The $200 Billion Agentic AI Opportunity for Tech Service Providers

For technology service providers, agentic AI represents both a profound disruption and a major growth opportunity. Longstanding delivery models are likely to come under pressure as enterprises accelerate the adoption of systems that can autonomously plan and execute end-to-end processes and achieve business outcomes. While efficiency gains will reduce demand for some traditional services, BCG’s analysis shows that agentic AI will ultimately expand the total addressable market for technology services—unlocking up to $200 billion in net new value pools in the next five years.

To understand this shift from both sides of the market, BCG conducted two complementary surveys: a demand-side survey of more than 115 enterprise executives across six industries and a supply-side survey of over 75 executives at technology service providers. We found that one-third of enterprises are already scaling agentic deployments, and two-thirds expect providers to build and operationalize priority use cases. Although providers acknowledge growing demand, they report gaps in readiness across enterprise priorities, efficiency commitments, and commercial models.

Taken together, these perspectives reveal a market in transition. Providers that respond with clarity and speed—reshaping portfolios, delivery models, and talent—will be positioned to lead the next era of growth. Those that hesitate risk being left behind as enterprises push agentic adoption forward.

The Net Effect for Tech Services Is Market Expansion

Agentic AI refers to systems capable of autonomous, multistep reasoning, decision making, and execution across workflows—not just generating outputs but driving outcomes. These systems are rapidly expanding the boundaries of automation. The funding landscape signals where agentic adoption is gaining traction first. (See “Follow the Funding.”)

Follow the Funding

By understanding where capital is flowing, technology service providers can gain a clear sense of where agentic AI is heading—and where to focus their own priorities. Investment patterns reveal which parts of the agentic stack are maturing fastest, where scalable solutions are emerging, and which use cases are closest to commercialization.

Our analysis of private investments across approximately 4,000 companies shows that capital is moving decisively into agentic AI. (See the exhibit.)

Since 2023, investment has grown by more than 60% annually, with capital concentrating in two areas:

• Agentic Enablers. These platforms and layers, which form the foundation for building and orchestrating agents at scale, account for more than 55% of total investment.
• Horizontal Applications. Cross-industry applications, such as conversational service agents and enterprise knowledge agents, account for approximately 40% of funding.

Enterprises are already realizing measurable value from horizontal applications. Verizon reported a 40% increase in sales productivity across its service teams after deploying an AI assistant that reduced call-handling times and freed up employees to focus on higher-value interactions. Nubank reported up to twelvefold efficiency gains in a major extract, transform, and load migration after deploying Cognition AI’s Devin, an autonomous AI software engineer.

Vertical, industry-specific agentic solutions are earlier in development but show growing activity in domains such as health care, life sciences, and banking, financial services, and insurance. Investment is gravitating toward high-friction workflows, such as clinical documentation and coding, know your customer and onboarding, and policy life cycle automation. In these areas, agents could materially reduce cycle time and improve human efficiency.

The convergence of capital and innovation around agentic platforms and horizontal solutions is laying the groundwork for enterprise-scale deployment of agentic AI, through repeatable, high-value use cases. For service providers, these funding patterns are a roadmap. The areas attracting disproportionate investment today will shape enterprise demand, ecosystem expectations, and the competitive landscape tomorrow.

Reality is more nuanced than the deflationary narrative. Agentic systems are often framed as a deflationary force for the tech services industry. The market narrative has centered predominantly on automation and effort reduction. However, even as efficiency gains reduce effort in parts of the current delivery pyramid, agentic AI is simultaneously unlocking new and sizable sources of demand. The net effect is expansion, not contraction, in the short to medium term. (See Exhibit 1.)

A historical perspective reinforces this conclusion. From 2010 through 2025, IT spending as a share of global GDP increased by roughly 1.5 percentage points, from approximately 4% to more than 5.5%. During this period, global GDP increased by $50 trillion, from roughly $70 trillion to nearly $120 trillion. Applying the 1.5 percentage point increase in IT spending to the $120 trillion economic base translates into approximately $2 trillion in incremental annual IT spending, bringing total global IT spend to nearly $7 trillion in 2025.

Over the past decade, major technology shifts, such as cloud adoption and digital transformation, have contributed roughly half a percentage point to IT spending as a share of global GDP. Looking ahead, agentic AI is expected to drive a comparable 0.5 to 1 percentage point uplift as adoption scales. Within overall IT spending, tech services spending is expected to see sustained growth as enterprises scale agentic adoption and invest in data foundations, orchestration capabilities, and new outsourced workloads.

Against this macro backdrop, a segment-level analysis shows a net uplift of up to $200 billion in the total addressable market over the next five years. In aggregate, this would result in a 6% to 8% CAGR for tech services through 2030.

Efficiency-led contraction is real but localized. Agentic AI is already driving material reductions in effort across workflows where the need for human judgment is minimal. In IT services, this includes automation of level 1 and level 2 incident management. In customer experience, agent-led interactions are increasingly resolving end-to-end inquiries.

Similar patterns are emerging across enterprise functions. In insurance, AI agents are automating claims intake, triage, and subrogation reconciliation. In finance and accounting, they are executing invoice processing, account reconciliation, and period-end close activities. In health care, agentic systems are rapidly replacing human effort in medical transcription and clinical documentation.

As a result, companies will continue to see meaningful reductions in effort across infrastructure managed services, customer experience, business process outsourcing, and application managed services. However, the net outcome will depend on how effectively service providers operationalize agentic AI-enabled services.

New value pools are emerging. BCG’s analysis shows that the efficiency-driven shrinkage will be more than offset by the expansion of value pools in three areas:

• Build-Deploy-Run for Agentic Solutions. Demand is rising for agentic application development, implementation, data operations, context pipelines, and infrastructure modernization. This work spans use case design and prioritization, agent workflow engineering, integration with core enterprise systems (such as enterprise resource planning and customer relationship management platforms), and embedding agents into customer- and employee-facing journeys. In banking, for example, providers are designing and deploying loan-origination agents that collect documentation, validate credit data, trigger underwriting workflows, and coordinate downstream approvals within core lending platforms.
• Expansion in the Addressable Scope of Work. As agents overcome language, context, and domain knowledge constraints, and as enterprises learn to harness internal and external data at scale, new categories of work become amenable to outsourcing. This begins with expansion into existing service categories, such as the offshoring of European-language-related customer experience support. It also unlocks new data-powered services. These include insurance risk and fraud services that combine proprietary claims data with third-party and ecosystem data, and population health optimization using clinical, operational, and social data.
• Oversight and Governance. Demand for recurring services will rise because agentic systems require continuous AI-for-operations support, exception management, and governance. These services include real-time monitoring of agent performance, drift detection, human-in-the-loop escalation frameworks, audit trails, compliance reporting, and model risk management. As agents operate across regulated workflows—such as financial reporting, underwriting, health documentation, and customer interactions— enterprises will require structured oversight covering security, data privacy, bias mitigation, and regulatory compliance.

As agentic AI reshapes the tech services demand landscape, the real question for service providers is not whether revenue pools will evolve, but how quickly their portfolios can adapt to capture the new sources of growth.

Enterprise AI Adoption Is Shaping Demand for Tech Services

These growth predictions have a basis in reality on the ground. BCG’s IT Spending Pulse shows that nearly 45% of enterprises expect to increase their spending on AI, including agentic capabilities. This shift is already translating into concrete demand patterns for service providers, as enterprises increasingly turn to them to operationalize AI.

Our enterprise demand survey reveals how the adoption journey of agentic AI is shaping demand for tech service providers:

• Large enterprises are moving beyond pilots. During the past two years, many organizations have emphasized AI and agentic AI pilots, alongside foundational data and process cleanup. While most enterprises are still running targeted agentic pilots, more than 40% of large enterprises report that they are already scaling implementation. Banking, financial services, and insurance are emerging as the clear adoption frontrunners. As enterprises consolidate the lessons learned and drive scaling, 2026 is shaping up to be a pivotal year, marking a shift from isolated agentic AI use cases to enterprise-wide deployment.
• Use case scaling is shaping demand. Agentic adoption is translating directly into service demand: 75% of enterprises report that they want to work with service providers to build or implement priority use cases.
• Momentum is accelerating. Agentic components are surfacing with increasing frequency in sourcing cycles, as more than 40% of enterprises report seeing agentic AI-enabled services more often in tech services bids across IT applications, business process outsourcing, and customer experience services. (See Exhibit 2.) Further, early value signals are emerging, as approximately 20% of the largest enterprises have already seen a 25% to 40% reduction of total cost of ownership (TCO) in deals with agentic AI.
• Agentic adoption is driven from the top. C-suite executives are more than twice as likely as their direct reports to engage service providers, making them the primary buyers. (See Exhibit 3.) This shift is accelerating decision cycles and elevating agentic transformation to the executive agenda.

Together, these signals suggest an inflection point in enterprise adoption. As agentic AI shifts from pilots to scaled deployment, service providers have an opportunity to redefine delivery models by embedding agentic AI in core workflows and thereby capture a disproportionate share of the resulting growth.

Expectation Gaps Limit Value Realization

Despite the early momentum, our demand- and supply-side surveys reveal several gaps between what buyers expect and what service providers are currently prepared to deliver. If left unaddressed, these gaps could constrain value realization.

Priorities are misaligned. Enterprises are prioritizing areas applicable across industries—such as customer service, finance and accounting, marketing, IT operations, and application development—primarily to drive productivity gains. Service providers are aligned on priorities in some of these areas, such as customer service, finance and accounting, and customer application development. However, they underprioritize other areas experiencing strong demand, particularly IT operations, cybersecurity, and application implementation.

Efficiency commitments trail expectations. Enterprises expect agentic AI to unlock productivity improvements of 30% to 40%, while most providers commit to only 6% to 15%. (See Exhibit 4.) This large gap creates friction in deal discussions, as buyers struggle to reconcile cautious commitments with their anticipated outcomes.

Articulated value does not materialize. Nearly 60% of enterprises report that they have not yet seen measurable TCO improvement in deals that include agentic AI. This indicates that the value articulated is not consistently visible in deals’ economics, often owing to a limited understanding of baseline performance and insufficient data collection and monitoring.

Adoption of value-linked commercial models remains elusive. More than 70% of enterprise decision makers indicate a preference for output- or outcome-linked commercial models for customer experience and business process outsourcing services. As a result, commercial discussions are shifting toward contracts tied to business outcomes.

However, as agentic AI capabilities and AI-enabled delivery constructs continue to mature, both buyers and service providers are struggling to operationalize outcome-linked contracts. Our survey found that approximately 60% of service providers still rely on time and materials or fixed-price structures when contracting for agentic-AI-enabled services. This persistent mismatch in commercial model expectations makes it increasingly important for service providers to clearly articulate value and anchor their commercial propositions in demonstrable outcomes.

The Winning Playbook for Service Providers

To close these gaps, service providers must realign offerings to enterprise priorities, strengthen value articulation, and evolve commercial models. Five imperatives stand out.

1. Reimagine and differentiate the portfolio. Providers must move quickly to embed agentic capabilities across existing offerings while building new services aligned to enterprise demand hotspots. Leading portfolios will reflect a deliberate mix of full-stack, industry-anchored solutions and horizontal agentic solutions. Winning providers will be explicit about where they can credibly drive end-to-end business transformation versus where they should focus on modular, repeatable services. In both cases, differentiation will be anchored in outcome-centric value propositions, not incremental feature additions.

2. Sell proven impact, not promises. Buyers are increasingly evaluating providers on the basis of measurable returns on investment. Leading enterprises report that proven returns are the most critical differentiator during the sales cycle. Demonstrating credible uplift through validated impact and engineered proofs of concept is becoming a prerequisite for commercial success.

3. Build and orchestrate the partner ecosystem. Winning providers are leveraging deeper partnerships with hyperscalers, enterprise software-as-a-service platforms, and emerging agentic-native players. More than 90% of providers report delivering agentic engagements leveraging ecosystem partners. The competitive advantage lies in orchestrating these partnerships to accelerate deployment, standardize patterns, and scale proven use cases.

4. Transform delivery operations. Agentic AI is reshaping how work gets done, reducing the total effort required for the same scope of work. Providers expect a 10% to 20% shrinkage in the service delivery pyramid over the next 24 months as agentic AI is embedded into workflows. This shift requires moving away from traditional delivery models toward AI-enabled ones, with humans orchestrating and supervising AI agents. At the same time, as agentic AI expands the overall addressable opportunity and unlocks new categories of work, total headcount is expected to grow, albeit with a different mix of skills.

5. Reshape talent strategy. The rise of human–AI delivery models is reshaping talent demand in three ways. Growth is slowing in traditional roles such as manual quality assurance testing and Java development. Core roles—including developers and quality assurance leads—are being redefined, with greater emphasis on AI-related skills. At the same time, new roles are emerging rapidly, including AI product engineers and chatbot developers, with demand growing at a 40% to 50% CAGR. To keep pace, service providers must actively rebalance their workforces through targeted hiring and rapid reskilling.

Agentic AI is changing how value is designed, delivered, and captured in technology services. As this shift accelerates, service providers will need to rethink their portfolios, delivery models, and commercial approaches. Success will not come from simply embedding AI into existing services. It will require a more fundamental reinvention around agentic AI capabilities. Providers that move early and act with clear intent will set the pace for the industry—positioning themselves to capture a disproportionate share of the up to $200 billion opportunity as agentic AI scales across enterprise functions.