The modern supply chain is facing a data crisis. Operations leaders are drowning in information but starving for insights. You have a Warehouse Management System (WMS), a Transportation Management System (TMS), an Enterprise Resource Planning (ERP) tool, thousands of spreadsheets, and countless email threads.
When a shipment is delayed, or a customer asks a critical question, how long does it take your team to find the answer?
For many logistics professionals, the answer is “too long.” Rising operational costs and chronic labor shortages mean you can no longer afford to have high-paid staff spending 20% of their day searching for documents.
This brings us to a critical question driving the future of logistics technology: Who will own your company’s AI layer? Glean’s CEO explains that the winner of the AI race won’t necessarily be the one with the best chatbot, but the one who can successfully unify enterprise data.
This article explores the concept of the “AI Layer” in logistics, breaking down the insights from industry leaders like Glean, and providing a roadmap for operations executives to harness this technology for efficiency and cost reduction.
What is the AI Layer in Logistics?
To understand the insights behind Who will own your company’s AI layer? Glean’s CEO explains, we must first define what an “AI Layer” actually is in the context of supply chain operations.
In simple terms, an AI Layer (often referred to as Enterprise Search or Retrieval-Augmented Generation/RAG) is a software infrastructure that sits on top of all your existing applications. It connects your fragmented data silos—WMS, TMS, Slack, Google Drive, Outlook, Salesforce—into a single, searchable intelligence engine.
The Problem: The Fragmented Supply Chain Stack
Logistics companies typically operate with a “best-of-breed” software approach. You might use SAP for finance, Manhattan for warehousing, and emails for carrier communication.
- The Disconnect: These systems rarely talk to each other perfectly.
- The Friction: To answer a question like “Why was the shipment to Walmart delayed?”, a manager must check the WMS for pick times, the TMS for carrier status, and email for driver updates.
The Solution: Permission-Aware Intelligence
The core concept that Glean’s CEO, Arvind Jain, emphasizes is permission-aware AI. This is the defining characteristic of a corporate AI layer versus a public tool like ChatGPT.
When a warehouse supervisor asks the AI a question, the AI should only access data that the supervisor is allowed to see. It acts as a universal translator and librarian for your internal company knowledge, ensuring security while democratizing access to information.
Why Now? The Urgency of AI Adoption in Supply Chain
The logistics industry is currently navigating a perfect storm of challenges that makes the adoption of an AI layer not just a luxury, but a necessity.
Exploding Data Complexity
Global supply chains generate massive amounts of unstructured data. According to industry estimates, over 80% of enterprise data is unstructured (emails, PDFs, contracts, slack messages). Traditional analytics tools (BI dashboards) only look at structured rows and columns.
Without an AI layer to “read” and index your unstructured data, you are making decisions based on only 20% of the available information.
The “Great Crew Change” and Labor Shortages
The logistics workforce is aging. Experienced dispatchers and warehouse managers hold decades of institutional knowledge in their heads. When they retire, that knowledge walks out the door.
- Knowledge Capture: An AI layer captures this historical context.
- Onboarding: New hires can ask the AI, “How do we handle customs documentation for Mexico?” and get an instant answer based on past company documents, rather than tapping a senior manager on the shoulder.
The Need for Speed in Global Logistics
Consumer expectations for delivery speed have never been higher. A delay of hours in finding a solution to a supply chain disruption can cost thousands of dollars in chargebacks or expedited shipping fees. The concept of Who will own your company’s AI layer? Glean’s CEO explains highlights that speed of information retrieval is directly correlated to operational agility.
Quantitative and Qualitative Benefits
Implementing a centralized AI layer offers tangible benefits for logistics operations. Below is a breakdown of how this technology impacts the bottom line.
Comparison: Traditional Search vs. AI Layer
The following table illustrates the operational shift when moving from siloed searching to a unified AI layer.
| Feature | Traditional Logistics Workflow | AI Layer / Enterprise Search |
|---|---|---|
| Search Scope | Search inside WMS, then search Email, then search Drive. | Single search bar scans ALL apps simultaneously. |
| Response Type | List of links or file names. | Direct answer summarized from multiple sources. |
| Context | strict keyword matching (e.g., must type exact Bill of Lading #). | Semantic understanding (e.g., “Show me the late shipments from last week”). |
Qualitative Advantages
1. Reduced Decision Latency
When a disruption occurs (e.g., a port strike or weather event), leaders need to know their exposure immediately. An AI layer allows an executive to ask, “Which shipments are currently routed through the Port of LA?” and receive an immediate synthesis of data from the TMS and PO systems.
2. Enhanced Customer Experience
Customer service teams in logistics often spend huge amounts of time chasing down “Where is my order?” (WISMO) tickets. With an AI layer, a support agent can type a customer’s name and instantly see the contract status, the latest shipment location, and recent email correspondence, resolving issues in seconds rather than hours.
3. Breaking Down Silos
The topic of Who will own your company’s AI layer? Glean’s CEO explains underscores the removal of barriers between departments. Sales knows what Operations is doing, and Operations knows what Finance requires, without needing constant meetings.
Implementation: Building Your Company’s AI Strategy
Adopting an AI layer is not “plug and play.” It requires a strategic approach to data governance and security. Here are the essential steps for logistics leaders.
Step 1: Data Governance and Hygiene
Before you implement an AI that searches everything, you must ensure your data is accurate. AI is garbage-in, garbage-out.
- Audit Sources: Identify which systems contain the “source of truth.” Is the correct delivery date in the TMS or the ERP?
- Clean Up: Archive old, irrelevant documents to prevent the AI from retrieving outdated SOPs (Standard Operating Procedures).
Step 2: Prioritizing Security and Permissions
This is the most critical aspect emphasized by Glean’s CEO. In logistics, you have sensitive pricing data, carrier contracts, and employee records.
- Permission Mapping: Ensure your AI tool respects the Access Control Lists (ACLs) of your source systems.
- Verification: If a user cannot see a file in SharePoint, the AI must not summarize that file for them.
Step 3: Integration with Core Logistics Systems
For an AI layer to be valuable in this sector, it must connect to industry-specific tools. Generic connectors are not enough.
- TMS Integration: Ensure the AI can index shipment comments and status updates.
- WMS Integration: The AI should be able to retrieve inventory snapshots or labor reports.
- Communication Platforms: Connect Slack, Teams, and Email, as this is where most exception management happens.
Step 4: Change Management and Training
Workers may fear that AI is there to replace them. Leadership must frame the AI layer as a “Co-pilot” that removes the drudgery of searching for files.
- Demonstrate Value: Show a dispatcher how they can save 30 minutes a day.
- Encourage Prompt Engineering: Train staff on how to ask the AI complex questions to get the best results.
Conclusion: Taking Ownership of Your AI Future
The question posed by the keyword—Who will own your company’s AI layer? Glean’s CEO explains—is ultimately about control and competitive advantage. If you rely solely on the disparate AI features built into individual apps (Salesforce AI, Microsoft Co-Pilot, Zoom AI), your data remains fragmented.
True ownership means implementing a horizontal AI layer that unifies your logistics ecosystem.
Recommended Next Steps for Operations Leaders
- Conduct a “Search Audit”: Survey your team to find out how much time they spend looking for information daily.
- Evaluate Your Tech Stack: List all SaaS applications used in your supply chain and identify where the data silos are worst.
- Explore Enterprise Search Solutions: Look for vendors that specialize in secure, permission-aware retrieval (like Glean) rather than just open generative AI models.
By owning your AI layer, you transform your logistics company from a reactive operation into a proactive, data-driven powerhouse. The technology is here; the challenge is to implement it before your competitors do.
