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large-scale data collection is to use machines and computers
to generate synthetic, but validated, data sets for use in
decision-making and risk-setting exercises. Regardless,
initiatives and partnerships around shared technology and
data may become increasingly incorporated or embedded
into business relationships to facilitate real-time access to
food safety information.
FUTURE DIRECTIONS
Retailer partnerships with regulatory officials, consumers, academic experts, and food industry stakeholders will
advance AI opportunities within food safety programs.
Continued discussions and showcases at subsequent IAFP
meetings on AI tools for food safety will further refine the
potential adoption and benefits of application at retail.
While the industry invests in data collection infrastructure,
foresight should be given to turning data into actionable
information. As the food industry gains access to more
information, the way food safety professionals manage risks
and make decisions will evolve. New operational risks may
arise, particularly around business interruption, perceived
liability, and data transactions. Early-adopting companies
will pave the way for assessing the feasibility and return on
investment for implementing these tools. Several shared
campaigns across the industry may be (i) keeping jobs in
the food sector by investing in food scientists and employees to be well-versed in data technology, (ii) defining how
AI and ML algorithms are used in regulatory processes, and
(iii) building inclusive partnerships with strategic technology stakeholders to strengthen overall food safety programs.

CONCLUSION
The retail food industry should prepare for a highly
automated future. Capitalizing on opportunities to develop
AI solutions that focus on strengthening food safety may
result in finding new applications from existing algorithms,
but food safety professionals will play an important role
in developing novel algorithms for specific food safety
problems. Retailers should be open to working alongside
AI tools that provide actionable information to leverage
experience and resources for managing food safety
risks. These technologies should not replace humans
involved in food safety but should help existing food
safety practices work more effectively. Perceived liability,
data quality, and accessibility remain the main barriers
to widespread AI adoption. Moving forward, the impact
of AI in the food industry will be enabled by increasing
industry-wide adoption of software and solutions that
provide connectivity and transparency of food safety data
throughout the supply chain.
ACKNOWLEDGMENTS
The authors thank FMI and iFoodDecisionSciences, Inc.,
for their support and dedication to advancing food safety
throughout the industry.
*Author for correspondence:
Telephone: +1 815.531.2968
Email: claire@ifooddecisionsciences.com

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