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AI's structural impact on technology services

8 July 2026

AI’s structural impact on the IT services industry; a hypothesis from some management consultants near you.

J-curve thesis: compression then expansion

  • Overall technology spend remains robust and is projected to reach record levels of over $6.5 trillion in 2026
  • The allocation of this spend is shifting away from traditional software builds and toward AI infrastructure and cloud operations
  • This shift creates potential a “J-Curve” effect for the IT services industry: an initial period of revenue compression as AI automates legacy services, followed by a larger expansion as firms sell new AI-driven workloads

Market compression

“What AI destroys”

  • Projected 18-36 month lag between when enterprises adopt AI internally and when they renegotiate their IT outsourcing contracts
  • By 2030, an estimated $192 billion to $311 billion of the outsourced market is at risk
    • Roughly 17.5% to 28.3% of the total outsourced IT services market
    • c. 2-4% annual headwind between now and 2020

The projected deflation by 2030 varies significantly across service lines:

  • Testing / QA (45-65% deflation): Standalone manual testing is disappearing as AI generates code and tests simultaneously in a single loop
  • Analytics / BI (40-55% deflation): Traditional standalone reporting dashboards are being aggressively absorbed into embedded AI platforms; additionally bespoke dashboards can be created quickly with agentic coding
  • BPO / BPS (25-35% deflation): High-volume, rule-based operations like Voice/CX and routine customer support are being rapidly automated by AI chatbots
  • Traditional application development and maintainance (ADM, 25-35% deflation): AI code generation tools are enabling smaller teams to maintain equivalent or higher output levels, reducing overall headcount requirements
  • Engineering R&D / Product Engineering (15-25% deflation): Software-heavy engineering compresses quickly, while physical hardware engineering is delayed by regulatory and testing constraints
  • ERP/SI & Infrastructure Management (10-15% deflation): AI compresses project timelines and automates routine monitoring, though this is partially offset by an increasing total volume of migration and multi-cloud work
  • Consulting (0-5% deflation): AI eliminates routine research tasks but simultaneously generates equivalent new demand for advisory services

Market expansion

“What AI creates”

  • While traditional services shrink, AI is creating a new addressable market of $700 billion to $1.2 trillion
    • Of which IT service providers are projected to capture $260 billion to $440 billion by 2030, based on an approx. 20-40% capture rate

These new addessable areas include:

  • Modernise: Opportunities include unlocking legacy systems (migrating outdated COBOL backlogs), replacing horizontal SaaS licenses with custom AI apps, and deploying Business Process as a Service (BPaaS) to substitute internal human labor
  • Build: Foundational growth areas include data engineering, designing private on-premise AI infrastructure, building sovereign AI stacks for governments, and outsourced custom silicon engineering
  • Trust: New revenue streams are emerging in AI FinOps (managing GPU and token costs) and ensuring AI governance, cybersecurity, and regulatory compliance
  • New pockets: Firms can build vertical “mini Palantirs” for decision intelligence, license domain-specific AI factories, and integrate physical AI and robotics at the edge

The impact on technology services firms

Operating model and valuation changes

The net market impact dips into negative territory between 2025 and 2028 due to early compression. The market is expected to cross into net positive territory by 2029, resulting in a base case net gain of $82 billion by 2030 and $120 billion by 2032.

  • Delivery structures: The traditional junior-heavy talent pyramid (60% Junior, 25% Mid, 15% Senior) will invert into an AI-native diamond structure (10% Junior, 20% Mid, 70% Senior) by 2030
  • Commercial models: Billing will migrate away from Time and Materials (T&M) toward outcome-based fees, subscriptions, and platform rent
  • Market multiples: Higher-margin, recurring AI revenue re-rates a service firm’s valuation from 1.0-1.5x revenue up to 3.0-5.0x revenue

Strategy and valuation

Over the next 18-24 months:

Strategy for technology services companies

  • Portfolio: map revenue risks
  • Skills: transition staff to AI orchestration roles
  • Deploy AI-augmented ‘pods’
    • i.e. a “mid-heavy” talent structure that is led by an orchestrator or senior domain expert, rather than relying on a large base of junior developers
    • ideally persistant across engagement to allow for the build-up of domain knowledge
  • Architect: build repeatable IP
  • Pricing: shift to value-based contracts
  • Update operating models

Technology services company valuations

  • Valuations typically based on deep domain expertise and long-term Master Service Agreements (MSAs)
  • Investors now discount a target’s headline EBITDA by the compression velocity of its service lines when determining aquisition valuations