The AI value framework

What the framework is for

Many organizations starting with AI have a long list of ideas and no way to compare them. The three paths give a leadership team a common language for sorting that list, so it can see how its ideas are spread across the paths and discuss them as one portfolio. Maybe more importantly, it allows leadership to make strategic decisions on what the portfolio of AI initiatives should look like.

We use it as the opening instrument in discovery work, and as the organizing structure in inspiration sessions and enablement programs.

The three paths

1. 1
   Boosting productivity

   Perform tasks and workflows faster, at larger scale, higher quality, and lower cost. Many organizations start on this path. The workflows are already understood, and the results can be measured within weeks. Typical areas include marketing and content production, customer support, software development, operations, research and analysis, and human resources.

2. 2
   Creating new value

   Build innovative solutions to accelerate time to value for your stakeholders. This path changes what the people you serve receive: customers, or employees in an internal function, or citizens for a public authority. The change comes through AI embedded in products, services and experiences. It takes longer than the first path and is usually led by product and business owners. It is also the path on which an organization can build an advantage over its competitors.

3. 3
   Driving disruption

   Challenge value chains, business models and modes of operation. This path asks which new ways of operating become possible, and which current ones become hard to defend, when AI makes intelligence cheap to use. It usually means making bigger bets, and requires multiple stakeholders to be willing to challenge many of their assumptions.

Balancing the portfolio

The framework is most useful when a leadership team looks at the mix of initiatives across the three paths. Organizations that stop at the first path can make savings, but they do not build a strategic position. Organizations that go to the third path without the first often lack the internal capability to implement it.

We look for a deliberate spread: near-term productivity work that funds and builds capability, measured bets on new value for stakeholders, and at least one structured exploration of how the model itself could change. The right proportions depend on your sector, your risk tolerance and your culture. Whatever they are, the portfolio should include initiatives on more than one path.

Where each path is owned

Two questions to ask first

We ask these two questions in discovery, before we introduce the framework.

1. 1
   What are you trying to change?

   If there is no answer beyond doing something with AI, the portfolio conversation is premature.

2. 2
   Should this process exist in its current form at all?

   Delegating work to AI requires the process to be defined. When organizations define a process for this purpose, they often find that much of its complexity is unnecessary. Simplifying the process can bring a larger benefit than automating it, and it can be done without AI.

How we use it in engagements

1. 1
   Inspiration sessions

   The framework anchors the session, and participants place their own priorities against it before any tool is demonstrated.

2. 2
   Discovery and direction

   We place each candidate use case on a path, score it, and compare it with the other use cases on the same path. The result is a prioritized portfolio, with impact and feasibility criteria.

3. 3
   Implementation and adoption

   The path decides how we deliver the work. For Boosting productivity, we combine enablement with workflow redesign. For Creating new value, we run it as product work. For Driving disruption, we run a structured exploration using scenarios.