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The future of autonomous systems: agentic AI in consumer products

Explore the game-changing potential of agentic AI in consumer products, enhancing efficiency and transforming customer interactions.


In brief
  • Agentic AI is revolutionizing the consumer products industry by enabling autonomous decision-making and enhancing operational efficiency.
  • Organizations can leverage agentic AI to optimize inventory management, improve customer satisfaction, and drive business growth.
  • Key elements of agentic AI include goal orientation, adaptive planning, and tool access, which facilitate seamless integration into existing operations.

Agentic artificial intelligence (AI) is poised to revolutionize the consumer products sector, presenting businesses with unique capabilities to enhance customer experiences, transform operating models and accelerate growth. As a tool, agentic AI presents an opportunity to create and capture enterprise value, unlike anything we’ve seen to date. By integrating agentic AI into their operations, organizations can harness the power of intelligent software that not only performs tasks but also makes decisions and interacts seamlessly with both technology and humans.

In a series of articles focused on the opportunities and disruption of agentic AI, we’ll explore the use cases along with challenges consumer products leaders will face on the journey. For this first article, we provide a foundational overview of how we got to this point in AI evolution, how agentic AI is different than what’s come before and what makes agentic AI unique from an operational and value creation standpoint. We’ll also illustrate what’s possible for consumer products leaders to transform business processes.

The journey to agentic AI

The evolution of artificial intelligence has been marked by significant milestones, beginning with rule-based systems that dominated the early landscape of AI. These systems relied on explicit programming, where developers defined a set of rules for the AI to follow. While effective for specific tasks, such as basic data processing or decision-making, these systems lacked the flexibility to adapt to new situations or learn from experience.

Then came machine learning, which revolutionized AI by enabling systems to learn from data rather than relying solely on predefined rules. This shift allowed for more nuanced decision-making and the ability to identify patterns within vast data sets. Deep learning further propelled this evolution, utilizing neural networks to process complex information and drive innovations in areas such as natural language processing and image recognition.

Generative AI (GenAI) is revolutionizing our AI landscape through Large Language Models (LLMs) that create new content, such as text, images or music, by learning from existing data. Prompts, the instructions used to query AI, can be written in plain language, opening the benefits of AI to the mass market. This brings us to agentic AI, which is uniquely characterized by its ability to autonomously perform tasks, make decisions, and engage with users and systems. Agentic AI leverages LLMs to adaptively plan and create a path to achieve a desired outcome. Understanding the historical evolution of AI and its applications is critical when integrating these technologies into your business landscape, as it ensures that you are addressing challenges with the appropriate foundational AI elements in place.

What makes something agentic AI

As industry definitions of agentic AI continue to develop, five foundational elements emerge as essential for identifying and defining the scope of your agents: goal orientation, adaptive planning, autonomy, tool access and continuity of execution.

  1. Goal orientation ensures that these agents are designed to achieve specific objectives, whether it be optimizing supply chains, personalizing customer experiences, or managing inventory.
  2. Adaptive planning enables agents to respond to changing circumstances, learning from interactions to improve their performance over time.
  3. Autonomy allows agents to operate independently, making decisions without constant human intervention. It’s up to humans, however, to determine how much autonomy a given agent has.
  4. Tool access refers to the ability of an agent to utilize various external resources and systems to achieve its objectives. This includes APIs, databases and other software tools that enhance the agent's capabilities in executing tasks effectively.
  5. Continuity of execution ensures that an agent can maintain operational effectiveness despite disruptions or changes in the environment. This involves having backup plans, monitoring systems and adaptive strategies to keep the agent on track toward its goals

Today, agentic AI is experiencing a maturation phase, trending toward greater autonomy, enhanced reasoning abilities and increased complexity in interactions. As organizations integrate these intelligent agents into their operations, they are discovering the potential for transformative impacts on customer engagement and operational processes.

Agentic AI in the consumer products industry

To fully grasp the transformative potential of agentic AI for consumer products companies, it is crucial to delve into specific use cases that tackle the industry's most pressing challenges. Among these challenges, optimizing inventory and swiftly adapting to unforeseen fluctuations in supply and demand stands out as a critical concern. In this context, let’s examine how an Inventory Optimization Agent can significantly enhance the efficiency and predictability of inventory management.

 

The primary objective of the Inventory Optimization Agent is to streamline inventory processes to achieve key goals such as reducing costs, minimizing stockouts, and improving turnover rates. By leveraging historical sales data, seasonal trends and market conditions, the agent will ensure that products are readily available when customers need them, ultimately boosting customer satisfaction and loyalty.

 

Through adaptive planning, the Inventory Agent will continuously adjust its strategies based on real-time data and changing market conditions. This responsiveness will enable the agent to navigate fluctuations in demand and supply effectively, aligning inventory levels with current market needs.

 

The agent will operate with a defined level of autonomy, enabling it to make independent decisions regarding inventory replenishment and allocation without constant human intervention, while still allowing for necessary human oversight to align critical decisions with broader business objectives.

 

With comprehensive tool access, the Inventory Agent will integrate various external resources, including data analytics platforms and inventory management systems, to enhance decision-making capabilities. This integration will provide a holistic view of inventory performance and supply chain dynamics, facilitating informed and strategic decisions.

 

To maintain continuity of execution, the agent will employ robust monitoring systems and backup plans, allowing it to manage disruptions while staying on track toward its goals. This proactive approach will minimize operational delays and enhance overall supply chain resilience.

 

The value brought by the Inventory Agent will be significant: it will reduce operational costs through optimized inventory levels and streamlined supply chain operations, enhancing overall efficiency. By automating processes such as order placements and shipment tracking, the Inventory Agent will empower consumer products firms to respond swiftly to market demands, driving profitability and competitive advantage in a dynamic environment. This multifaceted approach will ensure that the Inventory Agent is equipped to manage inventory efficiently and effectively, delivering tangible benefits to organizations.

Harnessing the future with agentic AI

Agentic AI has the potential to revolutionize the consumer products sector by tackling critical challenges and enhancing operational efficiency. By harnessing intelligent agents, organizations can optimize inventory management, elevate customer service, and provide personalized shopping experiences, ultimately driving growth and increasing customer satisfaction. As companies consider embarking on this transformative journey, several key considerations will be essential for successful implementation. Organizations must conduct a thorough data audit to assess their current capabilities and ensure they are prepared for agentic AI solutions, establishing robust data governance practices to facilitate better data integration and management. Additionally, companies should thoughtfully evaluate strategic objectives to determine the most relevant use cases for their agents, aligning agent goals with overarching business objectives to maximize impact.

Fostering an innovative work culture that encourages experimentation and supports the necessary resources for driving change will empower teams to embrace new technologies and methodologies. In future articles, we will delve deeper into the challenges and potential use case solutions for agentic AI, as well as outline the specific steps firms can take to initiate their journey toward this transformative technology. By prioritizing these foundational elements, companies can position themselves to leverage agentic AI effectively and unlock its full potential.

Jimmy Marquis, EY Americas Technology Consulting Executive Director, contributed to this article.

Summary 

Agentic artificial intelligence (AI) is set to transform the consumer products sector by enhancing customer experiences and operational models. Unlike previous AI iterations, agentic AI autonomously performs tasks, makes decisions, and interacts with users. Key elements include goal orientation, adaptive planning, autonomy, tool access, and continuity of execution. For instance, an Inventory Optimization Agent can streamline inventory management by leveraging data to reduce costs and improve customer satisfaction. As organizations adopt agentic AI, they must ensure robust data governance and align agent goals with business objectives to maximize impact and drive growth.

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