Accenture’s research shows two-thirds of organizations surveyed are planning investments in AI over the next year. That’s not surprising when you consider that, in some industries, these investments are expected to boost revenue by over 30 percent over the next four years.
Some AI applications link neatly to projected returns, making ROI calculations straightforward. An energy producer, for example, could tie its investment in an AI-powered predictive maintenance tool directly to increases in equipment uptime or reductions in maintenance costs.
Other applications are more complex and unpredictable, making it challenging to use typical approaches to calculate the ROI of AI. To what extent, for instance, could reductions in crime be tied to AI projects when many other factors may also be having an impact. Yet in any scenario, we need to make a solid business case for AI investment.
In situations where it is difficult to estimate the ROI of AI – be it because of inherent complexity or available capabilities—organizations can risk either losing competitive advantage by delaying investments or sinking money into the wrong AI initiatives.
So how can organizations get started on AI projects where future risks and returns are hard to quantify?
A staged approach to assessing the ROI of AI
When returns are difficult (or impossible) to calculate, or where risks need to be minimized systematically, we need to take a staged approach to AI projects. This allows us to accurately estimate the value or risk of future, larger-scale or live implementations. This staged approach can take three main forms:
A common feature of these approaches to assessing ROI of AI is that they usually are heavily customized to each organization’s data and circumstances. This is necessary when AI projects are the focus, because most often it is your organization’s context and data—used to train, test and refine the AI model—that will shape the ROI equation.
This differs markedly from traditional hardware or software investments where the costs and impacts are more neatly defined and predictable, to the extent that standardized ROI templates are commonly available for several types of investment.
Accounting for unexpected value
One final, but important, point when calculating ROI is that AI can deliver unexpected, additional insights. In other words, AI can deliver valuable results in addition to achieving (or not achieving) the primary objective of the project. When that happens, those insights should be included in the assessment of the project’s value. Planned or not, they are part of the project’s outputs. For this reason, you should be clear with your stakeholders that the exploratory nature of AI means you may be left with, or led to, additional or alternative results that may be no less valuable than your primary targets.