Integrating artificial intelligence (AI) into all areas of business is critical to a company’s ability to gain—or maintain—a competitive edge. Organizations report increased revenue and decreased costs in the business functions where they’ve implemented AI, according to 2023 McKinsey & Company research. And two-thirds of company representatives surveyed expect to do more AI integration in the next few years.

AI can transform operations, streamline processes, enhance decision-making, and drive innovation. However, the success of AI initiatives hinges on an organization’s ability to select the right projects—ones that align with their strategic objectives and can deliver tangible value.

In my years as a management consultant at Bain & Company, I witnessed a significant number of businesses launch AI projects that either failed to be completed, failed to be deployed to production, or failed to deliver the expected outcomes. Then, as an artificial intelligence and data analytics leader at Toptal, I regularly heard from enterprise and startup clients who said that even when a past AI project succeeded, it often took far longer to complete than they had initially imagined.

In my experience, there is generally a disconnect between internal data science teams and the rest of the business, which can lead to imprudent investments in AI. I recall one large insurance client telling me about an experience he’d had before seeking help from Toptal: The company was interested in leveraging AI to optimize their call center operations. And their in-house data scientists, excited by the potential cost savings of predictive staffing, built an extremely accurate model to forecast the number of calls by call type.

But the business was ultimately unable to act on the information. The forecast data was too granular and the forecast window too short for managers to enact realistic staffing changes to the call center. After the data team adjusted for practical business requirements (such as requiring enough time to have schedules released and reviewed by their staff; allotting enough time to recruit, hire, and train reps; and accounting for a lack of flexibility in continuous hours staffed), there was limited value available to optimize the call center’s operations.

The high failure rate and longer-than-expected timelines of many AI initiatives underscore the need for organizations to adopt a more strategic and systematic approach to evaluating these opportunities. By conducting thorough assessments and due diligence before embarking on an AI project, organizations can increase the likelihood of success and maximize their return on investment (ROI). The framework Toptal uses to evaluate AI opportunities addresses these issues directly, offering leaders a pragmatic method for classifying and prioritizing AI projects. The analysis evaluates initiatives based on two main factors: the value they deliver and the ease of implementation.

Assessing the Potential Value of AI Projects

Identifying the value associated with a potential AI project involves aligning a company’s AI initiatives and strategic goals, estimating financial impact, and understanding the opportunity cost of not embracing AI in the given use case. Value must be assessed upfront, prior to kicking off an AI initiative, to avoid potentially wasting money on an unnecessary project simply because it seems cool or trendy. For example, with the rise of generative AI (Gen AI) tools and chatbots, I witnessed many companies jump into building their own versions—and then struggle to demonstrate value and drive adoption. On the other hand, companies I’ve seen succeed with building Gen AI tools took a systematic approach and first identified areas of their business where Gen AI could reduce costs and increase productivity.

Data scientists will always be eager to explore and build with cutting-edge technologies, but they need coaching from business leaders on exactly which problems need to be solved. The best place to start is with the financials.

What Is the Financial Impact?