Retail is not a straightforward sector when it comes to AI maturity. On one side are organisations with a large, automated online environment, where personalisation, inventory management and pricing logic have long run on data. On the other side is the physical shop floor, where processes often still lean on experience, paper and verbal agreements between store and head office. Within one and the same chain, these two worlds can exist side by side, and that difference is larger than in many other sectors. Where financial services generally shows a flatter picture between head office and execution, in retail you regularly see a head office that is far ahead and a shop floor that still has to catch up.
That gap is the reason AI pilots in retail often do start, but fail to scale further. A pilot at head office works, because there the data, systems and people are already reasonably aligned. As soon as the same approach needs to move towards the shop floor or the distribution centre, the infrastructure to support it is often missing. Not because the ambition is lacking, but because the fundamental layer is not yet ready for it.
The hybridresourcing maturity measurement looks at seven dimensions, spread across five levels: from baseline to intelligence. Some of these dimensions are fundamental: the organisation itself, the IT infrastructure and data management. Another part depends on these. That is not a choice in ordering, but the way it works. A chain can only deploy AI at store level once the data from the tills, the inventory systems and staff planning is reliable and connected. Without that basis, every application remains a standalone experiment that does not scale.
In retail we see that the fundamental dimensions are often in order at head office, but not applied uniformly towards the locations. Data management can be mature at central level, while the data captured on the floor is still inconsistent, incomplete or delayed. That difference means the organisation as a whole scores lower than the sum of its parts would suggest.
What makes retail distinctive is not just the difference between online and physical, but also the difference in who you ask. A plot round, in which different people within the same organisation score separately, often shows a wide spread in this sector. An IT manager at head office may judge the infrastructure to be mature, while a store manager runs into the limitations of outdated systems on a daily basis. Both pictures are true, but they point to a different place in the chain.
That spread is itself information. It shows where within the organisation the picture of readiness diverges, and therefore where agreement needs to be reached first before a next step in AI becomes meaningful. That question of agreement is often larger in retail than in sectors with a more compact organisational structure, such as the ICT sector, where head office and execution are generally closer together.
The tension between a centrally managed head office and a dispersed execution is not unique to retail. That same tension can be seen in hospitality, where a chain of locations likewise has to rely on consistent basic processes before AI can work at location level, and in the real estate sector, where central management and decentralised maintenance have often grown apart from each other. In all these sectors the same order applies: the organisation, the infrastructure and data management must be in place first, before the dimensions that build on them can provide direction.
The maturity measurement gives no judgement on which department is lagging behind, and no advice on what should change first. What the measurement does do, is make clear at what level an organisation stands within each of the seven dimensions, and where the spread between the people who score is greatest. For a retail chain, that often means a confrontation between the picture at head office and the reality on the shop floor, and that is precisely the picture needed to determine where the foundation still needs to be laid.
The tool that carries out this measurement is under construction. Anyone who wants to use it once it becomes available can sign up for the waiting list. Nothing is being offered that does not yet exist, and nothing is promised about the outcome of the measurement for your organisation.
This page describes whether the organisation as a whole is ready to carry AI: whether the foundation of organisation, infrastructure and data is solid enough before further steps become meaningful. That is a different question from which part of the work AI can actually take over. For that question, the work scan from FTE TO AI is intended: it calculates per task which part of the work can be transferred to AI, from inventory administration to customer communication. Where the maturity measurement maps the capacity of the organisation, the work scan maps the content of the work itself. Both pictures are needed to know where you truly stand.
Vraag maar wat er moet staan voordat AI in uw organisatie kan landen.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.