Quantifying the value of data Aanalytics & AI initiatives

Quantifying the Value of Data Aanalytics & AI Initiatives

October 6, 2025 (last modified)
By Mark Dumay
Data Analytics & AI hold immense promise, but their business value often remains elusive. Discover how industry leaders cut through the noise to identify, quantify, and prioritize initiatives that deliver real impact.

Introduction: From Intuition to Impact  

Data analytics and AI initiatives are often celebrated for their transformative potential. Yet, without tangible value, they risk being perceived as experimental or auxiliary. For technology leaders, quantifying the business impact is essential to secure leadership buy-in and long-term commitment.

Consider a common scenario: your team builds an executive dashboard that visualizes sales performance. It looks sleek, updates in real-time, and is widely used. But how does it actually improve the business? Does it reduce decision latency? Improve forecast accuracy? Drive margin improvements? Without a clear value narrative, even the most sophisticated tools may struggle to justify their existence.

Understanding the Strategic Context  

To anchor analytics and AI initiatives in business strategy, we turn to Porter’s Value Chain. This framework distinguishes between:

  • Primary activities: operations, marketing & sales, inbound/outbound logistics, service
  • Support activities: infrastructure, HR, technology development, procurement

Data analytics and AI typically reside in the technology development support activity, enabling better decisions, faster execution, and smarter resource allocation. Their value is indirect, but not intangible. The following figure illustrates this.

Figure 1. Porter’s Value Chain Underscores the Strategic Role of Support Activities—such as Data Analytics and AI—in Enabling Value Creation Across Primary Business Functions.
Figure 1. Porter’s Value Chain Underscores the Strategic Role of Support Activities—such as Data Analytics and AI—in Enabling Value Creation Across Primary Business Functions.
Figure 1. Porter’s Value Chain Underscores the Strategic Role of Support Activities—such as Data Analytics and AI—in Enabling Value Creation Across Primary Business Functions.
Figure 1. Porter’s Value Chain Underscores the Strategic Role of Support Activities—such as Data Analytics and AI—in Enabling Value Creation Across Primary Business Functions.

By linking support functions to primary activities, we can trace how a predictive model (e.g., demand forecasting) enhances inventory management, reduces stockouts, and improves customer satisfaction. The degree of autonomy also matters:

  • Analytics: supports human decision-making
  • Predictions: informs decisions with probabilistic insights
  • AI & Machine Learning: automates decisions with minimal human intervention

Identifying Value: Lessons From Lean Thinking  

To uncover value, we borrow from the Lean methodology, which emphasizes eliminating waste and maximizing customer value. Four key levers guide this process:

  1. Strategic priorities – Align initiatives with business goals (e.g., growth, efficiency, innovation)
  2. Financial performance – Target cost reduction, revenue uplift, or margin improvement
  3. Voice of the customer – Address pain points, improve satisfaction, and enhance experience
  4. Voice of the process – Identify inefficiencies, bottlenecks, and variability

By triangulating these levers, organizations can pinpoint high-impact opportunities for data analytics and AI.

Benchmarking: Learning From the Outside-In  

Benchmark data provides a reality check and helps identify opportunity areas. For example:

  • In retail, AI-driven pricing optimization can yield 2–5% margin improvement
  • In manufacturing, predictive maintenance can reduce downtime by up to 30%
  • In B2B wholesale, demand forecasting can cut inventory costs by 10–20%
Figure 2. Estimated Value Potential of Data Analytics & AI Initiatives Across Industries, Expressed as a Percentage of Annual Revenue. Source: McKinsey.
Figure 2. Estimated Value Potential of Data Analytics & AI Initiatives Across Industries, Expressed as a Percentage of Annual Revenue. Source: McKinsey.
Figure 2. Estimated Value Potential of Data Analytics & AI Initiatives Across Industries, Expressed as a Percentage of Annual Revenue. Source: McKinsey.
Figure 2. Estimated Value Potential of Data Analytics & AI Initiatives Across Industries, Expressed as a Percentage of Annual Revenue. Source: McKinsey.

However, as Figure 2 illustrates, the value potential varies by industry maturity and AI readiness. Organizations with robust data infrastructure and agile processes tend to realize faster and larger returns.

A Structured Approach to Value Realization  

To move from ideas to impact, a structured approach is essential. We recommend the following five steps:

  1. Identify Value Levers – Use strategic, financial, customer, and process lenses
  2. Identify Opportunity Areas – Map analytics use cases to business pain points
  3. Screen Initial Opportunities – Evaluate feasibility, data availability, and expected impact
  4. Scope and Define Projects – Develop clear objectives, KPIs, and success criteria
  5. Prioritize Projects – Use a scoring model to balance value, effort, and strategic fit

This approach ensures that initiatives are business-driven, not technology-led.

How AdaptiQ Can Help  

At adaptiQ, we specialize in helping organizations identify and prioritize high-value analytics and AI initiatives. We combine deep domain expertise with agile delivery, ensuring that every project is aligned with strategic goals and delivers measurable impact.

Whether you’re exploring your first AI use case or scaling enterprise-wide analytics, we help you move from potential to performance.

Ready to quantify your next data initiative? Let’s Talk.