Insights from an interview series with Karan Jain and Sanjay Sankolli (Truist).
Enterprise AI isn’t failing because the models are weak. It’s failing because pilots rarely reflect the real enterprise environment.
Pilots succeed in isolated, simplified conditions. Production reveals the gaps.
The Biggest Mistakes Leaders Are Making
Sanjay highlighted three core buckets where things go wrong:
- Treating AI as a pure technology project instead of an operating model change
- Skipping a strong data foundation
- Underestimating regulatory, compliance, and operational realities
Most early experiments happen in ecosystems that don’t represent the organisation’s actual data, processes, or constraints. That mismatch becomes expensive when moving to production.
What Changes When AI Hits Real Enterprise Conditions
Vendors often optimise solutions for winning the pilot. Enterprises assume pilot performance will hold in production.
The reality is different:
- Data fragmentation – Years of M&A, regulatory patches, and siloed projects create “islands of automation.” Pilots use clean or curated data. Production does not.
- Integration complexity explodes in live environments.
- Governance and ownership become ambiguous when not addressed early.
- Latency, scale, and operational readiness introduce friction that pilots rarely test.
The result: strong pilot results, followed by significant challenges at scale.
Where AI Is Actually Delivering Value Today
Sanjay sees the clearest wins in augmentation and workflow acceleration, not full autonomy:
- Front office: Customer service deflection, predictive servicing, underwriting augmentation
- Middle office: Fraud detection refinement, KYC/AML alert disposition, claims triage
- Back office: Document intelligence, intelligent process automation
- Horizontal: Developer productivity and DevEx (one of the strongest areas)
Most current impact is bottom-line efficiency. Top-line growth is still emerging as organisations build confidence in the outputs
What High-Fidelity Evaluation Requires
Key lessons from the series:
- Run pilots with cross-functional teams (business + tech + risk + compliance) from day one
- Embed governance as a guardrail, not a late-stage gate
- Create a “digital twin” of the enterprise environment for testing
- Evaluate multiple vendors in parallel against clear, shared criteria
- Run pre-mortems – stress-test what can go wrong before committing
- Define production-ready success jointly across teams (business value + risk + ownership)
Bottom Line
Enterprise AI success is less about finding the best model and more about evaluation fidelity – testing solutions under conditions that closely match production reality.
Until organisations close the gap between pilot environments and real operational complexity, they will continue to see promising experiments and limited production impact.
The organisations pulling ahead are those treating AI adoption as a structural and operating model challenge – not just a technology one.
Closing the Pilot-to-Production Gap in Enterprise AI
If you are evaluating AI vendors or scaling AI across regulated environments, the real question is not which model performs best in a pilot.
It is which system has been evaluated under realistic conditions.
NayaOne helps enterprises bring reality into AI evaluation through a secure vendor evaluation layer designed to replicate operational, data, and governance constraints before deployment.
Learn how enterprises are reducing AI deployment risk through high-fidelity evaluation environments with NayaOne.
Book a guided walkthough or watch the video’s below:
Watch Part 1 of the CDO Interview Series with Sanjay Sankolli.
Watch Part 2: Where AI is delivering measurable value
Watch Part 3: Moving from AI theatre to operating models
Watch Part 4: How Truist solves AI latency
