Measuring AI value: A strategic framework for Boards and business leaders

In my early years at Amazon Web Services (AWS), I created a tool for building cloud business cases that went beyond measuring just total cost of ownership and now forms the basis of our approach to costing migrations. I later co-authored the Cloud Value Framework (CVF) which focusses on measuring cloud value across four areas: cost optimisation, risk reduction, increased agility, and resource efficiency. So it should come as no surprise that I often get asked by Boards and the executives I meet “How do we decide if we should make an AI investment and how do we measure its ROI?”

Traditional financial metrics like Return on Investment (ROI), Internal Rate of Return (IRR), Net Present Value (NPV), and payback period provide a starting point for measuring value. However, these are all lagging indicators - they measure value only after it has been created. While boards regularly make strategic decisions under uncertainty based on their risk appetite, AI has low barriers to entry and provides an opportunity to foster an experimental culture that can help organisations build confidence through evidence-based learning.

The current AI landscape offers numerous opportunities for low-cost, low-friction pilot projects that can demonstrate value quickly. For example, using generative AI services like Amazon Q for specific business functions, implementing document processing automation for a single department, or testing AI-powered customer service tools in a limited support queue. These pilots can deliver quick wins with minimal upfront investment, show tangible benefits within days rather than years, and provide the evidence needed to support larger strategic investments.

AI initiatives create value in uniquely powerful ways. They can enhance decision-making and customer experience across multiple business functions simultaneously. Their benefits often compound over time as systems learn and improve. And perhaps most importantly, they can fundamentally transform how work gets done, opening up entirely new possibilities for innovation and growth.

When it comes to measuring this value creation, boards and executives can easily get lost in a sea of metrics - from technical performance indicators to total cost of ownership calculations. But in my experience working with boards across multiple industries, there are really only five things they consistently care about: can we innovate and enter new markets, are we delivering value to our customers, are we operating efficiently, are we transforming responsibly, and are we improving our financial performance?

A Framework for AI Value Realisation

During my time leading the Professional Services Advisory practice at AWS, I developed the Well-Advised Framework as a companion to AWS’s Well-Architected Framework. While Well-Architected successfully guided technical teams in building secure, high-performing, resilient, and efficient cloud infrastructure, I saw a need for a framework that would resonate with c-suite conversations. Well-Advised emerged from hundreds of executive discussions about value creation, focusing on the fundamental business pillars that drive strategic decision-making.

The Well-Advised Framework provides a structured approach for articulating and measuring AI value creation across five key business pillars. For each pillar, we need to consider both leading indicators (which help predict future success) and lagging indicators (which confirm value delivery). This balanced approach helps boards make informed investment decisions while tracking the actual realisation of value.

1. Innovation, New Products and Services, and Market Entry

AI can be a powerful catalyst for innovation, enabling organisations to create new products, enhance existing services, and enter previously inaccessible markets. This pillar focusses on measuring how effectively AI drives your innovation pipeline and expands your market presence. Success here often leads to first-mover advantages and sustainable competitive differentiation.

Leading Indicators:

• Number of AI experiments in progress (e.g., 5 concurrent pilots across different business units)
• Speed of proof-of-concept development (e.g., reduction from 6 months to 6 weeks)
• New market opportunities identified (e.g., 3 new customer segments through AI-enabled services)
• Innovation pipeline velocity (e.g., 40% increase in ideas reaching prototype stage)

Lagging Indicators:

• New products/services launched (e.g., 2 new AI-enhanced products per quarter)
• Revenue from new markets (e.g., £2M in first-year revenue from AI-enabled services)
• Market share in new segments (e.g., 15% share in previously unserved market)
• Patent applications filed (e.g., 5 new patents based on AI innovations)

Focus on tracking the speed and effectiveness of your innovation process. Early indicators should show your organisation’s increasing capability to identify and pursue new opportunities, while lagging indicators demonstrate successful market entry and revenue generation.

2. Customer Value and Growth

Creating value for existing customers while attracting new ones is essential for sustainable business growth. AI can enhance customer experiences, personalise interactions, and identify new customer needs. This pillar measures both the immediate impact on customer satisfaction and the longer-term effects on customer relationships and market expansion.

Leading Indicators:

• Initial user adoption rates (e.g., 40% of target users engaging with new AI features in first month)
• Early customer feedback (e.g., 85% positive sentiment in initial user surveys)
• Service response improvements (e.g., 60% faster query resolution times)
• Customer engagement metrics (e.g., 3x increase in self-service portal usage)

Lagging Indicators:

• Customer satisfaction scores (e.g., 25% increase in CSAT scores)
• Customer lifetime value (e.g., 30% increase in average customer spend)
• Net Promoter Score trends (e.g., 20-point improvement in NPS)
• New customer acquisition rates (e.g., 45% increase in conversion rate)

Monitor early adoption and feedback closely to predict long-term success. Early positive indicators often translate into sustained customer value and growth, but watch for any negative trends that might require course correction.

3. Operational Excellence and Efficiency

AI can transform how work gets done, automating routine tasks, optimising complex processes, and enabling more informed decision-making. This pillar examines how effectively AI improves operational performance, reduces costs, and increases productivity across the organisation. The focus here is on sustainable improvements in core business operations.

Leading Indicators:

• Early process improvements (e.g., 30% reduction in manual steps within first month)
• System response times (e.g., AI model inference under 100ms)
• Initial automation rates (e.g., 50% of target processes automated)
• Employee adoption metrics (e.g., 75% of target users actively using AI tools)
• Early error reduction rates (e.g., 40% fewer errors in automated processes)
• Asset utilisation (e.g. 97% production line efficiency - up 11% - leading to greater output)

Lagging Indicators:

• Process completion times (e.g., loan approvals reduced from 5 days to 2 hours)
• Resource utilisation rates (e.g., 35% improvement in resource allocation)
• Quality improvement metrics (e.g., 60% reduction in error rates)
• Cost per transaction (e.g., £12 reduction in processing cost per item)
• Productivity gains (e.g., 4 hours per employee per week saved)

Focus on measuring both the technical performance of AI systems and their practical impact on business processes. Early indicators should show rapid improvement in process efficiency, while lagging indicators demonstrate sustained operational benefits.

4. Responsible Business Transformation

Successful AI adoption requires more than just technological implementation—it demands responsible change management, strong governance, and ethical considerations. This pillar measures how well the organisation manages the transformational aspects of AI, including risk management, sustainability, and the human impact of change.

Leading Indicators:

• AI governance framework adoption (e.g., 100% of AI projects following established guidelines)
• Training completion rates (e.g., 95% of staff completed AI awareness training)
• Risk assessment scores (e.g., all AI projects achieving risk score under threshold)
• Early sustainability metrics (e.g., 20% reduction food waste)
• Employee engagement levels (e.g., 80% positive feedback on AI initiatives)

Lagging Indicators:

• Regulatory compliance rates (e.g., zero compliance incidents in AI systems)
• Environmental impact reductions (e.g., 30% reduction in carbon footprint)
• Employee retention rates (e.g., 25% improvement in retention)
• Risk incident reductions (e.g., 50% fewer security incidents)
• Governance effectiveness scores (e.g., passing external AI audits with no major findings)

Balance the need for rapid transformation with responsible implementation. Leading indicators should show your organisation building the right foundations, while lagging indicators demonstrate sustainable and ethical AI adoption.

5. Revenue, Margin, and Profit

Ultimately, AI investments must contribute to the financial success of the organisation. This pillar looks at the bottom-line impact of AI initiatives, measuring both direct financial returns and the broader effects on business value creation. While these metrics are often lagging indicators, early signals can help predict future financial success.

Leading Indicators:

• Early efficiency gains (e.g., 15% reduction in operating costs in pilot areas)
• Sales cycle improvements (e.g., 40% reduction in time to close)
• Cost reduction trends (e.g., 25% decrease in customer service costs)
• Pipeline growth rates (e.g., 50% increase in qualified leads)
• Margin improvement signals (e.g., 10% reduction in cost of goods sold)

Lagging Indicators:

• Revenue growth (e.g., 20% year-over-year increase in AI-influenced revenue)
• Profit margins (e.g., 5 percentage point improvement in gross margin)
• Cost savings realised (e.g., £2M annual reduction in operational expenses)
• Market share gains (e.g., 3 percentage point increase in market share)
• Return on AI investment (e.g., 3x return on AI investment within 18 months)

While financial metrics are lagging by nature, look for early indicators that predict future financial success. Focus on metrics that show improved efficiency and effectiveness in revenue-generating activities.

Understanding Value Interrelationships

While we’ve examined each pillar of the Well-Advised Framework separately, in practice they form an interconnected web of value creation. Understanding these relationships is crucial for boards and executives making AI investment decisions.

Innovation and customer value are naturally intertwined - new AI-enabled products and services often lead directly to enhanced customer experiences and deeper market penetration. For example, when a bank implements AI-powered fraud detection, it not only creates an innovative service but also improves customer trust and satisfaction.

Operational excellence acts as a multiplier for other value areas. When AI improves operational efficiency, it often catalyses innovation by freeing up resources, enhances customer experience through faster service delivery, and directly impacts financial performance through cost reduction. Consider a manufacturer implementing AI-driven predictive maintenance - this operational improvement reduces downtime (efficiency), enables new service offerings (innovation), improves customer satisfaction (value), and boosts profitability (financial performance).

Responsible transformation underpins sustainable success across all other areas. Strong governance and ethical AI practices build trust with customers, enable confident innovation, and protect financial performance from compliance-related risks. Organisations that excel at responsible AI transformation often find it easier to enter regulated markets and attract both customers and talent.

Financial performance, while often viewed as the ultimate outcome, actually feeds back into enabling further value creation. Improved margins and profitability from early AI successes can fund more ambitious innovations, enable better customer experiences, and support more comprehensive transformation initiatives.

Understanding these interconnections helps organisations:

1. Make more holistic investment decisions that consider ripple effects across the business
2. Design AI initiatives that deliberately leverage multiple value pathways
3. Better sequence their AI initiatives to build upon early successes
4. Create more comprehensive business cases that capture full value potential

Getting Started

While the Well-Advised Framework provides a comprehensive approach to measuring AI value, you don’t need to implement everything at once. The key to success is starting small, learning fast, and scaling what works. Here’s how to begin:

Start With Your Priority Area

The most effective way to implement this framework is to begin with the area that aligns most closely with your immediate business objectives. If you’re looking to enter new markets, focus initially on Innovation metrics. If customer retention is a concern, start with Customer Value measures. Organisations facing cost pressures might begin with Operational Excellence indicators, while those in highly regulated industries often prioritise Responsible Transformation metrics. For those under margin pressure, Revenue and Profit metrics naturally take precedence.

Identify Your Initial Metrics

Once you’ve chosen your starting point, select two or three leading indicators that you can measure today using your existing data and systems. The key is to avoid the temptation to measure everything - instead, focus on metrics that are easy to collect with your current tools, clear to communicate to stakeholders, and directly tied to your business objectives. These metrics should be measurable on at least a monthly basis (real-time is preferred) to provide the regular feedback needed for learning and adjustment.

Set Up Basic Measurement

With your initial metrics chosen, create a simple dashboard for tracking. Start by establishing clear baselines - you need to know where you’re starting from to measure progress. Set realistic targets based on industry benchmarks or your own historical performance. Define how frequently you’ll measure each metric and, importantly, who will be responsible for collecting and reporting the data. Keep it simple at first; you can always add complexity as you learn what works.

Start Small and Learn

The best way to test your measurement approach is with a low-friction pilot project. Choose something contained with clear boundaries - perhaps a single department or process - and set a fixed time frame, typically around 12 weeks. This gives you enough time to see results while maintaining momentum. Involve key stakeholders early in the process, and make sure to document what you learn along the way. Don’t be afraid to adjust your metrics based on early feedback - the goal is to find what works for your organisation.

Conclusion

The art of measuring AI value creation doesn’t have to be complex. While traditional financial metrics will always play a role in investment decisions, focusing solely on lagging indicators like ROI misses the bigger picture. By measuring what truly matters to boards - innovation, customer value, operational excellence, responsible transformation, and financial performance - organisations can build a more complete and actionable view of their AI investments.

The low barriers to entry in today’s AI landscape present an unprecedented opportunity. Rather than getting caught up in complex measurement frameworks or waiting for perfect metrics, organisations can start small, learn quickly, and scale what works. The key is to begin with clear business objectives, choose meaningful metrics that predict success, and build measurement capabilities incrementally.

Remember that value creation in AI often follows a compound pattern - small wins in one area frequently catalyse larger gains across others. By understanding these relationships and measuring them effectively, boards and executives can make more informed decisions about AI investments and guide their organisations toward sustainable success in the AI era.

About the Author

Mario Thomas is a transformational business leader with nearly three decades of experience driving operational excellence and revenue growth across global enterprises. As Head of Global Training and Press Spokesperson at [Amazon Web Services](https://aws.amazon.com) (AWS), he leads worldwide enablement delivery and operations for one of technology's largest sales forces during a pivotal era of AI innovation. A Chartered Director and Fellow of the [Institute of Directors](https://www.iod.com), Mario partners with Boards and C-suite leaders to deliver measurable business outcomes through strategic transformation. His frameworks and methodologies have generated over two-billion dollars in enterprise value through the effective adoption of AI, data, and cloud technologies.