Calculating the ROI of AI strategy

 

Christian Cobb

The hype of AI has led to AI “champions”, AI “task forces”, and AI “working groups” at any given company, but we are seeing organizations pivot from aimless AI tinkering to driving real impact. 

Sam Altman has said we will see a billion dollar company run by one person in our lifetimes. Whether or not we do (I think we will), this hammers home the idea that 1) AI’s flexibility means it can support any part of the business and 2) benchmarks for ROI are going to change radically as AI native systems emerge.

In this blog, we’ll break down how you can calculate the ROI of AI Strategy with a simple, actionable approach to get ahead of the crowd. And how you might maximize that return.

A simple frame for AI ROI to set you up for success

Before diving into the details, let’s establish two things:

1. The definition of ROI hasn’t changed. You need to get more out than you put in.
2. We need to develop business cases, as (after all) investment decisions are made by humans. 

That is not new. What has changed is the flexibility of the technology we are looking to apply. 

That is, AI can support almost any process, function, business unit, or customer interaction.  We’ve found in the past 2 years that answering the questions of where and how to start are deceptively tricky.

 

To simplify, think of AI ROI in three primary buckets:

1. Cost Efficiency – Reducing costs, increasing productivity, and getting more out of existing assets.
2. Revenue Optimization – Enhancing customer lifetime value through upsells, cross-sells, and retention improvements.
3. New Revenue Streams – Unlocking new products, services, or business models.

These also are not new. Any investment will probably fall into one of these categories. This is the best frame to figure out where to go deeper.

Single use case, or flooded with options? Here’s how to prioritize

Organizations making build or buy decisions typically fall into two camps: either they have one clear AI use case or they’re overwhelmed by too many possibilities. 

If you have a single use case: focus on defining one key KPI that aligns with your strategic objectives. If you’re working on AI-powered customer support, for instance, your KPI might be reduced response time or decreased customer churn. Simplifying your objective helps tell your story to stakeholders and streamline execution.

If you are comparing multiple use cases: use the three ROI buckets (cost efficiency, revenue optimization, new revenue streams) to categorize and compare your use cases, apples to apples. This helps prioritize investments with the highest impact. If you really have a lot of use cases, you can start with a simple 1-5 score in each bucket to prioritize and then go deeper.

Three waves of AI adoption: where are you playing?

AI-driven transformation doesn’t happen all at once. AI adoption will unfold in three waves, much like previous revolutionary technologies such as electricity, digital, or mobile:

• Wave 1: Time, Cost & Efficiency – applying AI to existing ways of working, focusing on time savings, cost reductions, and efficiency gains
• Wave 2: Quality & Better Output – leveraging AI for better quality and enhanced outcomes. It’s not just about being faster or cheaper; it’s about delivering superior results and higher standards. 
• Wave 3: New Systems & Transformation – creating entirely new systems and ways to deliver and capture value, and redefining markets. 

It’s important to understand that success in the first wave doesn’t guarantee success in the third. Businesses must plan their AI strategies with a vision for all three waves, ensuring that investments made today help build towards transformative opportunities tomorrow. We advocate for a portfolio approach: invest across all three waves, with a clear vision for the third wave in mind.

Efforts across all waves can begin now and happen simultaneously

Investment in a given wave will naturally have different impacts on returns.

The Three Waves Framework provides a strategic roadmap for growth, ensuring organizations capitalize on immediate opportunities while building toward transformative, long-term change. Its principles are universal, applying to technology, business models, and industry evolution

Where to find higher AI ROI - stacking AI investments

One of the biggest missed opportunities in AI strategy is failing to connect use cases for systemic returns. Let’s look at an example: let’s say a company implements three AI solutions:

• AI-powered customer support to reduce response time.
• AI trend prediction to anticipate market demands.
• AI product design to develop better products.

Individually, each offers a return. But when interconnected, they create contagious ROI—customer insights from support feed into trend prediction, which informs better product development, leading to improved sales. The more these AI solutions talk to each other, the more improving one will have compounding, contagious return.

There’s no wrong way to start. Wave 1 improvements reduce costs, but these are the easiest to find and will be adopted quickly. Wave 2 makes for better customer experiences, raising revenue with repeat or new customers from differentiating features. Wave 3 starts to emerge when you have a synergistic system of AI tools that complement each other, adding to the return each provides

What does a good AI use case look like?

To ensure AI delivers real ROI, assess potential projects through this lens:

1. Does it solve a user problem? If it’s solving a real pain point, adoption is more likely.
2. Does it solve an organizational problem? If it improves efficiency or revenue, it justifies the investment.
3. Does it align with your business model? AI should enhance your competitive edge, not distract from it.

Bonus points if your solution also:

• Solves a leadership problem – e.g., enables strategic decision-making that helps steer the ship.
• Includes a clear capability vision – e.g., lays the foundation for Wave 3 transformation
• Activates your data – AI transformation is a data transformation, and figuring out data ASAP is in your best interest. If you wouldn’t bet something dear to you that your data is ready – then it’s not ready.

Key takeaways of calculating ROI of AI

 strategy

• AI ROI falls into three buckets: cost efficiency, revenue optimization, and new revenue streams.
• Prioritize AI use cases strategically by aligning them with measurable KPIs and business objectives.
• AI transformation happens in waves, from cost savings (Wave 1), to better output (Wave 2) to systemic reinvention (Wave 3).
• Interconnected AI use cases multiply returns, creating a contagious effect of business growth.
• Successful AI investments require a solid data foundation—if your data isn’t ready, you won’t be able to execute on your AI strategy.

The bottom line: be strategic about AI ROI

AI is one of the most flexible tools we’ve ever had. But flexibility without focus leads to inefficiency. By categorizing use cases, aligning investments to the right wave of AI maturity, and stacking solutions for interconnected value, businesses can unlock real, measurable returns.

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