When processes grow, they don’t always improve
Operations usually evolve over time.
New tools get added.Processes get adjusted.Workarounds become part of how things run.
At first, it works.
But as things grow, it becomes harder to see:
Where time is being lost
Where processes break down
And why simple tasks take longer than expected
What this page covers
This page looks at how operations teams are using AI and automation in practice, where it tends to fall short, and what changes when systems are set up more clearly.
What AI and automation mean in operations
In operations:
AI is used to analyse workflows, assist decision-making, and identify patterns
Automation handles how tasks move between systems and teams
Most businesses are already using both in some form.
The difference comes down to how well everything is connected.
Where AI is used in operations
AI is already being used across operational processes:
Workflow automation
Task management and routing
Reporting and performance tracking
Resource planning
Process optimisation
In most cases, this starts with tools—and stays there.
How operations teams are using AI today
These are useful.
They remove some friction.
But they don’t always make processes clearer.
Workflow automation tools
Moving tasks between systems
Task tracking platforms
Managing work across teams
Automated reporting
Generating updates on performance
Basic process automation
Reducing manual steps
⚠️ Where things start to break down
It’s rarely one obvious issue.
It’s the way things fit together—or don’t.
You might recognise things like:
Processes that rely on multiple systems to complete one task
Work being passed between teams without clear visibility
Manual steps sitting in the middle of “automated” workflows
Delays that are hard to trace back to a single cause
And then the bigger issue:
Not having a clear view of how work actually flows across the business
So teams:
Build workarounds
Add extra steps
Or rely on individuals to keep things moving
AI doesn’t really fix this on its own.
It can automate parts of a process—but if the structure behind it isn’t clear, you just get faster versions of the same inefficiencies.
And over time, that leads to:
Processes that are harder to manage
Increased dependency on specific people
Less visibility across the business
What better looks like in operations
Before
❌ Processes spread across multiple systems
❌ Limited visibility across workflows
❌ Reliance on manual steps and workarounds
After
✅ Clear, connected workflows
✅ Better visibility across how work moves
✅ Fewer manual handoffs between systems and teams
The shift isn’t about automating more.
It’s about making processes easier to follow.
Where Microsoft Copilot becomes useful
Copilot is already being used in operations to:
Summarise updates
Assist with reporting
Help manage tasks
That’s helpful—but limited.
Where it becomes genuinely useful is when it’s connected to how your business actually operates.
Instead of just summarising information, it can start to:
Show how work is progressing across systems
Highlight where delays are happening
Help identify patterns in workflows
For example:
“Where are tasks getting delayed most often?” “Which processes are taking longer than expected?” “Where are manual steps still slowing things down?”
These aren’t individual tasks—they’re system-level questions.
And they can only be answered when your processes are clearly structured.
Find out more about AI solutions
What this can look like in practice
Seeing how work actually flows
→ Instead of relying on assumptions, you can see how tasks move across sytems and teams.
Identifying where time is lost
→ Delays become visible - along with where they're happening.
Reducing reliance on workarounds
→ Processes become easier to follow, without needing manual fixes along the way.
Connceting systems properly
→ Instead of tools oeprating in isolation, they start to work as part of a single flow.
Why this doesn’t get fixed
Processes built around workarounds
Systems that don’t fully connect
Limited visibility across how work actually flows
Things still get done—just not as smoothly as they could.
What this usually involves
This isn’t about automating more.
It usually starts with:
Understanding how processes currently run
Looking at how systems interact
Identifying where delays and friction occur
From there, it becomes clearer what needs to change—and what doesn’t.
This is usually where things change
Most operations teams don’t need more automation.
They need a clearer view of how their business actually runs.
Once that’s in place, everything else becomes easier.
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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