Where Enterprises Lose Momentum with AI Deployment

The allure of turning AI into a competitive advantage is just too compelling to ignore for enterprises.  That’s why many of them are either thinking about, or already deploying, generative AI in some form.

However, as the level of AI adoption within these enterprises increases, executives are finding the implementation has more challenges than they anticipated.

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. 

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.

Snapshot of the AI Deployment Lifecycle 

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. 

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. 

1. Identify Problem

• Scope: Defining the business challenge or opportunity where AI can be leveraged.

• Potential Roadblocks: Lack of clarity or consensus on the problem.

• Business Units Involved: Executive Leadership, Strategic Planning.

• Inputs Required: Comprehensive understanding of business objectives, industry challenges, and potential AI capabilities.

2. Collect and Prepare Data

• Scope: Gathering, cleaning, and organizing data relevant to the problem.

• Potential Roadblocks: Insufficient data, data privacy concerns.

• Business Units Involved: Information Management, Legal & Compliance.

• Inputs Required: Data sources, data cleaning tools, and data privacy guidelines.

3. Select Model

• Scope: Choosing and fine-tuning a suitable AI model.

• Potential Roadblocks: Selecting an inappropriate model, overfitting.

• Business Units Involved: Research & Development, Data Science.

• Inputs Required: Problem definition, data, and machine learning frameworks.

4. Integrate via API into Tech Stack

• Scope: Ensuring the model communicates seamlessly with existing systems.

• Potential Roadblocks: Incompatibility issues, data integration challenges.

• Business Units Involved: IT & Systems, Software Engineering.

• Inputs Required: API specifications, integration tools, and system architecture details.

5. Test and Validate

• Scope: Assessing the model’s performance and reliability.

• Potential Roadblocks: Unforeseen bugs, poor performance.

• Business Units Involved: Quality Assurance, Data Science.

• Inputs Required: Testing frameworks, validation criteria, and performance metrics.

6. Deploy

• Scope: Launching the AI solution in a live environment.

• Potential Roadblocks: Deployment failures, unexpected behavior.

• Business Units Involved: Operations, IT & Systems.

• Inputs Required: Deployment plans, rollback strategies, and operational guidelines.

7. Monitor and Evaluate

• Scope: Ongoing performance tracking and analysis.

• Potential Roadblocks: Inadequate monitoring tools, performance degradation.

• Business Units Involved: Operations, Data Science.

• Inputs Required: Monitoring tools, evaluation metrics, and performance benchmarks.

8. Iterate

• Scope: Making necessary improvements based on feedback and performance data.

• Potential Roadblocks: Resistance to change, lack of actionable insights.

• Business Units Involved: Research & Development, Data Science.

• Inputs Required: Feedback loops, iteration plans, and updated data.

9. Scale

• Scope: Expanding the AI solution across various departments or functions.

• Potential Roadblocks: Scalability issues, inadequate resources.

• Business Units Involved: Operations, Executive Leadership.

• Inputs Required: Scalability assessment, resource allocation, and expansion plans.

10. Document, Train

• Scope: Educating stakeholders, documenting processes, and training users.

• Potential Roadblocks: Resistance to adoption, inadequate training materials.

• Business Units Involved: Human Resources, Training & Development.

• Inputs Required: Training materials, documentation tools, and communication plans.

11. Compliance and Governance

• Scope: Ensuring regulatory compliance and establishing governance mechanisms.

• Potential Roadblocks: Regulatory changes, compliance violations.

• Business Units Involved: Legal & Compliance, Governance.

• Inputs Required: Regulatory guidelines, auditing tools, and governance frameworks.

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 5 years or more. 

The Roadblocks Slowing Enterprise AI Deployment Down

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. 

For many enterprises, what stands in the way of a successful deployment:

1. 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.

2. 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.

3. 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.

4. 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.

How Keeping Humans in the Loop Maintains AI Momentum

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.

Why Enterprises Should Turn to a Strategic Partner

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. 

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:

1. 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. 

2. 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. 

3. 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.  

These three capabilities yield AI-enabled outcomes for enterprises. Interested in learning more? Speak to the team.