The Year-3 Test: 6 CXO questions that decide whether a CoE plan survives

Let’s start with the uncomfortable truth most leadership teams only admit in year two: A Center of Excellence (CoE)/Global Capability Centers (GCCs) doesn’t fail because the country was “wrong.” It fails because the economics you approved in year zero don’t survive year three.

Year one is usually fine. You hire, you launch, you ship a few wins. Everyone feels good.

Then the real world shows up:

• wages don’t rise evenly, they jump in some markets and stay flat in others,
• AI/ML hiring doesn’t behave like “just another role family”, it behaves like a separate premium labor segment,
• and your original model gets bent by the things no one modeled properly: compounding, retention pressure, and the cost of coordination.

So if you’re making a CoE/GCC decision in 2026, the question shouldn’t just be “Which country is #1?” It’s the question a CEO/CIO/CAIO actually cares about: “Will this still be worth it in year three, and can we scale there without the model breaking?”

The economic reset: two curves that decide whether your CoE stays a ‘win’ by year three

Curve 1: Wage inflation stopped being background noise. It became the shape of your ROI.

Most CoE/GCC business cases assume wage inflation like it’s a constant: “6% per year” and move on.

But the 2023→2024 wage deltas tell you that inflation is not a constant. It’s a slope, and slopes compound. Here’s what that means in plain terms:

• In some markets (like Poland and Serbia), wage inflation is running at 13–14%.
• In others (Canada, Malaysia, Costa Rica), it’s closer to 2–4%.
• In some cases (Argentina), the USD view swings hard because currency dominates.

Now, here’s why CXOs should care: you don’t just pay the wage curve once. Why? 

Because CoEs don’t stop hiring after year one. Most expand. And when you keep hiring into a market with a steep wage curve, you keep resetting your cost base upward, every quarter.

So the implication for a CXO is simple:

The cost advantage you approve is not the salary level. It’s the wage curve you lock into.

And that curve gets worse when you add two real-world effects:

• level creep (you hire one band higher to close candidates), and
• retention pressure (attrition forces you back into the market repeatedly).

That’s how a “good” COE starts losing its economic story in year two.

Curve 2: AI/ML commands a premium, so “country cost” is no longer the truth.

In 2023, many leaders treated AI hiring as a specialist edge case: “we’ll do a few pilots, hire a handful of ML people, partner for the rest.”

In 2026, the roadmap keeps pulling AI into the core. It’s becoming a layer inside product engineering. Even when pilots continue, teams need data engineering, MLOps, governance and security, platform engineering, and applied ML talent that can ship into production.

Now here’s the crucial executive implication of the AI premium numbers we get from CoE Hotspots Report of the World: In many markets, AI/ML doesn’t price ‘a little higher.’ It prices as a different market.

That premium does three things CXOs feel immediately:

1. It breaks the “average country cost” narrative.
   You can’t say “we chose X because engineers are cheaper” if the roles you actually need to scale are 30–90% more expensive than the local software baseline.
   
2. It distorts internal equity fast.
   When ML roles are paid at a big premium, you create pay compression across adjacent roles, data engineering, platform, even senior backend, and you trigger retention risk in your broader engineering base.
   
3. It changes your retention physics.
   High AI premiums usually mean scarcity + competition. That talent becomes the first to get poached. And you don’t just lose people, you lose the glue that makes AI production-grade.

And this is the nuance CXOs need to understand:

Some mature markets show more manageable AI premiums (India ~25%), while some emerging markets show extreme premiums (Vietnam/Latvia near ~90%+).

So “emerging” can look attractive on software economics, but AI economics can be the trap if you assume you can scale AI the same way you scale software.

That’s why 2026 CoE planning is about designing a 3-year talent and economics strategy that survives compounding and AI scarcity.

Now, once you see those two curves clearly, the six CXO questions become obvious.

Six questions CXOs ask in 2026, and what they’re really trying to avoid

1) “Does the cost advantage survive year three, or are we buying temporary arbitrage?”

A year-three cost advantage doesn’t come from a day-one salary chart. It comes from how the wage curve behaves once you’ve hired a meaningful base and you’re still adding headcount.

So a CXO should look for a plan that can answer three practical points without hand-waving:

• What wage inflation assumptions are we using for this country—based on what we’re seeing in benchmarks?
• What happens if we have to replace 15–25% of the team each year and rehire at the new market rate?
• What’s our stance on leveling discipline when the market pushes us to pay up?

If the team can’t walk through those three with numbers and a point of view, the “cost advantage” is a year-one story.

2) “When we scale AI roles, what happens to pay bands and retention?”

This is where a lot of 2026 CoE plans are still underbuilt.

AI premiums aren’t just a “compensation detail.” They shape how fast you can build AI capability and how stable the team stays once competitors start hiring in the same market.

So a CXO/CAIO should want a plan to show them, plainly:

• Where does AI pay sit versus local software pay in this country?
• Are we comfortable scaling AI in the same location, or do we want a deliberate split, scarce ML roles concentrated where the premium is manageable, and adjacent roles distributed where supply and economics are healthier?

If a CoE is going to touch product engineering in 2026, AI economics will show up in the model anyway. The only choice is whether you plan for it or discover it midstream.

3) “How fast do we get to a leadership spine?”

Every CoE hits a moment where headcount stops being the bottleneck and leadership becomes the bottleneck. If you hire 150 people and you’re still escalating architecture decisions and incident calls back to HQ, it doesn’t feel like scale. It feels like you added coordination.

So a CXO should always ask:

• Who are the first 25 hires, and how many of them are genuinely senior?
• How quickly can we hire engineering managers, staff-level engineers, platform leads, SRE leads, and the people who set standards?

This is why maturity signals matter in a practical way. Markets that are rising, often have a stronger pool of people who’ve worked in environments where ownership and modern engineering practices are normal.

If you can’t build the leadership spine early, year two becomes a long stabilization phase, and year three becomes a justification exercise.

4) “Are we building ownership, or are we building throughput?”

This question might sound a little philosophical, but when it comes to business it’s very operational.

Ownership means the site can hold pieces of the product and platform end-to-end: architecture, reliability, roadmap delivery, security posture, production-grade AI systems when needed. Throughput means the site is strong at delivering what others define.

Both can be valuable. The issue is when a plan is written as throughput and later measured as ownership. That’s where disappointment comes from.

So a CXO should look for a simple progression:

• What does the site own by end of year one?
• What does the site own by end of year three?
• What roles do we need to make that real, platform, SRE, architecture, data/MLOps where AI is in scope?

If the plan can’t state ownership clearly, the operating model will stay dependent, and coordination costs will rise as the CoE grows.

5) “Does the time zone help our operating cadence, or slow it down?”

Time zone is one of those things teams put into an appendix and then spend three years living with.

When CoEs are tied into product cadences, governance cycles, and incident response, overlap hours become a day-to-day multiplier. Low overlap can work, but it asks for more autonomy, tighter documentation, and clear handoffs.

So a CXO should know what cadence they’re signing up for:

• Are we running product squads with daily interaction?
• Are we running follow-the-sun operations?
• Are we splitting build and run responsibilities by time zone?

If that isn’t clear upfront, you pay for it later in meeting load, slower decisions, and leadership burnout.

6) “If something shifts, currency, policy, geopolitics, what’s our plan?”

This is the realism question. Every serious CXO asks it, even if the deck doesn’t.

The 2023–2025 movement shows that attractiveness can change sharply. Currency can turn compensation into a recurring negotiation. Policy and compliance constraints can tighten, especially as AI moves deeper into production systems. Geopolitical risk can create sudden friction in hiring, payments, travel, and vendor operations.

So a CXO should look for two things:

• Do we have a shock absorber in the footprint, another site or country that can take load if conditions change?
• Do we know what work can move in 30 days and what takes 90 days?

If there’s no answer, the model is brittle.

The point of all six questions

The top hotspots stayed familiar from 2023 to 2025. The real changes were quieter: the middle reshuffled, the emerging bench widened, wage curves diverged, and AI started pricing like a premium segment in many markets.

That’s why the decision frame has shifted.

For 2026, the winning CoE/GCC plans read like three-year operating plans: honest about curves, clear about AI talent, deliberate about leadership density, specific about ownership, realistic about cadence, and prepared for disruption.

That’s how the economics survive year three.