In the race to dominate the AI landscape, most enterprises are focused on the wrong race. They are obsessing over the processing power of GPUs and the sophistication of Large Language Models (LLMs), while ignoring the invisible friction that is actually stalling their progress: decision latency.
In the final part of CDO Magazine’s series, Sanjay Sankolli (Truist) and Karan Jain (NayaOne) hit on a hard truth: the bottleneck for AI isn’t the code; it’s the lack of infrastructure for technology adoption.
Connecting the Dots: From Pilots to Production
Throughout this series, we have tracked the evolution of the enterprise AI journey. We’ve explored why initiatives stall after promising pilots (Part 1), where AI is actually delivering measurable impact (Part 2), and how to evaluate solutions without losing control (Part 3).
But in this final chapter, we reach the ultimate hurdle. You can have the best use cases and the best evaluation criteria, but if your organisation lacks the “readiness” to act on them, you hit the Decision Latency Problem. As Sanjay notes, “The institutions that win won’t be the ones with the most AI. They will be the ones whose data, people, and decisions are ready for AI.”
The Infrastructure Gap: Why AI Stalls
Think about the modern enterprise stack. We have robust infrastructure for almost every critical function: CI/CD for code, ITSM for operations, and ERP for finance.
But what do we have for the pipeline between “we need this technology” and “it’s running in production”?
For most, that pipeline runs on spreadsheets, fragmented committees, and 14-month timelines. This is the Decision Latency Problem. As Sanjay notes, “The institutions that win won’t be the ones with the most AI. They will be the ones whose data, people, and decisions are ready for AI.”
From Gating to Guardrails
To solve for the “stalled pilots” discussed in Part 1, we have to move governance from a late-stage gate to an embedded system. VEI turns the evaluation phase into a high-speed guardrail. It replaces the scattered, opinion-based process living in SharePoint and Jira with a system of record that provides:
- Evidence-Based Decisioning: Structured frameworks that improve the quality of decisions, ensuring the “measurable impact” sought in Part 2 is actually achievable.
- Audit Readiness: A complete, compliant evidence trail for regulators, solving the “control” dilemma from Part 3.
- Time-to-Capability: Drastically reducing the time it takes to move from a validated idea to an operational asset.
Closing the Engineering Bridge
A recurring challenge in AI scaling is the tension between teams. Development teams are incentivised by velocity, while risk teams are incentivised by regulatory expectations. Traditionally, these are opposing forces.
VEI aligns these incentives. It provides engineering access – giving developers governed access to compute, models, and tools in minutes – while ensuring that governance as code is running in the background. It is the one layer where enterprises make technology decisions and where engineering teams prove them right.
Beyond the AI Wave
The “hidden drag” of decision latency is a choice. While the focus today is AI, the problem only grows with every technology wave.
The strategy is simple: Don’t just build for the current trend. Build the Vendor Evaluation Infrastructure that allows you to intake any technology – AI today, Quantum tomorrow – governed and compliant from day one.
