Don't cut; grow

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Is AI finally delivering value? Who cares – just start the cutting!

Is this a good strategy? Jensen Huang, CEO of Nvidia, doesn’t think so. “If your CEO is using AI to cut headcount, it means one thing: They have no imagination,” he said in a recent interview with CNBC. "They have no vision for what comes next. They got handed the most powerful tool in human history and their first instinct was to fire people."

Unfortunately, this is the prevailing attitude of many companies, who are cutting headcount in anticipation of AI at a rate 30 times more than those who wait to see if AI pays off. About 60% have already reduced or frozen hiring in anticipation of future AI productivity gains.

That’s the dour word out of a study published by Return on AI Institute, the research arm of Scaled Agile. At the same time, the study does show a majority of executives are perceiving value in their AI efforts. Across the different types of AI, 45% of executives say they are achieving a “great deal of value,” and another 45% are seeing a “moderate amount” of value. Only nine percent admitted that their organization is achieving a “small amount” of value, and none said they were achieving no value.

In terms of hard quantifiable numbers, the great majority of organizations are achieving either a slight positive return on investment (48%) or a substantial positive (39%) return on their AI investments relative to the costs they have incurred. No respondents reported that their organization roughly breaks even on its investments, and only 2% have seen overall negative returns.

Across the main categories of AI, generative AI is still in relatively imature stages, the report’s authors state. Only 9% of organizations currently identify generative AI as their most valuable AI type, compared with 50% for analytical AI and 40% for rule-based automation, which have decades of operational maturity.

Data readiness is the greatest obstacle to AI success, with a majority of executives, 55%, report data issues, the study also shows. Close to half, 47%, cite a lack of a cohesive framework, and 47% also cite technology complexity.

Another key factor is preparing and training the workforce and leaders. Organizations that invest in both upskilling and leadership AI fluency see a 23-percentage-point advantage in value realization.

Not covered in the data, but just as impactful, is short-term thinking, along with a rush to put AI to work before considering longer-term implications. “A focus on short-term value is why many enterprises never make the technology transformation that unlocks long-term value,” said Prem Naturajan, executive VP and head of enterprise AI at Capital One, quoted in the report. “We carefully measure the benefits of our AI use cases, and whatever AI we introduce, we put our backs into it—we invest in training, transforming business processes, and so forth. It’s never 'here’s a tool, go use it.’"

The report’s recommendations for better leveraging AI include the following

Think before you cut. “Be careful about anticipating benefits from AI with headcount reductions or hiring freezes before AI capabilities are actually in production.”

Be patient with AI. "It takes several years to achieve high levels of value."

Employ the full spectrum of AI types. "Analytical AI has the longest track record of delivering measurable value and deserves continued emphasis. Generative AI has real utility, with greatest returns from targeted deployment rather than just broad adoption, and agentic AI is an early signal worth exploring."

Broaden your range of AI applications. “Assess not only ways to improve internal processes and productivity from AI, but also how AI can be incorporated into customer-focused products and services.”

Invest in both employee training and governance. "Training without
governance creates chaos; governance without training creates bureaucracy."