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 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.
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.