This article was written by Diana Girdenytė, Strategist at Invest Lithuania
AI has become a defining investment priority for Global Capability Centers. Companies are building AI Centers of Excellence, deploying copilots and experimenting with autonomous agents to unlock productivity. Yet AI in Global Capability Centers will not deliver transformation simply because more tools are deployed. The problem is not AI adoption itself. It is that too many companies are using AI to automate processes that should first be redesigned. Automating a broken process does not transform it – it scales the inefficiency.
That is the real challenge for the next phase of GCC transformation: not how quickly organisations can adopt AI, but whether they are ready to redesign work around it.
Global Capability Centers are company-owned hubs that concentrate business, technology and specialist capabilities. Their mandates increasingly span finance, software engineering, data, cybersecurity, compliance and product development.
That makes GCCs natural environments for AI deployment, but automation is often applied before the underlying process has been simplified or properly understood.
The 2026 SSON and Insightful report The Automation Blind Spot describes this as the gap between AI adoption and AI absorption. Licenses, training completion and active users can show adoption, but they do not prove that work has changed. When AI agents inherit fragmented workflows and manual workarounds, they can reproduce the same complexity at greater speed.
AI should therefore not substitute for operating-model redesign. Value comes when organisations understand how work flows, remove unnecessary steps, clarify ownership, improve data quality and then decide where AI can change the outcome.
This is also changing what companies expect from their GCCs and GBS organisations.
For years, the business case centered on consolidation, standardisation and cost efficiency. Those remain important, but no longer define a high-performing center.
HFS Research’s 2024 work on AI-led Generative Business Services describes a shift toward value creation and business outcomes. Future GBS priorities in its research include customer satisfaction, revenue growth and transformation of existing processes. The direction is clear: stronger centers are moving closer to decisions about how the enterprise operates, not merely executing tasks designed elsewhere.
As repetitive work is automated, value moves toward process architecture, data stewardship, governance and cross-functional decision-making. GCCs that combine these capabilities are better positioned to become enterprise transformation hubs.
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AI readiness is not the same as having a large pool of AI specialists. For a GCC, it is an operating-model capability built from five elements:
In other words, the competitive advantage is not simply access to AI. It is the ability to absorb AI into how the organisation works.
Lithuania’s GBS and ICT ecosystem, which includes company-owned capability centers alongside broader business-service operations, offers a useful example of this transition. According to Invest Lithuania’s GBS & ICT Sector Overview 2026, the country hosts 105 GBS centers employing around 26,000 specialists.
Sixty percent of GBS centers perform end-to-end processes, 89% provide AI-related training, and 41% of surveyed centers plan to introduce new AI capabilities over the next two years. More than half already use generative AI in software development, data processing and customer experience. AI, data, engineering, cybersecurity and product-related capabilities are among the sector’s fastest-growing functions.
Together, these figures point to a shift from execution-focused work toward greater responsibility for process ownership, platforms, governance and enterprise transformation.
This shift is already visible in the mandates of individual centers: Cencora‘s Lithuania hub spans IT, cybersecurity, finance, supply chain and digital innovation, while Danske Bank‘s Lithuania operation delivers high-complexity work across technology, data, risk and compliance.
The pattern matters more than any single technology: broader scope, complex functions and greater ownership create an environment where AI can become a transformation tool rather than another software layer. Talent development is also embedded in the ecosystem: 72% of centers cooperate with educational institutions, helping companies shape and expand the skills needed as AI, data and engineering functions evolve.
Explore a growing business services ecosystem with skilled talent, strong expertise, and the flexibility to scale complex operations.
For international companies, location strategy should therefore change with the technology strategy.
The question is no longer only where to find AI talent, but where teams can own and redesign processes across business and technology as capabilities evolve.
This creates an opportunity for mature but scalable GBS locations such as Lithuania, increasingly defined by multifunctional services, technology talent, end-to-end ownership and a track record of moving centers toward higher-value work.
AI in Global Capability Centers will undoubtedly automate more work. But automation alone is not transformation.
Companies that capture the greatest value will use AI to simplify processes, integrate data, change decision rights and equip people to work differently. GCCs can drive that shift because they combine process knowledge, technology and operations.
For investment locations, competitiveness will depend not only on cost or AI talent, but on how effectively they help global companies turn AI from a deployed tool into a better way of working.
AI readiness is the ability to integrate AI into real workflows, supported by process ownership, reliable data and technology, governance, continuous upskilling and measurement.
It lets teams redesign full workflows, remove bottlenecks and measure business outcomes instead of optimizing isolated tasks.
Lithuania combines mature GBS and tech capabilities: 60% of centers run end-to-end processes, 89% provide AI training and 41% plan new AI capabilities over the next two years.
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