank" rel="" href="https://www.invisible.co/get-started/">Speak to the team</a>.

Where Enterprises Lose Momentum with AI Deployment

In today’s market, we see three major challenges standing in the way of enterprises seeking to leverage AI for a competitive edge:<ol><li>Talent with AI expertise is essential to building and using AI within the enterprise.</li><li>AI talent is tough to come by.&nbsp;</li><li>AI talent is costly.&nbsp;</li></ol>In fact, <a target="_blank" rel="" href="https://www.zdnet.com/article/more-skills-needed-to-help-ai-plug-skills-gaps/">67% of IT leaders</a> named the AI skills shortage as the number one barrier to deploying AI, according to Rackspace Technology. Yet recent AI-related job postings from July are <a target="_blank" rel="" href="https://www.computerworld.com/article/3702711/ai-skills-job-postings-jump-450-heres-what-companies-want.html">450% YoY</a>, highlighting that this hasn’t stopped them from trying.&nbsp;&nbsp;To further counter this shortage, enterprises are shifting talent within traditional technology and data roles to AI-related priorities, implementing AI point solutions to replace customer service representatives, and leveraging the flexibility of freelancers in a move that is shoring up the gig economy. But none of these have delivered the outcomes enterprises are looking for.&nbsp;Instead, the clearest path to ROI may lie in a strategic partner that merges the best of human and artificial intelligence within a single end-to-end technology solution. Let’s dive in.&nbsp;<h2>Why AI Expertise is Essential</h2>An effective enterprise AI solution can’t simply be patched together. To understand why, let’s look at the varying degrees of AI adoption within an enterprise.&nbsp;Many models depict enterprise AI adoption across three levels, with AI integration deepening within an enterprise’s operations at each step. <a target="_blank" rel="" href="https://www.egonzehnder.com/industries/technology-communications/artificial-intelligence/insights/is-your-c-suite-prepared-for-artificial-intelligence">Egon Zehnder</a> is one such organization.Here’s our take, including what adoption looks like at each stage:&nbsp;Level 1: AI is introduced in an organization<ul><li>A team, or teams, onboard a point solution like an AI-powered SaaS tool.</li><li>AI is added as a feature to a product or service.</li><li>A core AI solution is not centralized within the organization’s operations.</li></ul>Goals: Automate and increase the efficiency of repetitive low-complexity tasks, or improve the customer experience.&nbsp;Level 2: Business processes become centered in AI&nbsp;<ul><li>A foundation LLM is customized with fine-tuning to support one or more business functions.</li><li>Data science teams begin to shift focus toward maximizing the AI model’s value.</li><li>AI becomes a core product feature.</li></ul>Goals: Increase the efficiency and quality of an entire business function’s output and optimize value-creation for a key product.Level 3: An organization is fully AI-enabled&nbsp;<ul><li>An LLM or multiple LLMs working in concert deeply supports a core business process and has an impact across the entire organization.</li><li>Humans in an organization work seamlessly in tandem with the AI and the rest of their tech stack.</li><li>Existing business processes are redesigned to be AI-enabled; new business processes are structured around AI.</li></ul>Goals: Engage on every opportunity, maximize business productivity and outcomes, and widen the gap with competitors.Off-the-shelf AI tools tend to cater to enterprises at Level 1. But as enterprises scale to Levels 2 and 3, costs and complexity begin to mount.That’s because, at each level of adoption, the expertise and resources required scale exponentially. Based on our experience working with the leading AI model development firms and enterprises deploying AI, here is our assessment of the organizational requirements for Levels 2 and 3:<h4>Level 2 Requirements</h4>At this stage, an AI model is integrated within an enterprise’s operations at a larger scope. The stakes for having an accurate and robust solution are higher, so enterprises need:&nbsp;<ul><li>Skilled AI trainers to produce preference data for Supervised Fine-Tuning and perform model evaluations.</li><li>Trained experts to monitor data quality and manage the AI trainer workforce.</li><li>Tech infrastructure and data scientists to manage data securely and efficiently.</li><li>Properly structured and enriched proprietary data.</li><li>A <a target="_blank" rel="" href="https://www.forbes.com/sites/rashishrivastava/2023/09/01/ai-red-teams-google-nvidia-microsoft-meta/">red team</a> to uncover hallucinations and build feedback loops that steer a model toward aligned behavior.</li><li>Process mining and experience in shaping business processes around an AI model and human intelligence.</li><li>Tech infrastructure that moves data cleanly between AI, people, and the organization’s tech stack.</li></ul>Use case example: A healthcare firm utilizing a conversational AI tool that integrates with EMRs and is fine-tuned on preference data from domain experts.&nbsp;<h4>Level 3 Requirements</h4>When enterprises reach this level of adoption, they are shaping business-critical processes around one or more AI models working in concert. They need:&nbsp;<ul><li>All of the requirements of Level 2.</li><li>A technology platform capable of orchestrating one or more AI models, software integrations, and humans in the loop to maximize efficiency and value.</li><li>Research scientists with expertise in measuring.</li></ul>Use case example: A financial institution deploying <a target="_blank" rel="" href="https://www.invisible.co/blog/one-llm-not-enough/">multiple generative AI models</a> to improve fraud detection response accuracy and speed.For a business restructuring its operations around AI, the requirements listed above necessitate either a monumental shift in internal resources or a recruiting effort of similar cost and magnitude. Both routes threaten ROI due to their <a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tuning-too-costly-and-complex/">expense and complexity</a>.&nbsp;<h2>Why Choose a Strategic Partnership</h2>The strongest alternative is a partner experienced in AI model training and deployment. While many claim to transform enterprises with AI, few offer an end-to-end solution — this is where Invisible steps in.<h3>Invisible works alongside global enterprises, enabling them to leverage the full potential of AI through our process orchestration platform: a potent blend of a global team of experts, cutting-edge automation capability, and AI.&nbsp;</h3>This platform enables:<ul><li><a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tune-ai/">Customizing AI models</a> for specialized enterprise use cases.</li><li>The orchestration of multiple models and humans in the loop to deliver powerful outcomes through large-scale business operations.</li></ul>Invisible’s value proposition lies in our deep relationships with the world’s leading AI model development firms, for whom we provide human-in-the-loop data training that delivers more accurate and helpful models.&nbsp;This unique position grants our enterprise partners a head start in their AI journey with access to the latest thinking in AI and deep expertise in the most powerful models, allowing us to craft solutions that maximize model efficiency, while negating the need for additional recruitment, team shifts, or tech investments, ensuring businesses are AI-enabled and ROI-focused.<h3>Case Study: Strategic Partnership with a Big 4 Retailer</h3>A retail giant required a precise, high-throughput solution for enhancing product descriptions for its online marketplace. Cluttered third-party data obscured listings from search engines, leaving millions in revenue on the table.&nbsp;Invisible crafted a dual-AI solution combined with human expertise in which one AI model enriched data from scraped content, while another identified and flagged errors. Errors were then refined by human experts.The outcome? Invisible enriched a vast product range swiftly, surpassing other solution providers. The retailer witnessed a staggering 9x ROI within a month.Ready to harness AI's power for your enterprise? Discover how Invisible can be your strategic partner <a target="_blank" rel="" href="https://www.invisible.co/ai/">here</a>.

Overcoming the AI Talent Scarcity Problem Requires a New Approach for Enterprises

The reality is shifting for enterprises deploying AI. They’re realizing that one Large Language Model (LLM) alone can only perform small-scale tasks, and orchestrating multiple models at once is deeply complex.&nbsp;Making matters worse, most enterprises lack the necessary infrastructure to foster interoperability between the LLMs, people, and tech stack that are critical to performing enterprise-scale business processes. Building that solution now is too costly, <a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tuning-too-costly-and-complex/">too challenging</a>, and it may be too late.&nbsp;With these constraints, innovative companies should explore a solution that incorporates multiple specialized models at once that augment humans in the loop. In this blog post, we’ll go deeper into the limitations of deploying a single LLM, and how multiple models can be steered toward solving real enterprise-level challenges.&nbsp;Let’s dive in.&nbsp;<h2>The Limitations of a Single AI Model for Enterprise</h2>A single AI model will address individual business problems on a small scale, but will either be too generalized or too specialized to address enterprise-scale business problems. Here’s why:&nbsp;Generalized AI models, like GPT or BERT, are designed to perform a wide range of tasks. They're akin to Swiss Army knives in that they are versatile and have broad applicability, but the vastness of their training data and capabilities can make them less efficient or accurate for niche tasks.Specialized AI models on the other hand, like a fine-tuned foundation LLM, are tailored to perform specific tasks with high precision, much like a surgeon's scalpel. While they’re accurate and efficient within their designated domain, they aren’t useful outside of it.&nbsp;Both of these types of models amount to point solutions that enterprise-scale business problems and their processes aren’t typically solved by. Instead, enterprise challenges are multi-faceted and dynamic, requiring an agile solution that orchestrates multiple models at once.&nbsp;<h3>How to Solve the Interoperability Problem</h3>The challenge of combining and integrating models within one system is the downside of repeated technological breakthroughs in the field. New developments are emerging so rapidly that universal standards for how models should cooperate never had a chance to materialize.The status quo, then, looks like this for the foreseeable future: the leading firms use differing model architectures like TensorFlow, PyTorch, or Keras, which have incompatible data formats.&nbsp;<h3>Chaining LLMs Doesn’t Cut It</h3>An emerging framework for getting multiple LLMs to work in concert is by <a target="_blank" rel="" href="https://towardsdatascience.com/a-gentle-intro-to-chaining-llms-agents-and-utils-via-langchain-16cd385fca81">chaining</a> them together. When chained, a series of LLMs can perform more complex tasks, with the input of one model being determined by the output of the model in the previous step.&nbsp;<a target="_blank" rel="" href="https://blog.enterprisedna.co/what-is-langchain-a-beginners-guide-with-examples/">LangChain</a>, for example, is an open-source framework for chaining LLMs to develop workflows and applications beyond what a singular LLM may enable. However, LangChain has <a target="_blank" rel="" href="https://safjan.com/problems-with-Langchain-and-how-to-minimize-their-impact/">well-documented limitations</a>, including:<ul><li>It is inflexible with integrations</li><li>It is prone to breaking as a chain’s complexity increases</li><li>It primarily supports prototypes and demos</li></ul>For an enterprise deploying LLMs, chaining will be insufficient because it doesn’t enable orchestration that includes the people central to a business process. Any high-value use case will require a critical layer of human judgment and quality assurance in conjunction with what LLMs can produce, but the handoff between AI and people isn’t simple.<h3>Enterprise leaders should consider how each of these moving parts works in tandem, and whether they have a technology infrastructure that: A) Allows for the interoperability of multiple AI models, and B) Orchestrates their workforce to be augmented by the work done by those AI models. Most don’t.&nbsp;</h3><h4>Interoperability in Practice</h4>Here’s an example of a business use case that illustrates the importance of interoperability between AI models and people. An international bank that operates in 50 countries is likely regulated in 50 different ways.&nbsp;That bank, like any other, is at constant risk of fraud attempts. Each country they operate in has a unique regulatory framework for how the bank can detect and thwart these attempts.&nbsp;The bank decides to deploy multiple AI models, specialized for specific tasks, in order to mitigate risk and stay compliant. Their success relies on a system in which tasks are handed off seamlessly between an AI model and people providing human intelligence.&nbsp;Let’s look at the process flow:&nbsp; Step 1: Initiation:<ul><li>A transaction is initiated anywhere within the bank’s international network.</li></ul>Step 2: Regulatory Understanding:<ul><li>Model A: Interprets local financial regulations and translates them into actionable rules for the transaction.&nbsp;</li></ul>Step 3: Transaction Analysis &amp; Fraud Detection:<ul><li>Model B: Monitors the transaction in real-time, profiling it based on risk using deep learning.</li><li>Model C: Examines the transaction against regional fraud patterns and flags suspicious activity.&nbsp;</li></ul>Step 4: Human Review:&nbsp;<ul><li>Suspicious transactions flagged by Models B and C are automatically sent to regional compliance officers for review. These officers, using their local expertise, make the final decision on the transaction's legitimacy.</li></ul>Step 5: Feedback Loop:<ul><li>Post-human review, results are fed back into Model B and Model C, refining their detection capabilities.</li></ul>Step 6: Report Generation &amp; Audit:<ul><li>Model E: Consolidates the decisions made, reasons for suspicion, and final outcomes into a report for internal audits and regulatory bodies.</li></ul>Step 7: Conclusion:&nbsp;<ul><li>Transaction is either processed or halted based on AI analysis and human review.</li></ul>An overarching platform integrates the outputs from all models involved in this process. It schedules tasks, ensures real-time communication between models, incorporates human decisions, and triggers appropriate actions, like halting a transaction.<h2>How Invisible Enables Enterprise AI Deployment</h2>Our process orchestration engine is the ideal platform to support AI interoperability that also augments human input. In conjunction with our global workforce of trained operators, we enabled a <a target="_blank" rel="" href="https://www.invisible.co/results/">Big 4 retailer</a> to leverage multiple AI models to solve a uniquely complex, large-scale business problem.&nbsp;The client attracted over 50 million online visitors per month but had huge volumes of third-party product listings that contained missing and erroneous data fields that prevented thousands of products from appearing on search engines. This meant they couldn’t compete with the biggest player in the space, Amazon.&nbsp;Invisible applied generative AI to perform the heavy-lift data enrichment steps at an unparalleled scale. Another model flagged data containing errors, which were resolved by a dedicated team of operators who applied a finer touch.&nbsp;Our client saw a near-immediate 9x ROl. It took just 30 days.&nbsp;The next 10,000 products outperformed in their category by 140%. Overall, 44% of SKUs entered page one for search results, increasing both visibility and sales velocity.Ready to solve your complex business problem for good? See more about how we enable enterprise AI deployment <a target="_blank" rel="" href="https://www.invisible.co/ai/">here</a>.&nbsp;

One LLM Alone Can’t Solve Enterprise Business Problems

A pre-trained large language model (LLM) will only support small-scale generalized tasks. For an enterprise deploying an LLM to perform a specialized task like answering patient inquiries, generic responses aren’t enough.An enterprise-scale use case requires a model that is fine-tuned on a substantial amount of human-generated data specific to that need in order to create value. Otherwise, enterprises deploying generalized models risk overinvesting in a tool that under-delivers.While orchestrating the creation of a fine-tuning dataset is costly and complex, enterprises investing heavily in an AI model can’t afford to not fine-tune it. In fact, <a target="_blank" rel="" href="https://fortune.com/2022/07/26/a-i-success-business-sense-aible-sengupta/">80-90%</a> of enterprise AI projects reportedly fail because they either can’t reach or progress beyond the proof of concept phase, suggesting that models aren’t performing as desired.In this blog post, we explore how LLMs are pre-trained and why fine-tuning is a critical stage in enterprise AI deployment. Let’s dive in.<h2>How LLMs are Pre-Trained</h2>Large language models are pre-trained on billions of unlabeled and unstructured data points. In order to make sense of that data, the model needs to undergo a process of machine learning.There are three main types of machine learning that do this:<ul><li>Supervised Learning: The model learns from provided examples of inputs and their corresponding outputs, much like a student learning from a textbook with solutions.</li><li>Unsupervised Learning: The model finds patterns or groups within the data on its own like a child sorting toys without being told specific categories to sort them into.</li><li>Reinforcement Learning: The model learns by interacting with an environment and receiving feedback (rewards or penalties) for its actions, similar to teaching a pet new tricks with treats or scolding.</li></ul>For an LLM, the overarching goal of these techniques is to create new data or content that is coherent, realistic, and aligns with the characteristics of the training data. Because the training data comes from such a broad source (the internet), the end state constitutes a vanilla model that can only perform generalized tasks.<h3>Why Vanilla Models Need to Be Fine-Tuned</h3>Vanilla models on their own can perform small-scale business tasks like content generation. An enterprise-scale task would instead be a complex process involving multiple points of specialized outputs from a fine-tuned model, which requires the safe handling of proprietary data.Think of a vanilla model as if it were a new hire at your company. It’s unlikely that a new employee would be expected to perform high-value, domain-specific tasks prior to onboarding, training, and on-the-job experience.Like a new hire, a vanilla model falls short of meeting most enterprises' needs right out of the box for the following reasons:<ol><li>Lack of Specialization: Vanilla models are general-purpose and basic. Business problems, however, come with specific challenges and nuances that require specialized knowledge to address those challenges effectively.</li><li>Limited Adaptiveness: While they might work well for basic or introductory scenarios, a vanilla model won’t adapt well to diverse or unseen data. Businesses operate in dynamic environments where data can vary widely, and a model's (and an employee’s) ability to adapt is crucial.</li><li>Lack of Explainability: In business contexts, being able to explain why a specific prediction or classification was made is just as important as the output itself. Stakeholders might need to explain these decisions to others in the organization, customers, or even regulators. Newcomers and vanilla models don’t offer this level of interpretability.</li><li>Compliance Concerns: Especially in sectors like finance and healthcare, models must adhere to industry regulations. Models and people need to be trained compliantly, or else an enterprise could be exposed to the consequences of non-compliance.</li></ol><h2>Where Generalized Models Go Wrong</h2>Let’s break down a practical example using a real business use case. In this scenario, a national pharmacy chain deploys a chatbot, based on a vanilla model alone, on its website and mobile app.The chatbot is designed to assist users in understanding their medication, its side effects, interactions with other medications, and general health advice. Because the model hasn’t been fine-tuned, the pharmacy chain runs these risks:<ul><li>Vanilla LLMs may be able to provide generalized medical information, but can’t accurately represent specific dosages, contraindications, or recent medical findings that illuminate health risks to patients.</li><li>The model may fail to catch nuance within the context of a user asking about medications or describing symptoms, leading to incorrect or inappropriate medical advice that exposes the company to legal concerns.</li><li>Foundation models are meant for generalized audiences, and will likely lack the sensitivity needed to appropriately interact with a patient in distress.</li></ul>A foundation model may be more likely to inadvertently misuse a patient’s health data, harming their privacy and exposing the company to even more legal concerns.Realizing that such a model threatens to erode patient trust and do more harm than good, the pharmacy chain may reach a point where they turn away from the model. The solution to avoid limiting the use of these powerful tools is fine-tuning.A fine-tuned model, trained on data produced by experts in the medical field, would comfortably handle patient inquiries and provide better, expert-backed answers. <a target="_self" rel="" href="/contact-us">Speak to our team</a> to learn more about how we fine-tune LLMs for specialized use cases and support enterprises in deploying AI models for complex business processes.

Fine-Tuning is Key To Enterprise AI Deployment

With budgets and teams for AI innovation materializing at light speed, enterprises should know what it takes to train a model to solve enterprise-scale problems and yield a return on investment. In particular, an enterprise should care about HHH AI.HHH is a framework popularized by <a target="_blank" rel="" href="https://www.anthropic.com/index/training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback">Anthropic</a> for how helpful, honest, and harmless a model is. A model that meets these standards must be aligned with human preference through human-generated data in order to meet the needs of users and enterprises.In this blog post, we will explore what makes a model helpful, honest, and harmless, particularly focusing on large language models (LLMs). Then, we will explain how Invisible helps the leading firms and enterprises to align their models.Let’s dive in.<h2>Why is HHH Important?</h2>Building AI isn’t about just creating smart machines. Beyond solving complex enterprise business problems, HHH is a framework that ensures AI is beneficial for humanity. The alternative is a technology that risks harming the people who use it. Unfortunately, models are not HHH right out of the box.<h4>The Pitfalls of a Vanilla Model</h4>Because building a LLM from scratch is <a target="_blank" rel="" href="https://hai.stanford.edu/news/new-approach-trains-large-language-models-half-time#:~:text=But%20a%20handful%20of%20large,or%20hundreds%20of%20times%20that">extremely expensive</a>, let’s begin where most enterprises do: exploring vanilla models. A vanilla large language model (LLM) is a basic, general-purpose model without tweaks or special enhancements that generates text based on user prompts.Vanilla LLMs are trained on a massive corpus of internet text providing them with a broad scope, but this breadth can sometimes come at the cost of depth in specific topics. As a result, these types of models will be able to engage in a topic area that’s a mile wide but an inch deep.Vanilla models that have not been fine-tuned are prone to “hallucinating” answers, or generating content that seems plausible but is <a target="_blank" rel="" href="https://www.washingtonpost.com/technology/interactive/2023/ai-chatbot-learning/">factually incorrect</a> or can cause harm. For an enterprise business, that means deploying a vanilla model leads to two problems:<ul><li>Unhelpful Outputs: Enterprises may create, for example, a conversational AI application like a chatbot that’s supported by a vanilla model that is capable of answering a diverse set of questions, but will lack the domain specialization needed to be truly helpful to a user.</li><li>Harmful Outputs: Text datasets sourced from the internet are likely to include biased and even harmful content, and models trained exclusively on this data can pass this content onto internal users or customers.</li></ul>To avoid hallucinations and solve enterprise-scale problems through their AI investment, enterprises need to adopt a helpful, honest, and harmless AI framework centered around fine-tuning.<h3>Defining What HHH Really Means</h3>Let’s go deeper into what HHH means, with the help of <a target="_blank" rel="" href="https://www.arxiv-vanity.com/papers/2112.00861/">researchers at Anthropic</a>.<h4>What is Helpful AI?</h4>AI systems that are helpful:<ul><li>Are trained and fine-tuned with users’ needs and values in mind</li><li>Clearly attempt to conduct the operation prompted by the user, or suggest an alternative approach when the task requires it</li><li>Enhance productivity, save time, or make tasks easier for users within a given use case or range of use cases</li><li>Are accessible to users across a broad spectrum of abilities and expertise</li></ul><h4>What is Honest AI?</h4>AI systems that are honest:<ul><li>Provide accurate information when they can, and communicate clearly to users when they can’t produce an accurate output</li><li>Express uncertainty and the reason behind it</li><li>Are developed and operate transparently so users can understand how they work and trust what they generate</li></ul><h4>What is Harmless AI?</h4>AI systems that are harmless:<ul><li>Don’t comply when prompted to perform a dangerous task</li><li>Are trained within frameworks that transparently and actively mitigate bias</li><li>Don’t discriminate or demonstrate bias explicitly or implicitly</li><li>Communicate sensitively when engaging with a user on a sensitive topic</li></ul><h3>The Key to HHH AI</h3>The best way to reinforce a model’s helpfulness, honesty, and harmlessness is through human feedback. Reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT) are two increasingly popular techniques for aligning models with HHH in mind.<ul><li>RLHF: Human AI trainers rank, rate, and edit a series of model outputs to reinforce the generation of responses that align with human preferences.</li><li>SFT: Human AI trainers, often domain experts, produce demonstration data like example conversations between a chatbot and a user to train a model to align with a specific task.</li></ul>In general, enterprises can’t conduct these tasks on their own because they’re labor-intensive and complex. Thousands of hours of RLHF and SFT tasks need to be performed, at times with experts in specific industries or fields, to align a model to human preferences and use cases.<h2>How Invisible Trains HHH AI</h2>Invisible, the operations innovation company, promotes HHH with an innovative, end-to-end approach to fine-tuning models in three distinct ways:<ol><li>Ethically creating human-generated data at scale: With our platform, we do what no point solution or human-only solution can do on their own: optimally align AI. Our platform assigns AI trainers tasks that they are specialized in, presents a simple interface configured to those specific tasks, and collects data structured perfectly for a model to ingest. See an example of an RLHF task on our platform: <img src="https://invisible-strapi-cms-image-uploads.s3.us-east-1.amazonaws.com/blog_test_71057bfcef_1_e16342db16.gif" alt="blog_test_71057bfcef (1).gif" width="800" height="569"></li><li>Putting the best people behind the model: An innovative and agile recruitment engine enables us to find and hire AI trainers that are representative of the population at large and have expertise across domains. Through this approach, we are able to limit demographic concentration among teams training models and provide the unique level of domain expertise required to fine-tune models for enterprise use cases. For one leader in AI, we hired 250+ AI trainers in three months, including domain experts with advanced degrees.</li><li>Red Teaming: We root out inaccuracy, bias, and toxicity in a model through a technique called Red Teaming. Our expert team deliberately attempts to elicit a harmful and untrue output from a model, and then flags vulnerabilities in the model.</li></ol>Ready to achieve a return on your AI investment? <a target="_self" rel="" href="/contact-us">Let’s chat</a>.

How to Train Helpful, Honest, and Harmless AI

We're well into the AI hype cycle. Enterprises across industries are scrambling to understand how they can leverage artificial intelligence to augment their businesses.Unfortunately, enterprises are failing <a target="_blank" rel="" href="https://fortune.com/2022/07/26/a-i-success-business-sense-aible-sengupta/">80-90%</a> of the time as they try to implement AI into their business. Oftentimes this means they don’t reach ROI or their AI projects simply can’t get to launch due to internal friction.In general, innovative enterprises will take one of two approaches to AI deployment within their organizations. They either A) train an AI model from scratch or fine-tune an open-source model in-house or B) deploy third-party tools, often point solutions, within existing business processes.Too often these approaches begin from a mindset that "AI" is the panacea for complex operations problems. In this blog post, we will focus on the challenges enterprises face when taking these approaches to leverage AI.Let’s start with Challenge #1.<h2>Challenge #1: In-House AI Training and Fine-Tuning Is Too Costly and Complex</h2>The organizational requirements for training an enterprise-scale AI model from scratch or fine-tuning an existing model for a specific use case are extremely high. The main barriers to building a successful model or effectively fine-tuning one are cost and complexity.<h4>Costs of Training An In-House Model</h4>Costs for building a large-scale model like a large language model (LLM) <a target="_blank" rel="" href="https://www.linkedin.com/pulse/selling-data-44-cost-creating-ai-ayal-steinberg/">reportedly</a> start around $2-4 million, with millions in recurring costs to run them. These costs include expert AI talent, human data trainers, infrastructure, and compute.Enterprise leaders can be lured into spearheading in-house AI model training projects that either don’t yield an effective model or creep far past their forecasted costs. That’s because of an outdated approach to model development.In the beginning, AI researchers trained large language models (LLMs) using data easily collected on the internet. However, just because data is easily available does not mean it is honest, helpful, or harmless to the user.On the contrary, data easily collected from the internet can be harmful, biased, and toxic, the consequences of which can be worse for the enterprise than an ineffective model. Like a child, these nascent models require supervision and reinforcement of good behavior in order to align with user preferences.Supervised fine tuning (SFT) and reinforcement learning (RL) are the methods used by researchers to remove bias and harmful answers from responses generated by LLMs. But performing these techniques to align an AI model is too costly for enterprises to perform in-house.Thus, enterprise leaders can face project failure or perpetually chase ROI by exclusively managing AI model training internally.<h3>Costs for Fine-Tuning an Existing Model</h3>Fine-tuning an existing model is much cheaper. But the investment is still larger than enterprises might anticipate.One of the most burdensome costs enterprises will incur in fine-tuning is data preparation. That’s because out-of-the-box AI tools don’t fully support complex business processes.To effectively support a specific use case, open-source models will need to be fine-tuned on cleanly structured human-in-the-loop data. Enterprises will often chase fast AI solutions by feeding AI models with poorly structured, incomplete datasets that produce unuseful outputs.A more effective data preparation approach in the fine-tuning lifecycle first involves a strong data preprocessing phase. Data preprocessing is a largely manual process that turns unstructured data into a dataset ready for an AI model to be trained on.Then, the model will need to be fine-tuned on specifically created datasets, like demonstration data, to align the model with the needs of the specific use case. An example of demonstration data is a dataset of thousands of meticulously crafted conversation examples between a person and a chatbot for a customer support use case.These steps become too costly for internal teams because they can’t be automated. People-driven tasks don’t scale well.<h3>How Invisible Solves the Cost And Complexity Problem</h3>Scaling AI training and fine-tuning is complex and labor-intensive. Most enterprises aren’t equipped to do it without outside help.Many enterprise leaders will be familiar with and turn to BPOs. However, traditional BPOs can’t provide the quality of human-in-the-loop data required to produce or fine-tune an effective model.Invisible provides a complete <a target="_parent" rel="" href="/ai-training/">AI training solution</a> for enterprises that are building foundational models or fine-tuning open-source models for specific use cases. Our platform is 100% configurable to enable cost-effective, human-in-the-loop AI training and fine-tuning at scale.Our global team of AI trainers receives steps unique to their expertise through our platform. Each step is performed via a simple user interface specifically designed for that step’s goal.See that capability in action with an example of a Reinforcement Learning from Human Feedback (RLHF) step on our platform:<img src="https://invisible-strapi-cms-image-uploads.s3.us-east-1.amazonaws.com/blog_test_71057bfcef_51113bdd0e.gif" alt="blog_test_71057bfcef.gif" width="800" height="569">In this example, an AI trainer provides a prompt and then receives a sample of four outputs to rank for accuracy and helpfulness. They can also report if an output is toxic or harmful, providing metadata for why that’s the case.Without a solution like Invisible, enterprises are forced to build this infrastructure themselves and hire expensive talent, or turn to a BPO that provides poor-quality data.Ready to harness AI’s potential? <a target="_self" rel="" href="/contact-us/">Speak to the team</a>.

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