How do we turn growing AI expenditure into measurable business returns?

 Your employees have AI assistants, adoption is growing and teams are reporting time savings. That may be a worthwhile investment. But if competitors have access to the same tools, what are we doing differently with them, and does that difference show up in business performance? More fundamentally, are we funding the opportunities with the greatest potential, or those that are easiest to deploy?

To answer that, we need to look at the business change each investment could make possible. Saving time on individual tasks is one opportunity; using that capacity to serve more customers, reducing production losses or developing a new service are others. And the size of the opportunity doesn’t necessarily depend on how sophisticated the technology is. Standard software combined with your organisation’s information and expertise could achieve more than an expensive bespoke solution. Which means we need to be clear about the result we’re funding, what it will take to achieve it and whether it deserves priority over the alternatives.

So, what are today’s AI initiatives actually costing and delivering? How should we divide investment between improving existing work, changing core processes and creating new sources of revenue? Where is standard functionality sufficient, and where would a distinctive capability justify additional cost and effort? And what evidence should lead us to expand an initiative, redirect it or stop funding it?

At Making AI Pay, we’ll connect those investment choices with the work needed to realise their value, including costs and responsibilities that can disappear between departmental budgets. If you were allocating your AI budget again today, which commitments would you keep, and what would you fund differently?

Join CIONET Belgium’s Making AI Pay to sharpen the choices behind your next AI investment.