Tom Clayton
Co-Founder & Chief Executive Officer, IntelliAM
Tell us about your business – sell yourself!
Our business is IntelliAM – short for intelligent asset management. We’re an innovative, Aquis-listed technology company based in Dinnington, specialising in artificial intelligence (AI) and machine learning (ML) for the manufacturing industry.
We use AI and ML to help manufacturers get more from the machinery they already have – turning operational data into insights that improve performance, reduce downtime, and increase productivity.
The idea came from years of seeing the same issues on factory floors – underused machinery, unplanned downtime, and reactive maintenance – that cost businesses time and money. Traditional predictive maintenance methods haven’t kept pace, leaving manufacturers over reliant on individual engineers and operators.
In 2023, I co-founded IntelliAM with Keith Smith to change that and revolutionise the asset management industry.
Our technology is now used by many leading manufacturers, including half of the world’s top 12 food and drink producers.
By analysing equipment data, we help clients to optimise machine performance, reduce energy use and waste, fine-tune production, and prevent breakdowns, delivering real, measurable productivity gains.
What distinguishes your approach from the competition?
Our company is underpinned by the strong technical foundations and manufacturing expertise of 53 Degrees North (53N) – an asset care company I founded over a decade ago, which today functions as IntelliAM’s engineering consultancy division.
That deep domain expertise is our key differentiator, which our team has amassed through decades of service on production lines around the world.
Our ‘boots on the ground’ experience means we have a practical understanding of the challenges factory-floor operatives and engineers face every day.
While traditional predictive maintenance tools have been around for decades, many tech providers often lack this real-world insight – installing the wrong sensors in the wrong places, collecting irrelevant data, and generating false alarms that engineers can’t trust.
That’s where IntelliAM bridges the gap.
By combining advanced AI and ML technology with our domain expertise, we can turn billions of asset data points into meaningful, actionable insights that manufacturers can trust. This makes the unknowable, knowable – and that’s a game-changer for productivity.
What is the key ethos underpinning what you do?
We’re strong advocates for the power of AI and ML. We believe the answer to increased productivity can be found by tapping into the data hidden inside the plant and machinery of every factory in the world.
Our goal is to empower maintenance teams – giving them practical tools they need to make their machinery more reliable, reduce unplanned downtime, and improve efficiency.
With IntelliAM, we offer food and drink manufacturers a ‘predictive maintenance evolved’ solution that helps them stay competitive, improve performance, and meet the growing demands of global food production.
What are some common challenges you face in your line of work, and how do you address them?
One of the biggest challenges we face is encouraging manufacturers to adopt new digital tools. Many are understandably cautious, especially when it comes to introducing AI into traditional maintenance processes, but it just takes time, trust, and education to shift that mindset.
There’s also a common concern that AI could replace jobs. In reality, our technology is most effective when it works with skilled engineers. Human expertise is essential – engineers teach the system, guide it, and help make the results even smarter over time.
Another misconception is that digitalisation means ripping out legacy machinery and investing in expensive new kit. But that’s not the case. IntelliAM works with the equipment already in place, unlocking the hidden data to help manufacturers improve performance without costly replacements.
How can people get involved with what you do?
Ultimately, large-scale manufacturers not yet using AI and ML are likely missing out on valuable insights. So, for them, they can get involved by recognising that adopting AI isn’t a big-bang change, it’s a scalable journey that starts by liberating the untapped data they already have.
