Around 80% of artificial intelligence (AI) initiatives are doomed to failure, according to recent research. While some projects fail due to the lack of quality data or expertise in either AI engineering or relevant domains, a primary cause of failure is the lack of clear success criteria. Many enterprise AI or R&D teams become stuck in the experimentation and proof of concept (POC) stages of projects without a clear path forward, often using up budgets without providing ROI. These projects become fairly typical AI failures.
In this blog post, we’ll explore how AI and innovation leaders can prevent these AI failure scenarios by establishing clear success criteria supported by data and research.
Want to learn more about how you can launch better artificial intelligence projects that drive ROI and adoption at your organization? Learn how you can avoid the “AI Death Trap” of failed projects.
Many leaders are feeling the pressure to launch AI solutions from their boards and markets, making it easy to get drawn into an AI-first approach when choosing your projects. While an agentic application built on one of the world’s leading large language models may sound impressive, establishing this as the end goal is the most common way that leaders put the cart before the horse.
Your first step in building your AI project strategy is to start with the problem rather than the solution. Most organizations have plenty of problems that can be addressed with AI or machine learning — there’s almost always a process that can move faster or become more accurate, a manual task that needs to be scaled, or information that can be more easily accessed and made digestible.
To choose the right problem to address with your AI project, take the following into consideration:
Once you’ve chosen a specific problem to address with an AI application, you can start laying out your success criteria. If the problem is a slow or heavily manual process, for example, you can set a goal for how much time should be saved on the process with the use of your AI application.
While identifying a problem is important, the adoption and implementation of your AI application depends entirely on the people who are going to be using it in their day to day work. That’s why researching your end user audience via focus groups or individual interviews is key to launching a successful AI application in the real world.
While the questions you need to ask your audience depends on the use case and end user, here are some questions that can help guide your research:
End user research can help to establish more clear success criteria for your AI project, as well as establish rapport between them and the AI development team. This can make the experimentation and adoption stages of your AI project much easier and faster.
Organizations that include end user research as a part of a holistic AI model evaluation strategy are much better positioned to launch successful AI applications that deliver ROI.
Once you have a clear understanding of the problem you’re solving and the people your project will affect the most, your team will be much better prepared to propose potential AI tools or solutions.
At this stage, you can propose specific AI applications tailored to your problem and audience. These hypotheses should enable end user productivity and creativity while reducing time spent on repetitive, manual, administrative tasks.
Other elements to consider when building your proposal include:
Your hypothesis should also include research on the costs and value of the application, along with how much and when the business can expect to see ROI. Gartner’s model for calculating costs and value can be a useful place to start.
At this point, your goals and success criteria should be clear, taking into account both the specifics of your business problem and the end users that it will most significantly impact.
Want to learn more about how you can launch better AI projects that drive ROI and adoption at your organization? Learn how you can avoid the “AI Death Trap” of failed projects.