AI transformation needs a champion and it should be GBS leaders

Transforming a business with AI is not just a technical challenge; it’s an operational one. It depends on knowing how work flows across teams, how tools fit together, and how processes scale over time. It requires the kind of judgment that comes from running efficient systems under pressure, balancing quality, cost, and speed. These elements are the core of both the global business service (GBS) and shared services roles.

Your team already handles the work that spans functions and platforms. You’ve built processes that hold up under real-world complexity, and the pressure on shared services is only increasing as you face more volume, tighter timelines, and a broader scope of responsibility. At the same time, AI is beginning to change how that work gets done.

For many teams, the shift feels disruptive or unclear. But for shared services, it’ll feel like familiar territory. You’ve already built processes that span systems, manage complexity, and build for scale. AI is just a new way to execute that work, and that puts you in the best position to lead its adoption as the next step in the evolution of operations.

You already run the work that makes the most sense to transform first

Most companies don’t know exactly how AI will reshape their business yet, but they do know two things: they’re not going to rip out the core systems their operations depend on, and they’re not going to duct-tape together a dozen point solutions that can’t scale.

But you don’t have to choose one or the other. There’s a much more effective strategy: transforming the work between those systems, typically managed by humans, with one core solution that can help you tackle a variety of different workflows. That might be things like:

• Your offshore team extracting the right fields from a scanned invoice that lands in a shared inbox, coding it by hand, and entering it into the finance system.
• Your in-house team bundling the proper documentation when a flagged AML case gets routed to your queue, tracking down what’s missing, and following up until resolution.
• Your in-house team standardizing onboarding data that’s collected in the wrong format before it ever hits the internal tools.

These are exactly the kinds of workflows AI agents are built for; processes that, even if sometimes messy, can clearly be measured. But most importantly, when replaced with AI, they will dramatically improve the efficiency of your organization without the need for replacing your core systems or the need to manage several point solutions.

You get to simultaneously make your current systems more efficient and divert the efforts of your human team towards more strategic activities.

Owning AI early means you own the foundation for change

When you start automating human-intensive workflows, like onboarding, document handling, or payment approvals, you begin to build a repeatable foundation that other teams can emulate.

In a recent report from ScottMadden and APQC, benchmarking more than 400 shared services organizations, top-performing teams stood out not just for lower operating costs and leaner staffing, but for significantly greater maturity in automation and AI adoption. Twenty-five percent had already implemented some form of AI at nearly double the rate of their peers.

(Source)

That adoption could happen in a variety of ways, each of which lays the foundation for a process that’s repeatable.

For example, maybe your team handles extracting customer data from onboarding forms and routing it into the right systems. You choose to deploy an AI agent to make that process faster and more consistent. Now, the data is structured and flowing cleanly, and that same data can be used by another agent to run compliance checks or by a finance agent to set up billing. You’ve eliminated the need to reformat or revalidate that data to deploy a second AI agent. It’s already accurate.

More importantly, you’ve mapped the workflow to deploy an AI agent. You’ve got a playbook for setting up integrations and handling exceptions, so when another team wants to automate something like intake forms or triage alerts, you’re the one they come to.

More importantly, deploying that first agent gives you a repeatable blueprint. You’ve mapped the workflow, figured out how to handle exceptions, and built the right integrations. When another team wants to automate something like intake forms or triage alerts, they come to you because you’ve operationalized the process.

But deploying agents is only part of the job. Similar to any system involving people, you need a way to evaluate performance over time. That means testing for edge cases, spotting data drift, and continuously improving accuracy. You’ve already built processes to train people, measure output, and improve performance, and now you’d need to do the same with AI agents.

Those AI agents will give you visibility that people can’t. You get a live view of what it’s doing, where it’s stuck, and which workflows are creating recurring issues, whether that’s a specific vendor, tool, or policy. Some teams are already managing this with dedicated “agent inboxes” or dashboards, where all AI-driven work gets tracked and reviewed.

Over time, your department becomes more efficient, and you build a strategic framework for AI transformation. When the business is finally ready to replace a core system or rethink how teams are structured altogether to make room for AI, you’re the internal expert whose early success becomes the starting point.

To do this right, you need the right kind of AI solution

The workflows you manage probably don’t sit neatly in one function. You might move between finance, compliance, onboarding, and logistics; sometimes all in the same day. Any AI solution you adopt needs to do the same, which requires being able to use your current systems just like your human team.

You’re looking for a solution that can:

• Work with any system you currently use, like SAP, Workday, Marketo, etc.
• Execute workflows on top of those systems without building a new process on a new interface
• Handle structured workflows like invoice processing or document classification
• Also handle more complex workflows, with more exception handling and edge cases
• Manage both without forcing you to choose between scale and nuance

Ideally, you find one that can transform one process at a time. You want an AI solution to be able to grow with you across teams, systems, regions, and business lines without requiring new systems or separate integrations for every deployment. The more agents you build, the more reuse you should get.

Reindeer AI builds custom AI agents that operate on top of the systems you already use to handle workflows the way your team does today. Our builds execute work inside your existing tools using your existing logic, rules, and data. Each agent is built for a specific process using a small set of real examples from your team, and as you add more agents, they build on shared infrastructure by sharing integrations and data.

For example, one global 3PL’s shared services team used Reindeer AI to automate their broken track and trace escalation process. They turned to AI under pressure to reverse a steady decline in customer satisfaction. CSAT had dropped to half a point as customers waited too long for updates. Working with Reindeer AI, CSAT recovered within a quarter.

They deployed this first agent quickly, using only 20 sample shipments. With that agent in place, the team is now positioned to automate adjacent workflows, such as customer notifications, root cause tracking, or upstream inventory updates, using the same logic and infrastructure.

Because the logic, data structure, and integrations are already in place, each additional workflow takes less time to deploy and inherits the reliability of what’s already working.

Leading AI transformation puts you at the center of company-wide transformation

Most companies are still figuring out how to use AI in a way that moves the business forward. Strategy is written as it’s executed.

Shared services is in a unique position to be the team leading both strategy and execution. If you choose to be an early adopter of AI, you’ll also be the one capturing logic, cleaning data, and making that intelligence available to the rest of the organization.

The more AI agents you deploy, the more they’ll build on each other. Over time, you’ll become the department designing how work flows across the company. AI has the power to transform shared services from operational leadership to strategic leadership by proposing a vision and creating a repeatable framework to act on that vision.

Reindeer partners with shared services teams to build custom agents inside the systems you already use, expand capabilities over time, and create a foundation the rest of the business can grow on. If you’re interested in quickly deploying your first AI agent, contact us today.