In addition to questions about which AI project to invest in, I get a lot of INMA member questions about calculating return on investment (ROI) when it comes to AI use cases.

News industry leaders will swear up and down they do not want to prematurely snub out experimentation by focusing on ROI — yet those who are a rung or two down the ladder tell me that they absolutely need to include that when they make a business case for any AI project. 

I have seen a fair amount of hand-wringing about the question of calculating ROI when it comes to AI.

Leaders consider it imperative to use the technology, while team members are a mix of enthusiastic, skeptical, and afraid in varying proportions across the industry. This leads to spotty adoption — which means any cost savings are patchy and any ROI is not clear.

How can one wrap their head around this, then? 

I came across some useful advice recently suggested by Gartner, a company that does technology research and consulting work, which pointed out that fewer than 15% of the enterprises that have adopted AI have actually created any value.

Productivity does not equal value

Time saved is not money saved. 

“If you save 20% of a person’s working day, there is productivity leakage. And it is not until they use that new time they have got to produce revenue or to fundamentally change business processes across multiple full-time employees (FTEs) that you are actually going to get savings,” said Gartner’s distinguished Vice-President/Analyst Frances Karamouzis.

“We’re telling people to shift their focus from focusing on FTEs to focusing on processes … . Start planting the seeds for this because processes and the real benefits that come are from increasing the business outcomes — either because you reduce the latency of the process, you reduce the cycle time, you reduce the error rate, you somehow created some efficiency or some kind of efficacy, or you’re now creating new business agility.”

The broader the use case, the harder it is to show value

For example, rolling out an AI assistant across the company is a broad use case. It will not be equally useful to everyone on staff, and everyone will not use it the same way. 

Deployment does not equal adoption

Enterprises underestimate the change-management effort that comes along with AI implementation. 

“If you don’t focus on change management — changing their behaviour, changing their performance metrics, changing a lot of things about the workflow — the process you will enter into this sort of realm of what we call the inevitable sort of brick wall. And this brick wall is where we see a lot of clients now, where they started doing some efficiency based projects with AI, and they’re sort of struggling, first of all, to calculate or recognise value, but secondly, they’re struggling to get to the next level of, what’s the next use case? What’s the next big thing I should work on?” Karamouzis said.

Most companies are looking for efficiencies with AI

They are simply trying to maintain competitive parity or do basic work that is considered “table stakes” rather than using AI to gain a differentiating competitive advantage and gain market share or undertake game-changing business transformation. 

They are aiming only for low-hanging fruit and incremental improvements rather than trying to expand their revenue, market share, or profitability. 

This is not a place where it is easy to create and calculate value, though. Any return on investment here is highly correlated with execution.

Companies should instead create a portfolio of use cases 

Eighty percent of use cases should be in the category of creating competitive advantage and differentiation, with 5%-15% each in the categories of table stakes and business transformation, Karamouzis said. 

“If your focus is ROI, you want a large preponderance to (that category).”

Why don’t more companies do this? 

Because these use cases require collaboration across an organisation through multi-disciplinary, cross-functional teams, and it is easier to operate in siloes. 

This means that the portfolio of use cases instead ends up being 80% in basic table stakes work, she said.

In that case, companies should think about their AI initiatives in terms of “return on employee” instead, Karamouzis said, which means taking into account new digital dexterity skills or lower attrition rates among staff due to higher satisfaction with work instead of purely financial benefits.

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