Well-Advised: Measuring AI Value

The Cost Savings Trap

Most organisations evaluate AI investments primarily through the lens of operational efficiency. The business case centres on cost reduction, headcount savings, and process automation. These metrics are familiar, measurable, and easy to justify.

They’re also dangerously limited.

In my conversations with Chartered Directors and their Boards, I consistently see organisations that have optimised their AI portfolios for efficiency gains while missing innovation opportunities, customer value creation, and strategic differentiation. They’ve built sophisticated capabilities for doing existing things faster while competitors use AI to do entirely new things.

The Well-Advised Framework addresses this imbalance by measuring AI value across five strategic priorities that reflect how boards actually make decisions.

Five Dimensions of Value

Innovation, New Products and Services, and Market Entry captures AI’s potential to create new offerings, enter new markets, and establish competitive differentiation. When a manufacturer implements AI-driven predictive maintenance, the efficiency gains are obvious. The innovation opportunity - transitioning from equipment sales to uptime-as-a-service - often goes unmeasured.

Customer Value and Growth measures AI’s impact on customer experiences, personalisation, and market expansion. Leading indicators track engagement and satisfaction; lagging indicators confirm retention and lifetime value growth.

Operational Excellence and Efficiency covers the traditional efficiency metrics that dominate most AI business cases. These matter, but they’re one dimension among five, not the entire story.

Responsible Business Transformation measures how thoughtfully the organisation manages AI-driven change. This pillar - unique to Well-Advised compared to frameworks like the balanced scorecard - recognises that sustainable AI success requires strong governance, ethical practices, and responsible implementation. Organisations excelling here find it easier to enter regulated markets and attract both customers and talent.

Revenue, Margin, and Profit captures the bottom-line impact. While these metrics are often lagging indicators, early signals in pilot areas can predict future financial success.

Why Balance Matters

These pillars form an interconnected system. Innovation and customer value are naturally intertwined - new AI-enabled products often lead directly to enhanced customer experiences. Operational excellence acts as a multiplier, freeing resources for innovation while improving customer satisfaction and financial performance. Responsible transformation underpins sustainable success across all areas.

For AI portfolio management, this suggests balanced investment rather than concentration. Initiatives scoring 3-4 across all five pillars often create more sustainable value than those scoring 5 in a single dimension. The organisation pursuing only operational efficiency builds a portfolio that optimises costs while competitors capture markets.

Three Types of Indicators

Traditional business cases rely heavily on lagging indicators - metrics that confirm value after it’s been created. For AI initiatives, this perspective misses crucial signals.

Leading indicators track early signs of future value: engagement rates, prototype velocity, training completion, pilot efficiency gains. These metrics help boards understand whether investments are on track before results fully materialise.

Lagging indicators confirm delivered value: revenue growth, cost savings, customer satisfaction improvements, market share gains. These validate that investments have paid off.

Predictive indicators model future scenarios: churn probability, demand forecasts, risk projections, market opportunity simulations. These help boards understand what could happen under varying conditions, enabling more informed strategic decisions.

Effective AI measurement requires all three types working together across all five pillars.

Applying Well-Advised

When evaluating AI initiatives, I recommend creating a strategic alignment matrix that maps each proposal against all five pillars with specific metrics for each dimension. This structured format helps boards quickly assess how initiatives support organisational priorities across all dimensions, guiding discussion toward strategic impact rather than getting lost in technical implementation details.

The framework also guides portfolio balance. If your current AI investments cluster heavily in Operational Excellence while Innovation and Customer Value remain underweighted, you may be optimising for efficiency while missing strategic opportunity.

Foundational

Measuring AI Value: A Framework for ROI Beyond Cost Savings - The original framework introduction with detailed indicator examples

Application

Building AI Business Cases That Boards Approve - Using Well-Advised as the strategic purpose building block
Finding AI Opportunities - Strategic alignment assessment using the five pillars
Presenting AI Business Cases to Boards - Value chain visualisations for board communication