Funding should follow evidence of AI cost and ownership

Every production agent should report its outcomes, costs, quality drift, failures and owner into one operational view.

In Episode 3 of ADAPT Insider’s AI Economics Series, The Control Tower and Monday Morning Questions, Vijayan Seenisamy, Author of The Pilot Trap | Enterprise AI Delivery, continues his conversation with ADAPT’s Head of Programs and Value Engagement Byron Connolly to outline a practical model for governing AI agents in production.

The final episode covers the control tower, the questions every agent must answer before launch and the evidence required for continued funding.

Catch up on Episode 1 to understand the pilot trap, then Episode 2 for the negative unit economics and silent drift that follow in production.

Listen to the full Episode 3 on Apple Podcasts, Spotify or YouTube.

Key takeaways:

• A control tower shows how agents perform across outcomes, costs, quality drift, failures, handoffs and ownership.
• Production approval requires clear answers on value, cost, drift, failure recovery and ownership, signed off by business, technology and risk.
• Cost per successful outcome and production ownership determine whether an initiative receives product or experimental funding.

The control tower shows the whole agent fleet

Individual teams see the agents operating inside their own workflows.

They lose sight of the wider system once agents exchange work, drift at different rates and produce different costs.

Vijayan compares the environment to aircraft moving through changing weather.

Air traffic control sees each path, monitors changing conditions and intervenes before routes collide.

An AI control tower serves the same operational purpose.

It brings each agent’s outcomes, costs, drift, failures, handoffs and owner into one view so problems can be traced across the fleet.

The owner then has the information and authority to act when performance changes.

Five questions set the production gate

Every agent entering production must answer five questions:

• What outcome does the agent deliver?
• What does each successful outcome cost?
• How will quality drift be detected?
• What happens when the agent fails?
• Who owns the agent in production?

Three people then sign off on the answers.

The business owner accepts responsibility for value, the technical owner for reliability and the risk or governance owner for controls.

Missing answers expose work that remains unready for production.

Missing signatures expose accountability that has not been assigned.

Two Monday questions guide funding

Every Monday funding decision should begin with two questions: what does a successful outcome cost, and who owns the agent in production?

An initiative with evidence for both belongs in the production portfolio.

Missing evidence places it in the experiment portfolio, with funding tied to a defined question and time limit.

Experiments have value when they produce evidence.

Problems arise when organisations fund them as production systems before establishing cost, reliability and ownership.

Managing AI as a portfolio gives leaders a consistent basis for continuing, redesigning or stopping each investment.

Test the economics before funding another AI agent

Use the ADAPT AI Economics Calculator to estimate the cost per successful outcome, including build, run, retry and human intervention costs. Calculate now.