Tax technology benchmarking: the gap between perception and reality

Tax teams are under pressure from every direction at once: more reporting, increased scrutiny, higher stakeholder expectation, relentless change. The default response is often to look to a new system or AI. 

But in practice, the biggest constraint we see working with businesses is rarely the result of software. It’s the combination of processes, data, governance and capacity. 

Over the last few months, we have run a tax and technology review and benchmarking programme. The sessions are deliberately simple: a small number of open questions across process and governance, technology, data and the team itself. 

The answers we’ve received are not always simple, but they do coalesce around common themes. These are worth exploring.  

The first thing we find is that many Heads of Tax believe their peers are far more advanced than they are. That creates a sense of urgency and sometimes panic and a tendency to jump to solutions. 

But when we get to specifics, the market is not full of quietly humming, AI-driven tax operating models. There are a few outliers, and pockets of excellence. Mostly, there is experimentation, tactical automation and a lot of work in progress. 

The gap between what people think other teams are doing and what they are actually doing is huge. 

Related to this, almost all teams feel they are being left behind by most other teams, which logically cannot be true. The explanation for this perception is likely to be that organisations tend to benchmark themselves against an imagined best practice that is both rare, and usually easier described in a PowerPoint presentation than implemented in real life.

These insecurities about measuring up often push teams to have big ambitions for levelling up. However, for most teams, the real, high-impact opportunities are often not a multi-year enterprise resource planning (ERP) journey or an advanced new tax engine. 

They are more prosaic, more defined and cheaper: 

• Tactical automation to remove repetitive manual steps 
• Reducing the risk and resource drain of massively complex Excel workbooks 
• Improving data quality at source so tax isn’t constantly fixing it downstream 
• Tightening handovers between tax, finance and shared services 

Such projects aren’t always exciting. They are, however, effective. 

Most tax functions have robust governance. The inconsistency tends to appear between different taxes, different geographies, or different parts of the process. 

In a surprising number of cases, governance is also maintained through strong personal connections rather than through repeatable mechanisms. That works brilliantly – until it doesn’t, due to holidays, role changes, restructures or other ways in which the team loses that one person who just knows how it works. 

The teams we speak to are skilled, adaptable and constantly learning new mandates. 

But nobody says they have slack. Nobody says they are over-resourced. That makes contingency planning difficult and makes it very hard to prioritise longer-term improvement over the next deadline. 

A consistent symptom of this it that while business partnering is frequently cited as a practice people want to do more of, few are able to. 

Data is frequently inconsistent, siloed, and not designed with tax in mind. The result is rework: reconciliations, manual adjustments and translation layers between finance reporting and tax requirements. 

In addition to the problems it causes, it prevents improvements. It limits what organisations can realistically do with automation or AI, for example. If the data foundation is shaky, the best technology in the world simply helps you get the wrong answer faster. 

This finding is less about intent and more about incentives and context. 

Tax and finance teams often prioritise control, explainability, deadlines and auditability. IT teams, meanwhile, tend to prioritise standardisation, security, scalability and enterprise roadmaps. Both are sensible. But without a shared framing, they can accidentally become blockers rather than enablers for each other. 

If the findings above seem familiar and you are looking for a pragmatic starting point to move froward, there are several areas that often create quick momentum:

• Map where Excel is doing system work.  Identify the few spreadsheets that carry disproportionate operational risk and effort. These are often the best candidates for tactical automation or redesign 
• Prioritise upstream data fixes. Pick one or two data issues that cause repeated rework and fix them at source. Even small changes can have compounding benefits 
• Make governance repeatable. Where governance relies on individuals, capture it through checklists, standard packs, defined controls, documented assumptions and clear ownership 
• Create a common translation layer with IT.  Agree what good looks like – not just technical success, but operational outcomes (cycle time, error rates, controls, audit trail and resilience) 

A third-party review and benchmarking report can surface latent issues that otherwise can be seen as the day-to-day friction of doing business. They can refine these into a clear view of strengths and opportunities, and benchmark against similar functions. This can be useful for getting external stakeholder or budget-holder buy-in 

It can also provide an articulation of the opportunity cost of manual processes – for instance, automation can create more effective capacity in the team, which may reduce third party advisory fees. With the opportunity defined, the review can also set out practical next steps, since knowing where to start is itself a barrier to action, and give a strategic sense of future control and direction. 

Just as importantly, it also provides a reality check on what others are doing, which can be invaluable for building the business case for follow-up action.