How AI Is Poised Spark Tech Services Growth

The technology services industry, a sector that has consistently experienced steady growth over the past three decades, is currently facing a period of stagnation. Historically, consulting, systems integration, and outsourcing have expanded at rates of 5 to 15% annually. However, over the last two years, growth has flatlined, with the industry fluctuating between 1% growth and a 2% contraction. This shift can be attributed to various factors, including the increasing adoption of Artificial Intelligence (AI) and the subsequent changes in the way businesses operate.

Understanding the Current State of the Industry

The AI revolution is transforming the technology services industry in profound ways. While AI investment is on the rise, it’s not translating into growth in tech services. Instead, several forces are at work, reshaping the industry’s structure. One major factor is insourcing, where enterprises are reducing the share of discretionary projects they outsource to third parties. This doesn’t mean they’ve stopped doing these projects; they’re just increasingly doing them themselves using AI-assisted development tools.

A few years ago, discretionary spend was a major driver of growth for service providers. Today, a growing proportion of that spend is absorbed internally. Teams can now accomplish with a small number of developers, aided by AI tools, what once required large external delivery teams. This shift towards insourcing is a significant factor contributing to the industry’s current stagnation.

The Impact of Global Capability Centers (GCCs)

Another factor contributing to the industry’s contraction is the growth of Global Capability Centers (GCCs). For twenty years, the industry’s story has been one of labor arbitrage, with work shifting offshore to lower-cost locations, such as India or the Philippines. Now, however, many enterprises are choosing to build their own centers rather than rely on external providers. This trend is also a form of insourcing, and it’s accelerating rapidly.

When you combine these two trends – AI-enabled insourcing and the proliferation of GCCs – you get a significant reduction in third-party demand. This reduction in demand is a major contributor to the industry’s current contraction. As AI continues to advance, it’s likely that this trend will persist, leading to a period of consolidation and restructuring within the industry.

AI Efficiency and Its Impact on Revenue

AI isn’t just changing who does the work; it’s changing how efficiently the work can be done. As automation and AI-assisted delivery increase, the same projects can now be delivered faster and at lower cost. Companies that once charged $1 million for a development project can now profitably bid $500,000 and still make their margins. This efficiency is beneficial for clients but exerts downward pressure on industry revenue overall.

Put simply, the industry is doing more work for less money, and that translates into flat or contracting top-line growth. It’s highly probable that we’ll see modest industry contraction over the next six to twelve months as AI productivity gains take hold. This contraction will likely be driven by the increasing efficiency of AI-powered delivery models, which will reduce the need for external services.

The Deflationary Impact of AI

Every major technology shift creates both disruption and opportunity. Historically, when a disruptive technology enters the market, it lowers the cost of technology, and as a result, consumption expands; people buy more because they can afford more. That’s the long-term hope for the tech services industry. But in the near term, AI’s impact is deflationary, not expansive.

We are in the early stage of AI transformation, where enterprises are still experimenting, building governance frameworks, and exploring safe deployment. That work, while necessary, doesn’t yet generate large-scale commercial demand for third-party services. Meanwhile, AI’s first wave of productivity is being realized inside enterprises, not through outsourcing.

Rethinking the Operating Model

The real unlock from AI occurs not when you add AI tools to your existing operating model, but when you change the operating model itself. Simply automating what you already do produces limited results. It’s the equivalent of paving over old roads rather than redesigning the city. The productivity gains are real but small.

The significant ROI comes when enterprises reimagine how they operate, including how decisions are made, how work is structured, and how technology, data, and people interact. When companies reach that stage, the scope of work expands dramatically. To embed AI into an operating model, enterprises typically need to re-platform, modernize their data, and often migrate to the cloud to access and unify that data.

Defining the Future Model

The key is sequence. Many companies are starting with the technology, investing in AI pilots, cloud infrastructure, and data modernization, without first defining what their future operating model should look like. This is the same mistake we saw in the early stages of digital transformation. Enterprises modernized their infrastructure and migrated workloads, but because they didn’t simultaneously redesign their operating models, the ROI fell short.

The right approach is to start with a vision of the future operating model, then work backward to identify the technology and data requirements to enable it. That’s what unlocks both the transformation and the business case for investment. When enterprises reach that stage, the work becomes far more complex and integrated, requiring consulting, system design, data engineering, change management, and often organizational redesign.

The Next Growth Cycle

This brings us to the question every industry leader is asking: When will this next phase begin? Right now, large-scale operating model change is still rare. There are a few promising examples, but not yet enough for a pattern to emerge. As with every previous technology shift, most companies will wait to see credible AI transformation proof points before committing.

But once those early exemplars demonstrate success, adoption will accelerate quickly. And when that happens, the scope of transformation work will expand significantly, ushering in a new growth cycle for the tech services industry. There’s also a broader economic principle at work – Jevons’ Paradox: as the cost of a technology decreases, its use increases. AI is likely to follow this same path.

The Industry Reset

The near term will be challenging. AI-driven efficiencies, insourcing trends, and price compression will continue to put pressure on revenue and margins. But these forces are also laying the foundation for the industry’s reinvention. As enterprises move from experimenting with AI to transforming with AI, the complexity and scope of work will grow.

That’s where tech services firms have an opportunity to lead — helping clients reimagine operating models, re-platform architectures, and build the systems of execution that define the next era of business. So yes, we are likely to see contraction before expansion. But once operating model transformation takes hold, we will look back on this period not as a decline, but as the reset before the next wave of growth.