From Labor Arbitrage To AI: The Next Big Disruption In Tech Services

Forbes

2 days ago

The tech services industry has seen many transformative forces over the past several decades, but none has reshaped the industry as profoundly as labor arbitrage. In the early 1990s, the idea of moving application development and maintenance offshore to tap into cost-efficient talent pools seemed experimental. I remember working with a client in 1991 as they began cautiously exploring the model. Little did we know then that this was the beginning of a shift that would forever change global technology delivery.
From 1991 through the early 2000s, labor arbitrage didn't just supplement the IT services market – it redefined it. With the arrival of Y2K, organizations around the world urgently needed cost-effective IT support to update legacy systems. This catalyzed demand and helped solidify the offshore delivery model as a go-to strategy. From there, it fueled decades of rapid growth and led to the rise of Indian technology firms as global powerhouses. These firms perfected the delivery model and rode the wave for over 30 years, building mature, scalable, and highly cost-efficient global service delivery engines.
I believe at this point we can safely say that the market has moved out of a growth stage and into maturity.
Labor Arbitrage at a Crossroads
Over the past several years, growth in the traditional labor arbitrage-driven services model has slowed. Even before COVID, there were early signs that the model was approaching saturation. COVID did add a new layer of demand through accelerated digital transformation, but that momentum, too, I believe, is tapering off.
We've now reached a point where the traditional tech services market is growing at a modest clip of 3–5% annually, roughly in line with overall tech spend. For a maturing industry, this is not insignificant, but it's no longer the explosive expansion that characterized the 1990s and 2000s.
That's not to say some firms won't grow faster and some will, but the market as a whole looks like it's settled into a mature phase. And as that slowdown takes hold, we're seeing a new disruption taking shape: Artificial Intelligence (AI). Interestingly, the challenge posed by AI feels strikingly similar to the one labor arbitrage introduced decades ago to the traditional systems integration, application development, and outsourcing landscape.
AI, particularly generative AI, isn't a buzzword anymore. It's an operating model shift. It brings with it a set of capabilities and consequences that mirror, in many ways, what labor arbitrage did in the 1990s: a massive potential to reduce the unit cost of service delivery, drive productivity gains, and realign value creation.
AI Productivity: A Powerful Lever
We're already seeing tangible productivity gains from AI tools. Many service providers are achieving 30 to 40% improvement in delivery efficiency. When you compare that to a labor arbitrage-only model, typically, clients achieve something like a total benefit of between 20 and 25% cost reduction once you factor in transaction costs and friction.
If service providers can sustain 40% productivity gains – and that figure is expected to rise to 80% over time – AI becomes a significantly more powerful force than labor arbitrage alone. If you can get the 20% from labor arbitrage and layer on the 40% from AI, that's an ideal combined benefit. But there's a critical caveat: much of AI's advantage depends on speed, iteration, and real-time business interaction. That's difficult to sustain when teams are operating in time zones a continent away.
Our research into offshore pricing shows that the rates holding up strongest are coming out of Central and South America, regions that share time zones with North America. That pricing has held up. We believe there's a strong correlation there. Time zone alignment enables more synchronous collaboration, which in turn allows organizations to capture more of AI's productivity gains. We're also seeing some of the highest output levels from these same regions, reinforcing this directional trend.
Another area where we're seeing the most aggressive implementation of AI is in the startup community, where companies are co-locating their teams and achieving the highest productivity, sometimes achieving 10 times improvement in productivity. I'm not suggesting that same outcome is achievable for a large enterprise, at least not today; they have different constraints, but certainly what startups are doing is keeping those engineering teams very close to the business so that they can move at a high velocity.
So, the question isn't whether AI will be used, but what will the tech services industry look like once AI is fully embedded?
We can envision two plausible futures:
This scenario is evolutionary. Enterprises maintain the offshore-centric model, but layer AI tools on top to improve speed and quality. AI becomes a turbocharger, accelerating delivery and reducing the cost of development and support. Clients benefit from improved turnaround times and reduced spend.
And like previous cycles – client-server, cloud, digital – this cost reduction could stimulate new demand and reignite market growth. It's easy to imagine an uptick in demand as the cost of developing and maintaining technology drops. However, labor arbitrage + AI presumes that most of the delivery can remain offshore or at least remote, with limited real-time collaboration.
The alternative is a more disruptive path. AI doesn't just augment offshore delivery; it takes the place of many functions. If developers using AI can now complete in a day what used to take a week, the bottleneck isn't labor anymore; it's business responsiveness.
In this model, rapid development cycles demand tight alignment with the business. Constant communication, faster feedback loops, and iterative decision-making become essential. And that has profound implications for delivery geography.
If teams need to work in lockstep with the business, then being in the same time zone, or even the same building, suddenly matters a lot more. Many development and support tasks could shift from far-flung locations back to nearshore or onshore hubs, simply because they need to operate at the same pace as the business, and again, co-location or time zone overlap becomes a competitive advantage.
The Trade-Offs and Transition
One of the very significant things AI alone also suggests is that the business will have to restructure itself. If it's going to take full advantage of AI, it will begin to move at the speed of technology, which is a very substantial transition. AI implementation is not an easy switch. To capture its full productivity gains, organizations must transform not just their delivery models, but their operating models. That includes how they structure teams, where those teams sit, and how closely they integrate business and technology functions. In short, to unlock AI's full value, the business must be ready to move.
The implications here are substantial – not just for delivery, but for how businesses structure themselves around technology. Will we see more of an evolutionary pattern, or will we see a reinvention that radically changes the operating model, both in its location, how it's priced, and how it's delivered. We'll likely see both models unfold in parallel. The question is who will go in which direction, and how will that affect the fortunes of the tech services firms and their clients? The answer may define the next 30 years of global technology services.