GBS & Shared
Services Transformation, Built on Agentic AI.

Most global business services transformation programs stall in the design phase, a target operating model, a tower structure, and a business case, while the manual work underneath keeps piling up. Auxiliobits takes the opposite approach: we build the automation layer that makes your GBS model actually run, tower by tower, entity by entity, starting with the real process instead of the org chart.

We’re not a GBS consultancy. We don’t design your operating model or write your transformation roadmap. We’re the technology partner who shows up after that roadmap exists or instead of one and builds the agentic AI, RPA, and workflow orchestration that turns a GBS or shared services transformation from a slide into something running in production, measured against a real baseline, in weeks rather than quarters.

Whether you’re consolidating finance, HR, IT, and procurement into a single GBS structure, running a global business services transformation across newly acquired entities, or trying to get an existing shared services center to actually behave like a GBS model on paper claims it does, this page shows you where we fit, how we work, and the proof that it holds up at scale.

What This Page Covers

This page is your map to how Auxiliobits supports Global Business Services (GBS) and shared services transformation as the technology partner that builds and runs the automation layer, not as a consultancy that hands you a deck and a roadmap. If you’re a GBS leader, SSC director, or finance/HR/IT operations executive evaluating how agentic AI fits into your multi-tower, multi-entity operating model, you’ll find four things here: the case for why most GBS transformations stall before they deliver anything measurable, where Auxiliobits plugs into an existing GBS structure tower by tower, how a typical engagement runs from first conversation to production automation, and proof, real client outcomes at GBS scale, not modeled projections. This is deliberately not a strategy deck. Operating model design, tower consolidation decisions, and governance structures are decisions your organization and your consulting partners are best placed to make; we’re not going to tell you whether Procurement should report through Finance or stand alone, or how many tiers your service catalog needs.

What we bring is the execution layer underneath those decisions, the automation, orchestration, and agentic AI architecture that makes a GBS model actually run the way it was designed to on paper, across every entity and ERP that model has to account for. That distinction matters more than it sounds: many organizations that have already been through a GBS design engagement with a large advisory firm come to us next, specifically because the roadmap is finished and somebody now has to build the thing it describes.

If that’s where you are, the “How We Work” section below is probably the most relevant place to start. If you’re earlier in the process and still building the internal case for transformation, “The Problem With Most GBS Transformations” is a good place to begin, since it covers the failure patterns worth designing around from the outset. From here, you can go deeper into any GBS topic, fundamentals, operating model design, agentic AI architecture, or M&A-driven scale, using the categorized links at the bottom of this page.

The Problem With Most GBS Transformations

Most GBS transformation programs begin with the wrong focus. They start with a platform decision, an operating model diagram, or a re-org and only later get around to asking what’s actually slowing the organization down tower by tower. That sequencing is backwards, and it’s why so many GBS transformations spend 18 months in design before a single process gets measurably faster. By the time the target operating model is signed off, the business case that justified it has aged, the sponsor has often moved on, and the SSC or GCC underneath the new org chart is still running on the same manual workflows it had before the transformation began.

01The operating model trap

A GBS operating model, the tower structure, the governance layer, the service catalog, the SLA framework are necessary. It is not sufficient. An operating model describes how work should flow between Finance, HR, IT, and Procurement towers, who owns which decisions, and how service levels get measured. But an operating model diagram does not extract data from an invoice, does not reconcile a ledger across twenty company codes, and does not route an HR case to the right specialist. Too many transformation programs treat the operating model as the transformation itself, when it’s really just the blueprint. The building still has to get built, tower by tower, process by process, and that’s the part that gets deferred, underfunded, or handed to whichever vendor happened to win the platform RFP.

02The technology-first trap

The opposite failure mode is just as common: an organization picks a platform, an ERP module, an RPA suite, or an agentic AI vendor before it has mapped how work actually moves through any given tower today. Technology selected before process discovery tends to automate the wrong things well: it speeds up steps that shouldn’t exist in the first place, or it automates the documented SOP instead of the workaround that finance, HR, or procurement staff actually use to get things done. The result is a shiny pilot who looks impressive on a steering committee deck and never scales past the one team that helped design it, because it was never built against the real, messy, exception-heavy version of the process.

03The consultancy trap

There’s a third failure mode that’s specific to GBS transformation programs run by large advisory firms: the deliverable is a target operating model, a business case, and an implementation roadmap, and then the advisory engagement ends. Someone else, usually an internal team already stretched thin running the current-state operation, is left to actually build and deploy the automation the roadmap called for. The gap between “roadmap complete” and “automation live in production” is where most GBS transformation value quietly evaporates. A design partner hands off a plan. A technology partner has to still be in the room when the bots go into production, when the first exception fires at 2am, and when the third acquired entity needs onboarding into the automation layer six months later.

04It's a process problem, not a capacity or governance problem

This isn’t a capacity problem, and it isn’t a governance problem either. It’s a process problem, and neither adding headcount to a shared services center nor redrawing its reporting lines fixes it. Every quarter that a GBS tower goes without automation, transaction volume grows, another acquisition adds another ERP instance to reconcile, and the SSC’s answer is another req for another analyst. That’s not a transformation; it’s linear cost scaling with a new name on the org chart. The only durable fix is removing the manual layer that’s still sitting underneath the “transformed” org chart: the manual invoice intake, the informal escalation paths, the exception queues nobody owns, and the month-end close that still depends on one person’s tribal knowledge of which transaction codes to run in which order and in what sequence.

05Process-first, technology-second

We don’t start with tools, and we don’t start with an operating model redesign; that’s your call to make, with whoever you’ve engaged to help you make it. We start with your process—mapping how work actually moves through a tower today, not how the SOP says it should. That means sitting with the team doing the work, not just interviewing the tower lead. It means documenting the exceptions, not just the happy path, because in most GBS towers the exceptions are where 60–80% of the manual effort actually lives. Only after that discovery is complete do we automate execution on top of what you already run , and only after that automation is stable in production, do we talk about consolidating further, standardizing across entities, or scaling the model to the next tower or the next acquired business? Process-first. Technology—second. That sequencing, more than any platform choice, is what separates a GBS transformation that shows results in weeks from one that’s still “in design” a year later.

Why this matters more now than it did five years ago

Agentic AI changes the calculus here, but not in the way most vendor pitches suggest. The pitch usually implies that agentic AI removes the need for careful process discovery, that a sufficiently capable model can just “figure out” a messy, undocumented process on its own. In practice, the opposite is true: agentic systems are more sensitive to process quality than rule-based RPA was, not less. This is because an agent makes judgment calls at each step based on context, and bad context—such as an undocumented exception path, an approval rule nobody wrote down, or a company code that behaves differently from the other nineteen—can lead to confidently wrong decisions rather than an obvious failure that a human would catch.

What agentic AI does change is what becomes possible after discovery: rule-based RPA could only automate the parts of a process with entirely predictable branching logic, which meant the exception-heavy, judgment-dependent 20–30% of most GBS workflows stayed manual regardless of how much of the tower got automated.

Agentic architectures, the kind combining large language models with structured tool use and multi-agent orchestration, can now handle a meaningfully larger share of that judgment-dependent work: reading an unstructured vendor invoice in an unfamiliar layout, deciding which of several plausible approval paths an HR case should follow, and reconciling a discrepancy that requires understanding context rather than matching a fixed pattern. For a GBS tower running across multiple entities, where process variance is the norm rather than the exception, that shift is the difference between “80% of the tower automated, 20% permanently manual” and a tower that can actually approach full coverage over time.

Where Auxiliobits Fits

Auxiliobits doesn’t replace your GBS operating model or tell you how to structure it. We build the automation layer that makes each tower run at GBS scale across entities, ERPs, and geographies, and we do it tower by tower, so value shows up before the full model is even finalized. Three patterns cover most of where we plug in: a single finance tower that needs to run at GBS-grade volume and audit standard, multiple towers that need to hand off work to each other without a human in the loop re-keying data, and the specific, high-pressure case of integrating a newly acquired or newly consolidated entity into an existing GBS footprint.

01Finance GBS Towers

Finance is usually where GBS automation has to prove itself first, because finance towers carry the highest transaction volume, the tightest audit requirements, and the clearest, most defensible ROI math of any tower in the model. A finance GBS tower running across multiple entities and multiple ERPs has to reconcile data that was never designed to be reconciled together, with different chart-of-accounts structures, different approval hierarchies, different close calendars, and sometimes different currencies and tax regimes layered on top. That’s not a single-process automation problem; it’s a cross-entity data and workflow problem, and it’s where we’ve built our deepest bench.

Inside a finance GBS tower, the work typically breaks down into a few recurring categories, and we’ve built production automation across each of them.

Invoice intake and AI-powered data extraction from the dozens of formats vendors actually send—email, PDF, portal, EDI.
Two-way and three-way matching against purchase orders and goods receipts, with mismatches routed to the right approver rather than dropped in a shared queue.
Approval workflow orchestration that reaches the right person with full context instead of sitting in an inbox for a week.
Exception handling and categorization — since exceptions, not the clean transactions, are what consume most of a finance tower's headcount.
Multi-ERP reconciliation and consolidation, run nightly or on whatever cadence the close calendar requires.
Month-end and period-close automation, including the kind of multi-step, multi-entity closing sequence that used to require a finance analyst manually running transaction codes across dozens of company codes over an entire weekend.

Crucially, none of this requires standardizing your finance tower onto a single ERP first. The automation layer sits on top of SAP, Oracle, NetSuite, Microsoft Dynamics, or whatever combination your finance GBS tower has inherited through organic growth or acquisition—which matters, because “standardize the ERP first” is precisely the kind of multi-year prerequisite that keeps GBS transformations stuck in planning. It’s also worth being direct about why finance tends to be the tower we start with when an organization is evaluating a broader GBS transformation and hasn’t yet decided where to begin: the ROI math is the cleanest to build a business case around, the audit and compliance requirements make “process-first” discovery non-negotiable rather than optional, and a finance tower’s transaction volume means even a partial reduction in manual effort translates into a number a CFO will recognize immediately. That combination tends to make finance the easiest tower to get budget approval for the first one, and once the pattern is proven there, extending the same discovery-build-stabilize model to HR, IT, or procurement is a much easier internal conversation than starting cold in a tower with less obviously measurable ROI. See our AP and finance automation work for the tower-level detail, and the material ledger case study below for what the process looks like applied across 65+ manufacturing plants in a single close cycle.

02Multi-Tower Orchestration

A GBS model is only as strong as its weakest tower, and most of the real transformation value—the value that shows up in a CFO or COO’s numbers, not just in a single team’s efficiency metrics— lives in the handoffs between towers, not inside any one of them. HR data has to feed payroll accurately and on schedule. Procurement approvals must gate AP, ensuring that no additional day of latency is introduced. IT access provisioning has to trigger automatically when HR processes a new hire or a termination, not three days later after someone remembers to file a ticket. When those handoffs are manual, every tower ends up compensating for the others’ latency with its own buffer of manual double-checking—which is precisely the kind of hidden, uncosted manual work that never shows up on an SSC’s efficiency dashboard but absolutely shows up in cycle time.

We build the orchestration layer that connects HR operations, IT service delivery, procurement and supply chain workflows, and legal and compliance processes so that automation doesn’t stop at the tower boundary. Concretely, that means agentic workflows that read the outcome of one tower’s process and automatically trigger the dependent step in another tower’s system, without a person acting as the integration layer; anomaly detection and approval routing that spans systems rather than living inside a single platform’s workflow engine; and exception handling that routes issues to the tower that actually owns the fix, instead of bouncing between shared inboxes and generic ticket queues while a case ages past its SLA. This is also where agentic AI earns its keep over rule-based RPA alone—cross-tower exceptions rarely fit a single predictable pattern, and a system that can reason about context (which entity, which policy version, which approver is actually available) handles that variability in a way a purely rules-based bot cannot.

03Post-Merger & Multi-Entity GBS

This is where our track record is strongest, and it’s deliberately the third pattern rather than the first, because post-merger integration is the hardest test any GBS model faces. It forces every tower—finance, HR, IT, procurement, and often a legal/compliance layer on top — to absorb new entities, new ERPs, and new process variants simultaneously, on a timeline the board is watching and a synergy target the deal model already assumed. Most GBS transformation failures that are examined in a post-mortem can be traced back to this exact moment: the operating model was designed for the pre-acquisition footprint, and nobody built the execution layer that could actually absorb a new entity in weeks instead of quarters.

We’ve built the automation layer for exactly that scenario, most notably for a $2.8B advertising network integrating 70+ agencies post-merger, where the client’s own long-term goal was evolving a fragmented SSC into a true, centralized GBS model, and where the deal’s projected integration synergies were genuinely at risk until the automation layer went in. The pattern that made that engagement work and that generalizes to any multi-entity GBS. is a configuration-driven architecture rather than a hard-coded one: entities, company codes, and process parameters live in a config table, not in bespoke code per acquisition, which is the difference between a new entity taking eleven weeks to onboard manually and taking six working days once the automation layer is in place. See the full case study below for the complete breakdown, and the material ledger close case study for the same configuration-driven principle applied to a single finance process running across dozens of legal entities.

How We Work

The same 6–8 week model that gets AP automation live also generalizes to any GBS tower—finance, HR, IT, or procurement—because the sequencing problem is the same everywhere: understand the real process, across every entity that touches it, before you touch a system. We deliberately don’t run 12-month transformation programs with a single “big bang” go-live at the end. A GBS tower is made up of dozens of distinct sub-processes, and each one can be discovered, automated, and stabilized on its own 6–8 week cycle, which means the organization starts seeing measurable results within two months of kickoff, not two years.

Weeks 1–2| Process Discovery

We sit with the tower team, not in a meeting room and not relying solely on the documented SOP, and observe, question, and document the process as it actually runs across every entity, ERP, and geography involved. For a multi-entity GBS tower, this step alone is materially harder than it is for a single-entity team, because “the process” often isn’t one process; it’s a family of near-identical variants that accumulated as different legal entities were onboarded at different times, under different local requirements, and sometimes on different ERP versions. Discovery has to surface those variants explicitly, not paper over them with a single idealized flowchart. We map every intake channel, every approval and exception path, and every system integration point per entity, and we quantify a manual-effort baseline in hours and dollars, the number every stakeholder will use to judge whether the engagement actually worked. This is the step most GBS programs skip or rush, and it’s the single biggest reason so many of them stall: automation built against an assumed process instead of the real, entity-by-entity one breaks the first time it hits an exception nobody documented.

Weeks 3–6| Automation Build

We deploy RPA, AI extraction, agentic workflow orchestration, and ERP integrations across your existing systems, UiPath, Azure AI, your ERP stack, and whatever ticketing, communication, or reporting tools the tower already depends on—built against the exact process and exact entity variants documented in discovery. No rip-and-replace, no forced platform migration, and no multi-year ERP harmonization project as a prerequisite. The automation layer sits on top of what the tower already runs, which is what makes a 6–8 week cycle realistic in the first place: we’re not waiting on a separate ERP program to finish first. Where a tower spans multiple entities, we build the automation against a configuration-driven architecture rather than hard-coding logic per entity — new entities, plants, or company codes get added via a config table update, not a code change, which is what makes the model absorb future acquisitions or new plants without a redevelopment cycle. Throughout the build, we test continuously against real exception scenarios pulled from the discovery baseline, not synthetic happy-path test cases.

Weeks 7–8| Stabilization & Scale

We optimize performance, drive exception rates down to an agreed target, and train the tower team on the new process, including how to work exceptions with the automation flags rather than absorb them silently the way manual processes tend to encourage. We measure results against the exact baseline established in discovery, so the improvement is a defensible before-and-after number, not a modeled estimate. And we build the roadmap for what comes next: expanding the same automation pattern to the next entity in the tower, the next tower in the GBS model, or the next acquisition on the integration calendar.

What happens after the first 6–8 weeks

The first cycle is deliberately scoped to a single process or a single tower, because that’s what makes the model fast and low-risk, nobody is signing off on a multi-year, multi-tower transformation before seeing a single measurable result. What happens after that first cycle depends on where the results point. In some engagements, the fastest path to value is depth: expanding automation coverage within the same tower, moving from the highest-volume process to the next-highest, or extending an already-proven pattern (like the configuration-driven, multi-entity close automation in the material ledger case study) to additional entities that weren’t in the original pilot scope. In others, it’s breadth: taking the The same discovery-build-stabilize cadence is applied to a second tower, allowing HR, IT, or procurement to run its own 6–8 week cycle either in parallel or in sequence, depending on the team’s bandwidth to absorb change. And for organizations in the middle of active M&A, it’s often about onboarding: applying the same automation layer to a newly acquired entity as a defined, repeatable step in the integration playbook, rather than a one-off project each time.

What stays constant across all three paths is the baseline discipline: every expansion gets measured against a discovery-phase baseline, not a projected one, and every new entity or process gets built against its own documented reality rather than assumed to match the pattern that worked elsewhere. That discipline is what keeps a GBS automation program from drifting into the same trap as the operating-model-first transformations described above, where the roadmap looks coherent on a slide but the on-the-ground reality diverges from it a little more with every quarter that passes.

Proof

Case studies about a single AP process or a single reconciliation task are useful, but they don’t answer the question a GBS leader is actually asking: does this hold up across dozens of entities, multiple ERPs, and the kind of operational chaos that comes with acquisition-led growth? The two engagements below are chosen specifically because they answer that question at GBS scale, not process scale.

$2.8B advertising network — post-merger, multi-entity GBS at scale

A major holding group in the global marketing and advertising industry had completed a strategic series of acquisitions bringing more than 70 independent agencies—spanning media buying, creative production, digital strategy, and performance marketing—under a single corporate umbrella, managing $2.8B in annual billings across North America, EMEA, and APAC. The integration exposed the problem immediately: each acquired agency operated on its own financial stack, its project management system, and its own client reporting cadence. Finance teams manually reconciled billing data from different ERP systems at the end of every month. Operations staff spent 40+ hours a week exporting, transforming, and re-entering timesheets, vendor invoices, and purchase orders across platforms that had never been designed to talk to each other. Creative workflows had no standardized handoff protocol, causing project delays averaging four to si x days per campaign cycle.

The organization’s long-term goal was to evolve its SSC into a genuine global business services model—centralized, standardized, and scalable. But without automation, that evolution was structurally blocked: the $38M in integration synergies projected over three years were at risk, operational complexity compounded with every new acquisition, client-facing teams absorbed the backend inefficiency directly, and attrition risk climbed across key accounts as staff burned out on manual reconciliation work. As the client’s CTO put it, the organization had acquired the right agencies but was still running them like 70 separate companies, with the back office held together by spreadsheets and overtime.

Auxiliobits deployed a phased Intelligent Process Automation architecture combining agentic AI, robotic process automation, AI-powered document processing, and custom integration middleware—designed to sit above the existing agency systems rather than forcing platform migration, preserving agency autonomy while creating unified data flows at the network level. The build included multi-ERP financial consolidation, with robots extracting, normalizing, and reconciling billing data from multiple ERP systems nightly into a single ledger with exception flagging for human review; AI-driven vendor invoice processing extracting structured data from unstructured PDF invoices with 98.4% field accuracy, posting approved invoices automatically to each agency’s ERP; cross-agency timesheet aggregation pulling employee time logs from six different project management platforms into a central payroll and billing system; agent-driven creative brief and asset handoff triggered by project milestones instead of manual email chains; and automated client performance reporting generating weekly and monthly dashboards from live campaign data.

The Results

Luxury apparel manufacturer — multi-entity finance close across 65+ plants

A global luxury apparel manufacturer operating a European distribution and retail network spanning more than 65 plants across 20 countries—from flagship wholesale operations in France and Germany to retail and concession stores across Scandinavia, the Iberian Peninsula, and Eastern Europe — faced a demanding, recurring month-end ordeal: manually executing the Material Ledger Closing process across every EU company code in SAP ECC. The process required skilled SAP finance staff to log in and navigate dozens of transaction codes, run status checks and price determinations, execute six sequential costing sub-processes, and lock periods, all while monitoring for errors across every plant. The manual workflow unfolded across Thursday night, Sunday morning, and Monday, consuming eight to ten hours of high-skill finance staff time every month — and a single missed step or status code anomaly could require a full restart from the point of failure. As the client’s Director of Finance Operations for the EU described it, the cost accounting team was spending an entire weekend managing SAP transaction codes instead of analyzing what the numbers were actually telling the business — not a sustainable model as the company continued expanding across Europe.

Auxiliobits deployed a robotic process automation bot using UiPath to replicate and enhance the complete close workflow, from prerequisite validation on Thursday night through final period-lock confirmation on Monday morning. The robot autonomously handles every SAP interaction — logging into the SAP portal, executing transaction codes, validating output statuses, managing the six-step costing run with parallel processing across 20 parallel processes, closing monthly periods, and raising structured ServiceNow incidents whenever an exception is detected. After a human resolves the exception, the robot resumes automatically from the correct step rather than restarting the entire sequence — a structured, machine-readable email handoff protocol that turned what used to be an all-or-nothing manual process into a resilient, restartable one. The entire automation is driven by a configuration table listing every EU plant and company code, meaning a new entity or plant gets added via a table update rather than a code change.

The Results

The common thread

Both engagements share the same underlying lesson for GBS leaders: at multi-entity scale, manual reconciliation isn’t a staffing problem you can hire your way out of — it’s an architectural gap, and closing it is what makes the rest of the GBS model actually work. In both cases, the client’s stated strategic goal was explicitly a GBS or SSC evolution, and in both cases that evolution was structurally blocked by the same thing: a manual layer underneath the org chart that no amount of governance redesign could fix on its own. And in both cases, the architecture that solved it was the same—configuration-driven, ERP-agnostic, built against the real process across every entity rather than an idealized single-entity version of it. That’s not a coincidence; it’s the pattern every multi-entity GBS tower eventually runs into, whether the trigger is organic growth, acquisition, or simply the accumulated weight of running the same close process across twenty company codes for a decade.

Explore GBS Topics

This hub page gives you the overview. The links below go deeper into the specific questions GBS and shared services leaders tend to ask next, organized into four categories so you can go straight to what’s relevant rather than scrolling through everything we’ve written.

Fundamentals

Start here if you’re building the case for GBS transformation internally, or if you want the vocabulary and mental models before diving into architecture or operating-model detail.

Operating Model

For leaders weighing how a GBS tower should actually be resourced and governed—automate in-house, outsource, or do some hybrid—and what to look for in a partner either way.

Agentic AI Architecture

We’ve covered the platform shift from RPA to agentic AI elsewhere — the posts below go straight to what that shift looks like applied to an entire GBS function, not a single automated task.

ON THIS PAGE

01 What This Page Covers 02 The Problem With Most
GBS Transformations 03 Where Auxiliobits Fits 04 How We Work 05 Proof 06 The Common Thread 07 Explore GBS Topics

Not sure where your GBS model
is bleeding the most time?

Most GBS leaders can point to one tower they suspect is the weakest link, but few have a structured, tower-by-tower view of where manual effort is actually concentrated — the view you need before committing budget to any transformation initiative, agentic AI or otherwise.

A focused conversation with our automation and AI experts can help you identify where operational friction is concentrated across your shared services environment, evaluate the processes with the strongest automation potential, and determine where a focused transformation initiative could deliver measurable value first.

What you can expect: a practical discussion around your current operating model and bottlenecks; identification of high-value automation opportunities across finance and shared services processes; and a realistic view of how Auxiliobits could approach a focused 6–8 week engagement before scaling transformation more broadly.

 No obligation. No generic transformation pitch. Just a focused starting point for a conversation your organization is probably already having informally.

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What This Page Covers

The Problem With Most GBS Transformations

01The operating model trap

02The technology-first trap

03The consultancy trap

04It's a process problem, not a capacity or governance problem

05Process-first, technology-second

Why this matters more now than it did five years ago

Where Auxiliobits Fits

01Finance GBS Towers

02Multi-Tower Orchestration

03Post-Merger & Multi-Entity GBS

How We Work

What happens after the first 6–8 weeks

Proof

$2.8B advertising network — post-merger, multi-entity GBS at scale

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Luxury apparel manufacturer — multi-entity finance close across 65+ plants

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