Last year, AI in supply chain planning shifted from buzzword to baseline expectation for many small-to-medium-sized businesses (SMBs). Those that adopted AI-driven tools reported better forecast accuracy, improved service levels, and faster decision-making. The ones that waited found themselves falling further behind as volatility increased and manual processes couldn’t keep pace.
2026 likely won’t slow down. The same pressures that made AI essential in 2025, such as demand variability, supplier unreliability, and rising costs, are intensifying in some markets. But this is what’s different: AI isn’t experimental anymore. It’s proven. And the businesses prepared to leverage these five trends will have a significant advantage over those still debating whether AI is worth the investment.
Quick insights for decision-makers
- AI-powered forecasting moved from “nice to have” to “expected” as manual and ERP-native models created visibility gaps in volatile markets.
- Predictive visibility is replacing descriptive dashboards, giving SMBs the ability to anticipate problems rather than just track current positions.
- AI is transforming supplier collaboration by tracking lead time patterns, flagging reliability issues, and enabling proactive communication.
- Working capital optimization through AI helps SMBs identify hidden cost creep, reduce “inventory tax,” and free up cash without sacrificing service levels.
- Planners’ roles are evolving as AI handles routine decisions, allowing teams to focus on strategy, negotiations, and scenario planning rather than data cleansing.
Trend #1: AI-powered forecasting moves from “helpful” to “expected”
The 2025 Netstock Benchmark Report, which surveyed more than 2,400 SMBs around the world, found that AI-powered forecasting was the most used application in both 2024 and 2025. This advanced capability was leveraged by 52% and 63% of SMBs, respectively. This enabled more accurate forecasts that identified demand shifts ahead of time, leading to better business operations and service levels. On the other hand, businesses using manual forecasting or enterprise resource planning (ERP)-native tools easily miss demand shifts, leading to stock-outs during peaks and excess inventory during slower seasons.
AI-powered forecasting changes this by analyzing demand patterns across multiple dimensions, then applying the forecasting method that fits each SKU’s unique behavior. When demand spikes unexpectedly, AI adjusts forecasts in real time rather than waiting for humans to notice the trend and manually update projections.
The 21% increase in AI forecasting year-over-year (YoY) reflects the prominence of this trend. What was considered advanced technology is now table stakes. Customers expect reliable delivery. Investors expect efficient inventory management. Competitors are already using AI to optimize their forecasts. SMBs still relying on spreadsheets or basic time-series models in their ERP are operating at a permanent disadvantage.
Trend #2: Real-time inventory visibility becomes predictive, not just descriptive
Most ERP systems show you what inventory you have right now. That’s descriptive visibility, and it’s the minimum requirement. Descriptive visibility doesn’t help you avoid problems. It just confirms they’ve already happened.
Alternatively, predictive visibility tells you what you’ll need at each location over the next 30, 60, or 90 days. It identifies where excess inventory sits across multiple sites and calculates whether redistributing that excess makes more sense than placing new orders. It spots when a stock-out could occur and recommends action before customers notice.
For multi-location SMBs, this distinction really matters. One warehouse might have three months of safety stock for an SKU, while another location runs out next week. Descriptive visibility shows both facts separately. Predictive visibility connects them and recommends a transfer before the stock-out happens.
AI makes predictive visibility possible by continuously analyzing historical demand, current inventory positions, incoming supply, and forecasted needs across your entire network. At a high level, these actions deliver excellent service levels (above 90%), which were reported by 46% of SMBs in 2025, up from 41% the previous year. This improvement showcases that the businesses leveraging this trend aren’t deterred by turbulent times. Instead, they’re using purpose-built inventory management technology to adapt operations, save time, and even expand their business.
Trend #3: AI becomes the engine behind supplier collaboration and lead-time management
In 2025, 27% of SMBs adopted AI for supplier performance tracking and management, up from 20% in 2024. When evaluating data from the recent benchmark report, this relative increase of 35% appears to be a direct result of SMBs reacting to and resolving top supply chain challenges.
The particular challenge associated with this trend is lead-time variability, which ranked as the top challenge for 68% of SMBs in 2025. Suppliers who used to deliver consistently now miss windows regularly. Promised two-week lead times stretch to four or five weeks. Emergency orders become routine because you can’t trust original delivery dates.
AI supports supplier collaboration by tracking actual delivery performance over time and detecting patterns that traditional systems miss. This balances lead time volatility, allowing businesses to maintain production schedules and service levels.
If a supplier consistently ships late during certain months, AI flags it so procurement can adjust ordering schedules proactively. If lead times for specific materials trend longer, safety stock recommendations update automatically rather than waiting for manual recalculation.
This intelligence enables better supplier conversations, too. Instead of reactive complaints when deliveries arrive late, procurement teams can share data-driven performance trends and work collaboratively on solutions. Shared dashboards and automated reporting make these conversations more productive because both parties see the same information.
For SMBs managing dozens of suppliers across hundreds or thousands of components, AI-driven supplier performance management means less time firefighting and more time building strategic relationships with vendors who actually deliver on their commitments.
Trend #4: SMBs adopt AI to improve working capital and reduce “inventory tax”
Recent benchmark data showed increasing cost pressure from excess inventory. Every industry sector in North America carried excess close to, if not above, half of its inventory value. (Manufacturing: 50%; Retail: 48%; Wholesale: 49%; Other: 52%). Many of these SMBs carrying excess inventory couldn’t explain why or identify which SKUs tied up unnecessary capital.
This is the “inventory tax,” which can be defined as capital locked in stock that isn’t moving, space occupied by items that should have been sold months ago, and carrying costs that quietly erode margins. AI makes this visible by analyzing inventory turnover patterns, highlighting slow-moving SKUs, and recommending actions to free up working capital without sacrificing service levels.
Scenario modeling becomes essential here. What happens if we reduce safety stock on low-velocity items? How does demand respond if lead times extend another week? What’s the financial impact of redistributing excess from Location A to Location B versus placing a new purchase order? AI runs these scenarios in seconds, showing CFOs and operations leaders exactly how decisions affect cash flow.
Trend #5: AI elevates planners with faster, automated decision support
Supply chain planners face mounting pressure: balancing training for entry-level staff while learning new technologies themselves. The competencies required to succeed keep changing rapidly, and many senior team members need reskilling to stay current.
At the same time, AI demand planning has become central to business performance, with 55% of SMB reporting this as an AI use case in 2025. Businesses recognize that planners create total value across end-to-end operations, but the workload has grown unsustainable. Planners spend too much time on data cleansing, manual report building, and routine decisions that could be automated. That’s where AI comes in.
AI changes this dynamic by handling repetitive work. It automatically cleanses data, identifies exceptions, generates forecasts, optimizes safety stock, and recommends replenishment actions. What used to take planners 15 hours per week now happens automatically, freeing them to focus on higher-value activities: negotiating better supplier terms, modeling growth scenarios, collaborating with sales on promotional planning, and developing strategic inventory policies.
“What used to take us a full day using our manual process, now takes minutes, and as a result, we have re-purposed a large part of an employee’s role into another area of the business,” says James Politeski, President of DCL Supply USA.
This shift elevates planners. The planners who embrace AI as a decision support tool gain leverage. They make better decisions faster because AI surfaces insights they couldn’t see manually. They respond to disruptions proactively rather than reactively. And they contribute more strategically because they’re not drowning in spreadsheets.
“The ability to have all the information in one place has freed up some of our employees’ time, enabling them to focus on other things, which has been a huge ROI for us,” said Chuck Albanese, Director of Materials, Keir Surgical.
The AI planning maturity curve: Where SMBs should aim in 2026
Most SMBs fall somewhere along this progression:
- Manual: Spreadsheet-based forecasting, reactive decision-making, limited visibility. Planners spend most of their time on data entry and basic calculations.
- Reactive ERP-dependent: Using native ERP forecasting modules, but still largely reactive. Systems record what happened but provide limited intelligence about what should happen next.
- Predictive AI-assisted: AI tools supplement ERP data with advanced forecasting, automated recommendations, and predictive alerts. Planners shift from reactive to proactive.
- Fully optimized AI-driven: End-to-end AI integration across forecasting, replenishment, supplier management, and scenario planning. Systems continuously learn and adapt, with human oversight focused on strategy and exceptions.
The goal for most SMBs in 2026 isn’t reaching “fully optimized” immediately. It’s moving up one level from wherever you currently operate. If you’re still largely manual, adopting AI-assisted forecasting represents a massive improvement. If you’re already AI-assisted, enhancing supplier collaboration and scenario modeling capabilities makes sense.
The businesses that delay adoption, waiting for perfect timing or complete certainty, will find themselves stuck in the manual or reactive categories while competitors pull ahead. Starting early in 2026 ensures you see full-year impacts from improved planning. Better service levels, lower inventory costs, and stronger margins compound throughout the calendar year.
What to look for when selecting AI solutions
Not all AI-powered platforms deliver the same benefits or value. When comparing vendors, focus on these criteria:
- Security and data protection that meet enterprise standards. Your forecasting data is strategic. You should treat it accordingly.
- Real outcomes, not just features. Look for case studies showing measurable improvements in forecast accuracy, service levels, and inventory optimization from a business similar to yours.
- Integration with existing systems. AI tools should work with your ERP, not replace it. Bidirectional data flow ensures recommendations feed back into workflows planners already use.
- Built for SMBs specifically (not just enterprises). Implementation should take weeks, not quarters. The platform should be designed for lean teams without dedicated IT departments.
How Netstock helps SMBs leverage AI today
Armed with knowledge of these emerging trends and insights into what to look for, the question most SMBs should ask isn’t, “Should we adopt AI?” It’s, “How do we actually start?”
Netstock’s AI Pack is built specifically for growing SMBs. The platform integrates quickly with ERPs and doesn’t require specialized IT resources to maintain it.
More than this practical advantage, however, what makes Netstock different is the solution’s focus on practical outcomes. AI-driven forecasting automatically selects the best method for each SKU. Automated replenishment recommendations account for lead times, seasonality, and supplier performance. Multi-location visibility shows where excess sits and where transfers make sense. Scenario modeling lets you test decisions before committing capital.
The results show up fast. “Someone with no training can log in and get value on day one,” said Jacob Moody, CIO at Bargreen Ellingson.
“It’s like a second set of eyes on your work and an extra reminder to keep the team on track and ensure we’re catching things before they become problems,” said Marc Marchese, Assistant Operations Manager at Metalworks. “Before, we were always reacting. Now, with richer analytics at our fingertips, even insights we hadn’t asked for, we’re much more prepared, and our customers notice the difference.”
This value isn’t theoretical. If you don’t take our customers’ word for it, consider that Netstock’s Opportunity Engine has generated over $1 million in identified savings for businesses by surfacing optimization opportunities that could have gone unnoticed in manual processes.
