AI investments can look very promising on paper and still struggle to deliver real business value. Across industries, the pattern is familiar: a pilot works well in a demo, gets everyone excited, and then sits for months without making it into production. The problem is often not a lack of budget or ambition. The value gets lost at one specific point, when the team that built the AI hands it over to the team that has to run it and with different goals and metrics on each side.
Where the value leaks
Many enterprises have already changed their org structure. The teams that build AI and the teams that run it may now sit under the same VP, or even the same director. On paper, that looks like one team owning the outcome from start to finish.
But the KPIs often tell a different story, the build team is still measured on go-live dates and features delivered. The operations team is measured on uptime, tickets, and cost per incident -metrics designed for traditional software, not AI agents that make decisions, use tools, and can affect financial or compliance outcomes.
Once a pilot reaches production, the build team’s job is often considered done. The operations team then takes over a system it did not design and may not fully understand, while being held responsible for keeping it running. We have seen approval agents in financial services pass every pilot review, only to be used less, because no one owned the policy updates when real-world transaction patterns changed. The org chart says one team, but the scorecard says two - and the second team is left holding the risk.
What this costs beyond the missed go-live date
The cost of this gap goes beyond a delayed launch. AI costs can grow after production, often landing with finance teams that were not part of the original business case. Model drift can also go unnoticed because the operations team may not have the context to update the system, while the build team has already moved on to its next project.
And every stalled AI pilot makes the next one harder to justify. Gartner projects that more than 40% of agentic AI projects will be cancelled by 2027. In many cases, the problem is not the AI, its lack of single team clearly accountable for the AI from build to run.
Redesign the incentive, not just the org chart
Fixing this takes more than putting two teams under the same manager. It means changing what they are measured on. The build team’s responsibility should not end at go-live. It should continue into production, with metrics such as adoption, cost per transaction, and audit outcomes. Governance, monitoring, and cost controls should also be built in from the start, with the operations team involved before the system goes live.
This is the thinking behind AIONIQ, Parkar’s platform for AI transformation. AIONIQ Build takes AI from an enterprise’s existing data foundation to governed agents in production, with monitoring, governance, and cost controls built in from the start. AIONIQ Operate then runs that same AI layer 24x7, with the same team continuing to monitor agent behaviour, enforce policies, and manage AI costs.
There is no separate handoff or second team inheriting a system it did not build. The team that builds the AI stays connected to the team that runs it, with both measured against the same production outcomes.
A model we have already run in production
We have already seen this approach work in production. A financial services client had spent 18 months with an approval workflow stuck in the proof-of-concept stage. Working as one team across build and run, Parkar moved it to a governed AI agent connected to SAP and Workday in just 8 weeks.
The solution included built-in policies, human approval where needed, and a complete audit trail from the start. Today, it handles 70% of routine approvals, has reduced compliance reporting time by 30%, and has passed regulatory audits with the full agent trail intact.
There was no handoff between teams because the same team that built it stayed involved in running it.
Start with the assessment
If your AI programs are stalling somewhere between the pilot and the production budget review, the first useful step is not another workshop. It is a 5-day AI Readiness Assessment that produces a scored backlog, ranked by business value and production feasibility, so you know exactly which initiatives are worth the built-and-run commitment on AIONIQ.