Why AI Pilots Stall: Data, Complexity, and the Build vs. Buy Debate With Ashwin Mehta
AI pilot programs are consistently failing to deliver measurable business value, with a primary cause identified as a lack of clearly defined problem statements guiding these initiatives. Ashwin Mehta, an AI strategist with experience leading enterprise transformations, emphasized that many organizations initiate AI pilots without specific objectives, resulting in projects that struggle to demonstrate impact or justify further investment. This lack of focus often leads to stalled initiatives, rather than progress into scalable production environments.
The discussion outlined how mid-market and small businesses typically implement AI by acquiring SaaS tools with embedded AI features, rather than building bespoke solutions. Ashwin Mehta observed that while “build versus buy” considerations have shifted as orchestration and database platforms become more accessible, custom development still brings additional risk, skill requirements, and long-term maintenance burden. Even as technical barriers decrease, organizations are cautioned to weigh lifecycle costs and operational support needs before pursuing custom builds.
Data management was highlighted as a recurrent challenge, both from an organizational readiness perspective and regarding regulatory risk. Ashwin Mehta underscored the importance of establishing a single source of truth for business-critical data and classifying information by its regulatory sensitivity. Without such data discipline, adoption of AI tools—especially in regulated sectors—becomes a source of uncertainty, with organizations defaulting to restrictive or prohibitive AI policies due to inadequate risk visibility.
For MSPs and technology leaders, the operational implications are clear: pilots without rigorous scoping and problem definition are unlikely to progress, and sustainable AI adoption requires purposeful data governance and clear frameworks for project prioritization. With the complexity of AI implementations extending beyond technical issues to include cost volatility, compliance, change management, and skills gaps, providers must approach each initiative with a structured, risk-aware mindset and ensure ongoing oversight as both technology and regulatory landscapes evolve.
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