Portfolio of AI bets

What is a portfolio of AI bets?

Most companies approve AI projects one pitch at a time. A portfolio view changes the question from “is this a good idea?” to “is this a better bet than everything else competing for the same capital?”

Talk through your portfolio → Read the short answer →

4,000+ experiments · 50+ teams · Pretotyping before build

Decision system

01 Score Value · evidence · adoption · risk · time
02 Test Pretotype the assumptions that could change the call
03 Decide Fund · fix · kill

One comparable view of every AI bet, so the next dollar follows evidence rather than enthusiasm.

The short answer

A portfolio of AI bets is the complete set of AI projects, agents, and workflows an organisation is funding or considering, managed together as investments. Each bet is scored on expected value, the evidence behind it, and the cost of being wrong, so leaders can fund the few that will pay off and stop the rest.

Key facts

  • Every AI initiative is treated as a bet with an expected payoff and a real downside, not a guaranteed win.
  • Bets are compared on one page across value, evidence, and risk, rather than judged in isolation.
  • The portfolio is actively managed: bets get funded, held, or stopped as evidence comes in.
  • Capital flows to the highest-evidence, highest-value bets rather than the loudest internal champion.
Portfolio view

Make the funding call visible

Confidence sits on the x-axis, evidence depth on the y-axis, value changes the bubble size, and each bet carries a clear next decision. This example uses illustrative data, not customer information.

Example enterprise portfolio

Illustrative data · no customer information

16 bets · 4 decisions

12 6 0

Stop

Fund

Park

Prove next

Confidence →

Fund Prove next Park Stop Bubble size = value at stake
01

Why “bets,” not “projects”

Calling an AI initiative a project implies it will be delivered. Calling it a bet is more honest: it has a probability of paying off and a real chance of failing. That framing matters because it forces a number: how confident are we, and what is it worth if we are right?

When every initiative is a bet, you stop asking whether each one is individually defensible and start asking which ones beat the alternatives. That is the difference between a backlog and a portfolio.

02

What goes into the portfolio

A portfolio of AI bets spans everything competing for AI budget and attention, whether or not it has shipped:

  • Live deployments: agents and workflows already in production, judged on whether they actually moved the P&L.
  • In-flight pilots: work underway but not yet proven, judged on the evidence collected so far.
  • Proposed use cases: ideas pitched but not started, judged on expected value and feasibility.
  • Shadow AI: tools teams are already using without a mandate, carrying both value and governance risk.
03

How each bet is scored

The point of a portfolio is comparability. Each bet is scored on the same dimensions so a CFO can weigh a customer-service agent against a document-processing workflow without comparing apples to oranges.

We score every bet on business value, the strength of the behavioural evidence behind it, adoption likelihood, governance and compliance risk, and time-to-impact. The result is a single ranked view of where capital should go next.

04

Why most AI spend needs this

The numbers explain the urgency. MIT’s NANDA initiative found 95% of enterprise generative-AI pilots produce no measurable P&L impact, and McKinsey’s 2025 State of AI survey found that although 88% of organisations now use AI, only 39% report any enterprise-level EBIT impact. When most bets fail, approving them individually guarantees waste.

A portfolio approach surfaces that waste early. Instead of discovering a year later that a flagship project never paid off, you rank it against everything else up front and redirect the money before it is gone.

Sources

Evidence behind the page

95% of enterprise generative-AI pilots produce no measurable P&L impact. MIT NANDA, State of AI in Business 2025 ↗ 88% of organisations use AI, but only 39% report any enterprise-level EBIT impact. McKinsey, The State of AI 2025 ↗ Only 22% of companies have moved beyond proof-of-concept, and just 4% create substantial value from AI. BCG, Where’s the Value in AI? 2024 ↗
Questions

Frequently asked

What is a portfolio of AI bets? +

It is the full set of AI projects, agents, and workflows an organisation funds or considers, managed together as investments. Each is scored on expected value, evidence, and risk so leaders can fund the few that will pay off and stop the rest.

How is it different from an AI roadmap? +

A roadmap sequences work that has already been approved. A portfolio of bets is upstream of that: it decides which work deserves approval in the first place by comparing every candidate on value, evidence, and risk.

Who owns the portfolio of AI bets? +

Usually the executive or board members accountable for AI ROI, including the CEO, CFO, CIO, or COO, supported by transformation and innovation leaders. The portfolio gives them one comparable view for funding decisions.

Bring us the portfolio. We’ll help you decide what to fund, fix, or kill.

We look at the bets competing for budget, the evidence behind them, and where a Rapid Experimentation Operating Model would improve the next funding decision.

Talk through your portfolio → See AI experimentation