For every 33 AI proofs of concept an enterprise starts, only four ever reach production. In large companies, the average sunk cost of each abandoned initiative exceeds seven million dollars. Enthusiasm for AI is abundant. What is scarce is the criteria to decide what enters the portfolio, what advances and what stops before consuming more budget than it can justify. The AI portfolio grows through accumulation of successful demos, not through evidence of return.
Subcapability 03 of 05 · Artificial Intelligence
AI Value and Portfolio
AI initiative portfolio prioritized by real return: revenue, cost, margin. Not by demonstration enthusiasm.
What is at stake
The AI budget grew. The number of initiatives grew. Measurable results did not grow at the same rate. Most companies do not have a shortage of AI ideas. They have a shortage of criteria to decide what enters the portfolio, what advances and what stops before consuming more budget than it can justify.
What it is, in practice
How we work
Economic entry criteria
We evaluate each candidate initiative with explicit economic criteria: affected process, current volume, unit cost, estimated gain in revenue or cost reduction and defensible return timeline, before any budget commitment.
Value and feasibility prioritization
We classify the portfolio on two axes, business value and technical and data feasibility, to separate what goes to production this quarter, what stays under evaluation and what does not justify the investment now.
AI FinOps per initiative
We calculate inference cost at real production scale for each use case, making the ongoing cost of each model visible before scaling and avoiding invoice surprises that typically arrive months after the decision to expand.
Pilot exit criterion
We define before the start what must be true for the initiative to move to production, with a number, a deadline and a named person responsible for the go or stop decision. A pilot without an exit criterion has no end.
Return monitoring in production
We monitor the return of each initiative in production using an indicator defined before the start, measured in the affected process rather than in the model, with quarterly portfolio reviews to discontinue what does not deliver.
Measurable gains
What changes in the result when this subcapability matures.
Rate of AI initiatives that reach production
With economic criteria at the entry point and an exit criterion defined at the start of the pilot, the portfolio stops accumulating demos that never become products. Only what has a number and an owner advances.
Inference cost visible per use case
AI FinOps makes the ongoing cost of each model visible before scaling. The decision to expand gains a real cost basis rather than a pilot estimate built on controlled volume.
Average pilot duration before a scale or stop decision
A defined exit criterion turns the pilot into a process with a deadline. The loop of "we need more data" closes with a decision and a date, not with indefinite postponement.
Measurable return per dollar invested in AI
A portfolio with economic criteria per initiative and return monitoring in production converts AI from a cost line with different vocabulary into a results line with a number defensible in the boardroom.
Frequently asked questions
How do you calculate the return of an AI initiative before starting?
Start with the process AI will affect. Map the current volume, average time per unit and unit cost. Estimate the efficiency gain with AI, multiply by volume and compare with inference cost at real production scale. The calculation does not need to be exact to guide the decision. It needs to be defensible in a board conversation.
What is inference cost and why does it create invoice surprises?
Inference cost is what the company pays each time a model is queried. Traditional software has a fixed license. AI charges per use. At real production volume, that cost grows proportionally to the number of queries and can make an initiative that looked viable in a controlled pilot turn loss-making in production.
How do you break out of the eternal pilot cycle?
By defining the exit criterion before the pilot starts: what must be true to go to production, with a number, a deadline and the name of the person responsible for the decision. Without those three elements, the pilot becomes a permanent state, consuming budget and execution capacity without delivering.
What is the right size for an AI portfolio?
The criterion is real execution capacity. How many initiatives can each have an owner with sufficient dedication, a defined return indicator and a decision date? A portfolio larger than that capacity accumulates ownerless initiatives that are alive on paper and dead in practice.
How do you prioritize between initiatives competing for the same budget?
With two simultaneous criteria: expected value in revenue, cost or margin, and technical and data feasibility. Initiatives with high value and data-ready foundations enter first. Initiatives with high value but immature data enter after a data preparation phase. Low-value initiatives stay out, regardless of technical ease.
Other subcapabilities in this capability
AI Governance
Governance that converts AI from accumulated risk into scalable capability.
Business Team Adoption
Legal, finance and commercial teams using AI with guardrails, without waiting on IT and without exposing sensitive data.
AI Architecture
RAG, agents and MCP: the architecture that moves AI from experiment to production with verifiable data and traceable action.
AI Operations and Reliability
Models in production with complete lifecycle: versioning, monitoring, retraining and behavior supervision.
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