Generative AI Radar: CPG Industry Report

Executive summary

The market size of generative AI in the consumer packaged goods (CPG) sector is projected to reach $5.4 billion by 2033, growing at a compound annual growth rate (CAGR) of 9.5% during the forecast period from 2024 to 2033. Infosys Knowledge Institute surveyed 3,000 respondents from companies in 12 industries across Asia-Pacific, Europe, and North America, of which 298 respondents were from the CPG industry.

The study found that CPG companies leverage generative AI technology in a range of areas, spanning content creation, operational efficiency, and personalization. Yet they encounter obstacles in terms of creating business value, concerns about data privacy and security, and ethics.

However, by putting together a strong AI strategy, managing risk and ensuring responsible AI, and working cohesively with different departments in the organization, they can address these issues.

Generative AI spending on the rise

Globally, the value of generative AI in the CPG sector is projected to rise from $39.2 million in 2022 to $283.5 million by 2032, reflecting a CAGR of 22.5% over this period.

CPG companies surveyed by Infosys said their generative AI spending was set to grow by 65% in 2024 compared to that in 2023. In a rapidly evolving market, the technology helps them make more informed decisions regarding new products and their development. It streamlines processes, enhances customer experience, and helps companies gain a competitive advantage enabling greater speed and efficiency. In a more recent study, 97% respondents said that spending on AI would increase in the next fiscal year.

Source: Infosys Knowledge Institute

Where generative AI is being used

Generative AI is a transformative tool for CPG companies, helping them leverage data to better understand and engage their consumers. By analyzing patterns, preferences, and behaviors, AI can uncover actionable insights that drive customer loyalty and satisfaction. Here are a few ways generative AI can help CPG companies achieve these goals:

Content generation

Generative AI is a boon for CPG companies as it can help them quickly produce engaging content tailored for their consumers. Nestlé's approach to using AI for ranking online ads demonstrates a strong focus on efficiency and ROI optimization. By ensuring that only the most effective content reaches audiences, the company maximizes its advertising impact while minimizing wasted resources. In another example, Infosys helped a US-based pet food manufacturer implement generative AI for content creation and personalization at scale for marketing materials.

Customer service

Embedding generative AI into customer service helps CPG companies not only reduce the number of human agents involved and related costs but also speed up response time to customers significantly. Customer service organizations implement generative AI to help human agents access the right information quicker than conventional practices. For example, Traeger Grills implemented this technology to arm customer service agents with quick data and personalized recommendations, increasing customer satisfaction scores from approximately 72% to 93%.

Personalization

Faster-growing companies generate 40% more of their revenue from personalization compared to their slower-growing peers. Generative AI can help shrink this gap by aiding CPG companies in personalizing their offerings, responding to the way customers are willing to return to brands that offer positive personalized experiences: For example, M&M's gives customers the option to customize their candies by adding personalized messages or photos.

Product design and development

Generative AI in product development helps brands understand what their users want and then innovate accordingly. It also streamlines the process of conceptualization to development, reducing product cycle time: For example, Mattel uses DALL·E to create multiple options in visuals for new designs of its Hot Wheels cars.

“Infosys assisted a snacks company in leveraging an AI-driven digital product accelerator to optimize its process from concept to market, resulting in a 30% reduction in time to market,” says Neeraj Singhi, industry principal, consumer, retail, and logistics, at Infosys.

Marketing campaign management

The Infosys study discovered that CPG companies expect generative AI to have the most positive impact in the areas of content generation and creativity, followed by product development and design besides helping maintain a consistent tone and branding across the collaterals. Unilever automates its marketing content and customer communication creation with AI. Through this initiative, Unilever reduced the time spent on responses by 90%. Colgate-Palmolive's pilot program with a generative AI-enabled chatbot combines digital shelf data planning — selecting which products to display — and content creation helping employees across marketing, branding, customer service, and logistics with product detail page content tailored for consumers, to improve conversion rates and sales. By analyzing variables like title length and description effectiveness, the company ensures its brands resonate with specific audience preferences, maximizing sales potential.

Figure 2. Most impact expected in content generation

Source: Infosys Knowledge Institute

Emerging generative AI challenges

While generative AI offers CPG companies a range of applications, it also presents significant challenges such as delivering tangible business value and navigating risks and ethical concerns tied to its use.

Business value

Infosys research shows most CPG companies have yet to implement or generate value from generative AI — 61% of CPGs have either not started any generative AI initiatives or are still in the experimentation stage. Fewer CPG companies have created business value with generative AI — only 4% of CPG companies have generated business value with generative AI, significantly less than the overall (13%). This gap in value attainment could relate to not choosing appropriate use cases and evolving consumer needs. The latter challenges impact generative AI models’ ability to accurately account for these shifts and deliver reliable insights. The sector also navigates a wide and fragmented range of sales and marketing channels, spanning both physical and digital spaces, which makes it challenging for generative AI to generate unified strategies or insights.

Figure 3. Most impact expected in content generation

Source: Infosys Knowledge Institute

Ethics, bias, and data privacy

CPGs reported concerns around ethics and bias (28%) and data privacy (28%) as their biggest obstacles to adoption, with this sector expressing more concern than the whole sample. This is largely due to their reliance on vast amounts of customer data to extract insights critical for shaping pricing strategies, price pack architecture, and other business decisions.

Figure 4. Heightened concerns about ethics, bias, and data privacy

Source: Infosys Knowledge Institute

CPGs lack confidence with AI

Infosys research found that CPG companies are far less confident in managing generative AI than the overall sample, with only 57% of CPGs positive about their organization’s ability to manage generative AI, compared to 72% of the overall sample. While organizations are keen to adopt AI, few prepare their workforce to adapt to the new technology. Fewer than half of companies in the US (38%) and the UK (44%) actively invest in training their workforce to effectively use AI tools. This could lead to lower confidence in managing the technology.

Figure 5. Confidence in ability to manage generative AI

Source: Infosys Knowledge Institute

Leadership challenges

Many CPGs have yet to designate a sponsor. Infosys research shows that 22% of CPG companies report that they are yet to determine sponsors for generative AI initiatives, while 21% CPG companies report CIOs as sponsors, less than the overall trend (29%). Not having a dedicated leader to sponsor new technology could lead to a lack of funds, or inefficient use of investment on it stemming from a lack of direction.

Figure 6. Most CPGs don’t have a sponsor for their AI activities

Source: Infosys Knowledge Institute

To overcome their adoption challenges, CPGs need best practices that support their adoption. Achieving value from AI requires the right use cases, while training teams to use of generative AI reinforces the confidence in managing it. CPG companies need to solve their ethics and data privacy issues to benefit from this technology.

How to achieve better outcomes

Combating the issues posed by generative AI will not be easy for CPG companies. But getting the generative AI foundation right can pave the way for better outcomes.

• Maximizing business value from generative AI begins with defining clear objectives — whether it's driving revenue growth, increasing conversions, expanding market share, or enhancing customer experience. Companies need clear use cases that support their goals. Organizations should evaluate the ROI of each AI use case and pursue only those with the highest potential returns. By aligning AI initiatives with business goals, companies can ensure that low-impact use cases are filtered out, periodically revisiting and refining their strategy to focus on the most effective opportunities. Humans in the loop are necessary to monitor what sources the generative AI is getting its data from and to ensure that it is using the most high-quality data to deliver results that are as accurate as possible.
  
  Selecting use cases based on the collective expertise across the organization is a must. This helps zero in on use cases that make business sense from the organizational point of view and don't benefit only one or two departments. A siloed approach can lead to misalignment, underutilized potential, or duplicated effort. CPG companies should organize workshops or brainstorming sessions to gather insights from different departments before deploying AI. On how to prioritize AI projects, a former CTO of a global CPG company told us: "Our approach combines a robust technical framework with a clear focus on business value generation. To ensure we prioritize AI use cases with a positive ROI, we established a comprehensive Center of Excellence [CoE] for AI. At the core of this CoE is an AI governance team solely dedicated to reviewing, approving, or rejecting AI use cases across the organization globally — a number that can run into the hundreds. Every new AI project is assessed by this team based on its business case and potential for positive outcomes. For instance, one key criterion is the payback period; only use cases with a payback period of less than two years are approved. This structured setup ensures that teams are centrally aligned, equipped with the necessary knowledge, and guided by measurable KPIs to make informed decisions. By maintaining such stringent governance, we ensure that our AI initiatives consistently deliver tangible value to the organization.”

  By aligning AI initiatives with business goals, companies can ensure that low-impact use cases are filtered out.

• Companies need to establish specific functions that oversee responsible AI, risk management, and so on, which can focus on these areas fully and take accountability for the company's adherence to related policies and compliance. These teams will also be watchful of potential risks and help preempt threats before they occur. At the same time, they will be proficient in dealing with contingencies in case a breach has occurred before it escalates. This quick action can minimize the damage to the business. It will also guide other departments on the responsible AI, risk, and ethics-related protocols and ensure all teams are on the same page.
• Organizations need to implement role-specific generative AI training programs with hands-on exercises to increase confidence in managing AI among teams. For example, marketing teams need to be trained in learning to use AI for content creation, customer segmentation, and campaign optimization. Prompt-engineering training equips marketers with the skills to create the right prompts to generate engaging copy and impactful campaign strategies. This type of training teaches people not only how to use the technology best but helps them understand how the technology works. This added understanding increases the confidence that the teams have in managing the systems and staying up to date with newer versions of the technology.

By combining these practices, CPG companies can maximize the impact of generative AI. As the technology continues to evolve at a rapid pace, it opens up limitless possibilities for the future of the CPG industry.