At STL Digital, we believe retailers must reimagine how customers move between channels — in-store, mobile, social and web — to create connected, relevant relationships. Today, Generative AI is the accelerant that turns that ambition into reality. By augmenting merchandising, personalization, supply chain decisions and frontline service, generative models are helping retailers deliver consistently brilliant digital experiences across every touchpoint. In this post we’ll explain how, with examples, evidence from industry research, and practical guidance for leaders ready to adopt AI for enterprise use cases.
Why omnichannel demands smarter AI
Omnichannel isn’t just about being present everywhere — it’s about creating a seamless journey. The product seen on Instagram should be available in-store, for same-day pickup, with consistent pricing and returns. Consumers now expect this smooth continuity, and any friction stands out as omnichannel shopping continues to rise
Traditional rule-based systems and point solutions can replicate parts of an experience, but they struggle to synthesize huge, disparate datasets (browsing patterns, chat transcripts, inventory feeds, promotions, local footfall) in real time. Enter Generative AI: large models that can reason across many data types and produce human-friendly outputs — product descriptions, tailored offers, conversational responses, and even dynamic in-store signage — all personalized to a customer’s context.
What generative models deliver for retail
Generative models expand the toolkit across three high-impact areas:
- Hyperpersonalization at scale — Instead of static segments, models generate individualized product recommendations, marketing messages, and landing experiences that reflect a customer’s history, channel, and immediate context. That delivers richer Digital Experiences online and in-app, and drives measurable increases in conversion and lifetime value.
- Conversational commerce and service — Advanced chat assistants and voice agents can handle complex product discovery, returns, and store-locating tasks. They summarize policies, suggest cross-sells, and escalate to human agents with context attached — making omnichannel support truly unified. For contact centers, GenAI’s potential to reshape agent workflows and customer interactions is full of potential.
- Operational orchestration — From demand forecasting to fulfillment routing and markdown optimization, generative models can synthesize signals and propose actions. That improves speed and reduces waste — critical for competitive omnichannel networks. There are growing investments in these retail AI use cases.
Together, these capabilities deliver seamless Digital Experiences — ensuring consistent tone, accurate availability, and context-aware interactions across every channel, from mobile to in-store.
The Multimodal Future of Generative AI in Retail
According to a recent prediction from Gartner, by 2027, 40% of generative AI solutions will be multimodal, combining capabilities such as text, image, and video generation into a single cohesive platform. This shift will significantly impact industries like Retail, where Digital Experiences require dynamic, rich content that adapts to the user’s needs across various mediums and channels.
In retail, the integration of multimodal generative models means that retailers will be able to not only generate personalized product recommendations but also create dynamic video ads, on-the-fly images for promotions, and interactive virtual try-ons, all powered by a single AI solution. This multimodal approach allows for even greater personalization and engagement across touchpoints, ensuring that AI for Enterprise solutions deliver relevant, compelling content to customers regardless of the format they prefer to interact with.
This prediction aligns with broader trends in Generative AI adoption, which show rapid advancements in the ability to blend multiple forms of content generation into a single, seamless experience. Retailers who embrace multimodal solutions will be better positioned to create omnichannel experiences that feel both personalized and efficient, further enhancing the Digital Experience for customers.
As retailers scale AI across inventory, marketing, and customer service, operations will become more connected and seamless — highlighting the rising importance of multimodal capabilities in driving the next wave of retail transformation.
Evidence: what the research says
Industry research points to both the scale of value and the speed of AI application in Business adoption:
- According to McKinsey generative AI could contribute between $2.6 trillion and $4.4 trillion annually to the global economy, across 63 analyzed use cases. This represents a 15% to 40% increase in the overall impact of artificial intelligence. The research also notes that the potential economic value could roughly double if generative AI capabilities are embedded into existing software tools used for other tasks.
- Forrester projects that global technology spending will grow by 5.6% in 2025 to reach $4.9 trillion, fueled by investments in cybersecurity, cloud, generative AI, and the broader digital economy. The report highlights that generative AI is emerging as a major driver of enterprise technology priorities, particularly across industries such as financial services, government, and media, which together will represent 46% of global tech spending in 2024; by 2029 70% of tech spending will be on software and IT services. As organizations increase their reliance on software and IT services, Forrester emphasizes the need to balance AI talent availability, manage tech margins, and minimize technical debt while advancing digital growth strategies.
Practical omnichannel use cases — with short implementation notes
Below are concrete, high-impact use cases where Generative AI transforms omnichannel retail — plus where to start.
1. Product discovery that understands context
Use case: A conversational assistant that knows a customer’s recent searches, current promotions, store inventory and typical delivery preferences, and generates a short, persuasive product pitch.
Start small: Connect the model to a product catalog and a signal layer (recent page views + cart). Apply guardrails for pricing and returns language. Measure: uplift in add-to-cart, session NPS.
2. Unified customer support across channels
Use case: A unified GenAI layer processes chat, email, and voice transcripts to summarize issues and draft responses, maintaining context across channels.
Start small: Pilot in one support queue with human review, and measure handle time, escalations, and CSAT.
3. Dynamic in-store and online merchandising
Use case: Models generate localized content (images, copy, price suggestions) optimized for the customer segment visiting a store or site, updating in near-real-time for inventory and promotions.
Start small: A/B test generative copy and imagery for a narrow set of SKUs; measure conversion and return rates.
4. Returns and fulfillment orchestration
Use case: Predictive routing tools suggest the best fulfillment source — store, DC, or partner — and generate messages to manage delivery and return expectations.
Start small: Connect with OMS/WMS data and track delivery speed and fulfillment cost per order.
Always balance automation with human oversight, especially for pricing, policy, and sensitive customer interactions.
Enterprise considerations: data, governance and ROI
Deploying generative models at omnichannel scale requires focus on data, governance, and ROI:
- Data integration: unify product, inventory, CRM, and behavioral data through strong APIs and event streams to ensure consistent, trustworthy outputs.
- Governance and safety: enforce accuracy and compliance with guardrails like response templates, redaction, and human review for high-impact actions.
- Measuring ROI: take a phased approach — pilot, operationalize, then scale — to improve conversions, cut service costs, and reduce returns for sustained profitability.
Finally, adopt a platform mindset — embed AI as part of a composable stack (catalog, OMS, personalization, analytics) instead of a standalone tool. This approach minimizes vendor lock-in and enables continuous improvement.
People and process: the change management playbook
Generative tools don’t replace retail expertise — they enhance it. Success depends on three elements:
- Cross-functional squads: product, analytics, ops, and store managers co-own pilots.
- Human-in-the-loop workflows: start with human review, then automate gradually.
- Training and enablement: empower teams to use genAI to augment, not replace, decisions.
For contact centers and store associates, genAI boosts productivity and lets them focus on complex, high-empathy tasks — improving both satisfaction and performance.
Getting started checklist (for retail leaders)
- Identify 2–3 high-impact pilot use cases (recommendation, CX assistant, fulfillment routing).
- Inventory your data sources and fix gaps in product and inventory visibility.
- Choose a phased vendor strategy: proof-of-concept with best-fit providers, then standardize on a composable architecture.
- Build governance: output validation, privacy-preserving data handling, and escalation paths.
- Define success metrics: conversion lift, order accuracy, returns rate, and CSAT.
Additional Consideration for Scaling Generative AI
To successfully implement generative AI at scale, retailers must also ensure they have the right infrastructure and resources. Investing in cloud technologies, AI model training, and high-quality data pipelines is essential for maximizing the impact of AI for enterprise. Additionally, ongoing monitoring and optimization are crucial. Retailers should continuously refine their AI models based on evolving customer behavior, changing market conditions, and business priorities. By fostering a culture of innovation and agility, organizations can stay ahead of the curve and fully capitalize on the long-term potential of generative AI to drive growth, operational efficiency, and customer satisfaction.
Conclusion — the customer promise
At STL Digital, we see generative AI not as a flashy experiment but as the operational backbone of future digital experiences. When retailers combine real-time data, trusted governance, and human judgment, they unlock omnichannel experiences that feel effortless to customers and efficient for operations. That’s the future of retail — contextual, connected, and powered by AI that understands customers across every channel.
We can help map a tailored pilot plan — from use-case selection through ROI measurement — so your organization can move from experimentation to enterprise-scale impact with confidence.
