Intelligent Process Automation

Automate the right things.

Automation is easy to demonstrate.

The harder question is whether the process being automated is worth keeping in the first place.

Fern ICT helps organisations understand how work is actually done, identify unnecessary effort and determine where automation, integration or artificial intelligence can make a meaningful difference.

That might mean automating a repetitive task, connecting systems that currently depend on manual data transfer, introducing AI into part of a workflow, or simply redesigning a process that has become more complicated than it needs to be.

Sometimes the best automation is no automation at all.

Which processes are good candidates for automation?

Not every repetitive task is worth automating.

A process is more likely to benefit when it is performed frequently, follows reasonably consistent rules, involves significant manual effort or creates avoidable errors and delays.

We can help identify opportunities such as:

repetitive administrative work;
transferring information between systems;
repeatedly entering or reconciling data;
generating routine reports;
processing structured requests;
routing work according to defined rules;
extracting information from documents;
checking information against known criteria; and
assisting people with research and information retrieval.

We also look for processes where automation would introduce more complexity than it removes.

The objective is to find worthwhile opportunities rather than produce a list of everything that could technically be automated.

Should you automate the process or redesign it first?

A process that is inefficient when performed by people will often remain inefficient when performed by software.

Automation can make an existing process faster without making it better.

Before considering a technical solution, we can help understand:

what the process is actually intended to achieve;
where information enters and leaves it;
which activities genuinely require human judgement;
where people repeatedly enter, copy or reconcile information;
where delays and unnecessary hand-offs occur;
which activities are governed by clear rules;
where exceptions and unusual cases occur; and
which steps exist simply because the process has evolved that way.

This often reveals opportunities to remove unnecessary steps before automating what remains.

A simpler process is usually easier to automate, easier to operate and easier to change.

Automation, integration or AI?

There is more than one way to improve a process.

A problem involving several disconnected systems may be best solved through an API or integration.

A repetitive task may only require a small script or workflow.

A document-heavy process may benefit from information extraction.

A process involving natural language or large volumes of unstructured information may be a good candidate for AI.

And sometimes a change to the process itself is the most effective solution.

We can consider approaches including:

software automation;
APIs and systems integration;
workflow automation;
data transformation and processing;
document and information extraction;
rules-based decision processes;
AI-assisted analysis and information retrieval; and
AI-enabled workflow components.

The technology should follow the process rather than dictate it.

When should AI be used in a process?

AI can be useful where conventional automation struggles with ambiguity, language or unstructured information.

For example, it may help with:

extracting information from documents;
classifying or summarising information;
searching organisational knowledge;
assisting research;
interpreting natural-language requests;
preparing information for a human decision; or
handling parts of a workflow where the available information does not fit a simple set of rules.

But AI is not automatically the best solution.

If a process can be reliably and transparently implemented with a simple rule, script or integration, that may be preferable.

We consider AI in the context of the problem rather than assuming that every automation opportunity requires it.

Keep people involved where judgement matters

Not every decision should be automated.

Some activities need human oversight because the consequences of getting them wrong are significant, the available information is ambiguous, or the situation falls outside predictable patterns.

For AI-enabled processes in particular, useful questions include:

How reliable does the result need to be?
What happens when the system is wrong?
Who is responsible for checking the output?
What information is being exposed to the technology?
Can the decision or recommendation be understood afterwards?
What happens when the process encounters something it has not seen before?
Can a person intervene when the automated process behaves unexpectedly?

A useful automated process is not necessarily one with the least human involvement.

It is one where people spend their time on the things that actually require human judgement.

Improve the systems you already have

Automation does not necessarily mean introducing a new platform.

Existing systems often contain useful capabilities that are poorly connected or underused.

A relatively small change to an established process can sometimes achieve more than a major technology project.

We can look at the whole process and consider the simplest practical intervention.

That might be:

an API integration;
a small script;
a workflow;
an existing platform capability;
a data-processing service;
an AI service; or
a decision not to automate at all.

The question is not what new technology can be purchased.

It is what the organisation needs to improve and what is the simplest sensible way of achieving it.

How do you know whether automation has delivered value?

Successful automation should produce a measurable improvement.

That might mean:

less manual effort;
fewer errors;
shorter processing times;
faster access to information;
more consistent outcomes;
reduced operational cost;
better use of specialist staff; or
improved ability to handle increasing volumes of work.

The relevant measure depends on the process.

We can help establish what improvement should look like before implementation and assess whether the resulting solution actually delivered it.

Automation is not successful merely because the software works.

It is successful when the process is better.

From opportunity to implementation

Where there is a worthwhile opportunity, we can help turn it into a practical implementation plan.

This might involve:

understanding the existing process;
identifying opportunities and constraints;
assessing the potential benefit;
deciding whether to redesign, automate, integrate or introduce AI;
selecting an appropriate technical approach;
designing the target process and architecture;
testing the approach on a manageable scale; and
measuring whether it actually improved the outcome.

The resulting solution should be proportionate to the value of the problem being solved.

Not every automation opportunity needs a major transformation programme.

Independent advice

Fern ICT is independent of automation vendors, software platforms and AI providers.

That means recommendations are based on the process, the evidence and the circumstances of the organisation rather than a requirement to sell a particular technology.

The answer might be a new system.

It might be an integration between systems you already have.

It might be AI.

It might be a small piece of software.

Or it might be a simpler process.

The technology should serve the objective.

When does process automation make sense?

Independent advice can be particularly useful:

when a team is spending significant time on repetitive administrative work;
when information is repeatedly copied between systems;
when manual processes are generating avoidable errors;
when increasing workload is creating pressure to add more people;
when an existing workflow has become unnecessarily complicated;
when considering AI for an operational process;
when several systems need to work together more effectively;
when an organisation is evaluating an automation platform; or
when previous automation projects have not delivered the expected benefits.

You don’t need to know which technology you need before starting.

Sometimes the most useful starting point is simply to understand why the process works the way it does.

Start with the process

If a process is consuming too much time, generating avoidable errors or simply becoming difficult to manage, it may be worth examining before reaching for a new technology.

We can start with the process as it exists today, understand what is actually causing the problem and work out what could sensibly be improved.

Sometimes the answer is automation.

Sometimes it is integration.

Sometimes it is AI.

Sometimes it is a better process.

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