A financial analyst, a few years out of school, sits in front of four cloud terminals at a New York hedge fund, running $1,000 a day in AI tokens. His manager approved the budget without hesitation – the firm’s own math showed his productivity had multiplied fivefold, returning well over 200% on every dollar spent.
This is just one example of how a 26-year-old does his job today. Now imagine the same transformation applied across the loan origination operations of a top ten bank, with thousands of underwriters, risk analysts, and compliance staff. Or to the customer service operations of a Fortune 100 retailer handling two million interactions a week.
The potential gains are obvious, and no individual contributor or off-the-shelf AI tool can deliver them at enterprise scale. Reimagining business operations for human and agentic workers and building production-grade, AI-native solutions to run them is the opportunity that stands before IT services providers today.
The industry’s first principles were born out of an enterprise reality: technology kept becoming more powerful and more complex than companies could implement, scale, and operate on their own. Starting in the 1960s and supercharged from the 1990s forward, IT services providers built four durable value engines: implementation expertise, talent at industrial scale, global delivery, and managing complexity. Those engines survived every technology wave – Java, .Net, cloud, mobile – because they adapted while preserving the core economics.
Necessary shifts for the transition
Autonomous AI now challenges the industry at a deeper level. It changes how IT services firms create value, how they deliver it, how they price it, and ultimately how they themselves are valued.
Enterprise software was deterministic, relying on repeatable, templated logic delivered through packaged software and SaaS platforms. AI systems are different. The AI models that power them are not products to be configured, but reasoning engines that require context, guardrails, and domain-specific knowledge to work reliably inside enterprises. And because AI solutions must be built around reimagined workflows, service providers now have the opportunity to create and manage the full operating stack, taking accountability for business outcomes.
Making this transition demands four significant shifts. Providers must evolve from systems integrators into AI builders, creating the platforms, models, and tooling required to deliver outcomes. In the old world, software existed, and the job was to implement it; in the AI world, systems are inherently bespoke and must be built.
Talent must be reimagined away from the traditional pyramid and towards interdisciplinary skills, cultivating people who are simultaneously fluent in the business domain, operations, and technology.
Delivery must shift from labor-based models to delivering outcomes, with platforms and managed services becoming the new operating model.
Lastly, and perhaps most consequential, providers must shift from delivering projects to underwriting operational outcomes at scale. Enterprises’ appetite for outcomes was never in question. The problem was that the technology to deliver, measure, and guarantee them did not exist. The combination of cloud platforms, real-time data infrastructure, AI-driven automation, and agentic orchestration now makes it possible to instrument business processes end-to-end.
The opportunity—and the prize
No CEO ever woke up wanting to buy 500 FTEs. They always wanted faster time-to-market, lower inventory costs, and better customer retention. Now, for the first time, the technology exists to deliver exactly that.
When a services firm underwrites claims adjudication speed or commits to a measurable reduction in customer churn, it delivers efficiency, customer satisfaction, and the capacity to reinvest in growth. A global insurer that hands over claims operations to an outcome-based provider frees its best people to focus on product innovation, underwriting strategy, and market expansion, the functions that differentiate the business. A consumer goods company that outsources supply chain orchestration on outcome guarantees can redirect capital and leadership attention toward brand building, new market entry, and customer experience.
The providers moving fastest are investing in context engineering – the discipline of gathering and infusing an enterprise’s tribal knowledge into AI solutions so that probabilistic systems produce reliable, specific outputs. They are building repositories that encode decades of engagement knowledge into structured, reusable assets. They are establishing AI governance practices to ensure the security, auditability, and safety of AI systems. And they are restructuring commercial models from selling hours to underwriting results.
Everything can now come together as a service, allowing enterprises to focus on what defines their advantage while partners take responsibility for the work that enables it. The prize is access to a $6T+ market, more than three times that of IT Services today[1], once business operating budgets enter the pool.
IT Services firms with the courage to underwrite outcomes will define the industry and how the market values it.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
[1] Gartner estimates the 2026 IT Services Market at ~ 1.87 trillion. Roughly 11-14% of enterprise revenues are spent to operate corporate functions alone, including labor. 11% of global Fortune 500 companies’ revenue in 2025 ($41T) translates to ~$4.5T. Together, IT Services and operational spend would yield a market of over $6T. https://fortune.com/ranking/global500
