Silos to Synergy: Achieving End-to-End Intercompany Excellence

Intercompany accounting has a habit of starting as a “finance issue” and quickly becoming everyone’s issue. As organisations scale through M&A, expand into new jurisdictions, and face rising expectations for faster close and greater transparency, the friction created by disconnected processes, data and ownership becomes harder to ignore.

In our webinar Silos to Synergy: Achieving End-to-End Intercompany Excellence, Simon Atherton was joined by Ross Pflaeger and Jenny Holehouse to explore what “good” looks like for intercompany, how to build a pragmatic transformation roadmap, and where AI can accelerate progress when the fundamentals are in place.

What we heard from the room (polling insights)

Before diving into frameworks and solutions, we began by asking attendees our first polling question to pinpoint where intercompany pain is showing up most strongly today. The results were telling: 

This reinforces what many teams experience: when transactions aren’t booked consistently on both sides—or lack the detail to be understood locally—disputes multiply and period-end becomes a firefight.We also asked how teams work together across the end-to-end process. While collaboration is happening, the “synergy” many organisations want isn’t quite there yet:

This matters because intercompany performance is rarely constrained by one team’s effort alone. For example, controllership teams may be forced into manual “translation” work (tagging transactions, building offline reports) simply to give Tax and local finance what they need—work that can often be avoided by designing for shared outcomes and data requirements from the start.

Finally, we took a pulse on AI readiness in intercompany:

The message here is not “AI is far away”—it’s that many organisations still have significant value to unlock by standardising processes and data first. AI can be a genuine accelerator, but it works best when the underlying intercompany “plumbing” is strong.

A practical 3-step approach to intercompany transformation

Across the discussion, we returned to a simple structure for moving from “pain” to performance:

1. Identify the problem by understanding the current-state process, operating model and end-to-end data flow.
2. Define the transformation by designing toward leading practice across governance, data, technology and ways of working.
3. Calculate the benefit across Finance, Tax and Treasury to build a business case that reflects the true end-to-end value.

Building the roadmap: start where you are, but design for where you’re going

Intercompany transformations rarely happen in a vacuum. ERP programmes, M&A integration, and evolving tax and regulatory demands all influence timelines and scope. The organisations that make sustained progress are often those that invest time up front to define a shared future-state vision—agreeing how policies, contracting, calculations, invoicing, reporting and governance should work end-to-end—then sequence initiatives to unlock value along the way (not only at the end of a multi-year programme).

Where AI can help (and what to fix first)

AI is increasingly being applied to intercompany, but the highest-impact use cases tend to cluster around two areas: (1) improving the quality and accessibility of knowledge (policies, agreements, playbooks), and (2) accelerating documentation and control activities that rely on extracting and interpreting large volumes of information.

• Intercompany policy assistant: using an organisation’s LLM to help draft, refine and validate intercompany policy content against actual transaction types—spotting gaps and inconsistencies early.
• Transfer pricing documentation acceleration: assembling evidence, populating templates and supporting local file preparation at scale. 
• VAT/GST compliance checks: scanning invoice data to validate required local fields and flag high-risk items before submission.
• Audit preparation: drafting response packs and evidence summaries to improve speed and consistency when queries arise.

As with any finance and tax process, governance matters: teams need clarity on where the data comes from, how models behave, who has access, and whether there is an audit trail. Where AI output affects tax positions or statutory reporting, it should be AI-powered with human oversight—not AI-only.

Five practical takeaways to start moving from silos to synergy

1. Map your intercompany universe. Build a clear inventory of transaction types, volumes and pain points (a heat map is a good start).
2. Fix upstream first. Strengthen agreements, booking standards and data requirements so reconciliation becomes faster and more automated.
3. Design for shared outcomes. Bring Controllership, Tax, Treasury and key “data consumers” into the design early to avoid manual workarounds later.
4. Automate where it reduces disputes. Prioritise solutions that enable dual-sided posting, standard coding and transaction-level matching.
5. Be intentional about AI. Start with high-value, low-risk use cases (policy support, documentation, controls), and apply strong governance and human oversight.

Audience Q&A: questions we didn’t get to live(answered from the session)

We weren’t able to cover every audience question during the live Q&A. Below are responses we can provide: 

1. How do you recommend defining ownership between Accounting, Tax, Treasury, and business teams where intercompany mismatches arise from source system or master data issues rather than accounting entries?

2. Do you have clients that have debit notes processes in place for under receipting / over invoicing on price / quantity and how do they manage this from a reconciliation / payout clearing perspective?

3. Do all intercompany recharges require an actual invoice for the recharge or is a supporting spreadsheet and policy enough to just make the booking? If the recharges are all UK to UK and all the companies are in the same UK tax group for both CT and VAT?