The gap between knowing a supplier will be late and doing something useful about it often determines whether you hit margin targets or burn cash on expedites. Yet most procurement teams still learn about changes through scattered emails and phone calls — after their best options have disappeared.
It’s time to stop treating procurement and inventory as separate domains. When you build decisions around a real-time inventory twin — a unified view of what’s on hand, in transit, and on order at the PO line level — supplier signals become actionable intelligence. A three-day delay doesn’t trigger panic; it triggers options.
And now, with the emergence of AI agents, you can automate the reading of unstructured supplier updates and immediately translate them into inventory impacts, giving your teams time to make smart trade-offs instead of expensive saves.
Consider what this looks like in practice. Traditionally, when plans are disrupted, emails fly, meetings convene, someone eventually asks about inventory impact, and by then, you’re choosing between bad and worse. With an AI-powered inventory twin, the system instantly shows which DCs will drop below coverage, presents reallocation options from nearby sites, makes it easy to calculate the cost of partial consolidation versus expedited shipping, and routes the decision to the right person with context already assembled.
The outcome shows up where leaders care: fewer expedites, steadier first-promise performance, and dramatically fewer all-hands saves at quarter-end. Most suppliers default to email communication anyway, so let AI agents meet them there, parsing updates and maintaining the twin without forcing another portal on anyone.
What AI changes — and what it doesn’t
AI doesn’t set strategy. It reduces friction between good intentions and daily work. Agents can read unstructured supplier updates like emails, spreadsheets, and PDFs, along with EDI or portal feeds. They can ask for missing fields, map signals to the correct PO line, and push clean updates to teams that need them.
For a leadership lens, focus on three things:
- Access. Don’t force every supplier into a new tool. Let AI agents read emails, documents, and spreadsheets, fill in missing fields, and map updates to the right PO line, then post a clean record for everyone else.
- Speed. Earlier signals create real choices. Instead of a blanket expedite, you can reallocate from a nearby DC, consolidate partials, or upgrade a single leg.
- Confidence. Recommendations land better when the system explains cost and service trade-offs in plain terms. MIT Sloan frames this as improving the choices on the table — not just the prediction score.
Imagine a scenario where a supplier pushes a ready date three days before a retail reset. A live inventory view shows two plants dipping below coverage. Options arrive together: pull 20% from a nearby DC, combine partials to keep truckload economics, and upgrade one inland leg while keeping ocean as planned. The team picks the mixed option. The reset date holds, and airfreight stays off the table.
In a seasonal spike, several suppliers confirm quantity changes late. The inventory picture updates the same day, inbound shifts to a coastal DC, a cross-dock window moves up 24 hours to consolidate, and two suppliers get an automated request for missing ASN fields. Store availability holds through the weekend, and safety stock stays put.
But not every exception deserves attention. If bags of pistachios arrive two days late, it may not move the needle. If Super Bowl end caps arrive after the game, it absolutely does. The point of the inventory twin is to sort those situations in real time so teams don’t burn cycles where it won’t matter.
What to look for
Aim for “automate the routine, escalate the rare.” Capacity is finite. If 100 escalations land and your team can handle 20, the system should tell you which 20. Look for prioritization that ranks exceptions by business impact — revenue at risk, margin at risk, and promise risk — not by who shouted loudest. This is where AI earns its keep: assembling context, scoring impact, and presenting a short list leaders can act on.
Daily work should run on its own under clear policies: pulling supplier updates, fixing missing fields, syncing POs to inventory, nudging for confirmations, and moving dock times. When trade-offs are real or signals are fuzzy, the system should hand the case to a person with a short list of options, expected impact, and the reason it picked them.
This only works with a live picture of orders and inventory to act on. Look for a digital twin that stays current at the PO and line level, not just at the shipment. When a supplier moves a date or quantity, the twin should update the same day and downstream plans should adjust within hours. That twin needs a broad data network — suppliers, carriers, forwarders, ports — so you aren’t waiting on one system to refresh.
Expect agents that speak your language. They should understand common documents and terms, map them to the right order lines, and apply domain logic without heavy tinkering. Think practical judgment: recognizing when a partial makes sense, spotting consolidation opportunities, or noticing an Incoterms detail that changes who needs to act.
Keep controls simple and visible. Leaders should set guardrails by budget, service tier, or customer promise. The system should explain every recommended move in business terms, show expected cost and service effects, and keep an audit trail so finance and operations trust the outcomes.
Finally, look for a clean handoff. When the agent pushes a case to a human, it should arrive with context already assembled: the exposure window, options on the table, and a clear summary of why one path is preferred. That’s how you move fast without losing judgment, and how automation frees teams to focus on decisions that actually need them.
Automate the routine, escalate the rare
The aim is straightforward: let software handle the busywork, and bring people in when judgment matters. In practice, that means AI that can keep the inventory twin current, triage the day’s exceptions to the few that matter, and close supplier gaps by reading the channels people already use. When trade-offs carry real cost or service implications, the case lands on a person’s desk with options, expected impact, and the “why” already laid out.
That setup pays off across the table. A CSCO gets one view of inventory that moves as plans change. Exceptions surface early, and the team can test options in hours instead of weeks. Finance sees the lift as well. You spend less on last-minute freight and make faster, cleaner decisions that keep cash productive. McKinsey notes that early wins in working capital can create momentum for broader change, which matches what many leaders have seen firsthand.
Commercial leaders care most about promise reliability. When the digital twin updates the same day a supplier moves a date, customer-facing teams are not whipsawed mid-cycle. There are fewer calls to reset expectations, fewer partials that surprise the field, and a launch calendar that holds.
If there is one takeaway, make it this: wire procurement and execution around a living view of inventory, let automation clear the noise, and keep humans in the loop for the decisions that carry real cost and service trade-offs.
Matt Elenjickal is the Founder and Chief Executive Officer of FourKites. He founded FourKites in 2014 after recognizing pain points in the logistics industry and designing elegant and effective systems to address them. Prior to founding FourKites, Matt spent 7 years in the enterprise software space working for market leaders such as Oracle Corp and i2 Technologies/JDA Software Group. Matt has led high-impact teams that implemented logistics strategies and systems at P&G, Nestle, Kraft, Anheuser-Busch Inbev, Tyco, Argos and Nokia across North America, Western Europe and Latin America. Matt is passionate about logistics and supply chain management and has a keen sense for how technology can disrupt traditional silo-based planning and execution. Matt holds a BS in Mechanical Engineering from College of Engineering, Guindy, an MS in Industrial Engineering and Management Science from Northwestern University, and an MBA from Northwestern’s Kellogg School of Management. He lives in Chicago.
