NTT Data CDAO: C-Suite Leaders Have ‘Angst’ About GenAI, Still Go Full Throttle

Enterprises are accelerating their adoption of generative artificial intelligence (GenAI) at scale to gain a competitive advantage, especially in light of productivity and efficiency benefits across industries.

But C-suite leaders also face a thorny dilemma: How to innovate fast and yet do it responsibly to ensure privacy and avoid regulatory scrutiny.

The C-suite is saying, “I need to run fast. We need to drive business results, but I’ve got to do it in a secure, responsible way. I can’t have my data leaked to the outside world. I can’t drive an action that harms someone,” said Andrew Wells, chief data and AI officer, North America, of NTT Data, the digital transformation unit of Japanese telecom giant NTT, in an interview with PYMNTS.

“It was that dichotomy … that was causing a lot of angst in a lot of the CEOs (and CIOs) that I talked to,” Wells said.

He should know. NTT Data serves 75% of the global Fortune 100 companies. It is “probably the biggest company no one’s ever heard of,” Wells said. NTT Data is the fifth largest IT service provider globally, with $30 billion in annual revenue across 50 countries. It is also the third largest data center provider.

But this dilemma is not slowing down business leaders. Wells said he expects to book $2 billion in revenue for his Smart AI Agent tool alone by 2027.

AI agents are a step up from AI chatbots like ChatGPT in that they not only provide information but also perform multistep tasks for users autonomously. For example, they can plan a vacation.

AI agency is “the next big wave that’s hitting us right now,” Wells said, adding that today’s CEOs would be the last to manage all-human teams, in a nod to Salesforce CEO Marc Benioff’s comment at the World Economic Forum in January.

‘Backlogs’ of GenAI Use Cases

Wells said the GenAI demand is there: Companies have “backlogs” of use cases that are just waiting to be transformed by GenAI apps — but these apps have not yet been created or made available yet.

Wells cited an NTT Data survey of 2,300 executives last November showing that 97% of CEOs see GenAI having a “material impact” on their operations, and 70% of CEOs expect it to lead to “significant” transformation in 2025.

Those numbers dovetail with PYMNTS’ own research. A PYMNTS Intelligence report shows that most chief financial officers see GenAI as having a crucial and growing role in financial reporting.

Contrary to the public perception of AI hype reaching a plateau, Wells noted that executive enthusiasm remains strong.

“At the time we did the [survey] in November, there was this feeling in the zeitgeist that [GenAI] was overhyped and going to be on the decline, and that’s not what we were seeing at all in the data — everyone was full on.”

Executives also were “excited” and “amazed” by GenAI, the survey said.

Commoditization of GenAI

But at the fast pace of AI development, GenAI is already starting to be commoditized even though it is a fairly new technology.

“I look at GenAI as the phase that we’re in now and starting to get commoditized,” Wells said. “I think DeepSeek definitely shot the arrow across our bow to say that we can do this for a lot less and make models that have high efficacy.”

DeepSeek is a Chinese AI startup that developed foundation AI models that performed at par with top models from OpenAI, Google and Anthropic, but at a fraction of the cost.

GenAI’s rapid commoditization is reshaping how enterprises approach AI implementation. According to Wells, the focus is on building applications and agents on top of existing AI models rather than developing new foundation models from scratch.

“Right now, we have the GenAI layer created, and you’re going to start to see applications built on top of that, and those are more than likely going to take the shape of agents,” he predicted.

Wells said NTT Data is developing small language models for clients customized for their business processes. This makes the business processes smarter. They then layer AI agents on top to automate the tasks.

Wells used the example of routing a customer call or email to the right department or people. GenAI is used to interpret the call or email to capture not only the content but also the sentiment and tone, he said. An AI agent then routes the call or email to the right place or party.

Hallucinations and Other ‘Silliness’

As for tackling hallucinations, Wells said techniques are being used to mitigate them, such as GraphRAG (retrieval augmented generation). Companies could also train a small language model on internal data and employ fewer parameters in the AI model and then deeply training it on corporate datasets, to reduce hallucinations.

To ensure accuracy in AI agents, one tactic is to create governance layers — inserting a safety AI agent in the agentic workflow or a human in the loop, Wells said. Another way is to audit the AI agent’s decisions, among other techniques.

“There are guardrails you can put in place, but a lot of it depends on who’s architecting the solution, and it’s not going to be perfect,” Wells said. “We’re going to see silliness, and that’s where you as the person who’s doing the solution … has to be very purposeful and pedantic to make sure that you’re putting in the safeguards.”

But C-suite’s focus remains on innovation. Notably, 60% of executives plan to innovate first and then ensure GenAI is deployed responsibly, Wells said their survey showed. Only 30% was the other way around.

“It tells you the pressure that a lot of businesses are under — you’ve got to get out there and adopt this technology, and not necessarily for cost savings,” Wells said. “It’s really for competitive differentiation and driving better products and services in the market.”

“The people that run to AI and start using these tools are going to be the ones that capitalize on them” best, Wells concluded. “The ones that don’t use it are going to be the ones that get left behind.”

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