Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain domains, there’s little tolerance for iteration, especially in something like life sciences, when the AI application is facilitating new treatments to markets or diagnosing diseases. Even slightly inaccurate analyses and assumptions early on can create sizable downstream drift in ways that can be concerning.In analyzing dozens of AI PoCs that sailed on through to full production use — or didn’t — six common pitfalls emerge. Interestingly, it’s not usually the quality of the technology but misaligned goals, poor planning or unrealistic expectations that caused failure.
Here’s a summary of what went wrong in real-world examples and practical guidance on how to get it right.Lesson 1: A vague vision spells disasterEvery AI project needs a clear, measurable goal. Without it, developers are building a solution in search of a problem. For example, in developing an AI system for a pharmaceutical manufacturer’s clinical trials, the team aimed to “optimize the trial process,” but didn’t define what that meant. Did they need to accelerate patient recruitment, reduce participant dropout rates or lower the overall trial cost? The lack of focus led to a model that was technically sound but irrelevant to the client’s most pressing operational needs.Takeaway: Define specific, measurable objectives upfront. Use SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound). For example, aim for “reduce equipment downtime by 15% within six months” rather than a vague “make things better.” Document these goals and align stakeholders early to avoid scope creep.Lesson 2: Data quality overtakes quantityData is the lifeblood of AI, but poor-quality data is poison. In one project, a retail clie …