Many offshore investments in software delivery started as a rational bet on labor arbitrage. Some organizations expanded that bet into captive centers, long-term vendors, and operating footprints that create switching costs.
The math has shifted. In one study, distributed work items took about 2.5 times as long as co-located work. In a controlled experiment, developers using GitHub Copilot completed a task 55.8% faster. That combination reprices delivery toward teams that can decide, verify, and ship with ownership.
Let’s walk the decision logic from the original bet to what is winning now.
The Bet Behind Offshore Investments: Labor Arbitrage, Scale, and the Hidden Coordination Bill
Offshoring worked best when work was spec-stable and decoupled. If a problem was well understood, teams could describe it, hand it off, and deliver at a lower unit cost.
That logic scaled into bigger commitments, including captive centers, multi-year vendor contracts, and an operating footprint that persists even as the economics shift.
However, coordination was expensive. As Tyler Holmes, Axian’s CTO, put it, the trade came with “real pain: time zones and communication barriers.” It also eroded something harder to replace. “You lose institutional memory.”
If you are carrying a captive center or a multi-year contract, that bill is measurable. It also often shows up in slower decisions, heavier reviews, and ownership gaps across handoffs.
Leaders may still see output, but releases can feel riskier, and learning stays fragmented. The bet only works if recapture stays ahead of that bill.
GenAI Reprices the Local vs Offshore Talent Trade-Off
GenAI is moving the productivity advantage toward teams with context and proximity to decisions. Holmes’ point is blunt: “generative AI has made local teams much more productive,” which changes the return profile of labor arbitrage.
The lift comes from compressing time to first draft across specs, code, tests, refactors, and analysis. When the same people also carry product and customer risk, fewer clarification loops make it into delivery. If your product-adjacent team still owns the hard calls, GenAI amplifies their throughput first.
In a UK public sector trial, participants reported saving an average of 56 minutes per working day using AI coding assistants. That time tends to compound most where product ownership already sits.
Offshore models are already using AI. The local vs offshore talent question now turns on workflow integration and proximity to product decisions, because that is where verification and shipping speed can compound.
Repricing Offshore Investment Options: A Decision Framework for Rebalancing Without Torching Sunk Costs
GenAI changes the question behind offshore investments. You are choosing where coordination and ownership live, because that is where speed and predictability are won.
Unwinding an offshore footprint requires a strategic approach. There may be sunk costs, contracts, and delivery obligations. The approach is to rebalance work placement so the return curve improves without breaking the operating model.
Use this three-decision test to choose offshore investment options that still make sense:
- Context intensity: High-context work needs product nuance, customer impact judgment, and institutional memory. Low-context work is spec-stable and repeatable.
- Ownership criticality: High-criticality work carries a real business impacts, including regulated flows, reliability-sensitive services, and on-call learning loops. Low-criticality work stays isolated.
- Placement choice: Keep product-adjacent ownership when context or criticality is high. Use a hybrid when context is high, but implementation can be modularized behind hard interfaces and acceptance tests. Offshore still fits when context and criticality are both low.
Use outcomes to spot diminishing returns. Adding headcount and the lead time stays flat, coordination is likely swallowing the savings. If reliability trends the wrong way, changes fail more often, or recovery time (MTTR) takes longer, that can be a signal that ownership and handoffs are too fragmented.
Some helpful tactics include keeping offshore capacity for context-light streams, repurposing it to hardening interfaces, and stopping using it for ownership loops that need fast product decisions or want to benefit the most from GenAI tooling.
Axian’s view is consistent here: when offshore becomes a code factory, you inherit coordination overhead and time zone drag. If your offshore capabilities are just producing code (and not changing how they work to take advantage of GenAI), then your lift from GenAI tooling will be minimal.
Minimum Viable GenAI Enablement That Improves Predictability
GenAI payoff shows up when it reaches across the workflow. Tyler Holmes describes the goal as AI that can move “across the whole chain of work,” with delivery flowing end to end with “as little friction as possible.”
Below are three operating tactics that turn that goal into predictable delivery.
Workflow Placement
Use GenAI inside the SDLC to shorten cycle time. Put it to work drafting specs/stories, scaffolding changes, generating tests, summarizing PRs, and accelerating refactors and documentation updates.
Keep human gates for architecture, security, and release readiness.
Access and Information Flow
Holmes points out that AI-enabled workflows often need access to parts of the organization and information that is not already shared. Plan for the permissions, data sharing, and decision visibility that make that access possible, or GenAI will be trapped inside individual work tickets (instead of accelerating momentum across the whole SDLC).
Verification-First Patterns
In Stack Overflow’s 2025 survey, 46% of respondents distrust AI accuracy, versus 33% who trust it. Build review patterns, automated tests, and traceability so speed does not turn into release risk.
When these moves are in place, product-adjacent teams can take on more work without multiplying delivery risk. What you gain is predictable throughput and releases you can stand behind.
Prove the New Math with a Small Pilot
If you are carrying offshore investments, place the next bet as a small GenAI pilot. Use it to validate the new return curve before you rebalance at scale. Pick one high value product/work stream and commit to applying AI across the entire work structure (ie. from story refinement to release). Set aspirational goals that outperform the existing offshore setup (e.g. faster lead time, fewer defects, time spent creating/refining stories) and continue to enhance the work structure with AI until you’ve reached your goals (which should be lofty enough to mitigate the value of captive centers).
Example Improvements to Work Structure to Enable AI
- Code Gen inaccurate or ignores company conventions? – Add project specific context engineering, better stories (smaller, more explicit context/guidance for AI), and experiment with newer AI Code Gen models.
- Pull Request taking too long? – Add labels denoting when AI was used, add PR automated summaries (AI generated), and improve automated testing prior to PR review.
- Work Stoppages midsprint? – Prep larger “batches” of stories/work items and prep for a world where developers are responsible for larger bodies of work (composed of many small, well described stories). Old Paradigm: 1-2 stories assigned and ~1.75 stories get completed on average. New Paradigm: 6-8 stories assigned and ~6-7 stories getting completed on average.
- Testing taking too long? – Pour gas on automated testing and cover as much behavior as possible in automated tests. Use DevOps/automation to minimize manual testing. Tests become the default behavior spec for projects.
Measure lead time, deployment frequency, escaped defects, and the hours stakeholders spend clarifying work. If those indicators improve together, you have a valuable example of how to create highly productive (Gen AI enabled) workstreams that exceed output from captive centers.
When you have a workstructure that retains institutional memory, allows ownership/accountability with minimal number of handoffs, and uses Gen AI throughout the whole SDLC, THAT is when you’re best positioned to win with Gen AI (today, and for years to come).
Ready to try a GenAI Pilot with clear measurables and experienced engineering oversight? Talk to us at Axian about a de-risked GenAI enablement plan.