Gartner Barcelona: Shiny New Bags, Familiar Questions

Attending the Gartner Supply Chain Symposium/Xpo in Barcelona is always a useful moment to pause, listen and reflect. Gartner knows how to organise an event, but for me the real value is not in the scale of the conference or the number of sessions. The real value is in what the event reveals about where supply chain leaders are focusing their energy, and where many organisations are still struggling to turn ambition into practical business impact.

And the question I always ask myself when I am there is simple: why are we here? Why are we here as participants, and why are we here as sponsors? I have been visiting Gartner on and off for close to ten years now. Every time I come back, I see new language, new technology, new acronyms and new promises. Yet the actual business questions are not that new.

We are still asking how we use technology to improve and automate processes. We are still trying to optimise cost structures without damaging service. We are still looking for better predictive capabilities so we can reduce buffers, release working capital and make more confident decisions. We are still asking what talent we need, how we overcome cross-functional hurdles, and how we cascade decisions through the organisation so that they actually get executed.

That was my biggest reflection from Barcelona. The parameters are changing. The boundaries are moving. The external environment is more volatile, more connected and less forgiving. AI is accelerating the conversation, and the pressure on supply chain leaders is increasing. But the fundamental questions are still remarkably familiar. If the questions remain broadly the same, while the environment changes around us, then maybe the issue is not only whether we need new tools. Maybe the issue is whether our operating models, behaviours and decision processes are strong enough to use those tools properly.

As always, Gartner presented plenty of insights and statistics. And as always, many of those statistics showed a familiar gap: a significant number of companies are not achieving what they set out to achieve. A smaller group is able to deploy technology, embed it in the way the business works, and translate it into strategic outcomes. That difference matters. It is the difference between buying capability and building capability.

And then, of course, there was AI. What is there not to say about AI at the moment? It was everywhere in Barcelona. Gartner positioned the conversation around autonomous supply chains, human-machine collaboration, AI-enabled decision-making and the need for leaders to rethink operating models. That is the right conversation. But we also need to be careful not to confuse energy with progress.

AI will not fix poor data, it cannot judge what is poor data and what is not. Garbage in is still garbage out, even when the garbage is processed faster and presented more beautifully. AI will not automatically remove functional silos. It may even reinforce them if every function builds its own model, its own assumptions and its own version of the truth. AI will not replace the need for judgement, leadership and constructive challenge. In fact, it may make those capabilities more important.

Take demand sensing as an example. A better signal is useful, but it does not create value by itself. If commercial, supply, finance and operations are not aligned on how to respond, the business simply creates a faster version of the same debate. Or take autonomous planning. It can highlight exceptions, scenarios and risks at speed, but if decision rights are unclear, finance runs a separate version of the truth, or leaders still reward local optimisation, the technology will only expose the operating model problem more quickly.

There is a risk that algorithms narrow our field of vision. If we only look where the model tells us to look, we may stop asking the questions the model was never designed to answer. If we outsource too much thinking, we may diminish our own ability to interact, debate, learn and deal with conflict. Digital capability should not reduce human capability. It should extend it.

That is why I was inspired in one of the keynotes with the following: we have to relearn how to learn with AI. Not just learn how to prompt it. Not just learn how to automate tasks. We have to learn how to let it challenge us, surprise us and expose where our assumptions are weak. We need to use it to create better conversations, not fewer conversations. Better decisions, not just faster reports. Better collaboration, not just more dashboards.

The theme of human-machine collaboration is important because the future supply chain will not be built by technology alone. It will be built by people who understand how to use technology in the context of a business model, a culture and a set of decisions. The companies that succeed will not be the ones with the most pilots. They will be the ones that can scale what works, stop what does not work, and align the organisation around the decisions that matter most.

At the end of the conference there was a presentation connected to the Dylan Alcott Foundation. It was a strong reminder that, behind all the discussions about technology, autonomy, data, resilience and performance, there is still a very human question: what is it really all about? For me, the answer is dignity and respect. Living your life with dignity and respect. Working with dignity and respect. And, in a business context, designing systems, processes and technologies that do not leave people behind.

That also tells us something important about AI leadership. We already have divides in society and in organisations. Social divides. Economic divides. Capability divides. People are being pushed further apart by pressure, uncertainty, complexity and access to opportunity. If we are not careful, AI can widen those gaps. It can give even more leverage to those who already have the data, the skills, the confidence and the organisational power to use it. But it can also help us close the gaps, if leaders are intentional about access, education, decision transparency and inclusion.

That is the leadership challenge. Not whether AI is coming. It is already here. Not whether supply chains need to become more intelligent, adaptive and resilient. They clearly do. The challenge is whether we can combine the technology with the operating discipline, data foundations, cross-functional behaviours and human judgement required to make it useful.

So my conclusion from Gartner Barcelona is this: there is a lot of old wine in new bags. But the bags are very shiny this time. The danger is that we admire the bags and forget the wine. The opportunity is that we use the new bags to finally address some of the old problems properly.

AI, automation and autonomous supply chains will change how we work. But they will not remove the need for leadership. They will not replace end-to-end thinking. They will not solve poor decision-making by themselves. The winners will be those who combine technology with operating discipline, human judgement and the courage to change how the business really works. The real question is no longer whether we can deploy more technology. The real question is whether we are ready to build better businesses with it.