The pinnacle of the AI evolution lies in MAS, where a "society of minds" (specialized AI agents) collaborate to deliver an unparalleled discovery experience.
While a single Agentic AI can personalize and automate tasks for individual users, MAS enables customers to benefit from a team of specialized agents working behind the scenes. This orchestrated intelligence creates a more fluid, proactive, and deeply personalized shopping journey that no single agent could deliver alone – MAS is the result of Tech industry experts realizing and tackling the limitations of single AI Agents, as described by Cognizant’s CTO AI, Babak Hodjat (Mathews, 2024).
Imagine a customer journey in which one agent (The Empath) understands your mood and preferences, another (The Product Expert) scans thousands of products to find the perfect match, while a third (The Logistics Agent) checks what’s in stock nearby and how fast it can be delivered. A fourth agent (The Personal Stylist) helps you style your choices based on your own aesthetic, and a fifth (The Bias Mitigator) ensures your decisions aren’t being swayed by hidden biases or marketing tricks. This isn’t science fiction…It’s the natural evolution of AI in commerce.
To deliver these MAS-enabled customer experiences, retail businesses must undergo a strategic and operational transformation. A key shift will entail moving away from rigid workflows toward modular systems where specialized AI agents can ‘talk’ to each other and coordinate across functions like inventory, pricing, logistics, and customer service. Agility and cross-functionality will have to be embraced while processes and roles are redesigned to support the new set up.
The biggest challenge in MAS, as highlighted in discussions around agentic AI hurdles (Polak, 2025), is less about individual agent capability/implementation and more about the orchestration, governance, and ethical alignment of the entire system. How do you ensure that the emergent behavior of dozens of interacting agents consistently aligns with both user benefit and brand values? How do you debug or assign responsibility when a multi-agent decision leads to a poor outcome? Establishing robust frameworks for inter-agent communication protocols, conflict resolution, and transparent decision-logging is a monumental but critical undertaking.
If the travel sector can do it, so can retail
The travel industry, a high-stakes environment of perishable inventory and complex decision-making, has been an unwilling but effective proving ground for sophisticated AI implementation.
Retail must learn more than just the surface-level parallels, drawing from analyses like those from Mize (Izchak, 2025) and tourism AI specialists at IATourisme (Bellerose, 2025):
Hyper-personalization even under constraint: Travel AI doesn't just personalize, it personalizes against a backdrop of rigidly constrained and dynamically priced inventory (e.g., that one remaining window seat, that specific hotel room type during peak season). The algorithms that are able to balance customer preferences, inventory availability, cost, and revenue maximization offer a blueprint for retail's own challenges with limited-edition items, flash sales, or highly specific attribute-based demand.
Graceful failure and backup plan: Travel AI systems frequently encounter "no perfect match" scenarios or unexpected disruptions such as flight cancellations or hotel overbookings. The sophistication of these systems lies in being able to go beyond the initial match, and into alternative generation and proactive problem-solving during failures. Retail discovery engines will inevitably face similar issues, so learning from travel's recovery protocols is crucial.
The value of maintaining user’s perceived control: Early travel chatbots that were overly rigid or failed to provide escape hatches to human agents created intense frustration and led to countless sales losses. Maintaining perceived user control during AI interactions and facilitating seamless AI-to-human handoffs is of critical importance, especially when dealing with high-consideration purchases or complex service issues. AI should not try to replace humans, but augment them.
Data Ecosystem Interoperability: The travel industry's Global Distribution Systems (GDS) and subsequent API layers, while complex, underscore the necessity of data interoperability standards for AI to effectively query and aggregate options across myriad suppliers. Retail, with its often siloed brand and retailer data, faces an even steeper climb but must prioritize a similar path toward semantic data sharing for agentic systems to truly flourish across the ecosystem.
Plugging in tech? You're rewiring your business
As we have hinted across this article, the transition to an AI-driven product discovery model presents profound strategic challenges that demand more than technological solutions. It requires a new way for Retail & Consumer Goods companies to operate and do business.
To get this out of the way – yes, it does also require a huge technological shift. For years, the focus has been put into Big Data and collecting sheer volume raw information. This is not enough for the AI-era: Data will be an asset only if it is meticulously curated, semantically rich, and interconnected across product, customer, and contextual data domains. This requires a fundamental rethinking of Product Information Management (PIM), Digital Asset Management (DAM), and Customer Data Platform (CDP) strategies towards creating “AI-ready knowledge graphs”, or what’s the same, structured and interconnected data models that AI can understand and leverage for decision-making.
It's fair to say that leading with AI has a big technological component but isn't just an IT upgrade. It requires reskilling talent, redesigning workflows (e.g., how merchandisers interact with AI-driven assortment planning), and fostering an organizational culture of data-driven experimentation and human-AI collaboration (likely a big shift from any current company culture). Non-tech changes surrounding this tech implementation are a central topic of discussion among industry leaders, including in big forums such as World Retail Congress (Brien et al, 2025).
Now let’s say a company invests in rethinking its technology, processes, and talent, and implements one or more of the AI technologies discussed to improve product discoverability in eCommerce. How can they measure ROI? Definitely not through traditional last-click models. The impact of superior product discovery is often indirect and cumulative – reduced returns, increased customer lifetime value, enhanced brand perception. Attributing ROI through traditional models will be hardly adequate. New frameworks for measuring the value of "frictionless discovery" and "AI-assisted decision satisfaction" are needed in the dawn of a non-linear world.
But ROI is not the only framework to be redefined. As AI agents become more adept at understanding and influencing consumer choice, deep ethical questions arise. What is the AI's primary fiduciary duty – to the user, the platform, or the brands whose products it recommends? How do we ensure AI-driven persuasion doesn't become manipulative, especially for vulnerable consumers? Putting in place new, specific and actionable Ethical frameworks is non-negotiable.
As complex as their implementation is, foundational AI models are becoming more and more accessible. Retail leaders must think beyond “adopting it first”: Soon, when AI is broadly engrained into all major companies, the competitive advantage will stem from the quality of proprietary data sets, a truly equipped workforce, unique agent orchestration strategies and the cultivation of unique “AI brand personalities” that resonate with consumers and provide consistently good interactions. Long-term strategies over short-term gains.
A call to action for the brave
For leaders in the Retail & Consumer Goods industry, AI isn't a trend to watch passively. Consumer expectation is already shifting and demanding “hyper-relevant, personalized search experiences - whether they type, speak, or even upload images to find what they need” (Izzo, 2025).
To keep up with consumers and stay at the forefront of commerce, industry leaders will have to strategically lean in, experiment and invest in foundational data and talent to build the future. The journey requires courage and a willingness to rethink long-held assumptions, but the prize is a vastly more relevant and engaging commerce experience, driving revenue and brand awareness for the companies that succeed.
An early sign of success, and a sneak peek into AI’s full potential, is shown by the 30% increase in conversion rates seen by retailers who implemented AI-powered search in their eCommerce (Izzo, 2025). For an industry that has been competing for, investing in, and obsessing over that 2% plateau at the bottom of the conversion funnel, an increase of this magnitude is a big leap forward (and directly translated into dollar value).
Data supports there’s more to it: In 2025, 17% of shoppers reportedly bought a product because it was recommended by an AI shopping assistant or chatbot (Bonderud, 2025) – imagine what this could represent, at the awareness level, for a brand that has optimized their product marketing to capture AI’s attention. Additionally, Cognizant’s Agent Foundry framework has enabled clients to automate over 50% of post-purchase retail interactions (Cognizant, 2025), cutting order processing time by half and driving loyalty after conversion.
It’s clear that the AI Tech Triad has potential to impact the end-to-end funnel for the companies daring to dive in.
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