Taras Tymoshchuk is the founder and CEO of Geniusee.
The Deloitte 2026 “State of AI in the Enterprise” report, based on a survey of more than 3,000 executives, “found that only 25% of respondents have moved 40% or more of their AI pilots into production.”
That matches what I’ve seen in enterprise AI work. A convincing proof of concept (PoC) often creates confidence at exactly the point when leaders should become more demanding. The model performs well with curated data, a small user group and experts nearby to correct mistakes. Production removes those protections.
A business needs the system to consistently deliver useful, safe and economical outcomes in a live workflow. That means testing value, data, integrations, controls, ownership and economics together.
A demo can hide the business gaps.
Most PoCs begin by asking: Can the model perform this task? Leaders also need to define what changes in the business would occur if it succeeds.
When I review an AI initiative, I ask for the current baseline, target improvement, expected volume, acceptable failure rate and cost ceiling. “Better efficiency” is too broad. Reducing support-resolution time by 25% or cutting manual review by half gives the team something it can evaluate.
The project also needs an accountable business owner. Engineering, data, security and compliance may own different controls, but someone must own the operational result and decide whether the evidence justifies further investment. A pilot without that ownership can remain active long after its business case has disappeared.
Production begins where the demo ends.
PoCs often use clean exports, selected documents or a limited data snapshot. Live enterprise data arrives fragmented, outdated, duplicated and protected by different permissions. The system must also interact with existing tools, approval paths and user roles.
I’ve seen teams spend weeks comparing models while data ownership and system access remain unresolved. A stronger model still can’t restore missing context or decide which records a user is authorized to retrieve.
One of our recent projects illustrates what a more useful PoC can achieve. The goal was to generate multi-angle images with controllable 3D camera parameters. Before committing to a full build, the team tested three approaches against the use case. Two revealed constraints around third-party APIs and Gaussian splatting. A hybrid Gemini and Nano Banana workflow proved strongest on output quality, camera control and commercial viability.
The PoC did more than generate an impressive result. It reduced uncertainty around the model and architecture choice and gave the client a validated starting point for production, which was approved as the next phase. That’s the evidence a PoC should produce.
A pilot should use representative data and one narrow end-to-end workflow. It also needs a test set, quality thresholds and measures for groundedness, latency, safety and business impact. Edge cases, human-review rules, monitoring and rollback belong in the design.
McKinsey’s 2025 State of AI survey found that AI high performers were nearly three times as likely as other organizations to have fundamentally redesigned workflows. They were also three times as likely to report strong senior-leadership ownership and commitment. Production readiness depends on the operating model from the start.
Governance should enter at the same stage. If the system can read customer records, update a CRM or influence a regulated decision, identity controls, least-privilege access, audit logs and approval boundaries need to shape the architecture before the demo is approved.
The economics change at scale.
Small pilots can hide the combined cost of model calls, retrieval, infrastructure, monitoring, human review, incident handling and support. IBM and Oxford Economics surveyed 2,000 technology executives in early 2026 and found that 84% hadn’t fully operationalized AI financial management, while 85% lacked complete visibility into real-time AI spending.
We’ve learned to calculate review and exception handling before making a scaling decision. Production architecture should match the job. Some tasks need a powerful model. Others work better with a smaller model, deterministic rules or a hybrid flow that sends only difficult cases for deeper reasoning. The relevant metric is reliable business value per dollar spent.
Make the PoC earn the next investment.
Before approving a pilot, I’d ask four questions:
1. Is the value hypothesis measurable against a real baseline?
2. Will the pilot use representative data, permissions and integrations?
3. Are quality thresholds, failure handling and human accountability defined?
4. Do the unit economics still work at a realistic volume?
A useful PoC reduces uncertainty across each area. It shows whether the system can operate under real-world conditions and whether the business should fund the next stage. That turns the pilot into a disciplined investment tool and prevents the hardest questions from being deferred until production.
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