Business leaders everywhere are saying the same thing: “We need to use AI.” But for many supply chain teams, the pressure to implement AI can often come without a shared understanding of the problems AI can solve, or how it will actually deliver value.
AI delivers value in supply chain planning when it’s embedded directly into how planning decisions are made, as opposed to implementing it as a standalone experiment. Modern planning platforms like the Atlas Planning Platform from John Galt Solutions are designed around this principle, combining AI and machine learning into everyday planning workflows to improve key areas like demand planning, inventory decisions, and the speed of response amid volatility.
AI in supply chain planning is not about chasing technology trends – it's about solving persistent, often normalized problems that quietly erode revenue, working capital, and planner effectiveness.
If any of the challenges below feel familiar, your company may already be primed to benefit from AI.
1. Your Planners Spend More Time Cleaning Data Than Improving the Plan
If your team regularly says, “We can’t run the forecast yet. The data isn’t ready,” that’s a signal that shouldn’t be ignored.
Many supply chain organizations rely on dozens of internal and external data sources for demand forecasting: ERP data, POS data, promotions, market signals, weather, and more. Today, much of that work is still manual; cleansing files, harmonizing formats, rerunning models, and fixing errors before any real analysis can begin.
Where AI helps: AI automates data cleansing and harmonization, continuously preparing data in the background. Instead of spending hours fixing inputs, teams can focus on evaluating outputs, testing scenarios, and improving decisions. The value is more than just efficiency; it’s enabling planners to do the work that actually drives better outcomes.
2. You Rely on Static Forecasts That Can’t Keep Up With Reality
If your forecast is updated monthly or it takes weeks to generate, chances are you’re reacting too late to demand shifts.
Market volatility, social media trends, promotions, weather events, and economic changes can swing demand almost overnight. Traditional forecasting approaches struggle to incorporate these signals quickly or consistently, which is why AI is a game-changing lever for supply chain teams consolidating all this data.
Where AI helps: AI and machine learning enable demand sensing, continuously ingesting new signals to tune forecasts. That means better visibility, faster reaction times, and fewer surprises. Instead of waiting for the next planning cycle, you can respond while there’s still upside to capture.
3. Inventory Feels Either Too High or Never in the Right Place
If you’re holding excess inventory and still missing sales, the problem isn’t just too much stock, but where, when, and why inventory is positioned.
Traditional safety stock rules and replenishment logic often can’t handle today’s complexity including multiple demand patterns, variable lead times, supplier disruptions, and competing service-level targets.
Where AI helps: AI-driven inventory optimization uses more sophisticated scenarios to set intelligent safety stock, dynamically adjust replenishment, and position inventory where it creates the most value and improves service levels.
Teams looking to gain control of their inventory can use software solutions like Atlas Inventory Planning to leverage AI and balance everything from tactical target planning to Multi-echelon Inventory Optimization (MEIO), unlocking working capital while improving profitability.
4. Supplier Issues Catch You Off Guard
Late deliveries, early shipments, quantity mismatches… If supplier issues consistently disrupt your plans, you may be treating risk as a surprise instead of a probability.
Many organizations track supplier performance, but can they translate that data into forward-looking insight? Or answer questions like: How likely is this supplier to miss the next delivery? And what should we do now?
Where AI helps: AI enables supplier risk management by analyzing historical performance patterns and external signals to estimate the probability of disruption. That insight allows planners to proactively adjust production, inventory positioning, or sourcing strategies. This leads to reduced lost sales and operational firefighting.
5. You Know There’s a Problem, But Not the Cause
Perhaps the most overlooked sign: you’re constantly fixing issues, but you’re not confident you understand the root causes.
Without clarity on why problems occur, even the best technology will fall short. It’s like using a high-end kitchen appliance without knowing how to cook. The tool is powerful, but the results disappoint.
Where AI helps: Generative AI (GenAI) accelerates root-cause analysis by compiling, summarizing, and correlating data across sources. Instead of manually digging through reports and dashboards, teams can quickly surface insights that explain what’s really happening, and where to focus next.
Advanced supply chain planning software delivers explainable AI capabilities. For example, Galt Intelligence in the Atlas Planning Platform allows supply chain teams to use GenAI to ask questions and uncover relationships in data that aid in root cause analysis for supply chain issues.
Start With the Problem, Not the Technology
While much of today’s AI conversation centers on efficiency and productivity, the real payoff goes further:
- Capturing lost sales through better service levels
- Reacting faster to promotions and demand spikes
- Reducing lost opportunity from supplier disruption
- Optimizing inventory to free up working capital
- Supporting top-line growth while protecting margins
Before scoping AI solutions, organizations must first assess where friction exists, what’s causing it, and which decisions matter most. AI works best when it’s applied intentionally, step by step, aligned to business outcomes rather than pressure from the top.
That’s why many companies are best to work with a partner who can guide that maturity journey. For example, John Galt Solutions supports organizations through its Pathways to Evolve methodology, helping teams progressively mature their supply chain planning journey using the Atlas Planning Platform, applying AI where it makes sense, and where it delivers real value.
If your supply chain feels bogged down by manual work, slow reactions, and unclear root causes, you may be ready for AI to help solve the problems that have been holding you back. Let’s talk about it!
