But aren’t NTT Data’s customers aware it is using genAI to deliver some of its services, and demanding the company charge less as a result? Well, Pereira says, in some cases the answer is, yes. But in others the firm either charges a fixed price or has managed to move customers to a value-based pricing arrangement in which NTT Data gets paid based on a particular set of customer KPIs. The deal is often structured as a “success-based” payment, where if the KPI doesn’t move in the right direction, NTT Data makes nothing, but its compensation also ramps up in line with how much the KPI improves.
This kind of pricing model is the Holy Grail for many of those selling AI-based services. But as Pereira notes, many customers are uncomfortable moving to this kind of system because they dislike variable costs. “They expect to know what the cost of the project is beforehand,” he says.
The third stream is what Pereira calls NTT Data’s “productive model,” which is a tech platform the firm has developed for deploying generative AI models and solutions. One of the key considerations here, he says, was to build a platform that was “plug and play”—where it was easy to substitute in AI models from different vendors. Given how fast the technology is moving, he says, it’s essential not to get locked in to any given model or vendor. “We need to have our own platform, so we can decide our strategy independently from the strategy of the vendors,” Pereira says.
The fourth and final work stream is focused on “internal processes,” or the support functions of NTT Data itself, such as human resources, accounting, and marketing, and infusing AI into all of these departments to make them more efficient. Pereira says last year the firm automated 54,000 hours of internal work in this stream. This has included tasks such streamlining the onboarding of new vendors to its purchasing and procurement system and using AI to help screen the resumés of job applicants.
While Pereira is excited about the potential of AI agents, he says NTT Data is cautious about introducing them for client service tasks, for several reasons. One is simply that they are not yet that reliable. It’s “introducing huge risks, and I don’t think organizations are really prepared to manage this kind of risk [with] completely autonomous processes,” he says. Another reason is that NTT Data wants to emphasize the value its human consultants are providing. Automation can make the firm’s human consultants more efficient, but if the client comes to see AI as the primary driver of the firm’s value, then it’s in trouble. “Because if 100% of the value comes from the automation, then we are out of the equation,” he says.
The whole industry, Pereira says, must figure out how to draw the distinction between providing a service and providing software. Because, he says, if everything becomes software, then consultancies as a whole are finished.
With that, here’s the rest of this week’s AI news.
