For years, the software and services industries operated within a clear division of roles: software defined what could be done by codifying capabilities and workflows, while services determined how it was implemented and operated at scale through human effort.
Growth for tech services firms scaled predictably with workforce expansion, supported by leveraged delivery pyramids and utilization-driven margins. Artificial Intelligence (AI) improved productivity within this model, but it did not fundamentally alter its economics.
That equation is now changing.
As AI systems take on increasingly execution-heavy tasks, growth is becoming less tethered to headcount expansion. The traditional linkage between revenue and labor intensity is weakening, placing pressure on pyramid structures, pricing models, and margin assumptions. This is not a cyclical shift. It is structural.
And that structural shift is now becoming visible in real time.
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The Anthropic effect: Intelligence as the new execution layer
Anthropic’s recent announcements, expanding Claude into an agentic, tool-orchestrating platform and advancing enterprise code generation and modernization, crystallize this evolution and reignite what many now call a Software-as-a-Service (SaaS)pocalypse.
At a surface level, AI can now autonomously execute complex, multi-step business and engineering workflows that previously required dedicated SaaS tools or layers of junior talent.
At a deeper level, the change is more profound: AI is bundling intelligence, orchestration, and execution into a single programmable layer. What was once distributed across software platforms and services teams is increasingly being compressed into model-driven execution.
When execution itself becomes programmable, the economics of services change with it.
What does this mean for tech services?
For technology services firms, this shift introduces a long-elusive possibility: non-linear revenue growth. If execution no longer scales strictly with headcount, value creation can begin to decouple from labor intensity.
This is not a nail in the coffin for traditional services, nor will the transition materialize overnight. Enterprise complexity, regulatory constraints, integration depth, and risk sensitivity ensure that human expertise remains central in the near term. But the direction of travel is clear. Firms that use this window to recalibrate their operating models will define the next phase of growth in the AI-driven services ecosystem.
The question is no longer whether AI will reshape services economics, but how providers will respond.
We recognize that certain factors (e.g., commercial models) are inherently shaped by client preferences and constraints. However, in this blog, we focus specifically on the capabilities within providers’ control, particularly their asset portfolios and alliance ecosystems, to help them unlock non-linear revenue in an AI-first economy.
Recalibrating the operating model to drive sustained value (for yourself and your clients)
Reinvention in an AI-first world will not be a single strategic move. It requires deliberate shifts in what service providers build, how they deliver, and how they measure value.
This transformation will not be comfortable. It demands intentional cannibalization of legacy revenue streams and delivery constructs. While that may create short-term performance pressure, it is a necessary trade-off to secure long-term advantage as AI-led delivery models mature.
Asset-led delivery
If execution is becoming programmable, service providers cannot rely solely on scaling talent. They must embed leverage directly into delivery.
As outlined in Beyond FTEs: reimagining SI asset strategy in the age of AI this means building horizontal assets that rewire the Software Development Life Cycle (SDLC) and reshape how software is built, tested, deployed, and operated across industries and technology stacks, reinforced through deeper technology-provider partnerships to amplify impact.
However, building assets alone will not unlock non-linear growth. The real inflection lies in converting Intellectual Property (IP) assets into an economic engine. While many providers are investing in AI-led assets, far fewer have institutionalized mechanisms to enable and monetize them systematically.
Unlocking value requires alignment across portfolio discipline, market positioning, and commercialization strategy.
Alliances ecosystem-driven innovation
In an AI-first world, alliance ecosystems are no longer static badge collections but dynamic value networks. The partner landscape now extends beyond traditional hyperscalers and enterprise platform providers to include foundation model providers, hardware vendors, and niche data/AI-native firms.
But differentiation does not come from accumulating logos. It comes from activating them strategically. To keep their alliance ecosystems future-ready in an AI-driven landscape, providers must anchor their strategy around three critical questions:
Together, asset-led execution and modernized alliance ecosystems redefine how service providers create leverage in an AI-first world. The firms that integrate these internal and external capabilities cohesively will be best positioned to capture non-linear growth as execution models evolve.
As the AI landscape evolves at breakneck speed, which service providers will ultimately lead remains an open question. What is clear, however, is that the approach they choose today will determine their relevance tomorrow.
If you enjoy this blog, check out, Can services firms really build scaled software businesses? which delves deeper into another topic relating to AI.
To discuss more on this and other insights from our research on the technology industry, please reach out to Mayank Dawar ([email protected]) and Ankit Gupta ([email protected]).