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3 Tips for Setting and Managing Realistic Expectations for AI Projects  

This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI in Regulated Industries [And How to Overcome Them].

With nearly 88% of AI projects failing to reach production, it’s no wonder that some leaders—especially those in regulated industries—are hesitant to trust AI.

Why is it that so few models reach production?

Expectations aren’t aligning with AI performance—and regulated industry leaders are left disappointed, wondering where their projects went astray.

It’s critical that regulated industry leaders set realistic expectations and measurable goals to track the success of their AI projects. 

Your AI project should start with a strategy that includes these expectations and goals. Establishing meaningful metrics before the project’s launch can also help accurately measure the performance of the AI. Doing so ultimately ensures your organization is prepared to learn from situations where project performance deviates from what’s expected.

Use these three tips to establish and manage realistic expectations for your next AI solution.

1. Know When AI Is the Right Fit

AI is like a hammer: it’s a powerful tool, but of frustratingly little use when what you really need is a screwdriver. Before evaluating your goals and expectations for an AI project, determine if the solution to your problem should be AI. 

AI is at its most efficient when it’s used to augment processes involving an appropriate amount of insulated data. Choosing the right human-led tasks to augment allows workers to focus their attention elsewhere, increasing overall efficiency and productivity for your organization.

2. Define and Track Key Metrics

Sometimes, organizations make the mistake of only choosing key metrics tied to accuracy when defining the success of an AI model. Other metrics are overlooked, despite the insight an organization could gain from them. Here are a few examples:

  • How frequently you’ve learned something new from the model.
  • The frequency of biased outcomes.
  • The cost of retraining a model with a new data set.  

There’s no set list of metrics you must include in your AI monitoring, but your organization should choose performance indicators that make the most sense for your goals. These measurements can then be used in the future as a baseline. 

3. Include Humans in the Feedback Loop

AI requires testing, piloting, and continuous adjustments—it’s not a solution that can be set and left alone for weeks at a time. Involving humans in the review process allows organizations to align their expectations with actual outcomes.

Frequent communication throughout an AI project ensures that everyone involved remains aware of what’s happening. As changes in budget, goal, or data occur, the informed team can then adjust their expectations accordingly. Small adjustments to timing and budgets are far more manageable than major shifts in expectations.

AI for regulated industries is not a quick fix, but designing with trustworthy AI at the foundation can outweigh the costs of challenges—especially if you maintain realistic expectations.

Keeping these three tips top of mind while designing and developing AI can prevent disappointment in the results of your project. 

Uncover Other AI Design Challenges To Avoid

Interested in learning more about the AI design challenges faced by regulated industry leaders? Globally renowned AI strategist Cal Al-Dhubaib shares some of the top challenges he’s seen throughout his years as a data scientist—and how you can avoid them—in the complimentary resource, Top Challenges to Designing AI Solutions in Regulated Industries [And How To Overcome Them]. Click below to download the PDF.

Nicole Ponstingle McCaffrey is the COO and AI Translator at Pandata.