HACCP and AI: AI may support, but deciding at the CCP remains human work

HACCP is about control, not automation

HACCP is a system for controlling food safety risks. At its core is the critical control point, the CCP: a step where you must control a hazard, within established limits, with monitoring and corrective action. Anyone who takes HACCP seriously knows that a CCP is no place for a black box. So the question is not whether AI replaces HACCP, but where AI is useful within HACCP without you handing over control.

Where AI really helps in food

In practice, most of the gain is not at the CCP itself, but around it:

• Spotting a deviation sooner: AI flags trends in temperature, weight or fill level before a limit is exceeded. That gives your people time to intervene, instead of recording a deviation after the event.
• Predictive maintenance: a filler or cooling unit heading towards failure often shows up in the data before things go wrong. Less unplanned downtime also means less pressure on the processes around the CCPs.
• Less time spent searching documentation: AI helps to retrieve specifications, batch data and customer queries faster. That leaves the QA officer time for the judgement that really matters.

The boundary: at the CCP, the human decides

In all these cases, AI supplies a proposal or a signal. The decision stays with the human. At a critical control point, AI must not have the final say. A model that sounds plausible but occasionally hallucinates should not determine on its own whether a batch is released. The workable rule is simple: AI supports, the human decides and signs. That way, responsibility stays where it belongs and your HACCP plan remains defensible.

Without an audit trail, it is not an option

This is the point that carries most weight in audits. Under FSSC 22000, BRC and IFS, evidence is a precondition. A way of working that routinely runs without an audit trail puts the plant manager in an indefensible position as soon as the auditor probes further. That is why you record every AI-supported step:

• what the system proposed,
• on what basis,
• who checked it,
• and who signed.

Demonstrably unaltered evidence

With hash-chained logging, that trail is also demonstrably unaltered. At any time, you can show how a decision was reached. That is exactly what an auditor wants to see, and what makes the difference between 'we use AI' and 'we use AI in a controlled way'.

And the EU AI Act?

Anyone who uses AI in food also has to deal with the EU AI Act. The AI literacy provision in Article 4 has applied since 2 February 2025 and, since 27 July 2026, has asked for measures that support the AI literacy of employees who work with AI, appropriate to their role. For a QA team that interprets AI signals, that is not a formality but simply good professional practice. If you use AI in applications that may count as higher risk, an inventory and a risk classification go with that.

How to start sensibly

Start small and specific. Choose one process where AI can supply a signal or proposal, for example predictive maintenance on a filler or spotting a trend towards a CCP limit sooner. Set up the audit trail from day one, so that you never have to reconstruct evidence afterwards. Train the team that works with it. Only scale up once this one process has proved itself.

That way, HACCP remains the guiding framework, AI delivers value, and you keep an account that stands up to any audit: AI supports, the human decides and signs, and everything is recorded. To see how such a first process is set up in practice, including the audit trail from day one, visit our page on AI implementation in food manufacturing (in Dutch).