翻訳待ち：How Retail Leaders Can Scale AI Beyond Pilots

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

This article is sponsored by Unframe and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Retail has an AI operationalization bottleneck, converting AI investment and experimentation into governed, integrated production capabilities that deliver measurable business impact. The U.S. Census Bureau’s Business Trends and Outlook Survey found that roughly 14% of retail-trade businesses reported current use of AI in May 2026, below the 19.8% average across all businesses. A related Census Bureau working paper found that among firms using AI in any business function, 57% use it in three or fewer of the 15 business functions the survey tracks, most commonly sales and marketing, strategy, and IT. Core retail data and decisions are distributed across ERP, CRM, POS, warehouse management, merchandising, supplier, and legacy systems that continually change. Stanford’s Institute for Human-Centered Artificial Intelligence reports that 88% of organizations use AI in at least one business function. The World Economic Forum found that fewer than 1% of organizations have fully operationalized responsible AI practices. Carnegie Mellon University’s Software Engineering Institute, working with Accenture, identifies eight dimensions of AI adoption maturity — organizational strategy, workforce and culture, workflow re-engineering, risk and governance, data, engineering, operations, and ecosystem — and reports that only 8% of companies have scaled AI at an enterprise level. In a series on why enterprise AI deployment has become the defining bottleneck — and competitive battleground — for modern retail, Larissa Schneider, Co‑Founder and COO at Unframe.AI, and Chris Slovak, Global Field CTO at Unframe.AI, join Emerj on the AI in Business Podcast to break down the real operational blockers that stall AI in retail and explain the modular, agentic deployment model that finally moves AI from pilot to production at enterprise scale. This article examines the decisive operational insights that determine whether enterprise AI ever reaches production in modern retail: Unified AI program design for enterprise‑wide alignment: Establish a single architectural and governance hub that stops teams from building isolated pilots and ensures every solution snaps into one coherent enterprise AI system. Modular AI components for rapid enterprise deployment: Assemble AI workflows from reusable components so teams can launch and iterate solutions in weeks instead of rebuilding bespoke systems that stall and require constant re‑architecture. Distributed reasoning across retail systems for immediate value: Enable agentic systems to pull context directly from ERP, CRM, POS, and warehouse tools so AI can deliver accurate, real‑time outputs without waiting for multi‑year data‑centralization projects. Reusable Enterprise Context for Faster AI Deployment: Build a reusable layer of business knowledge, system relationships, and operational context so every AI deployment accelerates the next. Unified AI Program Design for Enterprise‑Wide Alignment Listen to the full episodes below: Episode 1: AI Deployment at Retail Speed – with Larissa Schneider of Unframe Guest: Larissa Schneider, Co-Founder and COO of Unframe.AI Expertise: Enterprise AI; Go-to-Market; Product Marketing; Enterprise Technology Brief Recognition: Larissa Schneider is Co-Founder and COO of Unframe, where she leads efforts to help enterprises deploy AI use cases. She previously held go-to-market and marketing leadership roles at Nutanix and Noname Security, including leading global marketing for Frame before and during its acquisition by Nutanix. She holds a master’s degree in International Marketing Management with Distinction from Hult International Business School. Larissa Schneider describes organizations where AI experimentation is happening faster than leaders can see, track, or evaluate. Employees across departments are building small automations, agents, and POCs to eliminate manual work — copying data between systems, stitching together workflows, or testing low‑code tools on their own. These efforts are well‑intentioned, but without a central program, they create a landscape in which AI activity is happening everywhere, and leadership cannot determine how it fits together or whether it can be trusted. She explains the dynamic: “You end up with these little islands of projects — trials and POCs running across every part of the organization, which naturally happens. And it’s great; we love people who are eager to try new technology and innovate their work. But how does all of that fit together? That’s the question we’re asking. Because to have reliable, governable, secure AI that truly moves the needle for the business, everything has to work in tandem — everything has to work together.” – Larissa Schneider, Co-Founder and COO of Unframe.AI For Larissa, governable AI means more than policy documents. Leaders need operational visibility into how AI workflows operate, who owns them, what data they use, and how outputs are evaluated. What AI is being built Which systems it touches What data flows through it Who owns the workflow How output quality is measured Without that visibility, pilots stall not because the model is weak, but because the enterprise cannot trust or scale what it cannot see. Larissa’s guidance for C‑suite leaders forms a clear structural playbook: Create one AI program that everything plugs into: A single architectural center prevents teams from building isolated tools that cannot be evaluated or deployed beyond their department. Define output‑quality benchmarks before scaling: Employees adopt AI only when outputs are consistent. Quality criteria must be shared across all workflows so leaders can measure reliability. Create visibility into workflows, decisions, and data movement: Leaders need to know where data originates, what actions agents take, and who is accountable for the outputs. Require every new use case to align with the same backbone: This ensures that AI activity across the enterprise is coherent, measurable, and compatible with future initiatives. Larissa describes this as a structural shift — AI fails when it is scattered. It scales when leaders create one place where all AI activity fits together. A unified program gives executives the visibility, trust, and architectural stability required to move from experimentation to enterprise‑wide deployment. Modular AI Components for Rapid Enterprise Deployment Larissa breaks down the component‑level insight that underpins a modular approach: “What we noticed very quickly is that AI use cases can come from all different parts of an organization, from all different industries and verticals and teams, but the underlying components that you need to put these AI use cases into practice are actually very similar. And those can range from very basic UI components and dashboards to enterprise connectors to your SAP and Salesforce instances and your data lakes. They can be things like reasoning and AI audibility, and you know, it really depends on the use case. But you would be surprised to see how much similarity we can see between inventory planning in retail and automated claims processing in insurance or lease abstraction in commercial real estate.” – Larissa Schneider, Co-Founder and COO of Unframe The surface‑level differences between enterprise workflows obscure the fact that most AI solutions rely on the same architectural building blocks, according to Larissa. Leaders often treat each new use case as a fresh engineering challenge, but the components repeat — connectors, dashboards, agents, reasoning layers, data‑extraction pipelines. The repeatability is the advantage. Once leaders recognize that, deployment stops being a reinvention exercise and becomes an assembly exercise. The work shifts from building to configuring. Here’s how Larissa translates that into action for executives: Build once, reuse everywhere: Components like SAP/Salesforce connectors, reasoning modules, and UI elements should serve dozens of workflows, not one. Let the architecture carry the complexity: The modular system should handle the heavy lifting so each new use case requires only light configuration. Treat integrations as shared infrastructure: Core system connectors shouldn’t be rebuilt for every department — they should be standardized and inherited. Customize only the final layer: The unique logic of a workflow is the only part that should require bespoke engineering. Scale horizontally, not from scratch: Once the components exist, new use cases become incremental rather than transformational. Larissa argues that most enterprise AI efforts are not about writing code. The larger challenge is defining workflows, outputs, user requirements, and business value before any implementation begins. From Larissa’s experience, modularity is what makes enterprise AI deployable at speed. When teams assemble solutions from reusable components, timelines compress, integration risk drops, and every new workflow inherits the stability of the ones already in production. Distributed Reasoning Across Retail Systems for Immediate Value Episode 2: The Predictive Model Reshaping Retail Operations at Scale – with Chris Slovak of Unframe Guest: Chris Slovak, Global Field CTO at Unframe Expertise: Artificial Intelligence; Data Infrastructure; Go-to-Market Strategy; Solutions Consulting Brief Recognition: Chris Slovak is Global Field CTO and Head of AI Architects at Unframe, helping enterprises achieve business outcomes with AI. He previously spent nearly eight years at Tealium, where he led global solutions consulting, helped grow the business to 20x ARR across three financing rounds, supported global expansion to eight international offices, and secured six patents. He also co-founded Challenger Interactive, which developed patented AI technology for gaming. Chris Slovak describes retail environments in which core operational truths are scattered across ERP, CRM, POS, and warehouse systems. Order state, supply status, inventory accuracy, and customer context each live partly in different tools. Retailers often try to solve this fragmentation by centralizing everything first — spending years aggregating, cleaning, and modeling data before any workflow can move into production. Chris argues that this approach stalls deployment long before AI can deliver value. Chris argues that treating data centralization as a prerequisite for AI has become one of the largest deployment bottlenecks in retail. He explains that agentic systems can operate directly on distributed systems, pulling the specific pieces of context they need in real time rather than waiting for a unified schema or a completed data‑warehouse migration: “Agents and agentic systems in particular don’t necessarily need one source of aggregated data truth. They can reason like a human can across multiple systems, so long as there’s context and semantic linking… You have ERPs and CRM, and then you have your point of sale systems, and they probably all semi-talk, but the truth is the state of order, supply, inventory probably to some extent live a little bit in each.” — Chris Slovak, Global Field CTO at Unframe Centralizing everything first also disrupts workflows. Teams must change how they work, adopt new tools, and reorganize processes to satisfy a data‑architecture ideal. Chris notes that this becomes a bridge to nowhere — systems evolve, businesses evolve, and the centralized model never reaches a stable end state. Allowing agents to pull data where it lies and how it lies avoids that disruption and removes the adoption risk. Chris points out the following actionable realities: Retail truth lives across ERP, CRM, POS, and warehouse [truncated for AI cost control]