{"id":85328,"date":"2025-11-18T12:14:42","date_gmt":"2025-11-18T17:14:42","guid":{"rendered":"https:\/\/www.mytotalretail.com\/?post_type=article&p=85328"},"modified":"2025-11-18T12:14:42","modified_gmt":"2025-11-18T17:14:42","slug":"why-retailers-are-still-missing-the-moment-with-ai-and-how-to-fix-it","status":"publish","type":"article","link":"https:\/\/www.mytotalretail.com\/article\/why-retailers-are-still-missing-the-moment-with-ai-and-how-to-fix-it\/","title":{"rendered":"Why Retailers Are Still Missing the Moment With AI. And How to Fix It"},"content":{"rendered":"

Retailers are putting more money into artificial intelligence every year. More than half (56 percent) have increased<\/a> their spending on generative AI since 2024. However, despite the investment, most still aren’t seeing real business results. Too often, AI is confined to narrow optimization, such as forecasting, pricing or personalization — meaning core retail processes remain siloed, slow or sub-optimal, failing to leverage both the investment and full value of AI.<\/p>\n

This disconnect continues to limit impact.<\/p>\n

Many retailers are still relying on static, product-first planning processes. These models were designed for a different era, when demand was more predictable and planning cycles followed seasonal timelines. However, today’s retail environment is shaped by social commerce, digital-first shopping journeys, and 24\/7 consumer engagement. A product can go viral on social media in hours, and macroeconomic shifts can alter demand within days. Without planning systems that adjust just as fast, retailers face stockouts, excess markdowns, and missed revenue.<\/p>\n

The good news? AI can transform planning when applied at the core, turning customer and market signals into smarter decisions. Here’s what it takes to make that shift.<\/p>\n

Customer Signals Are Out There, But Most Teams Can’t Use Them<\/strong><\/h2>\n

One of the biggest gaps in retail planning today is the failure to align with digital retail transformation.<\/p>\n

Today’s retail is 24\/7, always-on, and spans multiple touchpoints. Yet most systems are still driven by traditional product hierarchies and taxonomies that fail to reflect the truth about customers — e.g., their customer journeys, shopping missions, etc. What they lack is visibility into real-time market signals: what’s trending on TikTok, how sentiment is shifting, or what macro indicators are suggesting about future demand. These signals exist, but the workflows to act on them fast enough are often missing, leaving retailers out of step with the market, even when the data is available.<\/p>\n

Some retailers are beginning to transition to a more customer-centric, agile planning mode. Instead of treating planning as a quarterly exercise, they’re moving toward continuous cycles, updating forecasts, promotions and assortments based on what’s happening now. In fast-moving sectors like fashion and specialty retail, this is already becoming the norm. Other verticals are following.<\/p>\n

Differentiation is another key priority. Many retail experiences have become interchangeable. The brands that stand out use data to define their offer more clearly, tailoring product assortments, promotions and channel strategies to specific customer needs and local market dynamics. That level of precision requires sharper signals and a more connected approach across merchandising, supply chain, and finance.<\/p>\n

This is where AI can make a real difference. When demand and market signals are fused with internal data — e.g., sales trends, supply chain capacity, financial constraints — AI can surface patterns that humans alone would miss and turn them into better decisions.<\/p>\n

The most effective teams bring external and internal data together to inform planning decisions. When behavioral signals, market trends and operational realities are linked in a single environment and amplified by AI, retailers can respond more quickly, allocate inventory more effectively, and protect margins even in volatile conditions.<\/p>\n

Planning Has to Change Before AI Can Deliver<\/strong><\/h2>\n

Planning has always been central to retail performance. What’s changed is the volume, variety and velocity of data, as well as the need to make faster, more aligned decisions. The real challenge is more organizational than technical. People and process drive unified retail planning; AI is the mechanism to deepen that unification and enable more precise decision-making.<\/p>\n

Retailers that continue to rely on rigid, product-centric planning models will struggle to keep pace. Customer expectations won’t wait, and neither will the market.<\/p>\n

Where to Start: 4 Steps to Smarter AI Planning<\/strong><\/h2>\n

The first step is integrating external and internal data into a unified environment. From there, retailers can:<\/p>\n

    \n
  1. Use AI to monitor signals continuously<\/strong> rather than relying on static or quarterly planning.<\/li>\n
  2. Apply AI to scenario planning and forecasting,<\/strong> testing multiple demand and supply paths before making decisions.<\/li>\n
  3. Link insights directly to execution,<\/strong> influencing inventory, pricing and assortment decisions based on customer behavioral attributes.<\/li>\n
  4. Build cross-functional accountability,<\/strong> ensuring merchandising, supply chain, and finance teams all work from the same AI-driven insights.<\/span><\/li>\n<\/ol>\n

    It’s a challenge but also an opportunity. The tools exist. The signals are there. Now it’s about using them.<\/p>\n

    Matt<\/span>\u00a0Hopkins<\/span><\/a> is the global retail marketing director at Board<\/a>, an AI-powered planning platform.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    Retailers are putting more money into artificial intelligence every year. More than half (56 percent) have increased their spending on generative AI since 2024. However, despite the investment, most still aren’t seeing real business results. Too often, AI is confined to narrow optimization, such as forecasting, pricing or personalization — meaning core retail processes remain […]<\/p>\n","protected":false},"author":14816,"featured_media":84096,"comment_status":"closed","ping_status":"closed","template":"","coauthors":[139648],"class_list":["post-85328","article","type-article","status-publish","has-post-thumbnail","hentry","category-artificial-intelligence","category-inventory","category-inventory-management","tag-ai","tag-artificial-intellience","tag-demand-forecasting","tag-demand-planning","tag-forecasting","tag-inventory-management","tag-inventory-models","tag-social-commerce","editorial_type-trendsanalysis","collection-today-on-total-retail","vertical-retail"],"yoast_head":"\nWhy Retailers Are Still Missing the Moment With AI<\/title>\n<meta name=\"description\" content=\"Retailers that continue to rely on rigid, product-centric planning models will struggle to keep pace. 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