Every manufacturing leader has lived the same week. A top account calls in a rush order. The line is running at capacity. Somewhere in a regional warehouse sits three months of a slow moving SKU nobody wants, while the part that would unlock this week’s shipment is on backorder. Finance asks why working capital is tied up in inventory. Sales asks why the customer had to wait. Operations asks why nobody saw it coming.
This is not a data problem anymore. Most manufacturers already have the data. It is a decision problem, and it is exactly the problem the Fayette Chamber’s upcoming Advanced Manufacturing & Industry Network session is built to address.
On September 10, 2026, the Fayette Chamber will host manufacturing leaders in Peachtree City, Georgia for a session titled “AI, Availability & the Future of Supply Chain.” Taresh Grover, CEO of Pull Logic, will lead a practical discussion on how manufacturers can move past static reporting and toward AI powered decision tools that anticipate demand shifts, flag supply risk, protect product availability, and reduce inventory that is not earning its keep. The session is aimed squarely at operations, supply chain, procurement, sales, customer service, finance, IT, and executive leaders who all touch the same tension: growth versus cash versus service.
The timing is not incidental. Across manufacturing and distribution, AI has quietly moved from a side project to a budget line, and the data shows both real progress and a widening gap between adoption and actual transformation.
The Adoption Curve Has Bent, But Not Everyone Is Moving Together
The numbers on AI adoption in supply chain functions are no longer speculative. Demand for supply chain roles requiring AI skills rose 387% between Q1 2023 and Q1 2026¹, based on an analysis of more than 35 million job postings. That is not a signal of curiosity. It is a signal that companies are hiring for a capability they expect to need permanently.
The Hackett Group’s 2026 Supply Chain Key Issues Study found that 83% of participating organizations had deployed or were piloting AI in supply chain intelligence and analytics². Nearly three quarters, 74%, reported AI capabilities in sales and operations planning or integrated business planning, and 72% reported them in advanced planning and scheduling². On paper, AI looks close to mainstream in planning functions.
But mainstream adoption and mainstream transformation are not the same thing. Gartner found that only 17% of surveyed supply chain organizations were pursuing immediate, transformational redesign of processes and workflows around AI³. Most organizations are layering AI onto existing workflows rather than rethinking the workflow itself, and that distinction is where value tends to leak out.
Generative AI made this gap visible first. Gartner reported that 72% of surveyed supply chain organizations had deployed generative AI as of early 2025⁴, letting planners ask why demand shifted or request a summary of a supplier issue instead of digging through reports manually. Yet Gartner’s research also found that individual productivity gains were not consistently translating into team level or organization wide ROI⁴. A planner working faster does not automatically mean better inventory turns, higher service levels, or stronger purchasing performance. Faster answers are not the same as better decisions.
Budget allocation tells a related story. Gartner reports that 67% of supply chain digital investment now goes toward AI, yet 55% of chief supply chain officers say they remain unclear about the return their AI investments are generating⁵. Money is moving faster than measurement.
Where AI Adoption Stands in Supply Chain Planning, 2026
What "Availability" Actually Means in an AI Context
The FAMIN session frames the challenge around a word manufacturers know well: availability. It sounds simple, but availability sits at the intersection of demand accuracy, supply reliability, and inventory discipline, three things that are traditionally managed by three different teams looking at three different systems.
This is where AI based forecasting is showing measurable impact. According to McKinsey, AI powered forecasting reduces supply chain forecast errors by 20-50%, and cuts product unavailability by as much as 65%⁶. That is a direct line between forecasting quality and the moments manufacturers dread most, the order they cannot fill on time.
The inventory side of the equation shows a similar pattern. McKinsey research on distribution operations found that AI can reduce inventory levels by 20-30% by improving demand forecasting through dynamic segmentation and machine learning⁷. Separately, McKinsey found that early adopters of AI enabled supply chain management improved inventory levels by 35% and service levels by 65% compared to slower moving competitors⁸. Read those two figures together and the message is clear: the companies pulling ahead are not choosing between leaner inventory and better service. AI based planning is letting them get both at once, which directly contradicts the old assumption that better service always requires more safety stock.
Gartner’s newly released 2026 supply chain technology trends reinforce where this is heading next. As AI adoption scales, Gartner points to a growing need for decision governance, frameworks that ensure AI enabled decisions remain transparent, accountable, and auditable across complex supply chain processes⁹. In other words, the conversation is maturing from “can AI make a recommendation” to “can we trust and explain the recommendation it made.”
The Five Levers the FAMIN Session Covers
The session agenda maps closely to where manufacturers are actually losing money and losing customers. Attendees will walk through practical ways AI and digital supply chain tools help manufacturers:
- Identify availability gaps before they turn into lost sales
- Improve demand planning and replenishment decisions
- Simulate inventory and service trade offs before committing to a plan
- Reduce excess, obsolete, and slow moving inventory
- Improve customer service without simply adding more stock
- Connect commercial demand, supply planning, and operations into one responsive decision process
That last point is the one most organizations underestimate. Sales sees demand signals. Supply planning sees constraints. Operations sees capacity. Finance sees cash. When these functions work from separate spreadsheets and separate assumptions, decisions get made in silos and reconciled too late to matter. Connecting them into a shared, AI informed decision process is less about new software and more about closing the gap between what different teams know and when they know it.
From Siloed Reporting to Connected Decisions
Why This Matters More for Fayette County Manufacturers Specifically
Peachtree City and the broader Fayette County manufacturing base sit inside supply chains that stretch from automotive and industrial components to specialty equipment, much of it feeding customers who expect precision on lead times. These are exactly the mid market and enterprise manufacturers who tend to fall into the adoption gap Gartner describes, deploying pieces of AI in isolated tools without redesigning how planning, procurement, and operations actually make decisions together³.
Whether your company is early in its digital journey or already piloting AI, the value of a session like this is not the technology itself. It is seeing how peer manufacturers are structuring the decision process around it, and what that structure needs to look like before the tool matters at all.
A Practical Lens From Someone Who Builds This for a Living
This is precisely the territory Taresh Grover and the Pull Logic team spend their time in, not AI for its own sake, but AI applied to the specific, recurring decisions that determine whether a manufacturer hits its service targets without overbuilding inventory. Pull Logic’s own research has tracked how the shift from static reporting to AI powered decisioning is playing out across supply chains in 2026, including where the ROI is real and where it is still catching up to the hype¹⁰. The firm has also written specifically about how AI is changing inventory planning in practice¹¹, and about the next step many manufacturers are now evaluating: agentic AI systems that do not just recommend an action but can execute parts of the supply chain decision autonomously, within guardrails¹².
That agentic shift is worth watching closely. Gartner projects that supply chain management software with agentic AI capabilities will grow to $53 billion in spend by 2030, with roughly half of supply chain management solutions expected to include agentic AI capabilities by that same year¹³. Gartner has also placed autonomous planning past the peak of inflated expectations on its hype cycle, which typically signals that disciplined, real world deployments are starting to matter more than demos¹³. For manufacturers weighing whether now is the right time to move, that timing signal is significant. The tools are past the hype phase and into the phase where implementation discipline determines the outcome.
What to Bring to the Session
Manufacturing leaders attending on September 10 will get the most value by walking in with a specific question rather than a general curiosity about AI. A few worth considering:
Where, specifically, are lost sales happening because of availability gaps, and does anyone currently have visibility into that before the order is missed? How much working capital is currently tied up in slow moving or obsolete inventory, and has that number been benchmarked against what AI based inventory optimization typically achieves? And perhaps most importantly, which three teams in the organization would need to share a single decision process, rather than three separate reports, to actually close the gap between demand, supply, and service.
Those questions are exactly what separates the 83% of organizations piloting AI in analytics² from the 17% redesigning how decisions get made³. The session is built to help manufacturers move from one group to the other.
Sources
- [1] Gartner, reported via workforce analysis, June 2026: demand for supply chain roles requiring AI skills increased 387% between Q1 2023 and Q1 2026, based on more than 35 million job postings analyzed.
- [2] The Hackett Group, 2026 Supply Chain Key Issues Study: 83% of organizations deployed or piloting AI in supply chain intelligence and analytics; 74% in S&OP/IBP; 72% in advanced planning and scheduling.
- [3] Gartner, 2026: only 17% of surveyed supply chain organizations are pursuing immediate transformational redesign of processes and workflows around AI.
- [4] Gartner, February 2025: generative AI deployed by 72% of surveyed supply chain organizations; individual productivity gains not consistently translating to organization wide ROI.
- [5] Gartner, 2026: 67% of supply chain digital investment now allocated to AI; 55% of CSCOs report being unclear on the ROI of their AI investments.
- [6] McKinsey & Company: AI powered forecasting reduces supply chain forecast errors by 20 to 50%, cutting product unavailability by up to 65%.
- [7] McKinsey & Company, "Harnessing the power of AI in distribution operations": AI can reduce inventory levels by 20 to 30% through improved demand forecasting and dynamic segmentation.
- [8] McKinsey & Company: early adopters of AI enabled supply chain management improved inventory levels by 35% and service levels by 65% versus slower moving competitors.
- [9] Gartner, "Top Supply Chain Technology Trends for 2026": decision governance frameworks are becoming essential as AI adoption scales, to ensure transparent and auditable decisions.
- [10] Pull Logic, "AI Adoption in Supply Chain: 2026 Trends, Challenges, and ROI."
- [11] Pull Logic, "How AI Improves Inventory Planning."
- [12] Pull Logic, "Agentic AI for Supply Chain Automation."
- [13] Gartner, 2025 to 2026 supply chain hype cycle analysis: agentic AI supply chain management software spend projected to reach $53 billion by 2030, with autonomous planning past the peak of inflated expectations.
Frequently Asked Questions (FAQs)
What is the difference between traditional supply chain reporting and AI powered decisioning?
Traditional reporting tells you what already happened, last week’s fill rate, last month’s inventory turns, last quarter’s forecast error. It is backward looking by design, and by the time a report reaches a decision maker, the underlying condition has often already changed. AI powered decisioning works differently because it continuously ingests demand signals, supply constraints, and inventory positions to surface what is likely to happen next, not just what already occurred. Instead of a planner reading a report and manually deciding what to do, the system flags the specific SKUs, customers, or locations at risk and suggests a course correction before the gap becomes a missed order. The Fayette session focuses on this exact shift, moving manufacturers from a reporting posture to a decisioning posture across sales, planning, and operations.
How much can AI actually reduce forecast error and inventory levels for a manufacturer?
Based on McKinsey research, AI powered forecasting typically reduces forecast errors by 20 to 50%, and cuts product unavailability by as much as 65% in organizations that implement it well. On the inventory side, McKinsey has found that AI driven approaches can reduce inventory levels by 20 to 30% while simultaneously improving fill rates, and early adopters have reported inventory improvements as high as 35% alongside a 65% improvement in service levels relative to slower moving peers. These are not universal guarantees, results vary by data quality, product mix, and how well the organization redesigns its planning process around the tool. But the direction and magnitude of the numbers are consistent across multiple independent studies, which is why manufacturers are paying closer attention now.
Why are so many manufacturers piloting AI but not seeing organization wide ROI?
Gartner’s research points to a specific gap: while a majority of organizations have deployed or piloted AI in planning functions, only a small minority, around 17%, are pursuing a full redesign of the decision workflows those tools sit inside. When AI is bolted onto an existing process rather than used to rethink how demand, supply, and operations actually make joint decisions, the tool produces faster individual outputs without changing the collective outcome. A planner might get an answer more quickly, but if sales, procurement, and operations are still working from separate assumptions and separate meetings, the faster answer does not translate into better inventory turns or service levels. Closing that gap requires organizational change alongside the technology, not the technology alone.
What does "connecting commercial demand, supply planning, and operations" actually look like in practice?
In most manufacturing organizations today, sales tracks demand signals in a CRM or forecast spreadsheet, supply planning manages constraints in a separate planning system, and operations works from a production schedule that may not update in real time when either of the other two changes. Connecting these functions means building a shared, AI informed view where a shift in customer demand automatically flows into supply and production planning, and a supply constraint automatically flags the sales team before a commitment is made that cannot be kept. This does not necessarily require replacing every existing system. It requires a decision layer that pulls from all three and surfaces trade offs, more inventory versus longer lead time versus partial fulfillment, so the organization is choosing consciously rather than reacting after the fact.
Is agentic AI different from the generative AI most manufacturers already use?
Yes, and the distinction matters for planning purposes. Generative AI, the kind most manufacturers have already piloted, is primarily conversational. It helps a planner summarize a supplier issue or explain why demand shifted, but a human still takes the resulting action. Agentic AI goes a step further by taking bounded actions on its own, adjusting a replenishment order, flagging and rerouting an at risk shipment, or triggering an alert workflow, within guardrails set by the organization. Gartner has tracked agentic AI as one of its major supply chain technology trends, projecting that supply chain management software with agentic capabilities will grow to $53 billion in spend by 2030, with roughly half of supply chain solutions including agentic features by then. It is a meaningful next step, but one that depends on strong decision governance to be trustworthy.
Who should attend the Fayette Advanced Manufacturing & Industry Network session on September 10?
The session is designed for a cross functional audience because availability, inventory, and service are cross functional problems. That includes operations and supply chain leaders who manage the physical flow of goods, procurement leaders balancing supplier reliability against cost, sales and customer service leaders who own the customer facing consequences of a stockout, and finance and IT leaders who need to understand both the working capital impact and the technology requirements. Executive leadership is also a strong fit, since the decision to move from siloed reporting to a connected, AI informed planning process is ultimately a strategic one. Whether a company is early in its AI journey or already running pilots, the session is structured to give practical, applicable takeaways rather than a high level technology overview.
