From concept to shelf — The AI advantage in CPG product innovation

5 min readMay 7, 2024

79% of CPG C-suite executives participating in BCG’s Most Innovative Companies 2023 Report, rank innovation among their top three priorities.

Over my 30+ years of experience in driving transformation and digital success for CPGs, I have seen brands needing help to stay relevant in a highly competitive landscape. Product innovation presents a tremendous opportunity for CPG companies to capitalize on untapped market potential while securing growth and differentiation.

But success in innovation hinges on a fundamental question– how can CPG companies ensure their new offerings resonate with consumers and translate into real revenue? Modern consumers seek products with improved performance, advanced features, and sustainability, which makes the product development process all the more complex. Besides, the pressure to keep R&D costs low is as relentless as ever. The answer lies in leveraging the power of data and artificial intelligence to enhance their product development capabilities.

Did you know?

80% of product launches for FMCGs fail and growth strategies can fall flat .
Sources: NielsenIQ

How AI is improving R&D for product development

Integrating AI into the early stages of product development could allow companies to adopt processes that work faster, deliver better results, and bring them closer to their customers. Based on my learnings, I’d like to mention some of the ways product teams can leverage AI to improve outcomes:

  1. Improve product usability
    AI-powered analytics tools track and analyze user interactions with products or digital platforms. By understanding how users navigate through interfaces, where they encounter friction points, and what features they find most valuable, AI tools can guide the entire product development efforts to enhance product usability and user experience.
  2. Derive consumer insights
    AI-enabled demand sensing and sentiment analysis tools continuously monitor real-time data from various sources such as point-of-sale systems, online transactions, feedback, reviews, and social media interactions. AI and predictive analytics to identify patterns and correlations while gleaning deeper consumer insights or forecasting future consumer demand. AI and Gen AI tools enable procurement, product, marketing, pricing, sales, and service teams to easily collaborate and manage all the process workflows, ensuring market success.
  3. Accelerate product development
    Digital twins provide a risk-free product development environment, allowing design and engineering teams to explore more design options by minimizing costs associated with the production and testing of physical prototypes. AI or ML capabilities can also improve testing and validation by allowing new solutions to be evaluated in a wide range of real-world scenarios, including unusual and extreme operating conditions, or help detect anomalies in the design/formulations, etc in early stages.
  4. Reduce time to market
    AI is enabling the next generation of predictive modeling frameworks and generating concepts that reduce time to market. These frameworks inform product improvements by simulating the impact of proposed design changes using data collected from products operating in the field. The visibility across R&D experiments speeds up innovation and reduces the instances of experiment repetition.
  5. Driving sustainability
    With an increase in consumer demand for sustainable products and the evolution of sustainability-related regulations, manufacturers today need to commit to using natural resources responsibly. Developing new products that are sustainable and eco-friendly requires significant R&D efforts, including sourcing sustainable materials, improving supply chain management, and ensuring products meet sustainability standards. For instance, AI can drive sustainability in product development by facilitating the identification of ethically sourced, cruelty-free ingredients, optimizing resource usage for waste reduction in agriculture and manufacturing, and improving operational efficiency through predictive maintenance, which reduces energy consumption and equipment downtime.

Real-world success of AI in product R&D

R&D teams are often challenged with lack of data availability, siloed access, inconsistent formats, disparate sources and rudimentary practices. Sigmoid has successfully leveraged AI and ML to address these challenges bringing in effective resource utilization, automation, advanced analytics, real-time consumer insights and reduced time to market. Here are a few examples to illustrate the value add of data, analytics and insights in the CPG space when done right–

  1. An oral care leader wanted to understand consumer sentiment for product innovation. We used multiple NLP techniques to detect brand mentions across various social media channels. Deep learning models helped identify consumer emotions. The solution enabled the product R&D team to focus on innovation aligned with consumer preferences and trends, reducing new product launch time by 70%.
  2. The consumer insights team of an American multinational F100 consumer products manufacturer aimed to gain deeper insights into consumer preferences, brand affinity and purchase drivers. We used image analytics and machine vision for product and brand identification with 96% accuracy using images from consumer’s households. This enhanced their insights on consumer intelligence to improve product usability and empower consumer segmentation strategies.
  3. For a leading infant nutrition brand, we developed a data platform that integrated 30+ years of product and consumer survey data. The platform facilitated predictive analytics to derive consumer insights that drove product innovation aligned to consumer lifecycle stages. The solution reduced the number of reports to be analyzed by the R&D teams by 96%.


In the highly agile world of product development, the ability to swiftly develop and iterate prototypes is paramount. It is revolutionizing this process, empowering businesses to achieve rapid prototype development with unparalleled efficiency. By accelerating idea generation, optimizing design iterations, enabling real-time feedback, and facilitating predictive analysis, AI is reshaping the landscape of product development. Embracing AI-based R&D data platforms can equip businesses with the tools and insights necessary to navigate the complexities of the R&D and innovation spectrum. It can support rapid prototyping, democratize access to insights, reduce time-to-market, and elevate innovation.




About the author:

Shankar Viswanathan is the Chief Commercial Officer at Sigmoid. He brings over three decades of expertise in building foundational capabilities for CPGs across various business domains such as sales, marketing, media, supply chain, IT, analytics, and insights. He has a proven track record of successfully leading end-to-end enterprise transformations, resulting in strong and sustained financial performance in diverse, developed, and emerging markets. At Sigmoid, Shankar is dedicated to empowering clients in harnessing the power of data analytics and AI for effective business transformation.

Connect with Shankar on LinkedIn or send a query to to know more.




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