How to measure AI ROI and prove business impact

AI is on the minds of nearly every business leader today. The promise of intelligent automation, better decision-making, and new ways of working feels immense. Despite the urgency, a common challenge remains—turning AI potential into measurable business impact.

For many executives, there’s a gap between recognising AI’s potential and achieving measurable results. The journey requires a clear definition of AI readiness, a direct link between business priorities and targeted use cases, and a disciplined approach to measuring ROI. Without these elements, even well-intentioned initiatives risk stalling before they deliver meaningful AI business impact.

This guide explores key steps in determining ROI with AI—from assessing your readiness to sustaining value over time—with real-world examples of AI business impact from enterprise organisations.

Step 1. Align AI initiatives with business objectives

Key takeaway: Start every AI project with a clearly defined business goal to maximise impact and secure executive buy-in.

The first step towards a successful AI strategy is to evaluate your business goals. AI isn't valuable on its own. It's valuable when it moves the needle on something the business already cares about.

Before you invest, you need to know why you're investing. Are you trying to reduce costs, make quicker decisions, or increase customer retention? AI solutions that start with a clear objective are more likely to succeed. They also make it easier to secure support and justify the investment.

Having a single, unified interface for all of your AI capabilities can make a difference. It can proactively recommend next best actions and insights within the flow of work so employees can accomplish more.

Real-world example: Delta Airlines

Delta Airlines aligned its AI initiative with a core business objective—to connect employee happiness with customer experience. By using SAP SuccessFactors, they successfully filled nearly 50% of their managerial roles with customer-facing employees. The result created a virtuous cycle of positive customer and shareholder outcomes.

Step 2. Estimate ROI through use case modelling

Key takeaway: Model ROI through specific, high-value use cases and take a multi-year view to strengthen your investment case.

Before you implement any new AI technologies, you need a solid business case. For AI, that means modelling the return on investment through use cases. This is where you move beyond general ideas of efficiency and start to quantify the potential impact.

Consider the potential business AI value categories:

• Cost optimisation and efficiency gains: How many hours could you save by automating a manual process?
• Revenue growth: Could better personalisation lead to higher conversion rates?
• Risk mitigation and compliance: Could AI help you detect fraud or automate compliance checks, reducing your exposure to risk?
• Decision quality: What is the value of more accurate forecasting or fewer errors in your financial reporting?

One way these categories come to life is through the connection between the customer experience (CX) and your operational back end (ERP). A CX platform might have the front-end data on customer behaviour and order history. But the operational data—what allows you to generate quotations, handle delivery, and provide order and invoice status—is all in the back end. The two parts must be in step for a modern enterprise to deliver on what it promises.

An economic validation report by Enterprise Strategy Group shows that integrating AI into your CX and ERP systems can deliver a conservative ROI of 214% over five years—rising to 761% with maximum improvements.¹ The report also highlights that this kind of integration can lead to a 10% to 30% increase in average deal sizes, directly boosting revenue.²

When you model these benefits, remember to think about recurrence. Are these one-off savings, or will they compound over time? A multi-year view of the resulting cash flow can paint a much more compelling picture than a single-year projection. This is a critical step in building a strong business case that resonates with your fellow executives and helps you measure AI performance over time.

Real-world example: Microsoft

Microsoft faced challenges with manual processes and poor forecasting in its supply chain. By targeting these pain points, it achieved a 50% reduction in manual planning processes and a 75% increase in on-time planning. These improvements directly demonstrate how a well-defined use case can translate into tangible ROI.

Step 3. Quantify value through baseline comparisons

Key takeaway: Establish a clear performance baseline to measure AI’s true impact and highlight the cost of inaction.

To realise meaningful AI business impact, start by defining a clear performance baseline. Document current KPIs—such as processing times, error rates, customer satisfaction scores, or revenue per transaction—and project how AI could shift these numbers. This establishes a realistic payback period and break-even point.

The true value of AI often extends beyond simple calculations like “X hours saved at Y pounds per hour.” When automation removes repetitive tasks, teams can focus on strategic initiatives, drive innovation, and contribute to higher-value outcomes. This cascading effect amplifies ROI with AI far beyond the initial efficiency gains.

Equally important is recognising the cost of inaction. Delays in improving a critical process can mean missed revenue, diminished competitiveness, and lower customer retention. Framing AI as a strategic necessity—not a discretionary spend—strengthens your investment case.

Real-world example: Chobani

Take Chobani, for example. By using AI to streamline its financial processes, the company achieved a 75% reduction in time spent on expenses. This freed up its finance team from administrative work and allowed them to focus on more strategic initiatives such as financial analysis and improving compliance.

Step 4. Track real-world metrics post-deployment

Once your AI solution is live, shift from projections to performance data using an AI measurement tool. It’s important to focus on metrics that demonstrate whether the solution is delivering as intended:

• Measure before and after KPIs to confirm the results
• Monitor adoption and usage rates to ensure the solution is effectively utilised
• Track the downstream effects. Did improved throughput in one department lead to a faster time to market and a revenue boost in another?

A transparent consumption model gives you complete visibility into what’s used, how often, and where it’s delivering value. These insights allow you to optimise performance, communicate results clearly, and justify ongoing or expanded investment.

AI agents can be a powerful tool for this. These agents are infused with business process expertise, giving them the ability to reason, make decisions, and adapt to dynamic conditions. They can also automate time-consuming work across business functions such as supply chain, procurement, and finance.

Real-world example: Nestlé

Nestlé struggled with slow, paper-based expense processes that were prone to errors. By implementing AI-driven tools in SAP Concur, they were able to track and measure significant improvements. The company achieved a 100% elimination of manual expense management processes and saw a 3x increase in employee efficiency when creating reports.

Step 5. Include qualitative and strategic returns

Key takeaway: Consider both measurable financial gains and strategic, long-term benefits when evaluating AI’s success.

Not every return appears in a financial report—especially early on. It’s important to include qualitative and strategic returns when building your business case and progress reviews.

Some organisations achieved up to 300% improvement in daily productivity by automating routine processes like data entry, order processing, and customer support.³ Sales teams have also reported productivity increases of up to 90% through streamlined workflows and better access to customer data. These gains aren’t just about time saved—they free up teams to focus on higher-value work.⁴

Operational costs can also fall sharply. By simplifying operations and reducing the need for complex integrations, businesses can save up to 70% of the time they previously spent managing and maintaining systems.⁵

Some other things to consider:

• Faster experimentation cycles: AI can accelerate innovation
• Talent retention through innovation: People want to work for forward-thinking companies
• Competitive differentiation: Early adoption can give you a significant market advantage
• Future-proofing your infrastructure: Investing in AI today builds a more resilient, adaptable foundation for tomorrow

Finally, AI can significantly improve customer experience and retention. Companies have seen faster transaction completions, fewer service issues, and higher satisfaction rates—reducing churn by up to 55%⁶.

Real-world example: SA Power Networks

SA Power Networks faced the challenge of managing an ageing infrastructure across a vast, sparsely populated area. By using an AI-powered app, they not only saved £1M in a single year—they also achieved a 99% success rate in identifying poles likely to corrode. The AI solution also gave field technicians access to 50 years of asset history with a simple natural language query to improve safety and reliability.

Step 6. Build a feedback loop for continuous justification

Key takeaway: Create a feedback loop to refine models, discover new opportunities, and sustain AI’s business value over time.

AI technologies improve as they learn from new data. The most successful organisations establish a feedback loop that tracks outcomes, refines models, and applies insights to future initiatives.

This feedback loop ensures that your AI investment continues to deliver value long after the initial deployment. It also helps you identify new use cases, optimise existing solutions, and build on your successes.

A suite of tools can help you build, extend, and orchestrate AI solutions at scale. With centralised management and governance, AI agents can be aligned to business needs and reveal new opportunities across the organisation.

By continually exploring new use cases, developing bespoke solutions, and extending AI at your own pace, you transform AI from a single project into an ongoing engine for growth.

Starting your AI journey

Achieving a measurable ROI with AI is often more straightforward than it seems. You don’t need to have all the answers to get started. You just need the right plan, the right people, and the right support.

Whether you’re defining your first AI proof of concept or scaling AI enterprise-wide, choose technologies that are relevant, reliable, and responsible.

These systems should be protected by end-to-end security measures, including threat intelligence and vulnerability management, and governed by strong data policies to ensure responsible management, privacy, and legal compliance.

Embedded into core business processes, they deliver tangible results you can measure and build upon over time.

For executives, here’s a six-point leadership checklist to help your organisation maximise ROI:

• Begin every AI project with a clearly defined business goal to maximise impact and secure executive buy-in
• Model ROI through specific, high-value use cases and take a multi-year view to strengthen your investment case
• Establish a clear performance baseline to measure AI’s true impact and highlight the cost of inaction
• Use post-deployment metrics and transparent tracking tools to prove value and guide future AI investments.
• Consider both measurable financial gains and strategic, long-term benefits when evaluating AI’s success
• Create a feedback loop to refine models, discover new opportunities, and sustain AI’s business value over time