Hello and welcome to Eye on AI. In this edition…consultants’ automation dilemma…OpenAI unveils a shopping feature for ChatGPT…Bloomberg finds RAG can make AI models less safe…and brands race to figure out chatbot-mediated commerce.
Management consulting is high on the list of industries whose business model is threatened by generative AI. After all, analyzing written documents, doing research, writing reports, and coding software applications for clients is a lot of what consultants do. If your client can now use a “deep research” AI agent to do this work, do they still need to hire you? Even if the customer does hire you, they’ll probably expect you to use such an agent and bill for less time—upending the cost structure of a consultant’s business. So management consultants are having to come to terms with AI perhaps faster than other fields—and how they are doing so may have lessons for us all.
Recently, I spoke to David Pereira, who is head of generative AI for Europe and Latin America at NTT Data, the IT consulting and services provider that is owned by Japan’s NTT Group. Pereira’s job is not just about how NTT Data is using AI to help transform its clients’ business. It’s also about how NTT Data is transforming itself to meet the genAI moment. Pereira tells me that NTT Data’s own analysis indicated that 40% of the consulting firm’s revenues could be jeopardized by generative AI. So getting this transformation right is a high-stakes venture. “There was a challenge there, but also an opportunity for us,” he says.
Pereira says NTT Data has organized its response to genAI around four main work streams. The most important, he says, is “talent and cultural transformation.” The consulting firm is training all of its employees to understand and use AI. It is also thinking hard about how roles within the company will change—and proactively looking to reconfigure its workforce around the use of AI.
The second stream is called “value development” and concerns how NTT Data uses generative AI directly for and with its customers. Here, the company has put some big numbers on the board: It used generative AI to automate 2 million hours of software development—90% of it for clients—in the last fiscal year, which ended March 31. This coding assistance has meant that NTT Data has been able to lift the average profit margins of its IT services delivery projects by about 2%, Pereira says.
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.
