{"slug": "how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins", "title": "How Retail Finance teams are using Agentic AI to protect omni-channel margins", "summary": "Retail finance teams are using agentic AI to protect omni-channel margins by managing complexity across channels, fulfillment, and returns. The technology helps finance leaders turn real-time data into proactive, profitable actions, with Gartner predicting agentic AI will make 15% of daily business decisions by 2028.", "body_md": "The moments that move omni-channel margin are increasingly shaped by AI and agents. Context and control are how finance stays ahead of them.\n\nby [Sarah Duffy](/blog/author/sarah-duffy)\n\n*Why omni-channel complexity has pushed retail finance into the driver's seat of margin, cash, and markdown strategy.\n\n*Why ontology and governance have become boardroom words for finance leaders in an age of agentic commerce.\n\n*Why a data-smart AI coworker is the breakthrough unlock finance teams need to turn real-time data into proactive, profitable actions.\n\nAsk a retail CFO where the quarter's margin is landing and you will always get a hard-won answer, born from the discipline and rigor they bring to the business. And then a list: the orders shipped from stores at a higher cost to serve, the run of online returns booked against a different channel, the promotion an automated pricing engine leaned into harder than plan. Any one of those is the product of multiple systems, and each is increasingly shaped, and made faster and more complex, by automation and agents. The mission of finance departments is to understand the relationships among all of those variables, and more, to see how they move profitability, and to steer the business continuously in the right direction.\n\nFor younger shoppers, omni-channel commerce is all they have ever known. It was not long ago that buy online, pick up in store (BOPIS) was considered cutting edge, with today's fulfillment options having advanced well beyond it. This is simply how retail works now, giving customers one seamless experience across the digital and physical channels that keep growing, evolving, and becoming more immersive. Beyond the experience, omni-channel also changed the economics of the sale. It spread margin, cash, and markdown decisions across more channels, more fulfillment paths, and more ways to return. This is the environment in which retailers operate, and their finance departments are the constant through all of it, helping the business understand and act on rising complexity.\n\nNow, in the era of agentic commerce, that complexity is compounded by agents shaping how a price is set, cash is deployed, or a markdown is triggered. [Gartner expects agentic AI to make 15 percent of day-to-day business decisions by 2028](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027). The pace will vary by retailer, but the waves of agents reshaping internal systems, AI spend, and customer behavior are here to stay.\n\n*Channels, fulfillment, and returns move hour by hour. Margin is what finance protects.*\n\nFinance teams have always been good at finding the number, even when it is buried in complexity. But they are also the first to let the business know the numbers do not tell the whole story. What matters is the meaning behind them: the definitions, the channels, the cost drivers, and how each of those is changing as the business moves. An answer can be perfectly accurate and still not be correct, because it rests on a partial or dated picture of how the business actually works. Put plainly, is the number seen in the full context of the business?\n\nThat is what an ontology does: it captures meaning and keeps it current as the business changes. As Databricks CEO Ali Ghodsi puts it, most enterprise AI is guessing with false confidence, a context problem, not an intelligence problem. But, as with every technology, it is how the capability is delivered that makes all the difference. Which brings us to a new kind of ontology, built for the demands omni-channel retail places on it.\n\n*Accurate is the right figure. Correct is the same figure, rooted in the channel, the demand, and the cost to serve.*\n\nIn omni-channel retail, real time is paramount, and an understanding formed a few hours ago may already be out of date. So the ontology itself has to keep moving. It has to learn from the systems the business runs, sharpen with every question, and adapt as the business evolves, so the context stays live rather than captured once and left behind.\n\nThis is where [Genie](https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents) becomes the answer. Databricks built Genie as a data-smart AI coworker: a coworker a finance leader asks a direct question to and gets a trustworthy, sourced answer in return, grounded in Genie's ontology and governed at every step. It is built to help finance teams have more accurate answers and, more importantly, deliver trusted actions, beyond just providing readouts of what has happened.\n\nConsider the three questions on the minds of every retail finance team, each tied to one of three outcomes that compound, one feeding the next. For each, Genie does more than retrieve the data and answer. Its ontology learns the business, sharpens with every question, and shows its work:\n\n**› Where is margin really landing, by channel and product, after fulfillment and returns?**\n\nStart with margin. The markup finance plans and the margin it keeps are rarely the same number once fulfillment and returns are counted.\n\n**› Where is cash tied up in inventory that is overstocked, aged, or in the wrong location?**\n\nThen cash. The same unit can sit in the wrong place for weeks, quietly holding money the business could be putting to work elsewhere.\n\n**› Where is full-price revenue slipping to markdowns and returns?**\n\nThen full-price revenue. By the time a markdown is taken the erosion has already begun, so the value is in catching it while full-price demand still holds.\n\n*Three questions, three outcomes, one mechanism. Genie learns the business, sharpens with every question, and shows its work.*\n\nThat is the difference between reporting what already happened and continuously learning about your business, getting smarter with every interaction. And because every figure traces to its source, every permission holds, and the cost of the AI itself stays governed under one model, it is an answer finance can trust to act on. Genie readies the move, to reprice, to transfer stock, to hold a price, and a person in the loop makes the call.\n\nFinally, Genie's learning across all three comes together. Seeing where margin is truly landing shows where cash is trapped in inventory and where full price still holds. Make the right moves to free the trapped cash before the stock ages into a forced markdown, and hold full price where it's supported by demand. That turns three separate fights into one reinforcing mechanism: each move sets up the next, and the momentum compounds.\n\n*Each move sets up the next. Genie's learning across all three is what makes the momentum compound.*\n\nUnilever brought Genie to more than 1,200 finance and business users to move analysis off spreadsheets and into plain-language questions. Cyro Souza, Data & Analytics Sr Manager, Latin America Lead at Unilever, put it simply:\n\n\"With Genie, we've changed how finance works. What used to take days now takes minutes, and we can explore questions much more freely.\"\n\nAt Unilever's scale, that speed in testing hypotheses is expected to translate into multi-million-euro annual cost avoidance, driven by sharper customer analysis and continuous credit risk management. [Read the full story →](https://www.databricks.com/customers/unilever/genie)\n\nThis is the force multiplier built for what finance departments require. The critical people driving rigor and discipline across the business can now lean on a data-smart AI coworker that is always getting smarter, always current, always governed, truly understanding the business. Omni-channel will keep winning the customer, while a tool like Genie will help finance protect more margin with every sale.\n\n**See what a data-smart AI coworker looks like for finance. Databricks Genie is available today. ****databricks.com/product/ai-bi/genie**\n\n**What is changing for finance in omni-channel retail?**\n\nMore of the decisions that move margin, cost to serve, promotions, fulfillment, and returns, are made by agents. Finance's mission to protect profitability is unchanged; what has grown is the speed and complexity of change, which finance tools must understand and govern.\n\n**Does Genie make pricing, promotion, or markdown decisions?**\n\nNo. Those calls belong to merchandising, marketing, supply chain, and operations. Genie gives finance an accurate, governed view to see a forming risk early and guide or direct the owners who act on it.\n\n**Why do ontology and governance matter to a retail CFO?**\n\nOntology captures what the numbers mean for your business and keeps it current, so an answer is correct and not just accurate. Governance keeps every figure traced, permissioned, and cost-controlled. Together they make an answer safe to act on.\n\n**How is Genie different from an AI dashboard or BI tool?**\n\nA dashboard shows you what the data says. Genie is a data-smart AI coworker that helps you act on it, grounded in your ontology and governed end to end, with a person deciding.\n\nSubscribe to our blog and get the latest posts delivered to your inbox.", "url": "https://wpnews.pro/news/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins", "canonical_source": "https://www.databricks.com/blog/how-retail-finance-teams-are-using-agentic-ai-protect-omni-channel-margins", "published_at": "2026-07-14 15:00:00+00:00", "updated_at": "2026-07-14 15:23:53.922420+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-products", "ai-tools"], "entities": ["Gartner", "Databricks", "Ali Ghodsi"], "alternates": {"html": "https://wpnews.pro/news/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins", "markdown": "https://wpnews.pro/news/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins.md", "text": "https://wpnews.pro/news/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins.txt", "jsonld": "https://wpnews.pro/news/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel-margins.jsonld"}}