Turning AI projects into tangible value often feels like an uphill battle. Research suggests that 50% to 80% of these initiatives fall short – a figure shared by Ivo Bernardo from DareData. If you’ve ever wrestled with these challenges, you’re not alone.
Unlike traditional IT projects, AI introduces a level of uncertainty that adds extra layers of complexity. Issues such as unclear requirements and scope creep are magnified when outcomes aren’t guaranteed. That’s why it pays to set clear, realistic goals right from the start.
One common stumbling block is the lack of a well-defined success metric. Instead of juggling multiple objectives, focusing on a single, measurable target that aligns with your business goals can steer your project in the right direction.
Another challenge comes from involving too many stakeholders. While collaboration is key, too many opinions can dilute accountability. Often, having one or two dedicated champions helps keep the project focused and moving forward.
On the technical side, relying on tools designed for research—like Python notebooks—for production can lead to issues. For a robust deployment, it’s essential to integrate your solution with existing systems and secure strong support from your engineering team.
It’s also important to temper your expectations. AI is inherently probabilistic, which means 100% accuracy isn’t realistic. Setting achievable targets and clarifying what AI can—and can’t—do helps prevent disappointments down the line.
Finally, remember that not every problem needs an AI solution. In some cases, sticking with a simpler, rule-based system can preserve user trust and reduce maintenance headaches.
