Treat that as a workforce strategy because that’s what it is. The distributors, wholesalers and merchants that frame AI this way are already pulling ahead. The ones still running AI primarily as an IT project risk losing twice: first the people, then the knowledge they take with them.

Walk into almost any trade counter or branch in the UK and find the person everyone relies on. In a builders’ merchant, electrical wholesaler or industrial distributor, it is often someone in their 50s. They know which fittings actually cross-reference, which customer pays in 30 days and which one needs chasing, and which substitute to offer when a product is out of stock.

Almost none of it is written down.

I ran branch and call center operations at Grainger for the better part of a decade, and those were the people who kept the place standing. Last year, I watched a regional distributor lose three of them in a single quarter to retirement. The replacements were capable. They just didn’t know what the people who left knew. Service scores slipped, returns crept up, and the branch manager spent his days answering questions that used to answer themselves.

That’s the real AI story in our industry right now, and it has almost nothing to do with chatbots.

The Clock You’re Actually Racing

Here’s the uncomfortable part. The retirement wave may be the most important workforce issue on your five-year horizon, and many distribution leaders are managing it primarily as a hiring problem when it is also a knowledge problem. You can’t recruit your way out of a demographic cliff.

Look at the numbers in the trades that feed our sector. In UK construction and building materials, 35% of the workforce is over 50 and only 20% is under 30, with the average bricklayer now 52, according to 2025 data cited by ecomerchant. By 2035, more than a third of today’s workforce will reach retirement age, and roughly 750,000 workers are due to leave by 2036. Over the same period, the sector needs 251,500 additional workers by 2028 just to meet demand, against more than 140,000 unfilled vacancies as of December 2025.

The math doesn’t balance. The people aren’t there to hire.

This runs straight through the broader workforce supporting distributors, wholesalers and merchants. A March 2025 study by Flip and Workplace Intelligence, covering 500 UK frontline managers and employees in manufacturing and retail, found that 57% of the most experienced frontline workers are within five years of retirement. Sixty-eight percent of their managers fear vital expertise will be lost when those people go, and 78% aren’t confident their company is ready for the skills gap that follows.

The Organization for Economic Cooperation and Development made the broader demographic challenge clear in its 2025 Employment Outlook: Developed economies increasingly face labor scarcity as populations age and the ratio of older people to working-age populations rises.

Now set AI against that backdrop.

The Signal in the Data

Read that table from top to bottom and the strategy begins to write itself. The people who hold your operating knowledge are leaving. Distributors, wholesalers and merchants know they face a labor problem, and they increasingly see AI as a way to keep productivity rising with a workforce that may be smaller and less experienced.

What many haven’t done is connect those two problems directly.

Where Distributors Get Stuck

Three hard truths, and I’ve watched all three play out.

They buy tools before they capture knowledge. The instinct is to start with a chatbot or forecasting model. But your veteran employee’s know-how is part of the knowledge base AI needs, and if you don’t capture it while that person is still on the payroll, no model can magically recover it later.

In our December 2025 State of AI in Distribution survey, 52% of distributors named people as the biggest barrier to AI: a skills gap at 33% plus change resistance at 19%. Leadership buy-in ranked last. Read that carefully. The executives are increasingly convinced. The organization is struggling to execute.

They frame AI as replacement, and the workforce hears it. Almost no distributor in our data actually expects AI to become primarily a headcount-cutting exercise. Sixty percent expect it to increase the productivity of the people they retain. But if your branch team believes the model exists to replace them, they have little incentive to feed it what it needs.

That creates a dangerous contradiction. The veteran employees whose knowledge you most need to capture may be the least inclined to share it if the project is presented as a way to eliminate jobs.

They wait for clean data and a big platform. Nearly two-thirds of distributors, 63%, are still exploring or piloting rather than scaling AI. The leaders didn’t wait for perfect conditions.

As Grainger Chief Technology Officer Jonny LeRoy put it: “We’ve learned you’ve got to break down your problem into smaller chunks.”

That’s the difference. This is the kind of problem worth working through with people who have already done it, which is a large part of why we built the AI Forum for Distributors in the first place.

What This Means for Your Operation

Reframe the whole thing.

AI is part of your knowledge-retention strategy and your productivity strategy for a workforce that is going to change whether you prepare for it or not. For a UK builders’ merchant, an electrical wholesaler, a European industrial distributor or a multinational distribution group, the terminology may differ, but the operational challenge is the same.

The companies getting this right treat every approaching retirement as a body of knowledge at risk, and they act while the employee is still there to teach the organization.

They point AI first at high-volume, knowledge-dependent interactions: quoting, substitutions, order entry and technical lookups. Email order automation is already the most widely adopted customer-facing AI application in our survey, at 62%, precisely because it handles high volume and its return is relatively easy to see.

The appetite to invest is there. Sixty-five percent of distributors plan to increase AI spending over the next 24 months, with 88% naming productivity as their No. 1 reason for adopting it.

The leaders prove the point. Of more than 300 distributors we analyzed for The AI Execution Gap, only six reached the top AI maturity tier. One of them is Sonepar, the Paris-based global electrical distributor, which has committed more than €2.5 billion to logistics and €1 billion to its Spark digital platform.

The distance between those six and everyone else comes down to execution discipline, not simply budget or software.

What Changes Monday Morning

You can start this week. Five moves.

1. Build a retirement heat map. List everyone within five years of leaving and identify what only they know. That becomes your knowledge-risk map and helps establish your AI priorities.
2. Pick one knowledge-heavy, high-volume workflow. Product substitutions or quoting are strong first targets. Sit with your best person and capture how they do it, including the decisions, exceptions and judgment calls that never made it into the process manual.
3. Start with retrieval, not transformation. Getting technical product information, previous orders and account history in front of a new employee in seconds is a modest, provable win. It also takes pressure off your veterans immediately.
4. Put one senior owner on it. Don’t leave it to IT alone. Our data shows that technology-led efforts can stall when they aren’t tied closely enough to business outcomes. Name a senior person accountable for the result.
5. Measure a commercial number, not activity. Quote turnaround time, first-contact resolution or return rate. If you can’t tie the work to a number a branch manager, managing director or commercial director cares about, it won’t hold.

Notice what’s not on that list: a moonshot, a platform overhaul or a two-year roadmap.

Individually, these moves are modest. Running together, they build the muscle to capture institutional knowledge faster than your people retire.

That’s the game.

Come Work It Through With Your Peers

None of this is theoretical, and none of it is easy to build from a report alone. It is much easier in a room full of leaders wrestling with the same demographic math you are.

That’s what the AI Forum for Distributors, UK and EU is built for. It takes place Oct. 15, 2026, at the National Conference Centre in Birmingham, England, bringing together managing directors and senior commercial, operations, technology and digital leaders from distributors, wholesalers and merchants across the UK, Ireland and continental Europe.

The focus is practical: what’s actually working in distribution, lessons directly from distribution leaders, and a vetted group of technology companies already working in the sector.

Whether your company calls itself a distributor, wholesaler, builders’ merchant, electrical wholesaler or merchanting group, the problem is the same. If some of your best people are within five years of walking out the door, the time to build the capability that preserves what they know is now, not after they’ve gone.

Register for the AI Forum for Distributors, UK and EU, and come build it with people facing the same challenge.

The post Your Best People Are Retiring, But Your Best New Hire Is AI appeared first on Distribution Strategy Group.

Lowe’s Companies Inc. has reshuffled its senior leadership team, moving executives into new roles overseeing Pro, stores and artificial intelligence and making two additional executive appointments as the home improvement retailer looks to accelerate its next phase of growth.

The changes took effect Sept. 1 and span five executive vice president positions, according to a filing with the Securities and Exchange Commission. Lowe’s approved the appointments Aug. 28 and disclosed them in a Form 8-K filed Sept. 2.

Joseph M. McFarland III, previously executive vice president of stores, was named executive vice president of Pro and Home Services. Quonta D. Vance, previously executive vice president of Pro and Home Services, was named executive vice president of stores.

The moves put McFarland in charge of one of Lowe’s most important growth areas as the company continues expanding its business with professional contractors.

Lowe’s has been building out its Pro capabilities with expanded product access, digital quoting and purchasing tools, job-lot quantities, and direct-to-jobsite delivery. Its Pro Extended Aisle includes an expanded digital catalog with real-time inventory and pricing and a growing supplier network.

Lowe’s also elevated artificial intelligence within its technology organization. Seemantini Godbole, previously executive vice president and chief digital and information officer, was named executive vice president and chief information and AI officer.

In the expanded position, Godbole leads Lowe’s global technology organization, including engineering, product roadmaps, artificial intelligence, data and analytics, information security, and innovation, according to the company.

Lowe’s also appointed Adam D. Filipponi executive vice president of strategy and business development and Jennifer E. Wilson executive vice president and chief marketing officer.

The executive changes come after several years of investment by Lowe’s in Pro, digital, loyalty, fulfillment, and Home Services. The company said the appointments are intended to better connect those capabilities, establish clearer accountability, and allow Lowe’s to move faster on its largest growth opportunities.

The restructuring also puts Lowe’s Pro strategy under new leadership at a time when the retailer is expanding beyond the traditional store-based home improvement model. Its growing emphasis on professional contractors, larger orders, expanded supplier access and jobsite delivery increasingly puts Lowe’s into markets also served by building products and specialty distributors.

Lowe’s said the broader leadership changes are designed to strengthen execution of its Total Home strategy and position the company for its next phase of growth.

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Artificial intelligence infrastructure, warehouse automation and manufacturing reshoring are driving a new wave of U.S. industrial demand as companies seek more space for data centers, advanced manufacturing and highly automated distribution facilities.

Industrial net absorption across major U.S. markets exceeded 260 million square feet during the four quarters through the second quarter of 2026, surpassing the pre-pandemic annual range of 225 million to 250 million square feet, according to a Sept. 2 analysis from Morgan Stanley Real Assets published by Eaton Vance.

Industrial demand also accelerated from a year earlier. Trailing 12-month demand in the second quarter grew at twice the pace recorded in the second quarter of 2025, while the national industrial vacancy rate declined 10 basis points from a year earlier to 6.5%.

The stronger demand comes as construction of new industrial properties remains constrained. Construction activity has fallen 60% from its pandemic-era peak, while new development starts have averaged about 50 million square feet per quarter during the past three years.

Morgan Stanley Real Assets identified three forces behind the shift: the buildout of the data center supply chain, increased use of artificial intelligence and automation in warehouses, and expansion of advanced U.S. manufacturing.

Data center investment is already reshaping industrial demand in several markets.

Companies supporting data center construction and operations are leasing industrial space for semiconductor operations, equipment development, power technology, server testing, construction services and hardware storage.

In Dallas, data center-related companies have accounted for 30% of gross industrial leasing since the fourth quarter of 2024 and 40% of leasing for facilities larger than 700,000 square feet, according to Stream Realty data cited in the report.

Data center-related bulk industrial leasing in Dallas exceeded 7 million square feet during the past 12 months and accounted for nearly all the increase in leasing activity compared with pre-pandemic levels. Similar activity is occurring in Virginia and Midwest markets including Ohio and Kansas City.

The trend could have broader implications for distributors because the data center supply chain extends well beyond servers and computing equipment. Construction and operation of those facilities require electrical and power equipment, cooling systems, automation, construction products and other industrial supplies.

Warehouse automation is creating another source of demand.

Companies deploying artificial intelligence and next-generation automation are increasingly seeking large, modern distribution facilities capable of supporting autonomous mobile robots, conveyor systems, automated storage and retrieval systems and automated packaging equipment.

Leasing of U.S. industrial facilities larger than 1 million square feet reached 41 million square feet during the first half of 2026, twice the pace of the first half of 2025. Thirty-five leases exceeding 1 million square feet were signed during the period, compared with 18 a year earlier.

Ecommerce companies accounted for 50% of that large-building leasing volume. According to the report, the supply of modern facilities is tight, with only 12 bulk properties under construction and available for lease nationally. Some companies are now preleasing facilities scheduled for completion in 2027 and 2028.

Manufacturing expansion is adding another layer of industrial demand.

Advanced manufacturing now accounts for 19% of active U.S. industrial tenant requirements, according to JLL data cited in the report. The square footage associated with active manufacturing requirements has increased at a compound annual growth rate of more than 40% since 2020, with Texas, Georgia, Arizona and Ohio leading leasing activity.

Reshoring, increased logistics requirements and higher defense spending are contributing to the increase. The age of the country’s manufacturing infrastructure is another factor. More than half of U.S. manufacturing properties are between 30 and 60 years old, increasing demand for modern facilities capable of supporting advanced production.

Defense manufacturing is also contributing to growth in some markets. Aerospace and defense leasing in greater Los Angeles during the first half of 2026 was 30% higher than for all of 2025 and more than 140% above full-year 2024 levels, according to CBRE data cited in the report.

Taken together, the trends point to a changing mix of industrial demand. Traditional warehousing and logistics remain major users of industrial space, but data centers, advanced manufacturing, defense and highly automated distribution operations are accounting for a growing share of activity.

For distributors, the opportunity extends beyond construction of the facilities. Once operating, data centers, factories and automated distribution centers become continuing customers for electrical products, power and cooling equipment, automation and controls, material handling systems, safety equipment, fasteners, tools and maintenance, repair and operating supplies.

Morgan Stanley Real Assets said the U.S. industrial market appears to be moving from a cyclical recovery toward another growth cycle, with demand accelerating while development remains constrained. The firm expects AI infrastructure, automation and manufacturing reshoring to become increasingly important sources of industrial demand.

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That is the gap that matters.

The question is no longer whether AI matters. It is whether you can put it to work in the business. The wholesalers and merchants doing that are beginning to build an operating advantage over those still evaluating the technology.

According to 2025 Boston Consulting Group (BCG) research, companies that systematically embed AI into their operating models can achieve up to five times the revenue gains and three times the cost reductions of laggards. That advantage compounds as leaders lower their cost to serve, improve margins, and put more use cases into production.

The problem is increasingly less about technology than the discipline required to deploy it. Wholesalers that scale AI treat it as an operating discipline owned by the business. Those stuck in permanent pilots too often treat it primarily as an information technology project.

The Execution Gap

The broader numbers show the problem. Recent 2026 reporting indicates 88% of AI pilots never reach production or scale. For generative AI specifically, a 2026 Massachusetts Institute of Technology Media Lab report puts the failure-to-return-on-investment rate at 95%.

That is not necessarily an argument for waiting until the tools improve. It is an argument for fixing the process that turns a working pilot into a daily operation.

The early movers are already visible. DSG research on more than 300 North American distributors found only 26 that met the bar for integrated, production-level AI capability. Grainger, Wesco, Sonepar and Rexel are among the companies that have moved beyond asking where AI fits and are building it into their operating models.

For European wholesalers and merchants, Sonepar and Rexel make the point particularly relevant. This is not simply a North American trend.

European adoption data also shows a significant gap in company size. Eurostat reports that about 20% of European Union enterprises used AI in 2025. Among large enterprises, adoption was 55%, compared with 17% among small enterprises. The larger players are moving faster.

DSG’s State of AI in Distribution research shows a similar divide between individual use and operational deployment. Some 63% of distributors surveyed use ChatGPT, but only 15% have put a proven AI use case into daily operations.

The first number represents people using an AI tool. The second represents companies changing how they operate.

That is the execution gap.

The money is already showing up for companies that moved first. Rexel reported digital sales at 34% of group sales in 2025 and 35% for the first six months of 2026. In Europe, digital represented 44% of Rexel’s sales in 2025, supported by the adoption of digital tools and algorithmic quote and order entry.

That is a European wholesaler building an operating advantage that becomes harder for competitors to close the longer they wait.

Put the Business in Charge

One of the biggest barriers to closing the execution gap is treating AI primarily as an information technology project.

The reflex with new software is often to hand it to the information technology department. But AI changes how work happens at the branch counter, how salespeople negotiate, how buyers procure and how customer service teams process orders.

An information technology leader knows how to connect an application programming interface (API). That person may not have the operating context to redesign the quoting workflow for counter staff.

Without a clear process owner, an accountable executive sponsor and cross-functional involvement, pilots struggle to move into production. The wholesalers and merchants that scale AI put business leaders in charge of business outcomes.

Grainger’s differential investment model assigns high-stakes projects to leaders pulled from operations, people who understand the profit drivers and own the outcome. Sysco elevated its AI strategy to board-level oversight in 2026 and tied it to a $100 million cost-savings target.

The lesson is straightforward: Put accountability with leaders who own the process, the customer, and the financial result.

Start Where the Value Is Obvious

Getting started does not require a companywide transformation program. It means aiming AI at one high-volume, rules-based bottleneck and getting a measurable result inside a quarter.

Several workflows stand out.

Demand forecasting and the bullwhip effect. Wholesale distribution sits in the middle of the supply chain, making it particularly exposed to the bullwhip effect, where relatively small changes in customer demand can create much larger stock swings upstream.

Traditional forecasting models lean heavily on historical sales and moving averages. A retailer batches an order to capture a freight discount, or a manufacturer runs a promotion, and older models can interpret those artificial spikes as changes in underlying demand.

Machine learning models can incorporate point-of-sale information, weather, and economic indicators to identify anomalies and produce a cleaner purchasing baseline. Wholesalers using algorithmic demand sensing report 40% to 50% lower forecast errors, potentially reducing excess stock while improving fill rates.

Start here if working capital is tied up in the wrong stock.

Order entry and margin recovery. Order entry remains one of the back-office functions that can quietly limit revenue capacity. A business-to-business (B2B) wholesaler processing thousands of orders each month through email, PDF files and spreadsheets can still depend heavily on manual keying into an enterprise resource planning (ERP) system.

AI-based order entry can extract line items, map customer part numbers to an item master, validate contract pricing and draft the sales order. Exceptions can be routed to an employee rather than requiring every line to be processed manually.

DSG research puts productivity improvement from automating quote generation at 40% to 70%. Rexel Canada, for example, deployed agentic document processing with Onit and Hyland and achieved near-perfect invoice indexing accuracy within 48 hours in 2026.

Start here if customer service teams are spending too much time on manual order entry.

Pricing and getting salespeople to hold the line. In a high-volume, low-margin business, small improvements in price realization can have an outsized effect on operating profit.

Pricing ranks as the No. 1 AI priority for 27% of distributors surveyed by DSG in 2026. AI pricing engines can analyze customer price sensitivity, purchase frequency, order volume, and competitive position to generate recommendations at the quote level.

But generating the right price is only half the job. The salesperson must use it.

A 2025 study involving a B2B aluminum retailer found that machine learning price recommendations increased profit on treated quotes by 11%. If salespeople routinely override recommendations, however, the technology cannot deliver the intended result.

Start here if margin is leaking through discretionary discounting.

Accounts receivable and cash velocity. Invoice-to-cash has traditionally depended on labor-intensive matching of remittances to open invoices. Accounts receivable automation can issue invoices, ingest payment information, match payments and route disputes based on defined rules.

The models can learn customer remittance patterns and match payments even when reference numbers are missing or a customer short pays.

Start here if days sales outstanding is a constraint.

The Demand Side Is Moving Too

AI readiness is becoming a two-sided issue. Wholesalers and merchants need AI internally to operate more efficiently, but they also need their product and transaction data to be accessible to the AI systems their customers increasingly will use.

Agentic commerce means software acting for a buyer to search catalogs, verify pricing, confirm availability, and potentially execute transactions. B2B procurement, with its repeat purchases, technical specifications and negotiated contracts, is a natural environment for that technology.

DSG’s AI 2030 framework found 61% of organizations expect to deploy fully autonomous AI agents for complex functions within five years.

That changes the importance of product data.

An AI purchasing agent does not need to browse a homepage. It can query structured product information directly. Wholesalers therefore need clean, machine-readable catalogs containing accurate technical attributes, Global Trade Item Numbers (GTINs), and appropriate product classifications.

That also increases the importance of secure APIs that can expose appropriate contract pricing and real-time stock information to authorized systems.

The work required to support machine buyers is another reason to begin addressing product data now rather than waiting for agentic commerce to mature.

Get the Foundation Right Without Waiting

Data is one of the most common reasons companies delay AI deployments, but waiting for perfect data can become its own barrier.

No model will automatically reconcile a duplicated customer master or inconsistent item records. Some wholesalers freeze deployments while waiting for pristine data. Others run algorithms against fragmented ERP information and get confident but incorrect answers.

The better approach is to improve the data required for a specific use case while that use case is being developed.

The European Technical Information Model (ETIM), used across European and North American electrical and heating, ventilation and air conditioning markets, provides a shared product classification structure. In UK building materials, the Builders Merchants Federation (BMF) Product Data Template serves a similar purpose and feeds into Data Yard, the industry data pool developed by the BMF with the National Merchant Buying Society.

Master data cleanup is not something to skip. But it does not have to be completed across the entire business before the first AI deployment begins.

Build Governance in From the Start

European wholesalers also have a regulatory consideration their North American counterparts do not face to the same degree.

The European Union AI Act, in force since August 2024, establishes a risk-based approach to AI governance and can apply to companies outside Europe when their systems affect people in the European Union.

For wholesalers and merchants, some of the clearest requirements involve employment applications such as automated curriculum vitae screening, task allocation, performance evaluation, and workforce monitoring. These can fall into high-risk categories requiring governance, documentation, and human oversight.

AI systems affecting pricing or credit also require careful governance because of potential discrimination risks.

That is not a reason to delay deployment. It is a reason to build governance into the first use case rather than bolt it on later.

What Changes Monday Morning

Moving from evaluation to an operational deployment requires three decisions from the executive team.

Put a business leader in charge. Take AI strategy out of the exclusive control of information technology. Give a senior business leader with profit-and-loss responsibility ownership of the outcomes. Review what workflows changed, what financial return was generated and what process comes next.

Pick one hard bottleneck and commit to a 90-day result. Stop broad, undefined experiments. Pick a specific constraint, such as margin erosion or manual entry of emailed purchase orders, and apply a specialized tool to it. Establish one or two key performance indicators (KPIs) at the start so the business can determine within a quarter whether the deployment worked.

Start the data work in parallel. Do not wait for perfect data. Clean and standardize the information required for the first use case while building the capability to support the next one.

The objective is not to build an AI strategy on paper. It is to put one use case into daily operations, measure the result and use what the organization learns to tackle the next process.

The Two-Quarter Window

The window to treat AI solely as a future capability has closed. The opportunity now is to move it into daily operations.

The wholesalers and merchants building an advantage are not necessarily doing so because they have access to better AI. They are getting better at selecting specific business problems, putting operating leaders in charge, and turning successful pilots into repeatable processes.

The next two quarters provide enough time to do something tangible: Choose a bottleneck, establish ownership, put a use case into production, and measure the result.

Theory does not move market share. Execution does.

AI Forum UK & Europe, Oct. 15, 2026, at the National Conference Centre in Birmingham, will bring together wholesale distribution and merchant leaders to examine AI deployments, implementation costs, timelines, data preparation, and the lessons emerging from putting AI into day-to-day operations.

Register for AI Forum UK & Europe

Where European wholesale distribution leaders turn AI into competitive advantage.

The post The Two-Quarter Window: Why European Distribution Leaders Have to Move on AI Now appeared first on Distribution Strategy Group.

Artificial intelligence is moving closer to the center of distributor operations as software companies target some of the industry’s most persistent manual work: processing orders, preparing quotes, handling invoices, and keeping customer information current.

The latest moves come from Conexiom, WizCommerce and Rundoo, three companies attacking the problem from different directions. Conexiom is applying AI to order and invoice automation. WizCommerce is bringing it into customer relationship management, sales and quoting. Rundoo is embedding AI into a broader operating platform for independent hardware, paint, farm and feed and building materials businesses.

The common thread is significant for distributors. Rather than asking employees to use a separate AI assistant, the technology is increasingly being built into the systems where orders are received, customers are managed and transactions are processed.

Conexiom Targets Orders and Invoices

Conexiom has introduced Conexiom Relay, an AI-based system designed to automate order and invoice processing for distributors and manufacturers.

The Vancouver-based company said Relay can process information arriving through email threads, spreadsheets, handwritten notes, images, and text messages. The system is designed to turn that unstructured information into transaction data that can move into enterprise resource planning systems.

Conexiom already automates requests for quotations, orders, vendor order acknowledgments, shipping notices, and invoices. Relay extends that approach to less structured information and gives employees tools to review how the system handled a transaction.

That is an important distinction as AI moves from generating text to processing orders. An inaccurate summary or email can be corrected easily. A mistake involving a product, quantity, price, or customer account can create problems in purchasing, fulfillment, and billing.

Conexiom said Relay includes an Intelligence Console built around the company’s order and invoice transaction data. A Playbook feature converts customer service rules and employee corrections into repeatable workflows, while Conexiom Assist identifies exceptions and carries out processes based on those rules.

The company said it processes more than 1.5 billion line items annually for more than 600 customers and reports accuracy above 95% across its automation platform.

WizCommerce Brings AI Into Sales and CRM

WizCommerce is approaching distributor automation from the sales side.

The company rolled out WizCRM as an AI-enabled customer relationship management system designed specifically for wholesalers and distributors. It brings customer information, sales activity, quotes, orders, and ecommerce activity together rather than requiring sales representatives to work across separate applications.

Sales representatives can manage accounts and contacts, track opportunities, record meetings and visits, assign tasks and prepare quotes. Those quotes can be converted into orders and synchronized with a distributor’s enterprise resource planning system.

The system also incorporates B2B ecommerce activity, including customer searches, product views, shopping carts, and previous orders.

WizCRM includes WizPilot, an AI sales assistant that works with customer, quote, order, and sales information. It can help representatives prepare for customer meetings, create follow-up tasks, and identify customers that may be ready to reorder. Managers can use it to identify aging quotes and review sales activity.

WizCommerce also has been applying AI directly to order entry. Its technology can process incoming purchase orders and requests for quotations from emails, PDFs, spreadsheets, scans, voice notes, and handwritten documents and turn the information into structured orders and quotes for review.

The company said it works with more than 500 wholesale and distribution brands in the U.S.

Rundoo Raises $30 Million to Expand Supply Store Platform

Rundoo is taking a broader approach by building AI into software used to operate independent supply businesses.

The Redwood City, California-based company raised $30 million in August to expand its platform for independent hardware, paint, farm and feed and building materials stores. The round brought Rundoo’s total funding to $48 million.

Rundoo said it serves more than 500 independent stores in the U.S. and Canada. Its platform manages functions including point of sale, inventory, pricing, purchasing, and customer management.

The company is increasingly using AI across those operations. Its technology can use inventory and vendor information to help generate orders and provide pricing recommendations. Its AI assistant, Dooey, initially focused on reporting, allowing users to ask questions about sales and margins, but has expanded into ordering, email drafting, sales assignments, and other operating tasks.

Rundoo CEO Nick Hershey said the company sees an opportunity among independent supply businesses still operating on older systems.

The $30 million investment gives Rundoo additional capital to pursue that market and illustrates a different approach to distributor AI. Instead of selling an individual AI application, Rundoo is building the technology into the underlying system used to manage the business.

AI Gets Closer to the Transaction

The three developments point to a broader change in the distributor AI market.

The early wave of generative AI centered on content creation, search, summarization, and general-purpose employee assistants. Conexiom, WizCommerce and Rundoo are pushing the technology into processes tied directly to revenue and operations.

That makes the potential results easier to measure. Distributors can track how long it takes to process an order, prepare a quote or manage an invoice. They can measure the number of transactions employees process, the time required to respond to customers and the frequency of errors.

It also raises the stakes. Distributor transactions can depend on customer-specific pricing, product numbers, units of measure, inventory, substitutions, delivery requirements, and payment terms. Automating those processes requires more than generating a plausible response. The information must be correct before it moves downstream into purchasing, fulfillment, accounting, or the enterprise resource planning system.

That is why the more consequential development in distributor AI may not be another chatbot. Vendors are beginning to compete over something much more fundamental: how much of the work between an incoming customer request and a completed transaction can be automated accurately and with appropriate human oversight.

For distributors, the order desk is emerging as one of the first places where that question is being put to a practical test.

The post AI Moves into the Distributor Order Desk as New Tools Target Manual Work appeared first on Distribution Strategy Group.

Toolbx has launched an artificial intelligence order entry system aimed at reducing the manual work building supply distributors perform when processing customer orders and material lists.

The Santa Monica, California based software company said that AI Order Entry is now generally available to independent building supply dealers. The system processes material lists and purchase orders submitted through emails, PDFs, spreadsheets and job site photos, matches individual items against the dealer’s catalog and creates a draft order that can be sent to its enterprise resource planning system after employee review.

AI Order Entry is the first product in Forge AI, a new Toolbx product line focused on automating repetitive operational tasks.

The company is targeting a process that remains heavily manual for many distributors. Customer orders can arrive as unformatted emails, PDFs, spreadsheets, photos, text messages, or voicemails, requiring employees to identify products and manually enter stock keeping units, quantities, and units of measure. Toolbx said the process is common across lumber, hardware, plumbing and heating, ventilation, and air conditioning distribution.

Toolbx said the system connects to a dealer’s existing email inbox and automatically creates draft orders from incoming material lists. Users also can upload as many as 10 files at once, with each file generating a separate draft order.

The system extracts information including the customer, account, job site, requested date, or delivery or will call instructions and notes. Individual lines are then matched with stock keeping units and units of measure from the dealer’s catalog. Toolbx said a draft order typically is produced in less than a minute.

Items that cannot be matched with a prominent level of confidence are flagged for employee review. An order is not entered into the dealer’s enterprise resource planning system until an employee approves the draft.

Toolbx said it expects match rates of at least 95% when a dealer has complete product data. Dealers can train the system to recognize their products, customers and internal shorthand, and the matching rules adjust as the system processes additional material lists.

The company handles setup by accessing product data from the dealer’s enterprise resource planning system, configuring the matching process, and testing it against previous material lists before deployment.

Swift Supply, a sixth generation building materials dealer with five locations in Alabama and Florida, has been testing AI Order Entry through an early access program, said Scott McNutt, information technology manager.

McNutt said the system allows the distributor to process multiple orders while employees manage other customer work.

Toolbx provides an order to cash software platform for independent lumber yards, roofing suppliers and plumbing and HVAC distributors. Its platform manages orders, quotes, payments, and collections and includes online storefronts, digital ordering, and accounts receivable automation.

The company integrates with building supply enterprise resource planning systems including Epicor BisTrack, Epicor Eagle, ECI Spruce and DMSi Agility.

Toolbx said more than $2 billion in building supply orders and payments have been processed through its platform. The company serves more than 200 dealers and wholesalers operating in more than 1,000 locations across North America. More than 25,000 contractors and tradespeople order and make payments through the platform each day, according to the company.

The order entry product extends Toolbx’s use of AI into a transaction heavy part of distributor operations. The potential benefit is straightforward: reducing the amount of time employees spend converting customer documents into orders while preserving employee review before transactions reach the enterprise resource planning system. The effectiveness of the system will depend on its ability to accurately match products across the varied catalogs, terminology and order formats used by individual distributors.

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TD SYNNEX is expanding its PartnerFirst digital commerce platform with artificial intelligence, sales automation and new integrations as the technology distributor moves to connect more of its pricing, inventory, and customer data directly with the systems its resellers use to run their businesses.

The Fremont, California, and Clearwater, Florida-based distributor said PartnerFirst Digital Bridge now connects with Salesforce and QuickBooks Online, providing real-time access to pricing, inventory, orders, and customer lifecycle data.

TD SYNNEX is also expanding the reach of artificial intelligence assistants that can perform routine sales and customer service functions. After initially launching an AI assistant in Microsoft Teams, the distributor has added assistants to Slack and Webex.

Slack users can check real-time product pricing and availability. Through Webex, customers can check price and availability, look up order status, find their assigned TD SYNNEX contacts, and retrieve vendor-specific information.

The additions are part of a broader effort by TD SYNNEX to make its ecommerce and distribution systems accessible from the business applications its customers already use rather than requiring them to repeatedly move between separate platforms.

“Partners don’t need more dashboards. They need better methods to help them identify opportunities sooner, act faster and grow profitably,” said Reyna Thompson, president of North America at TD SYNNEX.

PartnerFirst, introduced in North America in September 2025, was designed to consolidate TD SYNNEX’s commerce, cloud, renewals, and other digital capabilities into a more unified customer platform. The latest additions extend that strategy deeper into sales, customer management, and purchasing workflows.

TD SYNNEX has added customer lifecycle analysis for select vendors, including Microsoft and Cisco. The tools provide reporting on opportunities by quantity and value and combine TD SYNNEX data with individual reseller profiles.

New campaign management capabilities allow partners to target and track communications with resellers and end customers. Opportunity reconciliation tools track close rates, revenue retention, renewals, upselling, and customer churn.

The distributor has also expanded cloud capabilities within PartnerFirst. Customers can see cloud customer data and contracts through subscription tools and use a unified quoting system with reporting and analytics.

Another new tool, QuoteSync, moves PartnerFirst quotes and related information directly into connected customer relationship management and professional services automation systems. TD SYNNEX said the integration is designed to reduce manual and duplicate data entry during the quoting process.

The company said customers making regular use of its digital offerings are seeing nearly 30% growth on average.

“We are seeing immediate impact on customer growth as they leverage PartnerFirst and Digital Bridge,” said Jessica McDowell, senior vice president of North America marketing and digital success at TD SYNNEX. “Customers that are regularly transacting across our digital offerings are seeing nearly 30 percent growth on average, outpacing customers not leveraging digital solutions.”

TD SYNNEX did not disclose the number of customers included in that comparison, the period over which the growth was measured or the average growth rate of customers that do not extensively use the digital tools.

The distributor is also expanding PartnerFirst beyond product transactions.

A new Services Marketplace allows customers to search for information technology services, review service information and submit inquiries through the platform. Available offerings include integration, installation, and managed services. The marketplace includes AI-powered search to help customers identify services and stock-keeping units.

Customers can also purchase products and monitor renewals through TD SYNNEX’s PartnerFirst mobile app for Apple iOS and Android devices.

The expansion is the latest step in TD SYNNEX’s effort to make its distribution platform more intricately connected to customers’ sales and operating systems.

The company introduced PartnerFirst in North America last September as a unified digital platform combining hardware, cloud, renewals, and other services. It has since expanded the platform internationally and continued adding automation and AI capabilities.

The significance for TD SYNNEX extends beyond adding another set of ecommerce features. By connecting pricing, inventory, orders, customer information, quotes, and renewals directly to Salesforce, QuickBooks Online and communications platforms, the distributor is positioning its systems deeper inside the day-to-day workflows of its reseller customers.

TD SYNNEX supports more than 150,000 customers in more than 100 countries. Its distribution business provides information technology hardware, software, and systems, while its Hyve Solutions business designs and manufactures computing, cloud, and connected infrastructure.

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As an Employee Stock Ownership Plan (ESOP), the stakes were personal. Every employee owned shares. When we found a way to handle more without adding cost, the share price got better, and everyone in the company felt it. This was never a slogan about cutting people. It was about not automatically bolting on a salesperson or a customer service representative (CSR) every time the business grew.

Here is the hard part. Most of what we did to grow for free was brutally manual. We rebuilt processes by hand. We fought for every point of margin. It was slow, and it was expensive to figure out. Distributors now have AI tools that make growing for free easier than it has ever been. The reason it works has almost nothing to do with the labor line, and that is the part most business cases miss.

Why Labor Savings Dominate the ROI Conversation

Most distributor AI business cases lead with headcount. Hours saved. FTEs avoided. Cost per transaction. There is a good reason for that. Labor is easy to measure. You know what a CSR costs, you know how long a task takes, and you can multiply your way to a savings figure your CFO will accept.

The easy number is the small number. McKinsey made this point directly in an August 2026 analysis. At most companies, even mature ones, Selling, General, and Administrative (SG&A) runs 5% to 12%  of revenue. A large cut to that line still cannot explain the returns leading AI adopters are reporting. The labor story is real. It is also nowhere near the whole story. Stop your business case at hours saved and you are measuring the least valuable thing AI does.

The Hidden Cost of Slow and Poor Decisions

Here is what almost nobody puts on a balance sheet. Decisions cost money. Not just the labor to make them, but the outcome when they are made slowly or made wrong.

Think about your own operation. A price held a week too long while a competitor moved. A reorder point that lagged real demand, so you either sat on dead stock or ran out. A quote that sat in a queue while the customer got antsy and called someone else. A credit approval that took three days on a deal that needed an answer in three hours. An exception nobody caught until it became a service failure and a phone call from an angry account.

None of those show up as a line item. The daily grind swallows them, invisible, and they are some of the largest operating costs you carry. McKinsey named decision-making one of the largest and least visible costs in a business, embedded in daily activity rather than captured anywhere you can see it. That is exactly why grow for free was so hard for us. The cost we were fighting sat buried in a thousand small decisions, and we had to dig each one out by hand.

The Distribution Decisions Where the Money Actually Sits

Not every decision matters equally. The money sits in a handful of high-frequency, high-consequence calls that your business makes thousands of times a month. In distribution, the list is short and familiar.

Pricing. The right price on the right order, set in the moment rather than off a stale matrix.

Replenishment. Reordering the right SKU in the right quantity before demand shifts under you.

Inventory positioning. Moving stock to where it will sell before a stockout, not after.

Quote prioritization. Working the quotes most likely to close and worth the most, instead of the ones that happen to be on top of the pile.

Credit. Approving good customers fast enough to keep the deal warm.

Exception handling. Catching the order, the shipment, or the account that is about to go sideways while there is still time to fix it.

Every one of those is a decision, made constantly, where speed and accuracy convert straight into margin. That is where the money lives. McKinsey advises companies to start where decisions are both expensive to make and economically important, and points to demand sensing as a natural first move for a distributor. Any operator already knows this in their gut.

How AI Changes Speed, Consistency, and Decision Quality

AI moves three levers at once on these decisions, and they compound.

Speed. McKinsey estimates AI can pull decision timelines from weeks or months down to seconds and cut the cost of deciding by more than 90%. A pricing or replenishment call that used to wait for a weekly cycle can now happen many times a day.

Consistency. Your best decisions stop depending on which person happens to be at the desk. The judgment gets applied the same way every time, at scale, without the good day or bad day variance that comes with any human queue.

Quality. AI can weigh more signals than a person can hold in their head, catching patterns in demand, inventory, and account behavior that a busy team would miss.

Here is the part leaders skip, and it decides whether any of this pays off. McKinsey found that companies layering copilots and dashboards onto their existing processes get only modest financial gains. Companies that redesign the workflow around AI see a 20%  Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) uplift and payback in one to two years. Same technology. The difference is whether you changed how the work flows or bolted a tool onto the old way. We learned that the hard way at Hisco long before AI showed up. You cannot grow for free by speeding up a broken process. You rebuild it first.

How to Measure a Decision Dividend

If the value sits in decisions, hours saved is the wrong scoreboard. McKinsey borrows a better one from the factory floor. You judge a plant by what it produces, not by how many machines are running. Apply the same test to AI. The measure is not how many tools you licensed. It is how many of your decisions are now informed, accelerated, or automated by AI. McKinsey calls that decision throughput and argues it is the metric that matters most.

Track it where it counts. What share of your pricing decisions are AI-informed today versus a year ago? How fast does a quote move from request to response now? How quickly does a credit approval clear? How many stockouts did you catch before they happened rather than after? Those numbers tie directly to margin and to revenue, and they tell you far more than a tally of hours saved ever will.

Where Human Approval Still Belongs

Faster and more decisions are not automatically better decisions. A high-speed engine bolted to a bicycle just wrecks faster. The discipline is knowing which decisions AI can make on its own and which still need a human in the loop.

McKinsey is blunt about this, and so am I. You define, clearly, when AI can act on its own, when it escalates to a person, and who owns the result. Skip those rules and two things go wrong. Either leaders refuse to delegate anything and throughput dies, or the system runs unchecked and the first bad call in a high-stakes lane turns into a real problem. Routine reorders and standard pricing are where AI should run. The large, unusual, or relationship-sensitive calls are where your experienced people keep the final say. Drawing that line is a leadership decision, not a technical one, and it belongs to the business, not to IT.

How Decision Automation Changes Management Roles

When AI takes over the routine decisions, the manager’s job changes. It shifts from making every call to setting the rules the system follows, watching the exceptions, and improving the logic over time. Less doing, more governing. That is a genuinely different job, and the people who thrive in it are not always the ones who were fastest at the old one.

This is where the veteran gets more valuable, not less. AI holds the information. Your twenty-year people hold the meaning, the context, the read on why this account or this pattern breaks from what the model sees. The edge comes from putting those together. The company that wins is not the one that bought AI. It is the one that built an organization able to turn better decisions into better results, week after week.

We chased the decision dividend at Hisco before anyone had a name for it, the slow and manual and painful way, and watched it show up in a share price every employee owned. The goal was never to shrink the team. It was to grow without the cost growing alongside it. Distributors now have tools we would have paid anything for back then, and most are pointing them at the wrong target, shaving minutes off tasks instead of sharpening the decisions those tasks feed.

Here is where to start Monday. Pick the one decision your business makes most often that moves margin the most, pricing on a common order type is a good candidate, and time it end to end. That number is your baseline and your first AI use case. Build from there.

What is the most expensive decision your business makes every single day, and how long does it take you to make it?

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TrueCommerce has named Anthony Gallo chief product officer as the supply chain technology provider expands its use of artificial intelligence across its platform.

Pittsburgh-based TrueCommerce said Gallo will oversee product strategy, management, and design, with priorities including AI-based B2B commerce, enterprise system integration, and data governance.

TrueCommerce operates a global supply chain network that processes 600 million transactions annually and connects businesses with trading partners in more than 40 countries, according to the company.

The company is working to combine data from that network with customers’ enterprise resource planning systems and AI to automate more supply chain and B2B commerce processes.

Gallo joins TrueCommerce from supply chain software company Kinaxis, where he was vice president of product management and platform innovation. He previously served as chief product officer at Tenovos and held product leadership roles at OpenText.

TrueCommerce provides electronic data interchange, supply chain integration, e-invoicing and related services connecting businesses with customers, suppliers, logistics providers, and internal systems.

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W.W. Grainger Inc. has acquired technology, intellectual property, and talent assets from Adroit Worldwide Media for $210 million in cash, adding technology designed to automate inventory management for industrial customers.

Chicago-based Grainger said the acquired assets will strengthen inventory management capabilities within its High-Touch Solutions — North America segment. The company plans to begin integrating the technology immediately and launch a commercial pilot within the next several months.

Grainger said the technology is expected to help customers lower the total cost of managing maintenance, repair, and operating inventory, improve product availability and free skilled employees for higher-value work. The company said the acquisition is not expected to contribute materially to near-term results.

Adroit Worldwide Media, or AWM, develops technology that uses artificial intelligence, computer vision, and sensors to automate inventory tracking and replenishment.

AWM’s current systems combine AI-powered vision and sensor technology with smart shelving, inventory analytics, and access controls. The company says its technology can track tools and consumable products across warehouses, cribs and other locations and link products removed to individual users, job codes, or accounts.

AWM also offers predictive replenishment technology designed to identify what inventory should be restocked and when. Other capabilities include tool tracking, smart shelves with weight detection, product mapping, automated inventory reporting, and real-time inventory visibility.

Those capabilities provide more detail around what Grainger described in announcing the acquisition as “frictionless technology for industrial B2B distribution.”

AWM has previously applied its technology specifically to industrial inventory management.

In 2020, AWM announced a global partnership with OptiCrib, a Shamrock company, to apply its Automated Inventory Intelligence and AWM Frictionless technologies to industrial and commercial storeroom management.

The OptiCrib system used high-definition optical sensors combined with weight-sensing technology to automate monitoring of on-shelf inventory. The companies said the technology was designed to provide continuous inventory accountability for durable and consumable materials.

The application puts AWM’s technology squarely into an area already familiar to industrial distributors: managing and replenishing products inside customer facilities.

AWM has also deployed its computer vision and frictionless technology in automated retail environments. In 2024, Denver-based Choice Market selected AWM as its preferred frictionless checkout and technology development partner for its automated Mini-Mart concept. The partnership was intended to help Choice expand the format across locations including multifamily developments, campuses, electric vehicle charging sites and hospitality properties.

AWM is headquartered in Aliso Viejo, California, and lists a production facility in Santa Ana, California. Its website also lists fulfillment or warehouse locations in Las Vegas; Salt Lake City; Sacramento; Boise, Idaho; and Santa Ana.

Grainger did not disclose in its acquisition announcement which specific AWM technologies or intellectual property were included in the transaction or how many AWM employees are joining Grainger.

The investment comes as Grainger’s High-Touch Solutions — North America business continues to post robust growth.

Sales in the segment increased 11.9% in the second quarter from a year earlier. Companywide sales increased 10.3% to $5.02 billion from $4.55 billion, while operating earnings rose 19% to $807 million from $678 million.

Grainger also raised its full-year 2026 sales forecast Aug. 4 to between $19.4 billion and $19.7 billion, up from its previous range of $19.2 billion to $19.6 billion.

For Grainger, the acquisition potentially extends its inventory management capabilities beyond supplying MRO products and into more automated tracking and replenishment after products reach a customer’s facility.

AWM’s existing industrial technology is designed to provide visibility into what products are on hand, who is using them, what has been removed and what needs to be replenished.

Grainger will now evaluate whether those capabilities can become a broader commercial offering within its High-Touch Solutions business.

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