When your business grows, finance gets harder to see clearly
At the start, finance is simple.
You know what’s coming in, what’s going out, and where things stand.
But as a business grows, things spread:
More systems
More people
More ways data is handled
And slowly, clarity starts to slip.
You still have the numbers - but not always the full picture, and not always when you need it.
What this page covers
This page looks at how finance teams are using AI and automation in practice, where it tends to fall short, and what changes when things are set up more clearly.
What AI and automation mean in finance
In finance teams:
AI is used to analyse data, generate reports, and answer questions
Automation handles the repetitive processes around it - like moving data, updating reports, and triggering workflows
Most businesses are already using both in some form.
The difference comes down to how well everything is connected.
Where AI is used in finance teams
AI is already being used across most finance functions:
Financial reporting
Forecasting and planning
Cash flow monitoring
Cost analysis
Internal queries and decision support
In most cases, this starts small - and stays fairly contained.
How finance teams are using AI today
Copilot in Excel
Helping analyse spreadsheets faster
Report summaries
Turning hours of reading into minutes
Invoice processing
Reducing manual data entry
Forecast support
Sense-checking numbers using past data
⚠️ Where things start to break down
It’s usually not one big issue.
It’s a build-up of small ones.
At some point, finance starts spending more time working around systems than using them.
You might recognise things like:
The same numbers being checked in multiple places, just to be sure
Slight differences between reports that take time to explain
Pulling data from different systems just to answer what should be a simple question
Month-end still feeling like a rush - even with automation in place
And then there’s the quieter issue:
Not being completely confident the numbers in front of you are the latest or the full picture
So you double-check.
Or rebuild the report.
Or ask someone else to confirm.
AI doesn’t really fix this on its own.
It can speed up parts of the process - but if the data behind it is spread out or inconsistent, you just get faster versions of the same problems.
And in finance, that creates a different kind of risk:
Decisions based on incomplete information
Time spent validating instead of analysing
Less confidence in what should be the most reliable part of the business
Most teams don’t notice this happening straight away.
It builds gradually - as systems grow, and the way data is handled doesn’t quite keep up.
What better looks like in finance
Before
❌ Rebuilding reports each time
❌ Checking numbers across systems
❌ Working from last month’s data
After
✅ Numbers already available when needed
✅ Fewer manual checks
✅ A clearer view of what’s happening now
The shift isn’t about adding more tools.
It’s about having a clearer structure behind the ones you already use.
Where Microsoft Copilot becomes useful
Copilot is already being used in finance teams to:
Analyse spreadsheets
Summarise reports
Assist with documentation
That’s helpful - but limited.
Where it becomes genuinely useful is when it’s connected to the rest of your financial data.
Instead of working from a single file, it can start answering broader questions like:
“What’s changed in our costs this quarter, and where is it coming from?” “Which areas of the business are becoming more expensive over time?” “What’s different compared to last month?”
Those aren’t spreadsheet tasks - they’re data questions.
And Copilot can only answer them properly when the structure behind the data is clear.
Most teams use tools like Copilot to work faster.
The difference comes when it’s connected to the right data - so answers are consistent, not just quick.
Find out more about AI solutions
What this can look like in practice
Seeing your numbers without waiting
→ Your key figures are already visible - without building a report first.
Understanding changes without digging
→ Instead of spotting a number and investigating it manually, you can ask: "Why has this changed?"...and get a breakdown based on live data.
Spending less time fixing spreadsheets
→ Less time spent matching figures, checking versions, and fixing small inconsistencies.
Seeing which financial decisions actually improved performance
...and which didn't.
→ Not just what was spent - but what changed as a result over time.
Why this doesn't get fixed
Systems added over time, not designed
Financial data spread across multiple tools
No single, trusted view of the numbers
It still works - so it doesn’t feel urgent to change.
What this usually involves
This isn’t about replacing your finance tools.
It usually starts with:
Understanding where financial data lives
Looking at how reports are currently built
Identifying where inconsistencies come from
From there, it becomes clearer what needs to change - and what doesn’t.
This is usually where things change
Most teams don’t need to move faster.
They need to be more consistent.
Once that’s in place, everything else becomes easier.
They’re trying to make sense of what they already have.
We were spending too much time pulling financial data together, and didn’t fully trust the numbers. That’s what they helped fix. We are now planning to implement more workflow automation with Hydrogen in the future.
S. Lewis-Dale
Head of Business Development
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