Key takeaways:
- AI oversight is lacking at the top. Only 28% of organizations using AI say their CEO is responsible for overseeing AI governance, and just 17% say their board is, even as more than half report having already experienced a negative consequence from AI.
- Literacy includes liability. For an executive, AI literacy means knowing what a system can and can’t be trusted to decide, what the risks are, and what to demand of a vendor.
- Don’t forget about compliance. The EU’s AI literacy obligation has applied since February 2025 and reaches companies outside the EU whose AI output is used there, which can include food exporters and their suppliers.
More than half of organizations using AI have already taken a hit from it, such as a bad output, a privacy lapse, or a compliance miss. More than half of organizations have experienced at least one negative consequence from their AI use.
Yet accountability for AI is thin at the top. Only 28% of organizations using AI said their CEO is responsible for overseeing AI governance, and just 17% said their board is. That’s the heart of the problem. AI is being deployed into quality control, maintenance, and food safety faster than the people accountable for those functions understand what they’ve approved.
AI literacy is meant to address this issue. For a food executive, literacy has little to do with coding or reading model architecture. It means understanding enough to govern the technology responsibly: what a system should be trusted to decide, where it can fail, and who answers for it when it does.
Literacy is about judgment rather than technical skill
AI seems like an IT department’s problem. But the data warns against that mindset. According to Deloitte, decisions about AI safety, security, and accountability all stem from a basic understanding of what AI is and what it’s capable of. A leader who can’t reason about the technology can’t govern it, and can’t tell a sound vendor claim from a dangerous one.
For a food executive, literacy means being able to:
- Judge what a given AI system should and shouldn’t be allowed to decide on its own, especially anything touching food safety.
- Recognize where a system can fail, from biased training data to confident-but-wrong outputs, and ensure a human checks the decisions that matter.
- Ask a vendor the right questions about how a tool was built, what data trained it, and how its decisions can be traced and audited.
- Understand the legal and regulatory liability when an AI-influenced decision goes wrong, because it’s the responsibility of the company, not the software provider.
As the tools get more powerful, oversight is lacking
AI is moving from tools that advise to tools that act, and the controls aren’t keeping up. A mere 20% of businesses possess a mature framework for governing autonomous AI agents, even though their adoption is projected to rise significantly.
Looking at the board level, fewer than 25% of companies have instituted structured, board-approved AI policies. Most organizations have principles or an ethics statement. Far fewer have the scaling rules, risk thresholds, and escalation triggers that turn good intentions into solid control.
For food manufacturers, the stakes are higher than for most industries. An AI system influencing a release decision, a temperature hold, or a supplier approval is operating where a wrong call can mean a recall or a public health event. The literacy to know which of those decisions an AI should touch, and how closely to watch it, is becoming part of the job of running a food company.
Compliance measures are underway
AI literacy is also turning into a legal requirement, and the first deadline is already behind us. Under Article 4 of the EU AI Act, the obligation for companies to ensure a sufficient level of AI literacy among staff who work with AI systems has applied since February 2, 2025. Formal supervision and enforcement by national authorities begin in August 2026.
The reach is what makes it relevant on this side of the Atlantic. The regulation applies to providers and deployers of AI systems used in the EU, and it extends to organizations outside the EU when the output of their AI system is used within the EU. A U.S. food company exporting to Europe, or supplying a customer who does, can fall within that scope.
However, as part of a “Digital Omnibus” package, EU institutions reached a political agreement in May 2026 to soften Article 4, shifting the wording toward requiring companies to support the development of AI literacy rather than guarantee a specific level. That change takes legal effect only once formally adopted and published, so the obligation as originally written still stands for now.
In short, regulators expect the people using AI to understand it, and they expect organizations to be able to show it.
How to build your own AI literacy
Addressing AI literacy starts at the top, and no one has to go back to school:
- Put a name on AI oversight. Decide who owns AI governance, at the executive and board level, rather than leaving it spread thinly across IT, legal, and operations.
- Learn your own highest-risk use cases. Focus your understanding on the AI systems that impact food safety, quality, and compliance, where a failure carries the most consequence, rather than trying to understand every tool at once.
- Build a vendor question list. Standardize what you ask any AI vendor, such as what data trained their tool, how its decisions get traced and audited, what its known failure modes are, and what liability looks like. Vague answers are a warning.
- Set the policy before you scale the tool. Define the rules, including when a pilot earns the right to expand, what needs human sign-off, and what triggers escalation, before AI is running across the plant.
- Document the literacy you build. Whether or not EU rules apply to you, a record of the training and guidance you’ve put in place is the evidence that you governed AI responsibly.
Governing AI well takes a different kind of knowledge than building it. The executives who do it ask sharp questions, assign clear accountability, and hold a firm line on which decisions a machine should never make alone. In an industry where an AI error can reach a dinner plate, that understanding has become part of the job for the people in charge.
