AI is not new to Katherine

Katherine first started working with artificial intelligence in 1989.

Her PhD in Electrical and Electronic Engineering included neural networks, chaos theory and analogue chip design. She developed two neuron models and explored how they could be used in different applications, with the longer-term aim of building them directly into electronic chips.

Then the AI bubble burst and the funding disappeared.

That experience gives Katherine a useful perspective on today's excitement around AI. She has seen the technology go through periods of high expectation before.

The difference now is that generative AI has become genuinely useful to businesses. It is faster, more capable and able to perform work that can create real value.

That is where Katherine believes the opportunity lies.

Not in using AI everywhere, but in using it where it makes sense.

Start with the work, not the technology

Katherine takes a practical view.

If a task is repetitive, can be explained clearly and follows visible steps, it may be a good candidate for AI.

But that does not automatically mean it should be automated.

Building a useful AI system takes time. It needs testing, maintenance and review. The value it creates should be comfortably greater than the effort needed to build and run it.

Sometimes AI is the right answer.

Sometimes a simpler process, better data or ordinary software will do the job perfectly well.

At AI dojo, we care more about the result than the technology used to get there.

Teaching that leads to real capability

Katherine is also a qualified teacher with a PGCE and has taught engineering and business at post-16, undergraduate and postgraduate level.

Her teaching has always focused on helping people use what they learn.

When she taught project management, she did not want students to spend the whole course listening to lectures and then sitting an exam.

Instead, she divided the class into teams.

At the end of each lesson, the teams were given subjects to investigate. Members divided the work between themselves, carried out their own research and brought what they had learned back to the group.

Katherine observed the discussions, checked their understanding and helped them work through problems as they appeared.

The students were not always convinced this was the favour Katherine thought it was.

Later, some came back and told her it had been one of the most useful classes they had taken at university.

The reason was simple. They had learned more than project management. They had learned how to work with other people, depend on colleagues and contribute to a team.

That same approach shapes how we work at AI dojo.

Understanding the theory matters. Being able to use it matters more.

The business of business

Katherine's experience goes well beyond technology.

As a management consultant, she has worked across sales, marketing, operations, negotiation, strategy and business growth.

She describes her speciality as the business of business.

That means understanding how the different parts of a company affect one another.

Does marketing understand what sales needs?

Does sales know what operations can actually deliver?

Is knowledge trapped with one person?

Where is work being repeated?

Where is a process creating more effort than value?

These are the kinds of questions Katherine has spent years helping businesses answer.

She is also the author of Master the Sales Process and Business Negotiation.

That wider experience matters because AI rarely fixes a business problem on its own. The process, people and technology all need to fit together.

From problem to working solution

The part Katherine enjoys most is taking something from problem to working solution.

That combines the two things she enjoys most: people and problem solving.

She likes understanding why something is not working, finding the friction and then helping to build something better.

Earlier in her career, Katherine architected storage used to archive very large volumes of banking transaction data.

Years after she moved on, that work was still quietly doing its job.

She likes that idea.

A good system does not need to demand attention. It simply needs to keep being useful.

Making more room for what matters

There is also a personal reason Katherine cares about removing repetitive work.

Since 2021, she has lived with Horner's syndrome and other health difficulties that have made parts of life more complicated.

It has changed the way she thinks about time, energy and the assumption that tomorrow will always offer the same possibilities as today.

That is one reason she believes technology should do more than help people produce more work.

Used well, AI can reduce some of the repetitive tasks that consume skilled time and energy.

That can create more room for better work, better decisions and life outside work.

For a small business owner, that might mean more time with customers, more time developing the business, or simply finishing the day with some energy left.

That matters too.

Get in touch to work with Katherine.