AI Is Everywhere In CPG R&D—So Why Are Results Not?
Manmit Shrimali, CEO & Founder of Turing Labs, paving the way to revolutionize traditional R&D practices.
San Francisco recently sued 10 of the largest food companies in the world. The federal government plans to eliminate artificial dyes from America’s food supply chain by the end of 2026. Consumers are scrutinizing ingredient labels with the rigor of legal documents.
This is not a passing headline cycle. It reflects a structural shift: Consumers and regulators are setting the terms, and CPG companies are forced to act.
The industry has reacted swiftly: accelerating reformulation, cleaning up ingredient lists and turning to AI to compress timelines. Yet a gap has emerged between the speed of regulatory demands and the technical maturity required to meet them at scale.
Your R&D Teams Followed The Market Playbook—So Why Are Results Lagging?
I spoke with an R&D leader at a Fortune 500 company who described a familiar pattern: Multiple pilots launched, vendors evaluated, capital invested and yet a team pushed forward under fatigue, with results still falling short.
The issue is not execution. It is the gap between what the market has promised and what the technology can deliver, where AI drives more output but not better results. Even in software engineering, teams ship more code yet work longer hours to keep up with rising expectations.
The ROI Problem
I've witnessed it repeatedly: A company brings in a powerful tool, and scientists who've spent years perfecting their craft push back. Not out of resistance but because expertise feels compressed into software. That concern is valid. Positioning AI as a replacement for scientific judgment loses credibility immediately.
The companies that move forward ask how to extend expertise to achieve what was once impossible. That distinction separates pilots that scale from those that collapse once executive attention moves on.
Flawed Tools Are Slowing Change
Every R&D leader has heard the pitch: Ingest regulatory filings, analyze competitors and surface insights in real time. Many have tried and hit the same wall.
Only a fraction of those outputs translates into meaningful product innovation. The rest is operational noise, reflecting the data these systems are trained on.
Negative results are rarely published. Journals favor breakthroughs, not dead ends. Proprietary data remains siloed. What remains is an incomplete, optimism-biased view of scientific knowledge. Systems then generate plausible recommendations that fail under real-world conditions.
That is why when I introduce our company's Luna AI platform, the most common question is: "This sounds compelling—but how does it actually work?"
These leaders' skepticism reflects a market promising transformation but often delivering little beyond a demo.
It's Not Just Culture—It's Incentives
The enterprise does not adopt technology. People do.
• The CEO needs margin expansion and a credible story for the board.
• The R&D vice president must outperform competitors with a shrinking budget.
• The bench scientist wants leverage, not replacement.
Adoption hinges on whose position is strengthened, and who has reason to resist it?
I see this tension daily: scientists on one side, digital teams on the other and leadership hoping proximity alone will drive alignment.
It won't.
And to further complicate matters, digital and R&D teams often do not speak the same language.
Two Cultures, Two Outcomes
Through years of working with R&D teams across CPG organizations and deploying Turing Labs at scale within enterprise environments, a consistent pattern becomes clear: Companies that stall and those that move forward operate from fundamentally different mindsets.
In fear-based environments, technology is framed as a cost-cutting tool rather than a capability amplifier. Product developers are treated as overhead. Adoption is centralized, and success is defined by fancy outputs, press releases and announcements. Progress depends on a lone champion. Innovation competes for scarce budget. Teams wait for perfect data, only to discover legacy systems fall short. Measurement is tied to activity, not outcomes.
Winning organizations operate differently. They treat technology as an amplifier of expertise, embed it within teams closest to the problem and define success through measurable gains in cycle time or cost. They align leadership early, start with available data, iterate quickly and generate intent-driven insights. Innovation is funded deliberately, with clear ownership and a direct line to business priorities.
How Leaders Escape Pilot Purgatory
• Pick A Painfully Specific Starting Point: AI can touch every part of R&D, but the temptation to "boil the ocean" creates paralysis. Progress comes from one constrained, high-stakes problem.
• Start With Reality, Not Perfection: Most critical data is scattered across systems, teams and spreadsheets. Large-scale investments in data lakes or ELNs are long-term bets; many teams wait years to see ROI. Successful organizations start with existing data, move quickly and generate intent-driven data as they go—building trust through results, not promises.
• Build Trust Through Guardrails: In scientific environments, guardrails determine whether a tool is trusted. Asking questions is easy; the real differentiation is in the final 20%—using the system correctly, validating what’s true and building guardrails that make it trustworthy. Overpromising doesn't just risk a project; it erodes trust permanently.
The Window Is Quickly Narrowing
Eighty-five percent of CPG innovations fail—not because the science is wrong but because organizations are not built to carry innovation through to the consumer. Now consider that alongside tightening regulatory and consumer pressure. The margin for error is shrinking.
The companies treating transformation as a technology procurement exercise will keep producing demos and exhausted teams. The ones that rewire culture, incentives and decision-making—not just infrastructure—will win. Because in the end, technology alone does not win markets. Execution, alignment and trust do.
