Katherine Leenhouts discusses her work in the data and AI space and offers her advice to professionals looking to emulate her career.

Katherine Leenhouts is director of data at PwC Ireland, but she didn’t initially plan for her career to go in that direction. “My path started at university. I started off considering a degree in something like Greek literature,” she told SiliconRepublic.com. 

“Instead, I got hooked on business after working for two small businesses during summer jobs. I switched majors and surprise, I loved my programming classes. They had the right mix of tangible results, challenge and creativity. 

“I interned at PwC, thanks to the guidance of one of my professors, and accepted a full-time role with one of their early data analytics teams. More than 15 years later, I’m happy to say my job has never been boring.”

In addition to AI, data and analytical skills, what abilities empower your work day to day?

Communication is a huge part of my day-to-day life. Whether I’m engaging with senior leaders from other organisations, collaborating with our own leadership or guiding interns or graduates on my team, the ability to adapt is key. I find you need to be quick on your feet. You need to be able to shift from understanding and digesting key information about a client project to explaining key changes in the data and AI space. That communication comes in many forms, whether through presentations, written proposals, requirement documents, or visual reports. My favourite is communicating through visual formats such as dashboards, slides, reports or other graphics. There is nothing more satisfying than seeing a complex idea land and give someone the insight they need to make a quality decision.

Do you use any skills that you didn’t expect to use at the beginning of your career?

Detective skills. I like a good set of requirements. When I started, I thought people would know exactly what they needed. What I discovered is that the initial task is just the starting point. I once had a client ask for a dashboard to track the status of her company’s internal audit projects worldwide. Through asking questions and getting deeper into requirements, I found she had a problem with long-running audits that went past their deadlines. These were then sometimes followed by long remediations. She didn’t want the status of internal audit projects, she wanted a dashboard that gave her a summary of where projects were stuck so she could unblock them.

We defined categories for delayed projects. She (and we) wanted data from the actual system auditors used to do their work, not from a manually updated spreadsheet. We delivered a dashboard that updated regularly, required no out-of-system updates, and gave her the information needed to take prompt, regular action to keep the business focused on improvements. The ability to question deeper and fully understand is one that is far more important than I realised at the start of my career.

How crucial are workplace AI, data and analytical skills, in the AI era?

AI fluency is now a basic requirement. Effective use of AI raises the standard of our work. Tools like coding assistants enable us to iterate more quickly. AI agents, LLMs [large language models] and others can bring the standard of work up several levels. It’s crucial that individuals know how to use AI to enhance and refine their own ideas. Without personal guidance, LLMs provide reams of good-quality but generic output. We expect an individual’s perspective and skill to shine through. When we interview individuals, we’re looking for people who think creatively, ask insightful questions, and excel at solving problems. Candidates that embrace the AI era with a mindset that values curiosity and innovation stand out.

What is exciting about a current role in AI and are there many challenges?

The field of AI is being created and refined daily. It reminds me of a child’s earliest years. One day they can’t crawl at all, the next they’re all over the house. AI is a lot like that. Every week the landscape changes. When you’re working in the field, you’re part of the story. That’s exciting. There are many challenges. For many organisations, data modernisation was a nice-to-have instead of a must-have. As a result, it can be challenging to apply advanced AI techniques. Organisations can be risk-averse. It takes compelling use cases to prompt changes to policies so that they balance the risks inherent in the use of novel technologies with the benefits and reduction of risk in current processes. On a personal level, adapting to new ways of working is a constant effort. What I like about our team is that this adaptation is often fun and engaging. 

What career routes are available to people skilled in AI, data and analytics?

There are two ways I see that people can be skilled in AI, data and analytics. In the first instance people have foundational technical skills, like programming in Python or SQL, working with data in cloud environments, creating and analysing insights or analysing the impact of AI on security. In that case, at PwC you’ll find a place in our tech, data plus AI team or in our cyber practice. In the second instance, if people are data-literate, know how to ask good questions and use AI tools to accelerate their work. 

AI has transformed workplace skills expectations, how can a strong leader encourage their teams?

Strong leaders set an example. They create spaces for teams to share knowledge and highlight best practices. Change is difficult, especially given the rapid changes in the AI space over the past three years. At PwC we help teams navigate these changes by embedding AI champions across the business to make it easier to adopt new habits. Our interns and graduates go through training on the tools available, ethical use and our ways-of-working.

Have you any predictions for how the year ahead may unfold in terms of AI and automation trends?

I think this is the year there’s a demand to start to see tangible returns from AI investments. We may see the first IPO of an AI company. I expect we’ll see more LLMs geared towards specific use cases, like supporting consumer health queries. We’ve already started to see more insights into what people “search” with LLMs – I hope we see more of this. We might begin to notice a clear distinction between companies that adopt AI to solve business problems and those that continue business as usual. Overall, I expect this field to continue to evolve at pace and keep pushing us to be innovative, think creatively and keep moving.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.