The Future of Generative AI in CPG: 7 Key Takeaways for Enterprise Leaders

Consumer Goods

Date : 02/26/2024

Consumer Goods

Date : 02/26/2024

The Future of Generative AI in CPG: 7 Key Takeaways for Enterprise Leaders

Explore the transformative power of Generative AI in the Consumer Goods sector with 7 key takeaways for leaders. Dive into personalization, innovation, and more.

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Generative AI in CPG
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The Future of Generative AI in CPG: 7 Key Takeaways for Enterprise Leaders

Table of contents

The Future of Generative AI in CPG: 7 Key Takeaways for Enterprise Leaders

Generative AI in CPG

Generative AI (genAI) is a superstar now; it is transforming how businesses operate, employees do their jobs, and consumers interact with brands, among many other things. Though still in its infancy, many questions about its use cases, risks, and impact need to be answered. Several Consumer Goods brands are experimenting with genAI in their workflows and developing production systems. It has, in fact, transcended mere CPG analytics and data processing procedures in company-consumer interactions and product development, reducing workloads to a great extent. 

Thus, understanding and adopting genAI is now an immediate reality for CPG leaders.

This article delves into how generative AI is shaping the future of the CPG sector and presents seven critical insights for enterprise leaders to navigate this emerging landscape effectively.

The Evolving Generative AI Landscape in CPG

GenAI has taken AI up a notch. Plans to adopt generative AI are nearly universal.

With several benefits offered by AI already—increased automation, improved customer experience, and the ability to anticipate and respond to market changes—Generative AI has taken it up a notch, maybe even more. Decision-makers are super interested in adopting or expanding its use cases. For example, using next-gen algorithms, enterprises can hyper-personalize experiences and address each individual’s needs, predict consumer trends accurately, and create targeted marketing campaigns with unparalleled accuracy. This results in improved customer loyalty and retention and better marketing effectiveness. On the product innovation front, generative AI is a powerhouse. It helps the product design process by generating many iterations, analyzing market data, and predicting what consumers will choose. This ability reduces product development time and ensures that new products fit the changing market requirements better.

Forrester expects that genAI will add convenience, remove friction from various experiences, and reshape jobs in ways we are only beginning to contemplate and disrupt organizations and industries.

The boardroom conversations.

The CPG industry, in fact, the entire market, is now obsessed with generative AI. The Gordon knot is to get genAI right. Today\'s deployment of generative AI for enterprises is done in multiple ways, such as training custom models. Some executives, seeing the magic of LLMs, want to start as soon as possible. They don\'t understand that it\'s difficult to replicate the same across domains and use cases. Additionally, the security teams are less thrilled and understand the known issues of factual inaccuracy, irrelevant outputs, privacy, data leakage, and intellectual property.

Accelerate Enterprise LLM and GenAI Competency with Databricks and MLflow

67% of companies are currently using generative AI. As generative AI technology evolves, training models and developing complex applications on top of these models is becoming easier, and many open-source models—leveraged by 16% of those surveyed—require fewer resources to run.

Source: Gartner

7 Key Takeaways for CPG Leaders

Key Takeaway 1: Enhanced Personalization and Customer Experience

Not just personalized but truly individualized.

GenAI enables CPGs to offer their consumers a sense of belonging and validation beyond the product itself.

In CPGs, personalization is a key differentiator in enhancing customer experience and improving brand loyalty. With genAI, CPG executives can now unlock unprecedented levels of customization and profoundly enrich the consumer\'s shopping journey.

How?

They can delve into the nuances of consumer data, going beyond basic CPG analytics to extract advanced and granular insights about individual preferences, behaviors, and needs. This enables tailored marketing strategies and product offerings with remarkable precision.

Personalization drives performance and better customer outcomes. Companies that grow faster drive 40 percent more revenue from personalization than their slower-growing counterparts

Mckinsey

Further, generative AI transcends the traditional transactional nature of consumer interactions, transforming them into deeply personalized connections. This enhances the immediate shopping experience and cultivates long-term loyalty and trust in the brand. With generative AI platforms, every interaction with a brand becomes a building block in a lasting relationship with the consumers.

With generative AI, customer experience future looks more personalized, engaging, and rewarding than ever before.

Key Takeaway 2: Streamlining Operations and Reducing Costs Through Generative AI

Generative AI is a game-changer for CPGs in streamlining operations and reducing costs. This can result in substantial cost savings and improved market responsiveness across supply chains and manufacturing processes.

How?

The application of generative AI in supply chain extends from predictive analytics to automation, fundamentally changing traditional operations and production management approaches. It can transform how one anticipates and responds to supply chain challenges, ensuring they can deliver products more efficiently. Indeed, by analyzing complex datasets, generative AI can identify bottlenecks, predict potential disruptions, and recommend proactive measures to mitigate risks.

Source: Gartner

16% of companies have fully integrated GenAI across supply chain functions, and 45% have it at least partially integrated.

In manufacturing, generative AI enhances process efficiency, reduces waste, and speeds up production cycles. This is achieved through its ability to optimize production schedules, predict maintenance needs, and ensure machinery operates at peak efficiency. By reducing operational costs, generative AI boosts profitability and enhances a company\'s agility and sensitivity to market fluctuations.

This increased responsiveness allows CPG firms to better meet consumer demands and adapt to changing market conditions.

Overcome 5 Key Challenges with a New Technology Architecture to Unlock Generative AI\'s Endless Potential

Key Takeaway 3: Revolutionizing Product Development with Generative AI

Rapid innovation and customization

Generative AI is empowering CPGs to quickly ideate, prototype, and refine product concepts by leveraging deep insights into market trends and consumer preferences, thereby significantly accelerating the product development cycle.

Generative AI disrupts the traditional product development model by enabling CPGs to analyze vast amounts of market trends and consumer behavior data. This helps identify opportunities for innovation or enhancement with unprecedented speed and accuracy. Many companies are launching GenAI products. Publicly available research indicates that AI can reduce the time to market for new products by up to 50% and increase the efficiency of research and development processes by over 30%.

Key Takeaway 5: Addressing Ethical and Privacy Concerns

With genAI, it is rather seamless for CPGs to develop AI solutions, unlocking real value for their enterprises. However, genAI can create fake identities, imagery, and content and hallucinate outcomes. In response, CPGs must establish and maintain guardrails to ensure new generative AI solutions are fair, nondiscriminatory, transparent, explainable, and accountable. Moreover, they must maintain human control over automated decisions and ensure data privacy. Responsible deployment of genAI solutions will become increasingly challenging as transformation speeds up and enterprise teams seek new solutions to market early.

“Responsible AI is a governance framework that covers ethical, legal, safety, privacy, and accountability concerns.”

Source: Venturebeat | Read more: The Imperative for Responsible AI in Business

While adherence to privacy laws like GDPR and CCPA forms the regulatory backbone for data protection, leading CPGs need to exceed these standards. Championing data transparency and empowering consumers with control over their personal information are pivotal steps in establishing deeper trust. This approach demands clear communication about data practices and a commitment to safeguard consumer data with utmost integrity.

62% of consumers today place greater trust in companies whose AI interactions are perceived as ethical, potentially increasing a company\'s revenue by up to 6.4%. 

The development and implementation of ethical AI frameworks are critical for guiding AI operations from inception to deployment. These frameworks, grounded in fairness, accountability, and transparency principles, serve as both a moral compass and a strategic asset for companies. They ensure that AI technologies are utilized in ways that respect consumer privacy and promote trust.

Transparency is crucial in demystifying AI operations for consumers.

Offering insights into AI decision-making processes without compromising proprietary technology is key to alleviating privacy concerns and enhancing trust. This transparency not only educates consumers but also reassures them about the ethical use of their data. CPGs should build capability around generative AI as a service to reduce risks, mature AI programs and processes, and demonstrate accountability to stakeholders.

Key Takeaway 6: Skilled Talent and Continuous Learning

CPG leaders are under increasing pressure to accelerate generative AI implementation into products, services, and processes.

Fundamental reskilling is needed across the organization to create value. LinkedIn found over the past five years that more than one-third of skills associated with technical roles have changed. Programmers and data scientists will develop models with GenAI assistance, developers will code with AI assistance, and project coordinators will track milestones with virtual assistants. HackerRank says 55% of developers already use AI assistants in the workplace.

As a result, skill turnover will accelerate, making it even more important for organizations to develop the relevant skills. To create value from these technologies, organizations need specific skills - skills that are neither easy to find nor affordable.

AI prompt engineer roles are compensated at up to $375,000 a year.

Business Insider

Implementation of AI requires the acquisition of new skills across the enterprise:

  • Expertise in AI implementation and innovation

Highly skilled AI specialists who can architect and develop AI and ML solutions and develop AI algorithms and models that are continuously evolving

  • Business + tech + social skills for scaling AI

Establishing an AI center of excellence with a blend of AI, strategy, and change management skill sets to scale the adoption of generative AI solutions across the enterprise.

  • Critical thinking and social intelligence

As per Microsoft, analytical judgment, flexibility, and emotional intelligence are new \"core competencies.\" The human in charge must determine whether the authoritative voice of AI can be trusted or if they should intervene and correct to safely use these tools. Understanding how AI works and the potential impact of AI-based solutions is important for governance around AI implementation. Thus, employees should build AI skills sooner rather than later.

For CPGs to fully leverage the advantages offered by generative AI, there is an unmistakable need to attract, develop, and retain highly skilled professionals. Equally important is establishing a culture that values and promotes continuous learning. By investing in developing AI skills and ethical understanding among their teams, CPGs can unlock new levels of innovation and efficiency, ensuring their competitive edge in a rapidly evolving market.

Key Takeaway 7: Preparing for a Competitive Future

CPGs need to prepare for a future marked by increased competitiveness, involving more than merely adopting new technologies but also going through a fundamental shift in mindset towards agility, innovation, and proactivity. For this to happen, CPG leaders must advance a culture that celebrates experimentation and learns from failure. This will make their enterprise more suited to explore new ideas, adapt to changes, and implement disruptive technologies like generative AI.

72% of business leaders recognize AI as a significant business advantage, underscoring its importance in maintaining competitiveness in the future.

Source: PwC

Bottomline: CPGs Need to Embrace the Inevitable, i.e., Gen AI

GenAI will infiltrate your organization whether you like it or not. Unbeknownst to you, your employees must be using AI tools, and your partners must be busy building genAI capabilities. GenAI is inevitable and will be a key enabler of most future-ready enterprises. Experimenting with internal generative AI use cases and building your AI muscles will ensure that rapid developments and disruptions don’t blindside you.

For CPG leaders, the path ahead requires some forward thinking. Instead of just implementing genAI, first focus on developing a data-centric culture and investing in learning that never ends.

Consider Tredence as your strategic partner for generative AI services and consulting

As a leading generative AI development company, Tredence has deployed over 50+ enterprise-wide data and customer data platforms, providing leading CPGs, retailers, and manufacturers with enhanced visibility, new capabilities, and the power to use predictive intelligence for optimizing all aspects of their business through our generative AI services. With a team of over 500+ expert data scientists, we specialize in generative AI consulting and have successfully deployed and fine-tuned generative AI LLM (Large Language Models) to solve complex problems for our clients. Our proficiency also extends to implementing Azure Open AI solutions for conversational AI solutions, showcasing our comprehensive expertise in genAI services.

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