AI innovation is progressing at a striking pace. Powerful new models, agents and specialised tools are emerging almost daily, driving strong demand from business leaders across every function. Yet most large organisations continue to evaluate these new capabilities through lengthy, sequential vendor onboarding processes built for traditional software – processes that often take six to nine months before any meaningful evaluation can even begin.
In this latest CDO Magazine conversation, Karan Jain, Founder and CEO of NayaOne, and Sanjay Sankolli, Chief Architect for AI and Data at Truist Financial Corporation, offer a refreshingly candid view of where enterprise AI efforts most often stumble.
At its core, the discussion reveals a quiet but persistent truth: many AI evaluations that appear successful in controlled settings ultimately prove misleading when the time comes to scale.
Watch the full interview (Part 1)
From Isolated Intelligence to Embedded Decision-Making
The discussion begins with a clear-eyed view of how organisations have evolved. From traditional business intelligence to machine learning-powered analytics and now to frontier models and agent-driven systems, the ambition has been to harness intelligence hidden in siloed data ecosystems and islands of automation.
Yet organisational readiness has not kept pace. The real prize is no longer isolated intelligence – it is moving to just-in-time intelligence that AI can act on, rooted in the organisation’s own context.
The Fundamental Misalignment and AI Evaluation Challenges
One of the most striking observations in the interview is how enterprises continue to approach AI. Too often, evaluations are framed as technology projects rather than as profound changes to the operating model. This framing leads to three recurring weaknesses:
- Data foundations that are simply not robust enough for production-grade AI
- Evaluations that are disconnected from the messy realities of live business processes
- Regulatory and compliance implications that are systematically underestimated
When the evaluation environment does not mirror operational reality, the journey from experimentation to enterprise AI scaling becomes far more difficult than anticipated.
The Last-Mile Challenge
Even when an evaluation delivers promising results, the transition to production exposes a different set of realities. True success depends on “everything around the model” – the surrounding infrastructure, governance structures, people and process integrations.
In practice, this is where most initiatives lose momentum. The true state of enterprise data emerges. Regulatory scrutiny intensifies. Integration complexity multiplies. Ownership becomes unclear. Change management is often treated as an afterthought.
Vendors naturally optimise their solutions to shine in evaluation settings. Enterprises, however, must contend with legacy systems, strict regulatory boundaries and the full weight of cross-functional dependencies. The gap between these two worlds is precisely where many otherwise strong evaluations falter. Read more about the hidden costs of evaluating AI.
A More Mature Approach – Pre-Onboarding Evaluation
The interview highlights an encouraging shift now visible across leading financial institutions. Rather than evaluating vendors sequentially, organisations are increasingly running parallel assessments of multiple solutions against the same business problem.
Nine times out of ten, the organisation ends up selecting a different vendor than it first expected. This parallel approach reduces bias, surfaces richer comparative insights and strengthens the organisation’s own decision-making discipline.
Building the Right Scaffolding – The External AI Evaluation Sandbox 2026
The conversation is ultimately a reminder that responsible AI deployment is not about slowing down innovation. It is about creating the right conditions for it to succeed at scale. Traditional sequential onboarding processes, which can stretch to six or nine months before meaningful evaluation begins, are increasingly mismatched with the pace of the market.
What is required is a secure, external AI evaluation platform – one that sits before any contract or core-system integration, uses synthetic data only, and enables consistent, audit-ready due diligence from the outset. Such an environment allows organisations to test and de-risk capabilities rapidly and safely through effective pre-onboarding AI evaluation, while preserving the simplification and risk discipline that boards rightly demand.
The Deeper Responsibility
The organisations that will lead in the years ahead are those that treat AI not merely as a set of powerful new tools, but as a structural change that demands equally thoughtful infrastructure. The difference between promising evaluations and durable enterprise value lies in the scaffolding we build between ambition and deployment.
Ready to build the right scaffolding for responsible AI in 2026?
NayaOne provides the leading external AI evaluation platform trusted by major financial institutions to test, compare and de-risk third-party capabilities before any commitment. Book a guided walkthrough.
