When companies invest in AI, the first question often asked is: “What’s the ROI?”
It's a fair question – but often the wrong one, at least in the way it's traditionally framed.
In the early 2000s, no one could give you an ROI calculation for building a website. But they could tell you: if you don’t, you’ll be irrelevant. And I think we’re seeing the same inflection point now. The mistake companies make is trying to apply the same ROI model they used for upgrading a server or rolling out a CRM.
AI is not a one-time purchase or a bolt-on tool. It’s a foundational capability shift. It changes how you think, decide and evolve (who does what, in what order, and with what automation handoffs), not just what you can automate.
The limits of traditional ROI
Classic ROI models focus on quantifiable, short-term outcomes:
- Cost savings
- Time reduction
- Productivity gains
These are important metrics, but when applied alone to AI initiatives, they tell an incomplete story. They undervalue AI's strategic potential and overemphasize efficiency over innovation. In fact, applying only traditional ROI logic to AI can de-incentivize bold initiatives that unlock long-term transformation.
I recently worked with a regional bank where Generative AI didn’t just accelerate client onboarding - it actually sparked a new way of working between compliance, legal, and front-office teams. That cultural impact wasn’t in the original ROI spreadsheet - but it created a ripple effect of innovation. That’s why we need a better framework.
We need to move beyond pure cost savings and look at AI value through a multi-layered lens.
The AI value pyramid
I use a simple but powerful mental model with my clients and colleagues: the AI value pyramid – a three-layer framework that reflects the full scope of AI’s impact.
Efficiency (base layer)
This is where most organizations begin their AI journey, the most obvious one, and where most traditional ROI models stop. It includes:
- Automation of repetitive tasks.
- Time savings for staff.
- Reductions in operational costs.
Think: fewer manual reports, faster document processing, and chatbots deflecting customer calls. Valuable? Yes, but not transformative. Stopping there would be like evaluating electronic mail (e-mail) based on how many letters it replaces.
Decision quality (middle layer)
This layer is often ignored – but it’s where real differentiation starts.
AI systems can uncover patterns, detect anomalies and generate insights that lead to:
- Smarter underwriting decisions.
- More accurate fraud detection.
- Improved supply chain forecasting.
- Better customer targeting.
These improvements may not always show immediate dollar value - but they compound over time, reducing risks and increasing precision in high-stakes areas.
Innovation and culture (top layer)
This is the most elusive and the most powerful. AI becomes a catalyst for:
- Creating new products and services.
- Rethinking business models.
- Developing a more data-driven, experimental culture.
It’s not just about what AI does, but what it enables. Organizations that embrace this layer don’t just improve processes – they reinvent them.
Why this matters
When you apply the traditional ROI lens to AI, you’re likely to:
- Prioritize the lowest-hanging fruit.
- Undervalue long-term gains.
- Undermine strategic innovation.
By adopting the AI value pyramid, leaders can:
- Justify larger investments.
- Set more realistic success metrics.
- Focus on sustainable transformation.
Don’t shrink AI to fit your spreadsheet
AI’s real ROI isn’t just in what it saves – it’s in what it unlocks.
If you are a business leader, next time, don’t ask, ‘What’s the ROI of this model?’ Ask, ‘What are we enabling by becoming an AI-native enterprise?’ That means faster risk responses, more personalized customer interactions, regulatory agility, and talent that’s ready for the next disruption.
