Agentic AI, defined as “AI systems and models that can act autonomously to achieve goals without the need for constant human guidance” (Harvard Business Review), has exploded onto the scene. AI-powered agents have increasingly occupied tech conversations involving every industry and domain from supply chain to customer service. Seemingly out of nowhere, the topic of agents has gained traction and been tossed around as a value-add, AI capability. And indeed it is — agentic AI can be seen as an enhancement to processes and routines in an autonomous way, freeing up resources to be used in more meaningful ways.
In fact, agentic AI is more than automation as we have known it. Agentic AI takes us to the next level with workflow orchestration that recognizes the relationship between tools (e.g., inventory systems and customer attributes in a CRM tool) and outcomes (e.g., a personalized product offering for an in-store customer) reshaping workflows from siloed actions into synchronized experiences. As it has also been put, AI agents are capable of making decisions and taking actions within set boundaries. So, what does this mean to the retail & CPG space? We will explore the “so what” for the industry and three ways in which AI agents can use existing tools to accomplish desired outcomes.
1. Agents as Marketing Assistants: Personalization Heroes
No matter the new hot topic or trend, personalization remains a priority and expectation. Using agents to facilitate personalization is a given and where we start in our exploration of agents in retail. Revisiting the definition of AI agents and their use of large language model (LLM)-powered systems designed to achieve objectives across multiple steps and leveraging tools autonomously as needed, agents are well positioned to elevate personalization both proactively and dynamically.
Agent-Driven Recommendations
AI agents can serve as vital champions to marketing teams when it comes to next-level recommendations. While product recommendations are not novel, agents offer a new, anticipatory spin on personalized recommendations. Agents enable teams to not only anticipate but adapt in the moment. With consumer expectations rapidly evolving and make-or-break interactions between brands and consumers at stake, agents can maneuver the dynamic landscape, orchestrating consumer engagement by tapping into multiple platforms and tools.
Handling complex data and understanding changing consumer preferences and shopper browsing patterns while adapting to real-time factors to form individualized recommendations is where agents shine. And the fact that agents can further individualize recommendations using human input or even learn the nuances between consumers to beef up product recommendations makes AI agents pertinent to enhanced consumer engagement.
The Consumer Experience and AI-Powered Agents
Personalization can not be mentioned without also mentioning content marketing. AI agents can play a significant role here as well, through facilitating the automation of cross-channel tasks, creating media, personalizing content delivery, and tracking consumer interactions. The win with agents for content marketing is evident, as agents can understand the target objective, unite the information, and take meaningful action on behalf of the marketer.
Building on existing LLM capabilities, agents empower marketing teams to create autonomous workflows that, seamlessly and without the need for manual prompting, progress from one generative AI task to another with decision-making adeptness. Agents allow marketers to do more, lifting much of the weight that comes with producing personalized experiences and readying marketers with AI-powered assistance.
Real-World Application
As we consider AI agents as personalization marketing assistants, we can imagine this taking shape across multiple scenarios revolving around, but not limited to, product recommendations, tailored experiences, and individualized content. And when we apply agents to these key areas of personalized marketing, they emerge as heroes by bringing together information and then making informed decisions with here-and-now guidance based on the synergized data, as illustrated in the example below (Example Agent Network). This type of application adds agility to teams, allowing them to dynamically adapt their approach to personalization with real-time customer context in place of passive actions associated with traditional AI.
How agents can be applied to personalization and support various personas in their activities
2. Agents as Supply Chain, Logistics, and Operations Partners
Uncertainty in supply chains has been a constant since the global pandemic. As a result, optimizing and driving efficiencies in supply chain processes like inventory management and logistics remain a priority. AI agents have the potential to inject efficiencies and improve performance across supply chains, offering an advantage — a supply chain partner — to supply chain managers. This advantage comes through the agent’s ability to accommodate nonstop shifts in supply and demand; with agentic AI initiated, agents can monitor processes, adjust to market conditions and determine optimal outcomes in order to provide the desired result to supply chain teams.
Supply chain improvements with agents extend to logistics, inventory management, and operational resiliency, among other areas in the supply chain. Beginning with logistics optimization, agents are able to evaluate variables in demand, anticipate disruptions, and run different logistics scenarios — all with a view of future impact on performance. This capability can set logistics teams up with better risk mitigation and improved planning.
Agentic AI applied to inventory management can also connect the monitoring of inventory levels to logistics information in order to inform adjustments to stock based on real-time demand. On top of this, autonomous agents can perform as replenishment partners and nimble middlemen between supply chain teams and suppliers. The value add that comes with agent facilitated cross-collaboration and guided process improvement, presents significant advantages to the supply chain.
The advantages that agents bring to the supply chain do not end with logistics or simply managing inventory levels; agents are also able to play a key role in supply chain resiliency. With agents in action, supplier performance can be synthesized with market conditions as well as insights from logistics and inventory planning in order to make informed decisions that build in resilience. Partnering agentic AI with people-managed workstreams has the potential to not only improve processes but also allow supply chain teams to more easily flex in response to disruption and change with limited human intervention.
3. Agents as Product Innovation Collaborators
Gaining and maintaining a competitive advantage necessitates innovation. And with agentic AI’s ability to connect to historical trends, scan various platforms for inspiration, and understand changing consumer preferences, agents are positioned to serve as collaborators with brands in creating what’s next in product innovation. This means that agents can support product design and, ultimately, the consumer experience by learning from consumer data, human input, and optimizing material usage, just to name a few actions agents can take. Agents then can identify emerging trends and create novel product ideas with a single prompt.
Innovation requires brands to adapt and forge a product roadmap that elevates the consumer experience. As a result, brainstorming sessions are a common occurrence for creative teams. Acting as creative collaborators, agents offer the capability to brainstorm, leveraging vast amounts of data and initiating a back-and-forth interaction between the creative team and the agent — effectively promoting a collaborative environment for product innovation. Agents present a unique value proposition for creatives by accelerating the inventive creative process and getting to the point of execution with greater ease. With agentic AI, creative teams have a powerful tool to aid in both generating and wrangling innovative designs.
More to Consider
Agentic AI goes a step further than traditional AI capabilities by providing guidance and autonomously assessing the desired action within prescribed requirements. And this benefit is why the market for agentic AI is expected to grow at a 45% CAGR over the next five years (Boston Consulting Group). We have explored three areas where agentic AI can impact the retail & CPG landscape, but these areas just scratch the surface of possibilities with agents. The out-of-the-ordinary possibilities of agentic AI present the industry with unique capabilities to transform, create and strengthen how businesses operate.
Agentic AI capabilities are quickly gaining in popularity for a reason — when integrated into ways of working, whether that be the consumer experience, next-level personalization, supply chains or product innovation — agents can become not only task masters but also decision makers that effortlessly connect with teams (humans) and systems. Retailers and brands alike stand to gain efficiency and success across the entire value chain by unlocking the full spectrum of possibilities that comes with agentic AI. The future of the industry is undoubtedly a future that will be shaped in part by AI agents.