Navigating the AI Frontier in Formulation Innovation - Part I

At Turing, my team and I have been on quite the roller coaster ride in our quest to revolutionize product development R&D through AI. I wish I could say it was a piece of cake – or in our case, a perfectly optimized ketchup recipe – but the truth is, it's been a journey with more twists and turns than a pretzel factory assembly line.

Every day we think about how we can better help R&D to deliver better and faster at scale. Sounds simple, right? Well, let's just say there were times when we felt like we were trying to mix oil and water – without the help of our AI platform that optimizes formulations from millions of combinations. But getting here? Let's just say it involved a lot of trials and even more errors.

But through persistence, a healthy sense of humor, and maybe a few stress-induced grey hairs (mostly mine, I'll admit), our team has gathered valuable insights and learnings along the way. And now, like proud parents who've watched their kid finally ride a bike without training wheels, we're excited to share these hard-won nuggets of wisdom in this series of articles and reflections.

So, buckle up and grab your favorite AI-optimized beverage (we're still working on that one), as we dive into the wild world of AI in CPG R&D. Trust me, it's been quite the adventure for all of us at Turing!

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Let’s start with three of the most common barriers:

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The Data Dilemma in CPG R&D

While many industries are abuzz with AI implementations, the CPG R&D sector faces a distinct challenge: data scarcity, combination of art and science, and multi-stage process. Unlike other domains with vast data repositories, CPG companies often lack the extensive datasets required for traditional AI models to function effectively. This scarcity is particularly pronounced when attempting to scale AI impact across diverse product lines and formulations.

It's tempting to view AI for CPG R&D as ready-to-use software, but we must approach generic AI models with huge caution. These models, trained on large, general datasets, may falter when confronted with the specialized nature of CPG formulations and the limited data available in our field. In fact, several companies have already burned their hands on generic approaches, learning the hard way that models which worked well on large datasets in fields like materials science don't directly translate to the intricacies of product formulations. The assumption that AI success in one domain will automatically apply to CPG R&D has led to costly missteps and disappointments.

Fortunately, recent advances in AI technology, particularly the Turing platform, are reshaping the landscape. This innovation means that CPG companies no longer need to spend years collecting or generating data before leveraging AI.The Turing platform's ability to work with limited data sets opens new possibilities for rapid AI adoption in CPG R&D, allowing companies to start reaping the benefits of AI-driven formulation optimization much sooner than previously thought possible.

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Beyond Technology: The Human Element in AI Adoption

While technological advancements are crucial, our experience has shown that the most significant hurdle in AI adoption isn't technological—it's human. Change management emerges as the paramount challenge, transcending the complexities of AI algorithms and data processing.

For AI to truly transform CPG R&D, product developers and leaders must embark on a journey of unlearning. Traditional methods that have been the backbone of R&D for decades need to be reevaluated and, in many cases, discarded. This process of unlearning is not just about adopting new tools; it's about embracing a new mindset that values data-driven insights and algorithmic recommendations alongside human expertise.

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The Ancient Tech Stack Dilemma: A Critical Roadblock

While discussing AI implementation challenges, we cannot overlook a critical issue that plagues many CPG companies: ancient tech stacks. This challenge is so significant that it deserves special attention.

Many established CPG companies are operating on legacy systems that have been in place for decades. These systems, while reliable for traditional operations, were never designed with AI integration in mind. As one CEO(business unit) of a major CPG company told me:

"Our tech stack is like a archaeological dig site. Each layer represents a different era of technology, and we're expected to build a spaceship on top of it."

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This analogy perfectly captures the magnitude of the challenge. These outdated systems often lack the flexibility, processing power, and data accessibility required for modern AI applications. They create data silos, making it difficult to gather and analyze the comprehensive datasets that AI thrives on.

Another R&D director shared their frustration:

"We have brilliant ideas for AI applications, but implementing them feels like trying to run a modern app on a computer from the 90s. It's not just slow; sometimes it's impossible."

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It's not merely a technical issue, but a business-critical decision that can define a company's ability to innovate and compete in the AI-driven future of CPG R&D.

At Turing, we've seen numerous projects stall or fail to reach their full potential simply because the underlying technology couldn't support the demands of advanced AI systems.

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Bridging the Gap Without Overhaul

While the ancient tech stack problem is significant, at Turing, we've developed a solution that strikes a delicate balance between innovation and practicality. Our approach is not about completely replacing existing systems, but rather augmenting them to unlock their AI potential.

The key to our solution lies in its ability to integrate seamlessly with your current infrastructure, minimizing disruption while maximizing impact. As one of our clients, a VP of R&D at a leading CPG firm, noted:

"What impressed us about Turing's solution was how it didn't require us to rip out our entire tech infrastructure. It was like adding a turbocharger to our existing engine, not buying a whole new car."

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Our platform is designed to work alongside your existing tools and processes, augmenting them with AI capabilities rather than replacing them entirely. This approach allows for a smoother transition and quicker adoption, as your team can continue to work with familiar systems while benefiting from advanced AI insights.

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A R&D transformation lead of the global CPG who implemented our solution shared:

"Turing's platform gave us the best of both worlds. We kept the institutional knowledge embedded in our legacy systems while gaining the power of cutting-edge AI. It's not another tool to learn – it enhances the tools we already use."

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As another satisfied client, a Senior Director of Product Development, put it:

"What I love about Turing's approach is that it respects our history while propelling us into the future. We're not throwing away decades of data and processes; we're supercharging them with AI."

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By augmenting rather than replacing, Turing's solution allows CPG companies to leverage the power of AI in their R&D processes without the massive disruption and cost typically associated with technological overhauls. It's about enhancing what you already do, not adding yet another tool to your already complex technological ecosystem.

This balanced approach enables companies to start their AI journey in R&D quickly and efficiently, paving the way for a future where AI is not just an add-on, but an integral part of the R&D process.

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As we navigate this AI revolution, it's important to acknowledge other potential obstacles:

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1. Demonstrating Value:

CPG companies could struggle to estimate and demonstrate the value of AI implementations if a generic approach to AI is taken.It's crucial to develop clear metrics and use cases that showcase the tangible benefits of AI in reducing development time, improving formulation efficacy, and driving innovation. A recent study by Gartner found that 49% of companies across industries cite finding ways to estimate and demonstrate value as a top barrier to implementing AI solutions.

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2. Talent Gap:

The shortage of professionals who understand both AI andCPG R&D creates a significant barrier. Bridging this gap requires innovative training programs and partnerships between tech experts and industry veterans.The same Gartner study revealed that 42% of companies view lack of talent as a major obstacle.

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3. Technical Complexity:

Implementing AI in CPG R&D often requires integrating multiple technological elements, from data pipelines to security controls. Companies must be prepared to invest in comprehensive AIeco systems, not just individual tools. According to McKinsey & Company, even a simple AI project can require 20 to 30 technology elements.‍

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4. Data Readiness:

While the Turing platform mitigates some data challenges, companies still need to focus on data quality, governance, and ethical use, especially when dealing with proprietary formulations and sensitive consumer information. The Gartner survey found that 39% of respondents express concerns about a lack of data as a top barrier to successful AI implementation.

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The AI revolution in CPG R&D is not just about adopting new technologies; it's about reimagining the entire product development process. By addressing the unique challenges of our industry and leveraging cutting-edge platforms likeTuring, we can unlock unprecedented levels of innovation, efficiency, and success in CPG formulation.

As we stand on the brink of this exciting new era, I invite fellow industry leaders to join us in embracing the transformative power of AI. Together, we can shape the future of CPG R&D, delivering better products to consumers faster and more efficiently than ever before.

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Share Your Challenges: We at Turing understand that every CPG company's journey to AI adoption is unique. If you're facing challenges in implementing AI in your R&D processes we'd love to hear from you. Email us at info@turingsaas.com and follow us on Linkedin.

Stay Tuned for Part 2: This article is just the beginning of our deep dive into AIin CPG R&D. Keep an eye out for Part 2 of this series. Follow Manmit Shrimali here to get the alert.