Sharpening a competitive edge with generative AI

The odds against a new product gaining market share have never been higher.

Shifting consumer preferences, crowded markets, cost and supply chain pressures, regulatory compliance issues, and the churn of new products are creating an increasingly complex launch environment.

Despite putting in years of research and development, and investing considerable human and financial resources, what stands in the way of new products hitting their revenue targets and contributing value to a product portfolio?

In many cases, the challenges start with an incomplete lifecycle view of a new product in the brand portfolio. When marketing, manufacturing, and supply chain teams operate in organizational silos, a lack of collaboration leads to delays, quality issues, and supply chain disruptions. Without a holistic product lifecycle view, it’s difficult to know when to phase a product into the portfolio, when to plan for peak volume, and when to phase out, while limiting impacts on other products.

95% of the 30,000 new consumer products launched each year fail to meet their commercial objectives.

Inadequate market research and consumer insights are also issues. Failure to conduct thorough market research and gather accurate information about customer satisfaction metrics results in products that do not meet customer needs or demand expectations. This leads to excess inventory, suboptimal sales performance, and supply chain inefficiencies.

Unrealistic timelines and tight launch deadlines also put immense pressure on supply chains. This can impact financial performance by expediting shipments and taking production capacity away from other profitable product lines.
 

Upstream decisions create a cascade of downstream challenges

At the beginning of a new product introduction (NPI) process, difficulties often emerge when identifying reliable suppliers, negotiating favorable terms, and ensuring stable supplies of new materials or ingredients. These activities can significantly impact the ability to meet production and distribution requirements. Lack of end-to-end supply chain visibility and coordination also contributes to communication disconnects, inventory management imbalances, and inefficient distribution—all of which compromise the success of new product launches.

Many aspects of the consumer products industry are highly regulated. When regulatory requirements and compliance standards are not addressed up-front during the product development phase, risks increase for delays, product recalls, or supply chain disruptions—with deleterious impacts on cost and brand reputation.

Neglecting to consider the sustainability impacts of new products on supply chain operations can also increase risks for regulatory non-compliance and missed opportunities to reduce costs and increase efficiency. Further, the level of supply chain transparency expected in the future will be significantly higher and needs to be considered during NPI.

Inadequate risk management and contingency planning is also a factor, especially when it comes to mitigating potential supply chain disruptions such as natural disasters, geopolitical events, or supplier sourcing and production issues.
 

Generative AI ushers in a new era for new product development

To address the challenges and manage the risks involved with new product development, the consumer products industry needs a more forward-looking, data-driven, and end-to-end approach for decision-making and digital transformation. By assisting and augmenting product development, supply chain integration, and lifecycle management with generative AI, industry leaders can accelerate innovation, improve cost management, increase understanding of market trends, and boost overall efficiency.

More than three-quarters of consumer product industry leaders agree that generative AI should be adopted quickly to keep up with the competition.

Along with many other business sectors, the consumer product goods (CPG) industry has started leveraging generative AI to boost operations and help employees make informed decisions in real-time. Multifaceted AI applications in CPG range from streamlining customer service through chatbots and virtual assistants to more complex implementations. These include leveraging unstructured data for demand sensing, demand forecasting, route optimization, and automating logistics management.

Beyond optimizing NPI and supply chain integration, and managing regulatory compliance across markets and regions, generative AI brings another dimension to long-term product management. The dynamic insights provided by this technology can embed another key business priority across the product lifecycle—sustainability.
 

The growing role of generative AI in the consumer products industry

Our latest research shows that CPG industry leaders are adopting generative AI at a rapid pace. More than two-thirds agree or strongly agree that this technology is important to the future of their CPG organization. Forty percent are piloting or implementing in product development, and 10% are currently operationalizing and optimizing this capability in the areas of product development and supply chain management.

Where generative AI has potential to contribute more value

For years, CPG leaders have been looking for ways to improve the end-to-end management of a new product, from initial design and development to product phase-out. Because generative AI is informed and trained on such a broad corpus of product and market data from so many stakeholders, it can have a pivotal role in orchestrating the process for greater visibility and efficiency.

Working with trusted suppliers in partner ecosystems, NPI research and development teams can use proprietary data from suppliers to train generative AI models and develop artificial intelligence algorithms. Enhanced gen AI capabilities, informed by large amounts of structured and unstructured supplier data, provide insights to technologies and solutions, such as digital twins, that would not otherwise be visible. Generative AI provides conversational knowledge, summarization of complex documents, content creation, and code creation capabilities to support product development, especially during the earliest stages.

The goal state, enabled by generative AI, is to optimize cost and performance at inception instead of incrementally after launch. It can change the paradigm; for example, by rewarding procurement managers for building cost savings at product launch instead of trying to cut materials costs and inventory levels for years after product launch.
 

Generative AI adoption in CPG: The decisive role of data and standards

As more consumer goods companies see the potential for generative AI to optimize new product launches and make supply chains more efficient, they are also running into many of the same barriers to adoption encountered by other industries. When analyzing the top six barriers indicated by CPG leaders, data-related issues comprise half of these obstacles.

To make data-driven decisions, understanding datasets is vital. The nature and quality of the data will also determine whether traditional automation, machine learning (ML) artificial intelligence or generative AI is the most effective approach to solve a specific problem. In terms of strategic fit, generative AI is not a one-size-fits-all solution. Traditional automation and ML remain crucial technologies in many application scenarios.

The lack of clear standards is causing 51% of Consumer CEOs to delay investments, and 63% of them lack consistent standards—such as data, privacy, and sustainability—in one or more areas of strategic focus.

Agreement on clear standards and the availability of timely and trustworthy data about customers, markets, and operations are the lifeblood of the CPG industry. Effective standard setting and management and governance of data will be essential to train, streamline, and run the models generative AI requires for successful adoption.
 

Optimize operations with AI and sustainability for greater benefits

A growing number of business leaders no longer see sustainability and operations as separate or competing initiatives. Instead, they strive to optimize investments and efforts to achieve business objectives in both areas. Three out of five consumer products leaders say they purposefully align sustainability goals with their business operational objectives.

Most of the lifetime sustainability costs of a product, as well as its carbon footprint, are set during the design phase of product development. When sustainability considerations are factored in at the earliest stages of a product lifecycle, they are more likely to be embedded in operations and yield greater business benefits. 

That’s why using generative AI tools during the critical NPI planning and product development phases can make such a significant immediate and long-term contribution to advancements in sustainability and operational goals. For example, 73% of organizations plan to increase their investment in generative AI for sustainability.

69% of consumer product industry executives say generative AI will be important for their sustainability agenda.

As with driving operational efficiency, it is essential for business to have the data platform and infrastructure in place and turn this valuable information into actionable insights for decision makers. Organizations that embed sustainability are better at converting their data into sustainability benefits, and 84% of consumer product executives agree that high-quality data and transparency are necessary to achieve sustainability objectives.

Business leaders recognize that generative AI creates new opportunities for tapping the potential of data for sustainability, and 83% of them agree that they are more likely to achieve great benefits to sustainable innovation and product/service development from their data capabilities.

More use cases, more value: Consumer product industry leaders are deploying generative AI in key areas for product development and supply chain functions.

Download the report to learn how consumer products companies are using generative AI to support product development, optimize supply chain management, improve regulatory compliance, and boost sustainability efforts. Then explore an action guide with practical steps companies can take at each phase of a product lifecycle to gain a competitive advantage in an increasingly complex marketplace.