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Why automation can create more services work
Deploying an AI model or agent is not the same as putting it to productive use across an organization. Customers need help selecting use cases, connecting AI to applications and data, modernizing infrastructure, and making systems reliable and secure in operation. Those tasks create work even as AI tools reduce effort in parts of conventional development and support.
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Boston Consulting Group (BCG) argues that agentic AI could unlock up to $200 billion in net new value pools for technology services over the next five years. That is BCG’s forecast, not a realized market total or a guarantee. Its authors describe the tension directly: “For technology service providers, agentic AI represents both a profound disruption and a major growth opportunity.” BCG’s analysis, published February 20, 2026, frames the outcome as expansion alongside a shift in the mix of services—not growth for every provider or offering.
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Where demand may move
Build, integrate, and operationalize AI
Organizations need services to turn priority use cases into working systems: application development, implementation, integration with existing software, and operational support. In BCG’s survey, one-third of participating enterprises said they were already scaling agentic deployments, while two-thirds expected providers to build and operationalize priority use cases. The demand-side survey covered more than 115 enterprise executives across six industries; a separate supply-side survey included more than 75 executives at technology service providers. These findings describe those surveyed groups, not all enterprises.
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Data and infrastructure foundations
AI applications rely on usable data, suitable infrastructure, and connections among services. Modernization and ongoing data and infrastructure operations can therefore become part of the opportunity, alongside initial implementation. Gartner forecast in March 2024 that AI and large language model tools would account for more than 30% of the increase in API demand by 2026. This was a forecast for that year, not a confirmed measurement of what happened. Gartner’s forecast points to possible pressure for more API capacity and integration, but does not establish how much services revenue it would produce.
Why the opportunity is uneven
Automation is also a direct efficiency pressure on providers: if AI allows work to be completed with fewer hours, customers may buy less of some labor-intensive traditional services or expect lower costs. OECD estimates quoted in the UK government’s 2026 AI adoption plan suggest about 50% of tasks in IT and information services are exposed to AI, approaching 80% in an expanded-capabilities scenario. The plan also cites potential total factor productivity gains above 10% over the next decade if adoption is high. These are scenario-based estimates, not observed job losses or guaranteed productivity results. The UK government plan presents them as potential effects of adoption.
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Exposure to automation does not mean an entire service line disappears. It does mean providers may need to deliver familiar work differently, while shifting investment toward integration, data, infrastructure, and operations. BCG says providers need to realign portfolios, delivery models, talent, and commercial structures. Providers that continue to sell effort in areas where AI reduces effort may face pressure; those able to deliver measurable customer outcomes may be better positioned to capture new demand.
What recent market figures do—and do not—show
Information Services Group (ISG) reported that combined technology services market annual contract value (ACV) rose 29% to $39.4 billion in Q1 2026, while infrastructure-as-a-service (IaaS) ACV rose 160%. ISG’s quarterly figures are a dated signal of contracting activity in that period, not revenue, a forecast of future growth, or proof that every category is expanding. They provide context for demand, but cannot settle whether AI will enlarge the market over the long term.
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What will determine which providers benefit
The central question is whether providers can convert AI capability into systems customers can use and trust—and whether the resulting work outweighs efficiency-driven reductions elsewhere. Readiness involves more than adding AI offerings to a catalogue:
- Skills: Build the expertise needed to develop, integrate, and operate AI systems in customers’ environments.
- Portfolio: Make room for implementation, data, infrastructure modernization, and ongoing operations as demand shifts.
- Delivery: Adapt methods to use AI productively while maintaining quality, governance, and dependable operations.
- Commercial model: Align engagement structures with delivered outcomes rather than assuming that more labor hours will remain the measure of value.
AI may therefore expand the technology services opportunity by creating new implementation and operational needs, even as it compresses parts of the work providers already sell. The market-level case is credible but conditional: forecasts and early market signals do not guarantee that individual firms, workers, or service lines will grow.
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