AI STRATEGY

Where AI creates measurable performance impact

ValueFabric connects AI opportunities to operating performance — identifying where AI impacts signals, defining the right decisions, and preparing execution through the AI Execution Fabric — before outcomes are fixed.

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AI initiatives start without clear priorities

Most organisations measure commercial success through top-line metrics.

  • Organisations are investing heavily in AI.
  • Ideas, pilots, and use cases are expanding across functions and systems.
  • But most initiatives are not connected to how performance actually changes.

As a result:

  • AI initiatives are selected based on ideas, not signals
  • Use cases compete without clear prioritisation
  • Investment is spread across low-impact opportunities
  • Outcomes are inconsistent and difficult to measure

AI creates activity — but not always performance impact.

Without a connection to financial, operational, and commercial signals:

  • Opportunities are misaligned with real business drivers
  • High-impact areas are overlooked
  • Initiatives remain disconnected from decision-making
  • Execution lacks focus

AI is explored — but not prioritised for execution.

AI is not prioritised based on signals and decisions

Most organisations define AI strategy through:

  • workshops and brainstorming
  • technology-driven roadmaps
  • isolated use cases within functions

But these approaches do not answer the core question: Where does AI change performance?

From AI opportunities to prioritised decisions

ValueFabric connects AI strategy directly to the Operating Intelligence system. It:

identifies where AI impacts financial, operational, and commercial signals
connects AI opportunities to real performance drivers
explains what is changing and why
defines which decisions should be taken
clarifies the expected impact of those decisions
prioritises AI use cases based on measurable outcomes
prepares execution through the AI Execution Fabric

This ensures that AI is applied where it changes performance — not where it is easiest to implement.

From AI opportunity → to decisions → to execution.

From AI opportunity → to decisions → to execution.

From AI opportunity → to decisions → to execution.

Focus on what changes performance

Commercial Intelligence monitors signals across three critical dimensions of commercial performance:

AI use cases are defined based on their impact on signals and decisions. This includes:

  • identifying the signals that indicate performance change
  • mapping where AI can influence those signals
  • defining use cases linked to decision-making
  • structuring initiatives around measurable outcomes

This creates:

  • a prioritised AI roadmap
  • alignment between AI initiatives and operating performance
  • focus on high-impact opportunities
  • structuring initiatives around measurable outcomes

Not more use cases — better ones.

Focused execution with measurable outcomes

With ValueFabric:

AI initiatives are prioritised based on performance impact

signals are translated into intelligence and decisions

decisions are prepared for execution

execution is enabled through systems and workflows

AI initiatives scale across the organisation

This enables organisations to move from experimentation to measurable performance improvement.

Focus drives execution.

Strategy connected to execution

Most AI strategies:

  • define ideas without prioritisation

  • focus on technology instead of performance

  • remain disconnected from execution

From strategy → to decisions → to execution → to outcomes.
ValueFabric connects all layers:

  • signals generate intelligence

  • intelligence defines decisions

  • AI strategy prioritises where to act

  • AI Execution Fabric executes those decisions

This ensures that AI strategy is not theoretical — but directly connected to outcomes.

Used by organisations investing in AI

This approach is used by:

ceo

CEOs driving transformation and performance

CIO & CTO

CIOs and CTOs responsible for technology strategy

LEADERS

innovation leaders managing AI initiatives

TEAMS

transformation teams responsible for execution

Different roles. One system.

From first signal to prioritised execution

Organisations start by identifying where signals indicate the highest potential for impact.

Step 1 — Business Risk Assessment

Identify where performance risks and opportunities are developing

Step 2 — Performance Signal Engine

Monitor the most critical signals continuously

Step 3 — AI Strategy

Define and prioritise AI use cases based on measurable impact

Step 4 — AI Execution Fabric

Translate decisions into execution systems

Start with signals — prioritise decisions — execute through systems.

Focus your AI investments where they change performance

Your organisation already has AI initiatives and opportunities. The difference is whether those initiatives are:

explored without prioritisation

or connected to signals, decisions, and execution

Start with a Business Risk Assessment to understand where AI can create the most impact — and how to prioritise it effectively.

Request Your Business Risk Assessment
Request a Strategy Session