IFT President: AI can transform F&B innovation, but adoption barriers block success
AI is reshaping how product teams ideate, formulate, and scale. It takes repetitive work off your plate, spots patterns you'd miss, and shortens the distance between concept and first viable prototype. Consumer interest is growing too - 41% of consumers see potential for AI in food product development, with Millennials leading the charge.
But there's a catch. Many organizations still wrestle with data gaps, siloed systems, and limited AI fluency. According to the Institute of Food Technologists' (IFT) president, Peggy Poole, the winners will be the teams that combine strong data discipline, tight integration, and a human-in-the-loop mindset.
Key takeaways
- AI boosts NPD efficiency by automating routine tasks, accelerating R&D, and informing formulation decisions.
- Consumers - especially Millennials - are open to AI-assisted formulation and personalized engagement.
- Barriers remain: data quality gaps, poor tooling integration across functions, and limited AI literacy.
Where AI delivers real value today (and where it doesn't)
AI already helps product developers move faster and think bigger. It can summarize trials, flag trends in consumer behavior and quality data, and pull relevant research into one place far quicker than manual searches. That frees up time for higher-value experiments and sharper decision-making.
What it doesn't do: replace food scientists. As Peggy Poole notes, AI should extend your capability, not erase your role. There's also a real risk of misinformation and bias if you don't control data sources or validate outputs with expert review.
Formulation: taste, texture, and multi-functional products
Complex products force trade-offs across attributes - sweetness, viscosity, melt, stability, cost, and more. AI helps you run scenario analyses across those variables before you step into the lab, so you can prune losing paths early.
It can surface unconventional ingredient sets and processing/storage options worth testing, then prioritize the most promising routes. The payoff: fewer blind alleys, tighter iteration loops, and clearer rationale for each change.
The internal capabilities most teams still lack
Two gaps show up again and again. First, AI literacy: many teams don't understand how systems reason, where they fail, or how to prompt and validate them. That limits usage and creates security risks when people paste sensitive data into tools without guardrails.
Second, integration: data doesn't flow cleanly between insights, R&D, pilot plants, quality, and commercialization. If your tools can't talk - or your data is misreported or mislabeled - model outputs lose context and value.
Food safety: useful pattern detection, with humans in the loop
AI can scan large, messy datasets to detect shifts toward control limits before they become excursions. That supports earlier interventions in quality monitoring and risk management. It's a strong fit for predictive shelf-life, sanitation verification trends, and environmental monitoring signals.
Still, data quality and bias control matter. Keep a scientist in the loop to vet training data, check assumptions, and review outputs. For broader context on this approach, see FAO's work on AI and food safety practices here.
What will separate leaders from laggards
Leaders will move faster with fewer iterations, streamline scale-up, and automate the admin that slows launches. More importantly, they'll use the time they gain to strengthen culture, deepen consumer connections, retain talent, and empower bolder experiments.
Laggards will keep adding tools without fixing data plumbing or training their people - and wonder why the ROI stalls.
Action plan for product developers
- Start narrow: pick high-signal use cases like formulation scenario planning, shelf-life prediction, or consumer trend clustering linked to clear KPIs.
- Fix the data: standardize taxonomies (ingredients, processes, claims), clean historical trials, and connect systems so data flows from insights to commercialization.
- Control your sources: build a curated, versioned knowledge base for retrieval-augmented AI, and block untrusted content from model prompts.
- Design human-in-the-loop steps: require expert review for risk-bearing outputs (safety limits, allergen risk, regulatory claims), and log decisions for audits.
- Validate and monitor: set baselines, run A/B pilots, track drift, and retrain with fresh data from real trials and consumer feedback.
- Secure it: set access controls, strip sensitive data where possible, and teach teams what's acceptable to share with AI tools.
- Upskill the lab and the line: build hands-on capability for scenario modeling, DoE acceleration, and predictive QC. Explore the AI Learning Path for Process Development Scientists and the AI Learning Path for Quality Control Specialists.
- Keep a strategic backlog: maintain a rolling list of AI opportunities across ideation, formulation, pilot, sensory, and post-launch analytics. For ongoing ideas and case studies, see AI for Product Development.
Bottom line
AI can make your team faster and smarter, but only if your data, tools, and people are aligned. Treat it like a force multiplier for skilled scientists - with tight controls, clear SOPs, and continuous learning.
Do that, and you'll reduce cycle time, de-risk decisions, and ship products consumers actually want - before your competitors do.
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