Build or Buy? Why the Future of AI Platforms May Demand Both: Insights from MachineCon NY

One AI question continues to divide enterprise leaders: is it faster, and more effective, to build in-house or to buy off-the-shelf? While the promise of proprietary platforms offers the lure of long-term differentiation, vendor solutions often boast speed and a lower up-front burden. The real challenge, as leaders made clear in a recent executive discussion from MachineCon New York, isn’t choosing one, rather, it’s learning how to orchestrate both.

The conversation, moderated by Arvind Balasundaram, Executive Director of Commercial Insights & Analytics at Regeneron, brought together senior leaders who’ve grappled with this question firsthand: Geeta Pyne, Senior Managing Director and Chief Architect at TIAA; and Ferris Zhang, Global Product Lead for Business Experimentation and Optimization at Mastercard.

What Speed to Insight Really Demands

While “speed to insight” remains a coveted metric in AI adoption, the consensus was that it’s often misunderstood. The rush to results can obscure the more fundamental need for problem clarity and iterative decision-making. Rather than treating speed as a race to code, leaders argued for a more principled process: one that includes fast, low-risk experimentation, clear “go/no-go” filters, and tight alignment with business impact from the outset.

This is an “express lane” mindset that not every project should advance at the same pace, and not every idea is worth three months of investment. By creating predefined lanes for experimentation, teams can quickly assess which use cases merit deeper commitment, before wasting cycles on misaligned or premature builds.