Most finance and tax teams know their processes are not as efficient as they could be. What is less clear - and less often articulated - is exactly where the inefficiency sits, what is causing it, and what solving it would actually be worth.
This article is a practical guide to that diagnostic step. Before investing in technology or process change, it is worth understanding what you are trying to fix.
Map your processes
The starting point is a clear picture of what your team actually does - not what the process document says, but what happens in practice.
Walk through a recent compliance cycle or year-end close. For each step, note:
- What triggers it (an email, a calendar date, another process completing)?
- What data is needed, and where does it come from?
- What happens to the data between receipt and output?
- Who is involved, and what judgement do they apply?
- What could go wrong, and how often does it?
This exercise rarely produces surprises about what the process is. It frequently produces surprises about how long individual steps take, how many people touch a piece of data, and how many of those touches add no analytical value.
Analyse time and resource allocation
Once the process is mapped, quantify where time goes. This does not need to be precise - rough estimates by step are sufficient.
The goal is to identify the ratio between:
- Time spent preparing data (extracting, formatting, loading, reconciling)
- Time spent reviewing data (checking, interpreting, making decisions)
In most finance and tax functions that have not invested in automation, the preparation-to-review ratio is skewed toward preparation. Qualified professionals spend a disproportionate share of their time on work that does not require their expertise.
That imbalance is the most important thing to identify - because it tells you where automation would have the highest impact.
Monitor data quality
Inefficiency often traces back to data quality problems upstream. Late or incomplete trial balances, inconsistent account coding, intercompany mismatches, and currency rounding issues all generate rework downstream - and that rework tends to be invisible in process maps because it gets treated as “just part of the job.”
As you review your processes, note how often manual corrections are made and what kind they are. Recurring corrections to the same types of issue are signals of an upstream data quality problem that should be fixed at source, not worked around repeatedly.
Evaluate data accessibility
In many finance and tax teams, the process of simply getting data is a source of significant delay. The ERP extract has to come from IT. The statutory reporting tool can only be accessed by two people. The prior-year workings are in a folder structure that no one has documented properly.
Map where data comes from and whether access is timely and reliable. Poor data accessibility - regardless of data quality - is a bottleneck that automation cannot solve. It needs to be addressed through access rights, documented data sources, and clear handoff processes.
Identify skill gaps
Processes that are heavily manual often persist not because automation is impossible, but because the team has not yet built the skills to implement it.
This is worth naming explicitly. If the main reason trial balance loading is done manually is that no one on the team has used Alteryx or built an API connection, that is a skill gap - and addressing it is within reach. It does not require a developer or an IT project. It requires a commitment to learning a tool, and some time to build a first workflow.
The teams that have made the most meaningful process improvements are usually those that invested in upskilling one or two team members, rather than waiting for IT to build a solution.
Solicit feedback from the team
The people who do the work know where the problems are. Asking them directly - in a structured way, not in the middle of a deadline - is one of the fastest routes to an accurate picture of process inefficiency.
Useful questions:
- What is the most time-consuming part of your role that feels like it should not need to be?
- Where do you most frequently have to wait for something before you can continue?
- What would you automate first if you could?
The answers often converge quickly on a small number of recurring pain points. Those are the right starting places.
Benchmark against industry
It is useful to have a sense of whether the inefficiencies you have identified are specific to your organisation or common across the industry. The latter does not make them acceptable, but it does help set realistic expectations for what improvement looks like.
Benchmarks to consider:
- What proportion of compliance cycle time is spent on data preparation versus review and analysis?
- How many manual steps sit between the ERP and a filed return?
- How many people need to be involved to produce a set of statutory accounts?
Tax technology vendors and professional associations publish benchmarking data periodically. It is imperfect, but useful as a reference point.
Conclusion
The goal of this exercise is not a comprehensive audit. It is a prioritised list - the two or three process inefficiencies that, if addressed, would have the most significant impact on the team’s capacity and output quality.
Most organisations that have gone through this exercise find that the biggest gains come from a small number of well-chosen changes. Identifying which changes those are, before investing in technology, is the most important step.
If you would like to talk through what this diagnostic might look like for your organisation, or what options exist to address specific inefficiencies you have already identified, get in touch.
