Your Supply Chain Digitalization Journey Doesn’t Start Where You Think

There is a lot of noise right now about autonomous supply chains, AI agents, and self-driving operations. And honestly? I find it exciting.

The direction the industry is heading is genuinely remarkable. The question isn’t whether AI will transform supply chain, it will. The question is where most companies actually are today in their digital supply chain journey, and what the real starting point looks like for them.

The gap between where the technology conversation is and where most supply chain teams operate day-to-day is enormous. Typically, if you are not a global manufacturer with a dedicated digital transformation budget, it can feel like digitalization is something that happens to other companies.

It doesn’t have to be that way. However, the path there looks very different from what most people think.

Before any intelligent system can work, before forecasting models, before optimization, before anything that resembles a self-driving supply chain, you need data you can trust. And across the industry, that is far less common than people admit.

The reason for this is not that teams aren’t capable. It is mainly that data quality problems are invisible until you go looking for them. The sales team is working off one version of the product list. Finance has another. The warehouse has a third. Nobody flagged it as a crisis because everyone found workarounds, manual reconciliations, personal spreadsheets, tribal knowledge that lives in one person’s head.

This is typically where supply chain digitalization actually starts for most companies. Not with AI. With the unglamorous work of understanding what data you have, where it lives, who owns it, and where it breaks down between systems and teams. That honest internal conversation, who owns what data, where it breaks, what the workarounds actually are, is usually the most revealing first step any company can take.

The instinct when a company decides to go digital is to look at what the biggest players in the industry are doing and try to replicate it. The problem is that those implementations are based on years of clean data, integrated systems, and dedicated technology teams. You can’t skip the foundation.

Another common mistake is trying to solve everything at once. Overhaul forecasting, inventory management, and reporting simultaneously, and six months later nothing is live because the scope became unmanageable and no one could agree on priorities. Momentum dies. People go back to their spreadsheets. And the next time someone proposes a supply chain digital transformation initiative, there is a room full of sceptics.

And then there is the jump-straight-to-AI move. The ambition is right, however here is what is worth understanding. Even a large AI implementation doesn’t skip the foundation work. Someone still has to map your business rules, clean your data, and build the logic. The difference is you are paying an external team to do it, typically inside a system your own people don’t fully understand and can’t maintain when the project ends. The step never gets skipped. You just decide who does it, how, and at what cost. Building that foundation yourself, progressively, means the knowledge stays inside your organization. That is worth something.

What actually works is starting with one painful, repeated problem that has a clear owner. Not a transformation. A fix. Something where you can show in a few months that Monday morning looks different from how it looked before.

Deborah Dull’s book The Self-Driving Supply Chain paints a compelling picture of where this is all heading, and I genuinely believe in that vision. What resonates with me is that the journey there is based on progressive capability. Digitalization, automation, and AI are a natural progression, each building on what came before. Standardizing a report or agreeing on a single data source today is mainly what makes automation reliable tomorrow and AI meaningful further down the road.

The measure of any step on that journey is simple. Does the output connect to a decision someone actually makes? A dashboard nobody checks isn’t progress. A forecast nobody trusts isn’t either. The question is always whether something changes in how someone plans, decides, or acts based on what the tool shows them.

The most impactful early steps in digitalization are typically about process clarity before technical complexity. Defining the business rules. Agreeing on a single version of the data. Removing friction that everyone had quietly accepted as normal. A weekly report that used to take two days to build now refreshes automatically. An order process that required retyping data from one system into another now runs without manual intervention. A planner who spent most of their time on routine calculations now focuses on exceptions that actually need human judgment.

That is how organizations start trusting their data. And that trust is what makes every subsequent step, including AI, actually deliver on its promise.

The tools are real. AI agents that autonomously replan based on disruptions, optimization engines running thousands of scenarios in real time, systems that learn from outcomes and get sharper over time. This is already happening in some organizations, and it will reach more of them faster than most people expect.

The companies getting there aren’t mainly the ones with the biggest budgets. They are the ones that started somewhere real, built something their team trusted, and kept going. Clean data, reliable processes, organizational confidence. That is a digital supply chain actually runs on.