How AI and Machine Learning Are Transforming Supply Chain Thinking
Lewis Reis, Chief Information Officer
Artificial Intelligence (AI) and Machine Learning (ML) have become the business buzzwords of the decade. But when nearly every organisation claims to be “using AI,” I often ask myself – how many truly understand what that means, or how to harness its real value?
In my world – supply chain management – I’ve learned that success doesn’t come from technology alone. It comes from the partnership between human expertise and intelligent systems.
From Data Chaos to Data Clarity
For years, supply chain leaders have collected data from every possible source — warehouse management systems, logistics partners, sensors, supplier portals, and customer orders. The problem isn’t a lack of information anymore; it’s an overload of it.
Every big company I’ve worked with has spent years collecting every single data point, just in case they ever needed it. The challenge now is that this data has become unruly — too many systems, too many silos, and no clear way to make it all work together.
I began my career in banking, where data quality and structure were non-negotiable. Bringing that same rigour into the supply chain world has revealed incredible opportunities to turn raw data into actionable insight — but only when human intelligence stays in the loop.
AI Can Process Data – But Humans Make It Meaningful
AI is brilliant at processing millions of rows of information and spotting patterns that would take humans weeks to uncover. But it’s not infallible. Like any system, it’s only as good as the data it’s fed and the questions it’s asked.
I often remind clients that AI models can be fast, repeatable, and statistically precise — but they can also be confidently wrong. AI is excellent at recognising what’s in the data, but it still needs human experts to give it a sanity check and ask, “Does this make sense in the real world?” That’s where experience truly matters.
Rather than replacing people, I see AI’s real power in augmenting human expertise. It takes on the repetitive, data-heavy tasks — cleansing, sorting, structuring — so that consultants and supply chain specialists can focus on the high-value decisions that require judgment, context, and intuition.
The Human Element in an Automated Future
The fear that AI will replace human jobs is as old as the technology itself. But history tells a different story. When Henry Ford introduced the assembly line, people thought it would put them out of work. When spreadsheets arrived, accountants thought the same. The truth is, jobs evolve — they don’t disappear.
In the warehouse, this evolution means AI might optimise layouts, predict demand, or flag inefficiencies — but it’s still people who solve real-world problems. As one of our senior consultants at Visku put it:
“If the warehouse floods or a truck is 15 minutes late, no computer in the world can fix that. Humans still make it happen.”
AI can enhance efficiency and safety, but human adaptability, creativity, and empathy remain irreplaceable.
Turning Data into Decisions
One of the biggest challenges in AI-driven logistics isn’t collecting data; it’s knowing what to do with it. Many organisations have built vast “data lakes” but lack a clear strategy for turning that data into business value.
The first question I always ask is: Do we have the right data – and enough of it – to make the decision we need?
When companies lack sufficient data, my team and I work closely with them to fill in the gaps, enrich existing datasets, and design models that can simulate real-world scenarios — for example, adding an extra warehouse between two regions to see how that would affect lead times and costs.
The trick is giving clients the clearest, most relevant data possible, so they can make the right decisions at the right time — with confidence.
The Vision Behind Puddle
Our latest initiative at Visku, Puddle, was born from the idea that AI shouldn’t be reserved for large enterprises with data science teams.
Puddle gives smaller businesses the same power to analyse, plan, and forecast. They can upload their data, work with one of our consultants to define what they want, and get meaningful insights – all without needing an in-house AI department.
For larger clients, Puddle integrates complex, fragmented datasets into a single environment, enriching and validating them with expert oversight. The platform adapts to each client’s needs — from basic reporting to advanced predictive modelling – making AI accessible, scalable, and secure.
AI and Machine Learning: More Than a Checkbox
There’s a growing temptation for companies to claim AI capability just to “tick the box.” But I always challenge that mindset. Everyone wants to say they’re using AI – but are they really? Are they using it to automate meaningful processes, or just to sound innovative?
True AI adoption means embedding it into how you operate – using it to enhance strategy, improve accuracy, and support human decision-making.
The Future: Collaboration Over Automation
AI will keep evolving – becoming smarter, faster, and more accessible. But I believe its success in the supply chain won’t be measured by how much it replaces human input, but by how well it complements it.
The future belongs to businesses that understand this balance: machines that process, humans that interpret, and organisations that connect the two.
In that sense, my goal isn’t artificial intelligence – it’s augmented intelligence.