AI is transforming how we live, work, and interact with the world. Read on to discover five critical tips for effectively employing AI in your business.
Artificial intelligence has been around for quite some time, but it has never experienced the level of global adoption that we see today. For companies looking to stay ahead, it’s essential to tap into the full potential of AI in business. But to truly succeed with that, the technical know-how won’t be enough.
Organisations need to equip their workforce with the right skills and mindsets to make AI work for them. Without this strong foundation, even the best tools will fall short.
In a recent episode of the Data & AI Mastery podcast, Peter Laflin, Director of Data and Analytics at Morrisons, shares invaluable insights drawn from over 20+ years of experience. He discusses how businesses can effectively leverage AI to shape strategy, optimise operations, and drive real-world impact.
Here are five takeaways from him for any company looking to succeed with AI:
While technical knowledge is undeniably essential in the data industry, there’s another quality that we don’t talk about enough: curiosity.
As Peter wisely puts it, “The mathematical side helps, and physicists, engineers, they all have similar training, similar thought processes, but it's curiosity I think that's the most important trait.”
Technical skills will only take you as far. It’s the drive to experiment with AI tools, try out new approaches, and develop innovative solutions that will ultimately determine your success. So, nurture this spirit in your workforce to stay competitive in this ever-evolving landscape.
Curiosity and commitment to continuous learning go hand in hand, and Peter knows this better than most. With his extensive experience in the data industry, he’s witnessed firsthand that the most successful people have an innate thirst for knowledge.
He says, “To work in data, you have to be interested in finding new ways to explore the things around you because that's a very fundamental part of the job.”
Data and AI evolve constantly, and staying ahead requires ongoing upskilling.
Employees need to be committed to learning, but it’s equally important for organisations to nurture this spirit, creating a culture of continuous development together.
When refining AI models and improving data quality, it’s easy to lose sight of the ultimate goal—satisfying your customers.
Peter reminded us of the bigger picture: the importance of building customer-centric solutions with AI. He captured this perfectly by calling it “bringing the art to the science.” It's a simple way to put it, but it speaks volumes.
He explains, “The way I try to coach my teams is to really help them understand that actually the job is to bring the art to the science. I mean that when we're finished and we've made all these really scientific choices along the way to use the right tools in the right way that actually the thing we did delighted the customer and gave them something better than they were expecting.”
So, combine your technical know-how with your creativity and understanding of human needs.
When working with data and machine learning, you can easily get caught up in refining models and perfecting algorithms. However, Peter reminds us that in the real world, sometimes “good enough” is exactly what’s needed.
The goal is to build a solution that meets the customer's needs. Continuing to optimise it beyond a certain point may lead to diminishing returns. So, it might be more valuable to shift focus to a different use case and create value there instead.
Finally, everything you do should focus on the final value you deliver.
Peter emphasises, “I think the key is to be clear on the whole system that you're optimising for. So it's really important that we have components, and you try to make all those components as good as possible. But really, our job is to look at the outputs that we're creating and the value that those outputs are generating.”
Yes, both the model and data quality are crucial, but the main focus should be on the final output—the one that delivers the most value. Whether that means dedicating more resources to more in model improvement or data quality, it’s crucial to consider the return on investment. And this ties back to the idea that sometimes “good enough” is in fact good enough. Don’t get stuck optimising something when the value has already been delivered.
Looking forward, these five key insights will help you stay on the right track—and mindset—as AI continues to evolve. Because evolve it will, and your job is to ensure that you and your team are always ready to adapt alongside it.