Susan Walsh on Data, AI Readiness, & Inclusivity In Tech

When you speak with Susan Walsh, it quickly becomes clear that she has a talent for making one of enterprise technology’s least glamorous problems surprisingly engaging. As The Classification Guru and Fixer of Data, Walsh has built her career around helping organisations tackle the messy, inconsistent information sitting underneath their technology strategies.

But her work goes beyond data cleaning. From AI readiness and supplier normalisation to women’s representation in technology, Walsh brings a distinctly practical perspective to some of the biggest conversations shaping the industry.

I recently sat down with Susan for an in-depth interview to discuss her work, the growing importance of trustworthy data, how AI is changing the enterprise, and what still needs to change to create a more inclusive technology industry.

 In light of Women’s Equality Day , we also explored her own experiences in technology and why she believes diverse backgrounds and perspectives are essential to solving the industry's biggest challenges.

Meet Susan Walsh, The Classification Guru

Walsh's career sits at the intersection of data, business, and practical problem-solving. Rather than approaching data as an abstract technical discipline, her work focuses on making information usable, trustworthy, and accessible to the organisations relying on it.

Her expertise spans data cleaning, classification, supplier normalisation, and taxonomy development, while her work has also expanded into education, public speaking, and AI.

“I’m Susan Walsh, aka The Classification Guru, Fixer of Data. I specialise in data cleaning, classification, supplier normalisation and taxonomy development, helping organisations turn messy, inconsistent data into something they can actually trust and use.

I’m also the creator of the COAT framework, which stands for Consistent, Organised, Accurate and Trustworthy, and Samification™, my supplier normalisation platform. I’m a two-time author of Between the Spreadsheets: Classifying and Fixing Data and Optimizing Sales and Marketing Data*, as well as a speaker, TEDx speaker and LinkedIn Learning instructor.*

Basically, I spend a lot of time talking about data in books, on stages, in courses and with businesses around the world, trying to make data less intimidating, more practical and, dare I say it, slightly more entertaining.”

Making Practical Data Skills More Accessible

As AI becomes increasingly embedded into everyday business processes, data skills are becoming relevant far beyond traditional data teams.

Walsh is particularly interested in making those skills accessible to people who may not consider themselves data or AI specialists. Her latest work explores how AI can be used to tackle one of the most persistent problems businesses face: cleaning and preparing their data.

“I’m particularly excited about making practical data skills more accessible. My third LinkedIn Learning course has just gone live, where I show people how to build their own AI Data Cleaning Assistant. It follows 5 Days to Cleaner Data and Data Cleaning Fundamentals.

What excites me most is showing people that you don’t need to be a data scientist or AI expert to use these tools. With the right rules, processes and human checks, AI can take some of the repetitive pain out of data cleaning while keeping people firmly in control.”

That focus on accessibility also extends to Walsh's perspective on women in technology.

What Does Equality in Technology Really Look Like?

With Women’s Equality Day providing a timely opportunity to reflect on women's progress across the technology industry, Walsh believes there has been genuine movement, but that visibility should not be confused with equality.

For her, the conversation needs to extend beyond representation and towards who gets access to leadership, investment, decision-making, and opportunities to become recognised experts.

“There has definitely been progress. We’re seeing more women visible in technology, data and AI, and importantly, more women building businesses and becoming recognised experts in these spaces. We’re also talking much more openly about issues such as gender bias and inequality.

But we still have a long way to go. Visibility isn’t the same as equality. There is still work to do around representation at senior levels, investment in female-founded businesses and making sure women are included in the conversations where technology is being designed and decisions are being made.

Even at events, I still see far too many “manels”, all-male panels, and I do my best to actively call these out. It’s also one of the reasons I set up the Women Who Speak group on LinkedIn, to help more women put themselves forward and become more visible as speakers and experts.

I’d also like us to get away from the idea that there is one type of person who “belongs” in technology. I didn’t start my career in tech. I came from sales and even ran a clothing boutique. Different backgrounds and experiences are incredibly valuable because technology ultimately has to solve real-world problems.”

Can AI Create a More Inclusive Technology Industry?

AI has the potential to lower some of the traditional barriers to entering technology. But Walsh also warns that the technology can reproduce the same biases that already exist within the data used to train and operate it.

That creates a tension between AI's potential to democratise access to technology and its potential to amplify existing inequalities.

“ AI can lower barriers to entry enormously. People can learn faster, experiment without needing huge budgets and do things that previously required specialist technical skills. That has huge potential to make technology more accessible. But AI learns from data, and data reflects the world we’ve already created. If that data contains bias, gaps or poor assumptions, AI can reproduce or even amplify them.

That’s why I keep the -data drum. We can have incredibly sophisticated AI, but if the data underneath it isn’t Consistent, Organised, Accurate and Trustworthy, we’re building something very clever on very shaky foundations.”

It is a point that connects directly to Walsh's wider work: technological sophistication cannot compensate for poor foundations.

Finding Your Place in Enterprise Technology

For women considering a career in technology, Walsh's advice is refreshingly practical. Rather than suggesting people need to become experts in every aspect of an increasingly complex industry, she encourages them to understand their own strengths and how those strengths can solve real business problems.

“Don’t feel you have to know everything before you put yourself forward. And you don't even have to be good at everything either; just focus on what you are good at, and strengthen those skills.

Technology changes far too quickly for anybody to know everything anyway. The people who will build sustainable careers are the ones who stay curious, keep learning, and understand how technology connects to actual business problems. And don’t underestimate experience gained outside technology. Sales, marketing, operations, finance, procurement, customer service, running a business- all of those experiences give you context that technology desperately needs.

You don’t have to become more technical than everybody else. Find the intersection between what you’re good at, what you enjoy, and the problems organisations need solved.”

That emphasis on practical business knowledge is also reflected in Walsh's assessment of the wider enterprise technology landscape.

Enterprise AI Is Moving From Hype to Reality

After several years of intense focus on what AI could potentially achieve, organisations are now confronting the practical realities of implementing it.

For Walsh, that means asking harder questions about data, accuracy, trust, governance, and how AI can actually be used within existing business environments.

“The biggest shift I’m seeing is the move from talking about AI to actually trying to operationalise it, and realising its limitations. For the last couple of years, there has been enormous excitement about what AI could do. Now organisations are asking much harder questions: Where can we genuinely use it? What data does it need? Can we trust the outputs? Who checks them? How do we scale it?

And that’s exposing some very unglamorous foundations that have been neglected for years, particularly data quality, governance and structure.”

AI Agents Are Getting Attention, But Don't Forget the Data

AI agents are currently one of the most talked-about developments in enterprise technology. Walsh believes the interest is warranted, but warns that organisations risk moving too quickly towards autonomous systems without first addressing the quality of the data those systems depend upon.

“AI agents, without question. Some of the attention is absolutely justified because the potential is enormous. But there’s also a danger of racing to build autonomous agents before organisations have sorted out the data those agents need to make decisions.

An AI agent with access to unreliable data doesn’t magically become intelligent. It can just make the wrong decision much faster and with considerably more confidence. So yes, be excited about agents. But perhaps clean the spreadsheet first. 😏”

It is perhaps the perfect encapsulation of Walsh's approach to AI: embrace the technology, but don't overlook the foundations that make it useful.

AI-Ready Technology Needs AI-Ready Data

That brings Walsh to what she believes is an emerging trend businesses need to take far more seriously: AI-ready data.

As organisations invest heavily in AI tools and strategies, the quality and structure of the data underneath those systems can easily become an afterthought.

“AI-ready data. Everyone's talking about being AI-ready, but what about the data?

There is understandably a huge focus on choosing AI tools and building AI strategies, but nowhere near enough attention is being paid to whether the underlying data is actually ready for them. Businesses should be asking: Is our data consistent? Is it structured properly? Is it accurate? Can we trust it? Do we know what our categories and definitions actually mean?

AI readiness isn’t just a technology problem. It’s a data quality problem, and I think more organisations are going to discover that very quickly.”

The AI Conversation Is Becoming More Practical

The industry's relationship with AI has changed considerably over the past year. Rather than focusing exclusively on what the technology might eventually be capable of, organisations are increasingly dealing with the practical questions of implementation.

For Walsh, that shift represents a healthier and more realistic approach to enterprise AI.

“They’re becoming much more practical and realistic. AI is not a magic wand. Twelve months ago, a lot of conversations were still about what AI could potentially do. Now I’m hearing much more about implementation, governance, accuracy, ROI and how humans and AI actually work together.

There’s also a growing recognition that AI isn’t something you simply switch on and walk away from. You need rules, good data, review processes and human judgement. That’s a much healthier conversation because we’re moving from “Look what AI can do!” to “How do we make this genuinely useful and trustworthy?”

Trust Will Define the Next Era of Enterprise Technology

For Walsh, all of these conversations ultimately lead back to one thing: trust.

As organisations introduce more AI, automation, and autonomous decision-making into their operations, the ability to understand and trust the technology, and the data underpinning it, will become increasingly important.

“Trust.

We’re going to have more AI, more automation and more autonomous technology making or influencing decisions. The differentiator won’t simply be who has the most technology. It will be who can trust what that technology is doing.

That means trustworthy data, transparent processes, appropriate human oversight and a willingness to question outputs rather than blindly accepting them. The technology is moving incredibly quickly. Our ability to trust the data feeding it needs to catch up.”

Final Thoughts

Speaking with Susan Walsh, one thing becomes  clear: the future of AI may depend just as much on the quality of our foundations as the sophistication of the technology itself.

From her work helping organisations fix data to her focus on making practical AI skills more accessible, Walsh consistently brings the conversation back to something easy to overlook amid the excitement around new technology: trust.

 Her thoughts on Women’s Equality Day highlight the importance of bringing different experiences, backgrounds, and voices into the technology industry, particularly as AI begins influencing an ever-growing number of business decisions.

As enterprises move deeper into the AI era, Walsh's message is straightforward. Don't simply ask what the technology can do. Ask whether the data is ready, whether the processes can be trusted, and whether the people using it have the skills and perspective to make it work.