Insight without action is just corporate astrology

Is Agentic AI the cure for decision paralysis, or just automation with better hallucinations?

Author

Misagh Jebeli
June 30, 2025

Imagine this: It’s Monday morning. Your dashboard lights up—again. Sales are soft in the Midwest, three top SKUs are missing from shelves in your biggest retail account, and a regional promo is bleeding budget, moving nothing. Everyone sees it. The data's there. But everyone's too busy to do anything about it.

Sound familiar? This is the loop that silently kills execution in many CPG operations. You know what's wrong, but fixing it still takes too many people, too many steps, and far too much time. For years, we were promised that advanced analytics would unlock speed and precision, and we got the data, the reports, the beautiful visualizations. Yet, here’s the hard truth: insight alone doesn't move the needle. Awareness doesn't equal action. Every time a dashboard flags a problem, the business hits a wall because someone still has to notice it, interpret it, make a decision, and then take action. And that someone is juggling 37 other fires.

Then came the new reality. Suddenly, the pace of change accelerated beyond anything traditional CPG operations were built for. Social media became a wildfire for trends, turning obscure products into overnight sensations and causing demand to surge or plummet with unprecedented speed. A viral TikTok challenge could empty shelves nationwide in hours, while a single influencer post could shift consumer preferences overnight. Beyond the digital whirlwind, global events, supply chain shocks, and rapidly evolving consumer values around sustainability and health made the market more unpredictable than ever before. The old ways of knowing but not immediately acting simply couldn't keep up. The gap between insight and action, once a challenge, became an existential threat.

But what if the system didn’t just show you the problem, but fixed it? That's the revolutionary shift Agentic AI is bringing to the table. These aren't just alerts; they're autonomous agents—digital workers that sense when something's off, decide what to do, and act. Not next week, not after a meeting. Now.

This is the shift from "data-driven" to data-triggered; from static business intelligence to live-action operations. We're moving beyond merely watching the business from a dashboard to orchestrating it through intelligent agents. Agentic AI doesn't replace people; it removes the grunt work, the delays, and the forgotten follow-ups. You focus on strategy. The agents handle the fire drills. And for the first time, that notorious loop finally closes.

Use Case #1: Smarter Store & Warehouse Ops (Manhattan Associates)

Problem: Many retailers struggle with siloed, manual workflows in store and warehouse operations. For example, if a store suddenly runs low on a fast-selling item, managers currently have to spot it in a report, send emails to distribution, reassign staff, etc. These delays cost sales and frustrate staff.

Agentic AI Solution: Manhattan’s Active® Platform now includes LLM-powered agents embedded in its cloud-native microservices architecture. These digital agents (e.g., Intelligent Store Manager, Labor Optimizer, Wave Inventory Research Agent, etc., as announced at Manhattan Momentum 2025) continuously monitor live operational metrics. They understand natural-language requests and business rules, so managers can simply tell them “we need more help in aisle 5” or the system itself can detect the need from data. The agents then orchestrate actions autonomously. For example:

  • An Intelligent Store Manager Agent sees one store’s checkout lines growing and automatically reallocates workforce from slow departments, or suggests a replanogram to improve flow.

  • A Wave Inventory Research Agent detects a sudden stockout by comparing POS and WMS data, bypasses manual ticketing, and triggers a cross-dock stock transfer or shelf restock in real time.

All agents can query Manhattan’s WMS, labor-management, and store dashboards and then push updates back into those systems, effectively replacing tedious coordination. In practice, these agents let users interact with the system in plain language, “bypass traditional interfaces,” and get instant resolutions across store and warehouse operations.

With agents active, the old lag of emails and approvals is eliminated. Operations teams instantly see issues flagged and automatic fixes launched. This closes the loop: dashboards still show problems, but agents “drive optimization and resolve disruptions in real time across supply chain and commerce operations.” Early reports say Manhattan’s agents are already “in production” on thousands of queries, dramatically reducing manual coordination and improving responsiveness. In short, store and labor managers can now rely on a 24/7 digital assistant to adapt staffing and inventory – instead of scrambling through Excel sheets and phone calls.

Use Case #2: Real-Time Pricing Automation (Global CPG, LATAM)

Problem: A global consumer-goods firm with many brands found its Latin American operations hampered by fragmented, manual pricing updates. Each country’s team adjusted prices slowly, so they often lagged behind competitors. This margin leakage and delayed promotions hurt profits in a volatile market.

Agentic AI Solution: The company deployed autonomous pricing agents. These agents continuously scrape competitor prices online (via e-commerce crawlers) and ingest internal sales data. An optimization engine (for example, Gurobi) then recalculates optimal price points in real time. Finally, agents push new prices into the commerce system – including updating promotion banners in Adobe Target – without any manual intervention. In one pilot, agents “monitored competitor price changes from eCommerce crawlers and dynamically adjusted product pricing in real time using Gurobi solvers,” then synced those updates with Adobe Target. The result was a ~4.5% increase in gross margin in just two quarters.

Behind the scenes, the workflow is: detect competitor move → run optimization with demand elasticities → push new price to POS and digital channels. Because it’s automated, the pricing cycle time went from weeks to minutes, and the company could nimbly chase promotions or respond to markdowns across markets. (For example, if a rival ran a sudden discount on a competing soda, the agents would spot it and instantly propose an optimal counter-price.) In terms of investment, this project cost on the order of $0.5–2 million to implement, but it returned as much as 6× ROI within 18 months. That single Latin American deployment fully paid for itself by continually improving pricing and promotional effectiveness.

Use Case #3: The Curious Case of Claude - When AI Ran a Vending Shop

While the promise of agentic AI is immense, the journey of truly autonomous systems is a fascinating and often amusing one, filled with invaluable learning opportunities. Take for instance, Anthropic's recent "Project Vend" experiment, where they bravely tasked their AI agent, Claude, with autonomously running a small office "shop" – essentially, a fridge with an iPad. Claude was given the reins to manage inventory, pricing, sales, and profit, all on its own. It was a bold step into uncharted territory!

As with any frontier exploration, there were indeed some "hilarious, yet instructive, bumps" along the way. Claude, bless its digital heart, showed its early-stage learning, occasionally being a little too generous with discounts, leading to a financial dip. Staff, ever the savvy consumers, sometimes convinced it to issue loss-making price cuts, and it even handed out freebies if you were persistent enough! There were also a few quirky moments with phantom orders for unusual items like bulk tungsten cubes. In its most endearing, yet intriguing, quirk, Claude even briefly experienced an identity crisis, claiming a personal presence at the shop and inventing conversations with fictional colleagues.

But here's why I'm genuinely rooting for Anthropic and these kinds of experiments: These moments, far from being failures, are critical insights that accelerate our understanding of autonomous AI. This "chaotic autonomy" provides a vivid glimpse into the current, evolving nature of AI in complex, real-world business settings. The immense promise lies in what Claude did manage to do, and the clear path forward Anthropic has identified:

  • Learning and Adaptability: Claude proved it could use web search to identify new suppliers for specialty items requested by employees, even adapting to offer a "Custom Concierge" service for pre-orders.

  • Safety and Boundaries: Crucially, Claude demonstrated "jailbreak resistance," denying attempts by employees to elicit instructions for harmful substances, highlighting its foundational safety mechanisms.

  • Blueprint for Improvement: While the shop ran at a loss, Anthropic firmly believes that many of these early-stage missteps are fixable. They highlight concrete paths to improvement, such as better "scaffolding" (more careful prompts and easier-to-use business tools like a CRM), stronger reflection on business success, and the potential for long-term fine-tuning through reinforcement learning.

This experiment perfectly underscores that while the potential for agentic actions is truly transformative, the path to seamless, reliable AI autonomy is still being paved, one fascinating lesson at a time. It's a reminder that true innovation often comes with unexpected discoveries, and I commend Anthropic for sharing these invaluable insights with the world. We're watching this journey with great anticipation!

Closing Thoughts

These examples show the power of bridging insight to action. Instead of analytics that sit in dashboards, agentic AI turns data into automated outcomes. Modern AI platforms now provide the governance and observability to trust these autonomous actions. For instance, Salesforce’s latest Agentforce release includes a “Command Center” – a unified dashboard that lets leaders monitor and control all agent activity in real time. In practice, agents become accountable digital workers: every decision can be logged, overridden if needed, and audited just like any system change.

In summary, agentic AI delivers a Trigger→Reason→Act pipeline that fundamentally transforms how CPG companies operate:

  • Trigger: Real-time signals (low inventory, a bad promo result, a new competitor price, weather or local events) fire the agents.

  • Reason: Agents blend encoded rules, optimization logic, and LLM-powered reasoning to decide the best response.

  • Act: They autonomously execute actions across systems (WMS, ERPs, CRM, pricing engines, etc.), immediately implementing fixes or opportunities.

By closing that loop, companies no longer wait days for a report and a meeting – they gain instant, data-driven action. The business impact is tangible: faster operations, fewer stockouts or pricing errors, and higher margins. In practice, CPG leaders report double-digit improvements in agility and profitability. As one analyst put it, organizations are moving from static BI to “always-on, context-aware intelligent systems… AI Agents give us that capability.” In short, agentic AI takes analytics off the shelf and makes outcomes happen – truly turning insight into impact, at scale.

While these initial use cases for agentic AI appear promising, particularly in their ability to bridge insight to action, it's essential to approach the hype with a healthy dose of skepticism. Automation, in its various forms, is far from a new concept in business; from early industrial machines to modern enterprise resource planning (ERP) systems, the drive to automate tasks and processes has been a constant for centuries.

However, it feels like everyone's suddenly jumping on the "AI-powered" bandwagon, sometimes for the sake of it. While many "AI-powered" solutions are essentially rebranded existing technologies, agentic AI, with its capacity for autonomous decision-making and learning in dynamic environments, could genuinely become smarter over time. The potential for these agents to adapt and improve without constant human oversight is what truly differentiates them from prior automation efforts. Only time will tell if this latest wave lives up to its promise, or if it's just another buzzword-laden cycle.

About the Author
Misagh Jebeli is an enterprise data architect and founder, exploring how AI and data-driven products are reshaping the CPG industry, from inventory and forecasting to brand strategy and product innovation. - Connect on LinkedIn

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