AI ROI for businesses? Measuring beyond the hype…

Who’d have thought just a few years ago that AI would be literally everywhere. Gone are the days when the only examples you can think of are Netflix and Google maps. Now we have AI in toothbrushes and AI grannies sending phone scammers crazy (our personal favourite use case so far):

But from boardrooms to marketing teams, leaders in businesses are being told they need AI to stay competitive. But amid the hype and noise, one crucial question often gets overlooked – what’s the actual return on investment (ROI)? Is it all worth the effort?

We’ve seen plenty of AI projects begin with enthusiasm but come unstuck due to unclear objectives, lack of training, poor data, or unrealistic expectations. Now let’s park all that and pretend briefly that we are operating in a perfect world and all these things have been sorted. How do you actually work out if what you have implemented is delivering real business value? How tangible do your measurements need to be?

The problem with traditional ROI measurement for AI

Many businesses default to the standard tried and tested ROI metrics – what costs have we saved, what are the efficiency gains, how big are our revenue increases? While these metrics are obviously still important, they don’t tell the full story. AI’s impact often extends beyond immediate financial returns. It influences decision making, it changes customers’ experiences, and it can potentially affect long-term competitiveness.

Here’s a simple everyday example to bring this to life:

Imagine you run a retail business and you have an AI-powered customer support chatbot on your website, where before you had a team of people answering calls or emails. Its immediate ROI might be measured in reduced costs of the support team that would have been handling those queries beforehand. But what about:

• Improved (you hope) customer retention?
• Increased capacity for the human agents to handle complex queries, potentially generating more revenue?
• Insights gained from automated conversations that can improve other processes?

These benefits may not be easy to quantify but could have a significant impact on your business performance and growth.

Consider a blended approach to AI ROI

To get a broader picture of AI’s ROI, try and move beyond the immediate short term financial fix and consider measuring across three dimensions:

1. Financial ROI – The old favourites: cost savings, revenue increase, operational efficiency. Clearly essential (and your FD will want these) but not the full story.
2. Strategic ROI – How does AI improve decision-making, customer experience, or competitive positioning? Some easy enough to measure, such as customer satisfaction, some trickier, like improved decision making.
3. Capability ROI – What new capabilities does AI enable that were previously impossible or inefficient?

If you do this, you’re thinking of AI as a long-term business enabler rather than a short-term cost-saving tool. This is a good thing. It feels like there’s too much clambering for the latter at the moment.

What are the challenges in measuring AI ROI?

Several factors make AI ROI measurement super challenging to pin down:

1. Long-term value creation: Many AI investments deliver compound benefits over time.
2. Opportunity costs: It is often difficult to quantify what might have happened without the specific AI investment.
3. Attribution issues: Isolating AI’s impact from other business factors might be nigh on impossible.
4. Hidden costs: Infrastructure, training, data management, maintenance to name but a few. Often these aren’t isolated costs to a single AI tool or investment.
5. Deployment delays: Time between investment and operational implementation. Although implementing AI in many instances can be super quick, it’s not always the case.

Build yourself a strategy to get this right