Most Enterprises Are Investing In AI In The Wrong Order

Hiren is CTO & co-founder of Simform. With 15+ years in tech engineering, he leads teams in cloud architecture, MLOps and secure agentic AI.

In many enterprise AI programs that I have seen, the model is not what blocks production. It’s the layer underneath it. In those programs, the data was neither trustworthy nor was the platform reliable. What AI needs to run was simply not there.

S&P Global Market Intelligence surveyed more than 1,000 enterprises across North America and Europe and found that 42% of companies abandoned the majority of their AI initiatives before reaching production. The year before, that number was 17%. On average, 46% of proof-of-concept projects never made it out. That gap between adoption and production is where the foundation problem shows up.

I use three dependent layers when evaluating the readiness of an AI initiative to scale: a governed, trustworthy data foundation, a reliable and observable platform and governed agentic systems on top.

Visible AI capability competes better for capital than invisible infrastructure. Model licenses and agent capabilities go in first because they produce quick results leadership can point to before the quarter ends. The data foundation comes later, if at all, because a working agent is easier to demonstrate than data lineage, access controls or governance maturity.​

When The Investment Order Is A Decision Someone Made​

In a recent client engagement with a manufacturing company, the team approved an AI initiative with strong talent, well-scoped problem and real investment. Production-scale capital went into AI capability before the data foundation that should have supported it. The foundation investment did not get approved until months into the program. By the time the gap surfaced, nothing the model produced could be trusted because the data lineage was missing.