In most consumer goods companies, the biggest shift AI brings is not in technology but in how decisions are taken. Forecasting, production planning and brand building already rely on large amounts of information. AI takes the manual data crunching out and places the emphasis on how confidently teams can make decisions when the information changes.
For an acquirer, the question is not whether a business has the latest tools, as modernising systems and processes would be part of the acquisition plan. It is whether the business can realistically adapt to the way AI changes decision making.
The usual indicators people look for can be misleading. Data quality, systems, and processes matter, but they can all be improved after acquisition. Most companies have data that needs work. Most have processes that have drifted over time. Most have rule books that sit partly in documents and partly in people’s heads. These are common (and normal) issues. They are fixable with time, attention, and the right people.
The part that is not easily fixed is the culture around failure and uncertainty. This is the element that determines whether a business will actually use AI when it is available. After all, the business has operated for years without using AI, even as it dealt with overstocking, stockouts, promotional misfires, marketing activity that did not land as planned and costly production runs.
Why everything except culture can be improved
Almost every consumer goods business, whether founder-led or part of a larger group, carries the same structural realities.
The data is rarely in good, let alone perfect, shape, but it can always be cleaned and tagged.
Category structures may not be consistent, but once you understand how the business thinks, they can be organised properly.
Processes often contain exceptions and workarounds, but these can be brought under control once the underlying logic is clear. If processes are not documented, this can be documented after discussions with the team. Decision making ‘rules’ followed can be documented following key decision maker interviews. These are almost never documented.
Technology is usually the simplest part to change once the fundamentals are stable.
None of these areas determine whether the business is future-proof. They only tell you how much work is required after the deal closes.
The problem appears when circumstances are uncertain, when there is a chance of getting something wrong, or when people must make a decision in a way that is not how they normally do it.
People will say they support AI. They will agree with the direction, join the meetings, and nod at the logic. Then, when a decision needs to be made, they default to the way they have always done things. This is not open resistance. It is usually driven by the fear of making a mistake.
AI needs people to use it, question it, correct it, and refine it. That only happens in a culture where mistakes are not punished, where uncertainty is acceptable, and where people can surface issues without worrying about the consequences. Without this environment, AI becomes something that is discussed but rarely used to make decisions that matter.
Why psychological safety is the real marker of AI readiness
AI improves only when people are willing to experiment and to look at what did not work. In a culture where the cost of being wrong is high, the learning curve that AI requires will not be tolerated.
A business can have imperfect data and still succeed with AI if people feel comfortable trying new approaches. A business with perfect systems will still fail if people feel exposed every time they test something unfamiliar.
This is why psychological safety is not just another factor. It is the only foundation that cannot be created quickly after acquisition. You cannot build trust in a matter of months. You cannot expect rapid change in a company where people have learned that they may be blamed for a negative outcome. You cannot introduce experimentation if the organisation has learned to avoid imperfect outcomes.
How to identify this during an acquisition process
You can usually see this in how people interact during the course of the day. People say what they are noticing without hesitation. Leaders are open about what needs attention. Teams talk plainly about changes, raise concerns openly and do not hesitate to contradict senior decision making. When something looks unusual, it is raised rather than worked around without comment. They may even talk about recent changes in their ways of working. You also hear people ask questions without worrying about how the question will be received, and you see decisions being discussed on their merits rather than based on hierarchy. There is no discomfort in admitting that something needs to be revisited, and no instinct to defend a plan simply because it has already been agreed.
These are some of the signs that the organisation will work with new information instead of ignoring it, and that AI will not be treated as something separate from everyday decisions.
What this means when evaluating a consumer goods business
When assessing a consumer goods business for acquisition in the AI age, the presence or absence of modern tools or clean data is not the deciding factor. These influence how much work will be required and how much it will cost to get the business to where it needs to be, but they do not determine whether AI will ever become part of daily decision making.
The real question is whether the organisation has the psychological safety needed to work with systems that have a learning curve and to adopt different ways of making decisions. Without this, people will agree in principle yet revert to familiar habits when it matters. They will avoid using new tools, hesitate to trust new information and quietly work round anything that feels uncomfortable. This is far more damaging than a lack of technology.
AI depends on many conditions, but this is the only one that cannot be created without meaningful disruption. It is also the condition that determines whether the business will become genuinely future-proof.
A company that feels safe to experiment will adapt to the AI age. A company that does not feel safe will resist it, even if everyone agrees with the strategy. That is the difference an acquirer needs to look for. For acquirers in consumer goods, where operational complexity and brand risk compound quickly, this cultural foundation is vital.
