By Adam Mills
AI Editor
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Artificial intelligence is beginning to unsettle the economics of IT services, raising new questions about how work is organized, priced and where value resides. Recent market signals have pointed in different directions, with some suggesting long-term growth and others hinting at pressure on traditional approaches.
Newsweek’s recent “AI Impact Forum” webinar, “Is the IT Services Industry Going Extinct?”, held on April 9, brought those questions into focus as Dr. Ranjit Tinaikar, former CEO of Ness Digital Engineering, spoke with Francisco D’Souza, co-founder and managing partner at the private equity company Recognize and former CEO of Cognizant.
One idea surfaced repeatedly: the cost of producing software is falling, while the importance of that software continues to rise.
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“If you believe that coding is going to become abundant, and it’s already on that path,” D’Souza said, “you would expect the value associated with coding to become significantly less.”
This imbalance creates what D’Souza described as a “paradox of value.” As development becomes easier and faster, the output itself becomes more central to how companies operate and compete.
“You could end up in a scenario where the total value of software actually increases quite dramatically, even as the value associated with creating it declines,” he said.
At the same time, greater abundance introduces new complexity. More code does not automatically translate into better systems. As AI generates software at scale, the number of integrations, dependencies and edge cases expands, increasing the surface area for risk. Security vulnerabilities, technical debt and accountability gaps become harder to manage when systems are produced faster than they can be fully understood.
That dynamic reinforces the need for a different kind of advantage. In a world where execution becomes cheaper, the ability to ensure systems are reliable, secure and fit for purpose grows more important—and more scarce.
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The implications are already visible in how services are delivered. For decades, the industry has tied revenue closely to labor, billing clients based on time and team size.. As software becomes faster and easier to build, that relationship begins to weaken. What matters more is how effectively companies apply technology to solve problems, integrate systems and deliver measurable outcomes.
Work that once required large, coordinated teams can now be broken into smaller components and executed in parallel, speeding up delivery.
As throughput increases, the link between effort and revenue starts to loosen. Pricing tied to hours worked or headcount becomes harder to justify when the same output can be produced with fewer resources.
In practical terms, work that once required dozens of engineers over several months can now be completed by smaller teams working alongside AI systems in a fraction of the time. That compression is already forcing companies to rethink how they price and deliver services.
“You’re going to have to move away from input-based pricing to output-based pricing,” D’Souza said.
The consequences extend beyond pricing. Organizations will need to rethink how work moves from problem definition through delivery, combining human expertise with AI systems at each stage.
Tinaikar emphasized the urgency. “You have to be willing to rethink your entire model,” he said.
That reassessment starts with where contribution is created. Earlier stages of engagement—defining the problem and setting direction—take on greater importance, while later stages shift toward integration, validation and ensuring systems work as intended, rather than simply executing tasks.
D’Souza framed this as a broader evolution in the role of services firms.
“In the past, we provided the hands,” he said. “In the future, we have to provide the head.”
As AI systems take on more of the execution layer, firms are increasingly judged on their ability to deliver reliable outcomes rather than the scale of resources deployed.
“What clients are really buying is certainty,” he said. “They’re not buying effort.”
The workforce impact is just as significant. AI does not eliminate the need for talent, but it changes how that talent is applied. Fewer people may be required for each unit of work, even as the total volume of work expands.
That combination—greater demand alongside greater efficiency—creates both opportunity and risk. Companies that adapt their operating structures can expand capacity and move faster, while those that remain tied to labor-heavy approaches may struggle to compete.
Tinaikar made clear this is not a slow transition. “This is not something you can ease into over time,” he said. “You have to make decisions.”
For D’Souza, the moment calls for decisive leadership. “You have to burn the boats,” he said.
AI, in this view, is not simply improving existing workflows. It is redefining how services are delivered, how performance is measured and how firms position themselves in the market.
Watch the full webinar discussion above, and head here to sign up for the next "AI Impact Forum" webinar, taking place on April 30, and featuring Tiger Tyagarajan, the former CEO of the advanced technology solutions company Genpact.
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