Impact of Agentic AI on Technology Services - Market Report
Agentic AI is reshaping how enterprise technology services are bought, built, and delivered. Enterprise spend is moving from pilot to production, the buy-versus-build equation in software is being reassessed, and GSIs are acquiring AI capability they cannot build organically fast enough. This report maps those shifts across enterprise demand, capability build-out, and the industry verticals where adoption is accelerating first.
Three dynamics are converging. Enterprise AI is moving from pilot to production, with early spend concentrated on the cloud, data, and governance foundations required for scaled deployment. Software providers are pivoting from copilot features to agentic platforms, fundamentally altering the buy-versus-build calculus. And AI Labs are investing heavily in partner ecosystems, creating a new class of implementation opportunity for services firms. The moves are already happening. HSBC is scaling AI across 600+ applications on Google Cloud. Novo Nordisk has cut regulatory document writing from ten-plus weeks to ten minutes using Claude on Amazon Bedrock. Allianz UK's AI underwriting tool is in full commercial production. The pace is building.
Think of the two as complementary rather than separate. The three-part series published earlier this year made a narrative argument: AI adoption inside enterprises will be slower, messier, and more human-dependent than most pundits claim. The isolated examples of document automation or process AI that are already happening — Novo Nordisk cutting regulatory writing from weeks to minutes, CommBank deploying an autonomous DevOps agent — are exactly what early-stage, friction-heavy adoption looks like. They are not evidence of large-scale displacement. They are proof of concept.
This report provides the data layer underneath that argument. Enterprise spend flowing into cloud, data, and governance foundations before AI agents can be deployed at scale. Regulated industries moving first while mid-sized firms remain in pilot mode. GSIs acquiring AI capability rather than building it, because organic build is too slow. All of this maps directly onto the adoption pattern the articles described.
Where the series offered the strategic why, this report offers the market what. Read together they make a more complete picture of where the technology services industry is heading — and what firms need to do to be on the right side of it.
Read the full series: Article 1 — Resetting the Conversation on AI · Article 2 — The Reality of Implementing AI Inside Enterprises · Article 3 — What AI Means for Technology Services and Consulting Firms
M&A is accelerating as a direct response to AI-driven enterprise demand. GSIs are acquiring rather than building, securing scarce AI, data, and engineering capability faster than organic growth allows. 85% of tech deals cited AI as a strategic driver in 2025. Deal activity is concentrating around engineering-led AI platforms — illustrated by Accenture's acquisition of Faculty (US$1bn+), Wipro's acquisition of HARMAN DTS (US$375m), and Coforge's acquisition of Encora (US$2.35bn). For firms considering a sale or capital raise, AI-native capability is increasingly central to buyer interest and valuation.
Regulated industries are moving first. Financial services, healthcare, and telecoms are leading enterprise-wide AI transformation, with larger organisations pulling ahead while smaller enterprises remain in pilot mode. Cybersecurity deserves specific attention — AI is multiplying enterprise attack surfaces through agents, APIs, and machine identities, driving a distinct spending priority the report projects will grow from $219bn in 2025 to $563bn by 2032.
It sits at two levels. At the infrastructure level, firms with established cloud and data capabilities are best placed to capture the foundational spend that precedes AI deployment — the data pipelines, governance layers, and cloud modernisation every enterprise needs before agentic AI can run reliably. At the advisory level, Anthropic and OpenAI are actively building partner ecosystems to move enterprise clients from proof of concept to production, and a new class of AI implementers is emerging around agent design, evaluation, governance, and code modernisation.
As Equiteq's thought leadership series argued: firms do not need to become AI product companies. They need credible capability to help clients identify high-value use cases, prepare data environments, integrate AI into workflows, and govern risk and accountability. That is where durable demand is forming.