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How AI in CPG Gets Products to Market Faster

Consumer packaged goods (CPG) companies are constantly on the lookout for shifts in shoppers’ demands and tastes. Whether a trend is emerging or fading, CPG companies must adjust their products accordingly. One way to stay ahead of the curve is with advanced technology, including artificial intelligence (AI). By using AI in CPG R&D, companies are getting products  from conception to commercialization faster and delighting consumers.

CPG brands need to improve R&D execution, as 85% of product launches fail within two years, according to a Foodpairing report. CPG companies can leverage AI to optimize supply chains, advance sustainability and strengthen loss prevention. But perhaps the biggest potential is in using AI to accelerate innovation in R&D and get to market before competitors. 

Learn more about the R&D challenges faced by the CPG industry and how AI can help. We’ll also explore common R&D use cases and how the right AI solution helps your company reach its goals faster.

Download Now: 3 ways CPG companies can use AI to overcome the biggest R&D challenges [NEW GUIDE]

CPG R&D Challenges

Developing the next big thing in the CPG industry isn’t easy. Behind every successful product launch are countless hours of R&D investigation, experimentation, trial and error, and retesting. Even the most experienced companies can struggle to invent the future. Here are some of the challenges enterprise CPG organizations face during the R&D phase.

Limited Resources

Developing new products requires significant investment in time, money and talent, but companies don’t always have the resources they need. The primary limitation for R&D teams is funding, especially when the cost of R&D is difficult to predict. Even teams with enough financial resources might suffer from technological limitations or a lack of skilled employees.

Businesses must optimize their R&D project management when they’re dealing with limited time, money and other resources. One way to do more with less is to leverage employee knowledge and experience. To unlock this knowledge, companies must remove organizational silos and roadblocks to collaboration. 

This may involve stepping outside traditional product lifecycle management software and leveraging tools and technologies that enable teams to quickly find the information they need, without getting bogged down or being forced to assume. Identifying qualified subject matter experts that bridge gaps in knowledge and experience is important, but it’s also more complex than offering an assessment or requesting credentials.  

Changing Consumer Preferences

CPG companies need to be able to quickly adapt to changing trends and consumer demands. But recognizing and anticipating consumer preferences is difficult, especially across regions and cultures. Companies can’t necessarily keep up with every trend or change in preference, as developing new products or altering existing ones is time-consuming and costly, with no guarantee of success. Implementing significant product changes can be particularly difficult for CPG companies with complex supply chains and manufacturing processes. 

Regulatory Compliance

CPG products face regulations across geographies, including those related to raw materials, formulations and safety. Many products, especially those for human consumption, have additional labeling, marketing and import/export regulations. Failure to comply can lead to fines and other penalties, including product recalls and legal action. 

Regulations are changing all the time, and companies need to track these developments and act accordingly. Regulations also impact the R&D process. For example, some raw materials might be prohibited in certain regions or require additional testing to prove its safety. 

Short Product Life Cycles

The CPG industry is known for its short product life cycles. Companies must be able to identify and develop new products quickly while still conducting market research and product testing. These condensed timelines add pressure to R&D teams and create challenges in recouping costs before a product becomes obsolete or falls out of favor with consumers.  

Product Recalls

Product recalls can have serious financial and reputational consequences for any company, so taking every precaution to avoid them is a top priority. In addition to causing business interruptions and loss of sales, recalls can erode consumer trust. To minimize this risk, companies can leverage AI tools to provide access to internal knowledge, resources and people to cut down on the chances of errors resulting in recalls.

CPG R&D Challenges: Limited resources, changing consumer preferences, regulatory compliance, short product life cycles

Benefits of AI for CPG Product Development and Innovation

Despite economic uncertainty and increasing market pressure, the CPG industry is predicted to grow at a 3.5% compound annual growth rate through the end of the 2020s. To realize this growth and even beat the industry average, CPG firms will need to find an edge — and AI-based platforms are part of the solution. 

Effective AI-enabled tools help businesses work more efficiently, avoid errors and prevent duplicated efforts through efficient collaboration. Here are some of the benefits of AI in CPG innovation.

Faster Product Development

Consumer preferences in many sectors shift so quickly that CPG companies can’t afford long R&D cycles. AI tools can accelerate this process in numerous ways.

One way AI speeds up product development is by automating tasks and doing them more quickly. For example, AI-enabled tools can speed up data analysis of consumer preferences and identify patterns and trends more quickly and accurately. This helps data scientists do their jobs more efficiently and make strong recommendations as CPG companies decide which products are likely to succeed and which should be avoided.

AI-powered tools can find insights in seconds that would otherwise require people to manually spend large amounts of time on mundane tasks, creating more opportunities for errors. Human analysts may also be prone to biases, such as confirmation bias, that could influence their conclusions. AI can analyze data objectively and provide more accurate insights into consumer preferences and behavior. 

AI tools can connect R&D teams with critical information in real time. For example, Starmind enables your employees to pose a question and get connected either with the answer they need or with an in-house expert who’s best equipped to assist. This type of process eliminates archaic search processes, keeps subject matter experts from becoming overburdened and enables employees to work more efficiently.   

Some AI tools allow CPG companies to create virtual prototypes of their products for testing and analysis before committing to the physical production process. This saves on costs and helps companies explore new ideas before committing physical resources and time. 

Improved Product Quality

Machine learning algorithms help companies identify which design, directions, ingredients or formulations are most likely to result in a high-quality product. While these predictions will need to be evaluated, companies can reduce time wasted on dead ends and focus on the most plausible possibilities.

AI can analyze large amounts of data related to R&D efforts and customer feedback to optimize product formulation. AI can assist in ingredient selection or suggest a new combination that was overlooked by humans. It can also ensure high-quality final products by optimizing development processes. 

For example, Procter & Gamble Co. (P&G) leverages smart manufacturing at scale for the complex production of diapers. This enables them to improve cycle time, reduce network losses, ensure quality and improve productivity through machine telemetry and high-speed analytics.  AI-driven tools can also analyze data related to product safety and quality, allowing CPG companies to identify flaws before the product hits store shelves.

Reduced R&D Costs

Companies can identify which products are most likely to succeed in the marketplace by using predictive analytics. This is a brand of data analytics that uses statistical methods, machine learning algorithms and data mining techniques to make predictions about future outcomes.

Predictive analytics help R&D teams understand why certain products are successful and how those findings apply to products under development. As most CPG product launches currently fail, any improvement here can save companies time and money while potentially increasing market share.

More Automation

AI-powered solutions help CPG companies automate components of processes such as quality control testing and product inspection. This reduces the amount of time and resources required while improving accuracy and consistency. 

Most data collection, analysis and decision-making has long relied on computing power. But advances in automation and AI are further streamlining these processes and requiring less manual work by humans. 

AI tools can also be used to automate the testing process, such as for product safety and efficacy, as well as simulate customer usage scenarios.

Improved Regulatory Compliance

The CPG industry is highly regulated. With AI tools, companies can analyze data, including from regulatory bodies, to ensure that their products meet all the necessary legal and regulatory requirements. 

For example, AI can scan product labels and packaging to ensure that all required information is present and accurate. Beyond the R&D stage, AI tools can scan advertising and marketing materials to ensure they're compliant with regulations governing claims and disclosures. This helps businesses avoid costly fines and penalties while promoting consumer trust. 

Nestle is using an AI system to offer creative rules and guidelines for the thousands of marketers working with its brand. This helps ensure compliance while allowing for personalization and innovation.

5 Benefits of AI for CPG Product Development and Innovation: Faster product development, Improved product quality, Reduced R&D Costs, More automation, Improved regulatory compliance

AI Use Cases in CPG Product Development

AI is affecting R&D across sectors, but it has particular promise for CPG companies. Here are some of the use cases the industry is already exploring. 

Personalized and Customized Products

CPG companies can use AI to help develop personalized products by using internal data such as search history, demographics and customer purchase history. AI tools can also take external data, such as customer feedback, market trends and competitor analysis, to inform product development. 

This personalization can be applied to the individual or to certain customer groups or demographic profiles. For example, Unilever recently used an AI-driven product design platform to create beauty products. The company used AI to analyze lipstick color combinations faster than conducting physical experiments and with a much lower use of resources.

Market Research

Predictive analytics tools powered by AI can gauge consumer preferences by analyzing conversations on social media platforms, survey responses or other data points related to the customer experience. Brands can use this information to drive R&D efforts, among other areas.

Kraft Heinz has created an AI-driven market research platform to gain insights into consumer preferences and behaviors, with product development being one of the beneficiaries. By getting better information on consumer preferences and analyzing it quickly with AI, Kraft Heinz expects to improve its ability to develop food products.

Product Testing 

Because R&D by definition creates something new, CPG companies require extensive testing to ensure that new products or formulations deliver the intended results — without negative side effects. 

AI tools automate much of this process. In particular, AI can evaluate massive amounts of test data more quickly than humans using older computing methods. An AI-based approach to product testing uses data analysis, machine learning and computer vision to spot defects and suggest design improvements before the product is released to the public. Feedback processes can also be streamlined, enabling R&D teams to get an outside perspective of products in real-time and quickly make improvements.

Organizational Knowledge 

PepsiCo R&D needed to create a knowledge-centric R&D ecosystem that fostered faster innovation, decision-making and time to market.

For PepsiCo R&D, the primary goal was to identify and connect high-quality subject matter experts, to reduce the risk of duplicated work, which would slow down the innovation cycle.

Starmind’s AI-powered platform helped PepsiCo capture those conversations, connect employees with existing knowledge and in-house experts. The result was improved R&D efficiency, collaboration and innovation.

Integrating AI for CPG R&D in Your Enterprise Organization

The potential of artificial intelligence in the CPG industry to revolutionize product development and R&D is immense. Companies can use AI to speed time to market, improve demand forecasting, ensure safety and make knowledge more accessible across the enterprise, among other benefits. Doing all of this will require buy-in from executive leadership, careful planning and a thorough implementation. 

To achieve the benefits of increased productivity, accuracy, cost savings and access to tacit expert knowledge, partner with AI experts such as Starmind who can help you deploy an AI-powered solution inside your business and R&D function. A knowledgeable partner can minimize disruption, maximize employee buy-in and ensure you have scalable processes that grow with the business.

With the right AI-powered solution, you can leverage your greatest asset — your people. Want to learn more? Check out these collaboration ideas for R&D.

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3 ways CPG companies can use AI to overcome the biggest R&D challenges [NEW GUIDE]

AI is transforming the CPG industry. Get ahead of your competitors. Learn about:

  • Streamline product development, connect experts, reduce errors, learn in real time.
  • Innovate globally with seamless marketing, research, and supply chain connections.
  • Eliminate duplication of efforts, connect researchers with in-house expertise and insights.

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