Transformation isn’t always about speed. Sometimes it’s about direction. As we approach 2026, shared services are taking a new course—driven by AI. In recent years, at EWS Limited, we’ve witnessed this change move from speculation to planning, from planning to action. For leaders making fundamental choices about how their organizations operate, a clear understanding of the facts is much more valuable than hype or promises.
This article highlights the seven findings we believe every leader in shared services should know for the road ahead. We reference the State of the Shared Services Industry Report 2026, and we add our own sector insights, built through partnerships with global enterprises managing payroll outsourcing, global mobility, and company formation in over 100 countries. If you’re a global mobility manager, HR director, C-level executive, or someone responsible for workforce direction, these findings matter to your next moves.
The adoption of AI in shared services is not an abstract trend. It directly shapes the way companies engage talent, manage cost, measure value, and deliver results quickly and at scale. At EWS, we work with IT leaders and HR directors who realize that the old metrics—like pure cost controls or hours saved—are no longer enough. Success is now measured by the ability to drive new value, adapt quickly, and support sustainable growth worldwide.
Let’s get into the numbers and stories behind the AI shift in shared services for 2026.
According to the latest State of the Shared Services Industry Report 2026, a striking 84% of organizations expect AI to significantly impact shared services within the next three years. This isn’t a quiet prediction; it marks a near-universal expectation at all leadership levels. The research, based on over 400 senior professionals’ responses, paints a clear timeline: the next three years will be decisive. When we see numbers this high, we see urgency and opportunity converging.
AI is changing the rules. The sector is not asking “if” but “how soon”.
There’s a shift from viewing AI as a far-off technology to making it part of today’s realities, even if it brings risk or uncertainty. Strategic investment and experimentation have started moving faster. We have seen this firsthand among our clients at EWS when discussing global workforce solutions in unfamiliar markets.
For more perspectives on the profound shifts AI is bringing to global mobility and talent engagement, you can read our additional thoughts at EWS: Impact of AI on Global Mobility.
AI is not just about automating transactional steps. 38% of surveyed leaders foresee agentic AI technology replacing some captive activities, and 46% more are considering such AI solutions. Agentic AI refers to technologies capable of reasoning, decision-making, and self-direction within defined boundaries. This is a meaningful stretch beyond typical robotic process automation.
The numbers reveal that many organizations are already on the edge of a major transition. Some activities that were once thought “too complex” or “too sensitive” to hand over to AI are now in scope. These include:
Our direct experience supports this. At EWS Limited, we see increasing requests for agentic AI pilots in our payroll outsourcing and Employer of Record (EOR) offerings, where the goal is not only speed but also learning from data to support people decisions.
Shared services were once measured almost purely by cost controls and completion rates. Now, the lens is wider. Still, cost management remains the top-used metric, with 85% of organizations tracking it. After cost, customer satisfaction (72%), volume growth (32%), revenue improvement (21%), working capital (21%), and margin improvement (19%) follow as measures of value, according to the same report.
This broadening focus reflects a reality we often discuss with our clients: cost savings are expected, but now value creation and innovation are the targets. But progress can feel slow—many companies still tie performance to effort-based allocation, not outcomes.
What gets measured gets managed. If growth isn’t measured, results are missed.
Imagine the potential when priorities truly shift. We see organizations who track volume growth and customer satisfaction increasing trust with leadership, raising their profile beyond “back office” and being given seats at strategic tables.
In this year’s report, 57% of organizations consider themselves at a medium level of automation maturity, and 9% say high. Last year, most rated themselves lower. We think this marks a real cultural and operational shift. There’s greater confidence in scaling digital tools, and fewer are held back by uncertainty or by trying to perfect small pilots before scaling up.
What does “medium maturity” look like? Typically, organizations at this level:
Our own journey at EWS Limited echoes this trajectory. We see our clients’ operating models evolving, and our solutions—especially in payroll outsourcing and data analytics—are shifting to match this new comfort with digital maturity.
The traditional center of shared services is finance. In 2026, this remains true, with 87% of organizations using shared services for order-to-cash processes and 86% for purchase-to-pay. But growth is strong in HR processes: 40% use shared services for payroll; 34% for benefits administration. And in a notable trend, data management is gaining ground: 65% of shared service organizations handle data processes, and 49% are engaged with business analytics.
What we see in the field aligns closely. Shared service models are becoming the nerve center not only for basic finance tasks but for people-related processes and corporate data initiatives. Data is becoming less of a support function and more of a key driver.
The best results, in our view, come when organizations treat data management as foundational, not a “nice-to-have.” We cover this further in our article about AI transforming global mobility and why new processes are coming into scope for shared services centers.
This is a point many leaders find frustrating. Even as shared services expand their scope, 55% still measure performance using FTE or effort-based allocation. Only 1% use outcome-based pricing. This choice often reflects legacy systems or comfort with established KPIs, but it limits the ability to see and reward true business improvement—especially when AI produces results unattached to labor effort.
To put it simply: if we measure by hours, we may miss the value of work done by artificial intelligence, which can achieve results faster and differently. Outcome-based metrics, while rare, can align incentives and focus the team on actual business results, including value-added growth and customer impact.
Measurement methods shape what gets funded—and what gets noticed by leadership.
We highlight this in our consulting with clients at EWS: to grow impact, make outcome metrics visible and central in shared service reporting.
Not all change is technical. Findings from a recent government shared services audit show much to celebrate, but also point to areas needing attention: gaps like no clear single owner, missing technical leadership, and fragmented interdependencies. These findings repeat across sectors and countries.
The key lesson: Even with the best automation or AI, transformation needs the right data, cultural readiness, and shared goals. Time, investment, and a focus on foundational consistency—like standardizing how data is collected or decisions are made—make all the difference between short-term improvements and sustainable change.
Change starts with people—AI only works if the culture is ready.
Cathy Gu, a prominent voice in the sector, highlights that many organizations are experimenting with or planning for AI, and success requires more than technology. We agree. As shared services leaders, we advise focusing energy on building consistent data practices, developing technical leadership, and bringing stakeholders together toward common outcomes.
The community around shared services is expanding. Over 850 attendees are expected at the SSOW Europe event in Estoril, Portugal in May, where speakers such as Iwona Sikora, Gunter Van Craen, Madeleine Roach, Joerg Mimmel, and Edvinas Katilius will share their experience. This signals growing momentum. We expect further ideas, success stories, and pitfalls discussed at such forums to set the direction for strategy in 2026 and beyond.
As organizations mature, leaders are gradually shifting focus from pure cost savings to new forms of business value support. In our coverage of global hiring trends, we see this growth mindset everywhere—especially in areas like international hiring, digital skill development, and agile workforce planning. The push for smarter investment, better data use, and faster adaptation increasingly comes from shared services teams themselves.
The State of the Shared Services Industry Report 2026 highlights a shift that matches this mindset. Cost may remain the main tracked metric, but more leaders now include measures like:
That said, the gap between what’s measured and what’s wanted is still wide. Our advice to peers in the sector is to make their “growth story” part of monthly dashboards, board presentations, and project reviews, not just a side note.
The rise in organizations claiming medium or high automation maturity has implications for the people side of shared services. Teams are not shrinking as quickly as some expect—instead, their roles are evolving. IT, HR, and finance professionals are finding themselves training AI, troubleshooting bots, or focusing on exception processes that demand nuance.
This means more need for upskilling and cross-training. At EWS, we’ve noticed that clients who invest in staff readiness and digital skills see smoother transitions and higher retention rates, even as AI takes on a bigger share of work.
While finance still dominates the shared services landscape, rapid growth is visible in:
Shared services centers that handle complex, high-volume processes—like payroll for global remote teams or cross-border analytics—are adopting AI pilots quickly. For example, our own work with international hiring has shown us that integrating AI-driven data management improves consistency, decision-making, and employee satisfaction, especially in distributed teams.
Many leaders ask us how to connect their AI and automation investments with business outputs instead of just counting hours or saving headcount. It’s a fair question—it’s about showing value to board members and funding future projects. Here are a few approaches we recommend to move beyond effort-based allocation:
Outcome-based success speaks louder than hours saved.
If there’s one lesson from recent audits and sector roundtables, it’s that even the best AI solutions can stumble without good foundations. Progress requires more than software—it needs:
At EWS Limited, we guide organizations to invest in these basics, building reliable platforms before scaling advanced automation. Cathy Gu’s remarks echo what we advise: get the culture and data right, or digital investment risks missing the mark.
Shared services are at a crossroads. AI is bringing the sector into a new era—one defined not only by faster or cheaper but also by greater business impact, ability to scale, and readiness for risk and change. Boards are asking for broader value—volume, growth, margin, customer impact. The best shared service leaders are responding by expanding what they track and report on.
If you’re ready to build capability in this new model, pay attention to the following:
The biggest change on the horizon? The recognition that shared services teams are drivers of value creation, not just cost centers.
The next three years are set to be transformative for shared services, as 84% of organizations expect AI to change the sector in fundamental ways. That means new responsibilities for leaders: preparing teams, adopting growth-focused KPIs, and investing in data and culture as much as in technology.
This shift will require focus, patience, and vision. At EWS Limited, we’re excited to help our partners and clients make sense of the changes, choose the right priorities, and invest in workforce solutions that keep them ahead. We believe that when strategy, people, and data come together, AI in shared services delivers not just savings, but future-ready growth and confidence.
If you’d like to learn more about the trends shaping AI in shared services worldwide, discover our insights at workforce planning in AI-driven logistics and infrastructure, or reach out directly to our team to discuss tailored solutions for your expansion and growth.
AI in shared services means using artificial intelligence to manage and improve common business processes that serve multiple departments or regions, such as finance, HR, payroll, and data management. Instead of manual effort or traditional automation, AI systems can handle tasks, make decisions, and even learn from data to improve results. Organizations like EWS Limited provide AI-driven solutions to help companies expand and manage their workforce globally.
AI can increase speed, accuracy, and the ability to scale shared services to new locations or business areas by automating complex tasks, analyzing large data sets, and supporting smarter decisions. For example, AI can process payroll faster, manage compliance risks, and improve customer or employee satisfaction while freeing up human teams for higher-value work. AI also helps generate insights to support business growth.
AI can require investment in new technology, integration, and training, but the long-term savings and growth from improved results often offset these costs. Many organizations start small, test AI in core processes, and expand as benefits become clear. The right approach helps control risk and spread investment over time.
Risks of AI in shared services include data quality issues, lack of staff readiness, challenges with change management, or not aligning new technology with real business needs. Successful adoption depends on good leadership, clear ownership of projects, and consistent, well-managed data—a finding supported by sector audits and reports referenced throughout this article.
Start by identifying processes that could benefit from automation or data analysis, assess current data quality, and build a roadmap focused on testing and scaling up solutions over time. Engage technical leaders, include staff in change plans, and make sure new KPIs reflect both savings and new forms of business value. For further guidance, you can reach out to EWS Limited to tailor the approach to your organization’s needs.
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