SYSPRO is rolling out an artificial intelligence platform designed to give manufacturers and distributors AI agents that can identify operational problems, recommend responses, and carry out approved actions within existing business systems.

The enterprise resource planning (ERP) software provider launched SYSPRO Torque on Sept. 2. The company said the platform is designed to connect AI directly with ERP, inventory, warehouse, and other operating systems rather than function primarily as a stand-alone chatbot or assistant.

Torque entered controlled availability in August with manufacturers and distributors in the food and beverage, industrial equipment, and fabricated metals sectors, according to SYSPRO. Participants range from midsize companies to distributors with more than $2 billion in revenue.

SYSPRO did not identify the distributors participating in the program.

The launch comes as software providers increasingly develop agentic AI systems capable of performing defined business tasks instead of simply generating information or recommendations. For distributors, that could extend AI into functions including inventory management, procurement, customer service, planning and finance.

SYSPRO said Torque can combine information from orders, inventory, suppliers, and operating schedules to identify problems and determine potential responses. The platform is designed to work with different ERP systems, although SYSPRO said it provides deeper integration with the company’s own software.

Torque also can connect with warehouse systems and other operational technology and operate across cloud, on-premises, and hybrid environments, according to the company.

A key part of the platform is determining how much authority an AI agent receives.

Companies can require human approval before an agent takes an action or authorize it to execute established, repeatable workflows automatically. SYSPRO said actions are logged along with the business rules and data used to reach a decision, providing a record of how the system reached and executed its conclusions.

SYSPRO said employees also can use plain-language instructions to describe the work they want an AI agent to perform. Torque then creates the agent without requiring the employee to write the underlying code.

That approach could make it easier for distributors to apply AI to specific operating processes without requiring managers to develop their own software. The extent to which companies allow those agents to act without human approval, however, remains under their control.

SYSPRO said Torque incorporates a manufacturing knowledge graph based on nearly five decades of industry practices. The platform can also connect with manufacturing execution systems, warehouse hardware, and other operational data sources.

Companies participating in the controlled rollout are working with SYSPRO to determine which decisions Torque should recommend, which actions agents can execute and how the results should be measured.

The company said Torque uses usage-based pricing, allowing customers to begin with a single workflow and expand to additional agents.

For distributors, the more significant test will be whether AI agents can move from limited pilots into routine operating processes while maintaining controls over the decisions they make and the actions they take.

The participation of distributors with more than $2 billion in revenue gives SYSPRO an opportunity to test that model in larger and more complex distribution operations.

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GrubMarket is expanding its use of artificial intelligence in food distribution with a new tool designed to help produce wholesalers make pricing and purchasing decisions using U.S. Department of Agriculture market data.

The San Francisco-based company has launched USDA Pricing AI Analyst, the latest addition to its GrubAssist AI platform. The tool allows distributors to ask questions about fruit and vegetable prices and, when connected to their internal systems, compare market benchmarks with their own sales and purchasing data.

The system draws pricing information from the USDA’s Agricultural Marketing Service. GrubMarket said users can ask questions in natural language rather than manually searching government databases, downloading files, and analyzing spreadsheets.

That could make the tool particularly useful in produce distribution, where prices can change quickly and vary by product, location, size, and packaging.

A distributor, for example, could ask how avocado prices are trending during the week or compare strawberry prices in California and New York. When connected to the distributor’s enterprise resource planning system, the tool can also compare those market benchmarks with actual transactions.

GrubMarket said distributors could use the system to identify orders sold below USDA shipping-point prices, determine whether their prices are above or below market levels and compare supplier costs with USDA benchmarks.

The tool also can analyze pricing by customer and sales representative, compare purchasing prices by supplier and buyer, track market price movements and compare prices across geographic markets, according to the company.

Users access the system through GrubAssist’s desktop or mobile chat interface and can type or speak their questions. If GrubAssist is connected to a distributor’s ERP system, the AI analyst can use the company’s sales and purchasing records in its comparisons.

USDA Pricing AI Analyst joins several specialized applications already available through GrubAssist, including Business Analyst, Inventory Analyst and Cash Flow Analyst.

GrubMarket said the underlying platform uses large language models, natural-language processing, semantic data mapping, generative AI, predictive analytics, and agentic workflows developed for food supply chain operations.

CEO Mike Xu said the tool is intended to simplify access to market information that distributors traditionally have had to gather and analyze themselves.

The product represents a more targeted use of AI in distribution than general-purpose chatbots or productivity applications. GrubMarket is applying the technology to pricing, purchasing and supplier analysis, functions tied directly to a distributor’s cost of goods and selling margins.

But the company has not yet disclosed results showing the financial impact of the new tool. Its announcement did not include customer adoption figures, pricing for the product or examples quantifying improvements in selling margins, purchasing costs or sales.

That leaves the business case to be demonstrated. Technology can make USDA and internal transaction data easier to analyze, but GrubMarket has not yet provided evidence showing how much that capability improves distributor financial performance in practice.

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Ingram Micro is putting artificial intelligence at the center of a multiyear strategy to change how the global technology distributor operates and interacts with customers and vendors.

The company said Sept. 15 that its Xvantage digital platform incorporates more than 400 AI and machine-learning models designed to automate processes, generate recommendations, and improve interactions between Ingram Micro and its business partners. The company outlined the strategy at its inaugural 2026 Capital Markets Day in Dallas.

Ingram Micro is positioning Xvantage as the foundation of a broader effort to transform the company from a traditional technology distributor into what management calls a “B2B intelligent operating system” for the global information technology market.

The company said Xvantage brings its AI and machine-learning models together on a single platform intended to reduce friction in transactions and improve business performance for Ingram Micro, its customers, and vendors.

The scale of the AI deployment is not new. Ingram Micro’s 2025 annual report, filed with the Securities and Exchange Commission in March, said Xvantage incorporated more than 42 million lines of code and 400 AI and machine-learning models. The filing said the company planned to continue investing in digital tools, automation, and AI as part of its growth strategy.

Xvantage also includes IDA, Ingram Micro’s intelligent digital assistant. The company said IDA and its real-time integration capabilities can embed recommendations and actions into systems customers already use, with the goal of accelerating sales cycles and increasing sales wins.

Sanjib Sahoo, president of Ingram Micro’s Global Platform Group, said the company’s strategy extends beyond using AI to generate recommendations.

“Our platform can predict, recommend, automate, and increasingly take action,” Sahoo said.

Ingram Micro said its strategy combines AI with the data, workflows and transactions generated across its global distribution network.

The company serves more than 165,000 customers and works with approximately 1,500 vendor partners, according to its most recent annual report. It operates in 57 countries and serves customers in more than 200 countries.

CEO Paul Bay said the company intends to use that scale and its technology platform to more intricately connect supply and demand.

“We are entering a new chapter as we build on those strengths to create the industry’s B2B intelligent operating system at scale,” Bay said.

The AI strategy is part of a broader growth plan Ingram Micro presented to investors.

For fiscal 2026 through 2029, the company is targeting compound annual net sales growth of about 4% to 6% and gross profit growth of about 5% to 7%. Ingram Micro cautioned that its multiyear financial framework is not guidance for any individual fiscal year.

Ingram Micro also identified higher-value solutions, services, and cloud offerings, along with its Enable AI program, as areas it expects to support growth and market-share gains across its four global regions.

Ingram Micro has already linked Xvantage to business growth. In its first-quarter earnings release in May, the company said “AI-led net sales” increased more than 60% year over year in its largest countries. The company did not disclose the dollar value of those sales in that release.

The Sept. 15 announcement, however, did not quantify how much revenue, profit or productivity improvement is directly attributable to the more than 400 AI and machine-learning models operating within Xvantage.

That leaves a key question for Ingram Micro and other distributors making substantial AI investments: whether embedding AI throughout core sales and operating processes can produce measurable gains at sufficient scale to justify the investment.

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Canadian wholesalers remain among the country’s slowest adopters of artificial intelligence, even as AI use across the broader economy continues to accelerate.

Just 7.9% of wholesale trade businesses reported using AI to produce goods or deliver services during the previous 12 months, according to recent Statistics Canada data. That put wholesale trade ahead of only agriculture, forestry, fishing, and hunting, at 4.5%, among the industries measured. Construction had the third-lowest adoption rate at 9.2%.

The results show a substantial gap between wholesale trade and the broader Canadian economy.

Overall, 19.2% of Canadian businesses reported using AI in the second quarter of 2026, up from 12.2% a year earlier and more than triple the 6.1% recorded in the second quarter of 2024.

Statistics Canada based the findings on its Canadian Survey on Business Conditions, conducted from April 1 through May 6. The agency received responses from 9,251 businesses from a stratified sample of 21,105.

Information and cultural industries led the country in AI adoption at 42.3%, followed by finance and insurance at 40.4% and professional, scientific, and technical services at 32.4%. The same three sectors also led to adoption a year earlier.

Among Canadian businesses using AI, data analytics was the most common application, reported by 36.6%. Text analytics followed at 34.5%, while 28.2% reported using virtual agents or chatbots.

Company size also tracked with AI adoption. Among businesses with 100 or more employees, 27.8% reported using AI, compared with 19.9% of businesses with one to four employees.

Cybersecurity or privacy concerns were the most cited barriers, reported by 13.4% of businesses. Cost was cited by 10.6%.

Statistics Canada did not provide a separate breakdown of the barriers facing wholesalers or the AI applications being used specifically within wholesale trade. The agency’s forthcoming TechStat program is scheduled to begin publishing dedicated data on AI adoption and its impact in 2027.

For distributors, the latest numbers suggest wholesale trade’s low adoption rate is not simply a one-quarter setback. The sector has trailed information, finance and professional services in every survey cycle since Statistics Canada began asking these questions in 2024, even as AI adoption across Canadian businesses has more than tripled.

The numbers also underscore the scale of the challenge facing distributors. While some large distributors are moving AI into pricing, inventory management, sales, customer service and other operating functions, Canada’s overall wholesale adoption rate remains in the single digits.

Closing that gap will depend not only on what the industry’s largest companies do, but also on how quickly midsize and independent distributors move AI beyond experiments and pilot programs and into everyday operations.

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Sysco Corp. plans to use artificial intelligence, automation, and other technology to cut at least $500 million in costs by fiscal 2029, targeting truck routing, warehouse operations, procurement, merchandising, and administrative work across its global distribution network.

The Houston-based foodservice distributor announced the three-year initiative Sept. 9, expanding a technology program that is already expected to produce about $100 million in savings during fiscal 2027.

The program puts a specific savings target behind Sysco’s effort to move AI deeper into its day-to-day distribution operations.

Sysco operates 333 distribution centers in 10 countries, employs about 75,000 people, and serves approximately 670,000 customer locations. The company generated more than $84 billion in sales during fiscal 2026, which ended June 27.

Sysco said much of the new efficiency effort will focus on its supply chain. The company plans to modernize truck-routing software, improve warehouse picking productivity and reduce miles driven.

It also plans to increase automation in merchandising and procurement, including strategic sourcing, while reducing indirect expenses and simplifying customer service and back-office operations.

“We finished fiscal 2026 with momentum, and that momentum has carried into the new year,” Chairman and CEO Kevin Hourican said in a statement.

Hourican said the $500 million program will be rolled out over the next three years and is intended to change how Sysco manages everyday work in areas including truck routing, merchandising, and sales.

The initiative builds on technology investments Sysco outlined when it reported fiscal 2026 results Aug. 4. Sysco said it expects approximately $100 million in savings during fiscal 2027 from AI, automation, and other efficiency initiatives.

Sysco now expects those efforts to expand through fiscal 2029, with at least $500 million in savings targeted over the three-year period.

The company also is tying the initiative to management incentives. Sysco said meeting its cost-reduction targets has been added to the company’s long-term equity performance program.

Sysco expects sales to reach about $90 billion

Sysco also reaffirmed its fiscal 2027 forecast, which it first issued Aug. 4.

The distributor expects sales to increase 6% to 7% to about $90 billion during fiscal 2027. The fiscal year includes a 53rd week.

For fiscal 2028 and fiscal 2029, Sysco increased its expected annual sales growth range to 4% to 7%, up from its previous forecast of 4% to 6%.

The company also increased its expected annual adjusted earnings-per-share growth range for those two years to 9% to 11%, compared with its previous target of 6% to 8%.

Those forecasts cover Sysco’s existing operations and do not include the expected impact of its pending Jetro Restaurant Depot transaction.

Sysco expects that transaction to close by the third quarter of fiscal 2027.

AI targets core distribution operations

Sysco’s program is notable because the company is targeting some of the largest and most labor-intensive parts of distribution operations.

Its routing initiative is designed to reduce miles driven while improving the use of trucks and drivers. In warehouses, Sysco is seeking to improve the productivity of employees who select products for customer orders.

Automation in procurement and merchandising is intended to reduce manual work involved in buying and managing products. Sysco also plans to simplify customer service and administrative processes.

The size of Sysco’s network means small improvements at individual distribution centers or along delivery routes could produce substantial savings when applied across the company.

The $100 million Sysco expects to save during fiscal 2027 will provide an early measure of the program’s performance as the distributor works toward its $500 million target by fiscal 2029.Do not miss any content from Distribution Strategy Group. Join our list.

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Wholesale distributors have embraced artificial intelligence as a strategic priority, but most have yet to deploy the technology broadly across their businesses, creating a sizable divide between AI ambitions and execution.

Distribution Strategy Group’s recently published State of AI in Distribution research found that 93% of distributors consider AI a strategic priority, while just 16% have deployed it across multiple business functions. That represents a 77-percentage-point gap between strategic intent and operational execution.

The findings are based on a survey of 233 wholesale distribution executives, 57% of them C-suite executives. They show an industry in which interest in AI has become widespread while large-scale implementation remains concentrated among a small group of companies.

That divide is the focus of DSG’s AI Top 25, a benchmark developed to identify distributors that have moved AI beyond experimentation and into production with measurable business results.

DSG evaluated more than 300 North American distributors during a two-year research effort, reviewing public disclosures, earnings call transcripts, executive interviews, case studies, and vendor confirmation. Companies whose AI strategies consisted primarily of roadmaps, partnership announcements or pilot programs that had not reached daily operating use were excluded.

The benchmark was designed to recognize 25 companies. DSG included 26 after an additional distributor met all its criteria rather than eliminating a qualifying company to maintain the original cutoff.

Download the complete AI Top 25 report to see the distributors setting the pace for AI deployment and the strategies behind their progress. [Click here.]

Six Practices Separate AI Leaders

The companies in the AI Top 25 span industrial, electrical, healthcare, technology and foodservice distribution and include publicly traded, privately held and employee-owned businesses.

Despite those differences, DSG identified six practices that consistently separate companies with more mature AI operations: clear executive accountability, moving successful pilots into production, stronger data, deployment across multiple business functions, measurement against business results and sustained investment over several years.

The findings indicate that access to technology itself is becoming less of a competitive differentiator.

Most distributors can buy many of the same cloud platforms, generative AI tools, pricing applications, and forecasting systems. What varies is the ability to integrate those systems into daily operations and demonstrate that they are improving the business.

That shifts the AI discussion from what technology a distributor owns to how effectively it uses it.

Leadership Starts with Accountability

One of the clearest patterns DSG identified was executive accountability.

At companies with more mature AI deployments, responsibility typically does not sit with a broad committee or move among departments. One executive owns the strategy, controls the budget and is accountable for results.

That structure gives operating teams a clear decision-maker for prioritizing investments, resolving conflicts, and determining whether an application should move from testing into production.

Brian Hopkins, DSG chief operating officer, said the pattern appeared consistently among companies in the benchmark.

“All these folks named a single owner,” Hopkins said. “If you have budget authority, you’re the owner. You’re not a committee.”

The title varies by company. Responsibility may be with a chief information officer, chief digital officer, another senior executive or, at smaller organizations, the CEO.

The common factor is authority.

DSG’s research indicates that implementation can slow when responsibility is divided among steering committees, innovation teams, information technology departments, and operating units without one executive empowered to make enterprise-wide decisions.

Moving Beyond Pilot Mode

Another dividing line is whether companies treat pilots as a temporary stage or an accomplishment in themselves.

Distributors have launched chatbots, pricing experiments, forecasting tools, sales applications, and other AI projects. Many, however, remain isolated tests rather than part of everyday operations.

DSG’s methodology intentionally distinguished between experimentation and production deployment. Companies were not included based solely on AI announcements or pilots.

Top performers start with a defined business problem and measurable target. If an application demonstrates value, they decide about whether to expand it rather than allowing the project to remain indefinitely in testing.

Successful applications can then be extended across branches, regions, product categories, or business functions.

“They stopped piloting a couple years ago and started making sure it worked across the business,” Hopkins said.

The distinction matters because the number of AI projects underway is not necessarily a measure of maturity. A distributor operating two AI applications at scale and producing measurable results may be further along than a company running a dozen pilots.

Data Quality Emerges as a Critical Divide

DSG’s broader research also found that technology is not the biggest obstacle to adoption of AI.

People are.

Skills shortages and employee resistance accounted for 52% of the challenges distributors identified. Skills gaps alone were cited by one-third of respondents, making workforce readiness the largest individual barrier identified in the research.

Data was another major dividing line.

Among distributors reporting strong data quality, 81% expressed confidence in the return on their AI investments. Among companies reporting poor or inconsistent data, only 18% expressed confidence.

The findings reinforce a fundamental limitation of AI: It cannot correct unreliable underlying information simply because a company deploys more sophisticated software.

Poor product records, inconsistent customer information and incomplete pricing and transaction histories can weaken AI results. Companies that invested in standardized product information, cleaner customer records, and data governance before scaling AI entered deployment from a stronger position.

For distributors early in their AI strategies, the findings suggest improving underlying data may be more important than adding another application.

AI Spreads Across Business Functions

The AI Top 25 also shows AI moving beyond individual departments.

Pricing systems can support quoting. Forecasting can improve purchasing and inventory positioning. Digital commerce activity can identify sales opportunities. Better demand forecasts can improve warehouse planning and customer service.

“The companies that started this process are now taking multiple AI use cases and putting them together simultaneously,” Hopkins said. “That’s going to be a differentiator over the next couple years.”

Connecting those systems can create benefits beyond individual productivity gains.

Better forecasting can improve inventory decisions. More accurate inventory information gives customer service teams better information about availability and delivery. Customer interactions generate additional transactions and behavioral data that can improve future recommendations.

As more applications draw from common data, the value of individual deployments can increase.

That gives companies that started building interconnected systems several years ago an advantage that competitors may not be able to eliminate simply by purchasing similar software.

Business Results Become the Scorecard

Measurement is another distinction between AI experimentation and more mature deployment.

DSG found that 41% of distributors primarily measure AI through productivity and time savings, while 18% have no formal measurement process.

Companies in the AI Top 25 take a more structured approach. They establish performance measures before implementation and evaluate AI against business objectives such as revenue, costs, productivity, customer experience, gross margin, fill rates and days of inventory on hand.

That provides management with a clearer basis for determining which projects warrant additional investment and which should be discontinued.

It also shifts attention away from technology activity.

Launching an AI application is not a business result. Improving gross margin, reducing inventory, increasing sales productivity, or cutting customer response times is.

AI Leaders Take a Multiyear View

Many of the distributors furthest along with AI began investing well before generative AI became a mainstream business issue.

They spent years developing capabilities in automation, analytics, machine learning, and data management. Those investments created an operating and data foundation that could support newer AI technologies.

“They’re taking big swings,” Hopkins said. “It’s not a one-time investment this year. It’s investment over a number of years.”

The findings suggest AI leadership may depend less on being first to adopt each new application and more on developing an organization capable of repeatedly identifying useful technology, deploying it, measuring the results, and expanding what works.

A Benchmark for the Rest of Distribution

The AI Top 25 also shows that AI maturity is not limited to the largest distributors.

Large publicly traded companies appear alongside privately held and employee-owned distributors and companies serving specialized markets. Their resources and technology budgets vary, but DSG found similar management practices among those that reached higher levels of AI maturity.

Executive accountability, data quality, measurable objectives, production deployment, and expansion across functions recur throughout the benchmark.

For distributors outside the Top 25, those findings provide a practical benchmark.

Executives can assess who owns AI and controls its budget, whether product and customer data are ready to support AI on a scale, how many pilots have reached production, whether applications operate across multiple functions and whether results are measured against established business metrics.

The 77-percentage-point gap between strategic priority and broad deployment shows how far much of wholesale distribution must go.

But the AI Top 25 also provides evidence of what comes next.

The companies identified by DSG are moving AI out of isolated experiments and into operations, tying investments to measurable business results and connecting applications across the enterprise.

For distributors trying to make the same transition, the benchmark offers a view not of what AI may eventually do, but of how leading distributors are putting it to work today.

Download the complete Distribution Strategy Group AI Top 25 report for the rankings, company examples and lessons from distributors that have moved AI from experimentation to execution. [Click here.]

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Continental Battery Systems is deploying an artificial intelligence platform across its sales organization to identify changes in customer accounts and recommend actions to sales representatives serving more than 45,000 customer locations nationwide.

The battery distributor has signed a multiyear agreement with Emerix to implement the technology, which will analyze account activity and flag potential revenue risks and sales opportunities for territory and inside sales representatives. The system also will provide representatives with the reasoning behind each alert and a recommended next step.

Sales managers will receive a broader view of account coverage and revenue health across their regions, giving them another tool to prioritize coaching and prepare for customer account reviews.

The companies expect the system to go live within 12 weeks.

The deployment extends AI beyond reporting and analysis and into day-to-day sales execution at Continental. Rather than requiring representatives to identify changes by reviewing customer and transaction data themselves, the system is designed to surface accounts that may require attention and route them to the representatives responsible for those customers.

For Continental, that means applying AI across a sizable distribution operation. Founded in 1932, the company distributes batteries, energy storage systems and recycling solutions and operates more than 160 locations and five regional distribution centers. Its customers include original equipment manufacturers, aftermarket providers, and major retailers.

“We’ve spent nearly a century building one of the largest battery distribution networks in North America,” Continental Chief Information Officer Jim Kitchen said. “Staying ahead now means putting technology to work inside the way we actually run the business, not around it.”

Kitchen said the technology is intended to help Continental respond more quickly to changes across its customer base, anticipate customer needs and improve customer experience.

The project reflects a shift in how distributors are beginning to apply AI. Instead of using the technology primarily to generate reports or summarize information, Continental is putting it into a workflow intended to influence which accounts salespeople pursue and what actions they take.

Emerix CEO and co-founder Ariel Palones said large branch networks generate more information than sales teams can review on their own.

“The value is never the signal,” Palones said. “It’s getting the right account in front of the right person while there’s still time to act.”

Emerix said its technology operates within companies’ existing business systems and can use data and other signals to determine a response and initiate actions. The company said its platform manages more than $10 billion in supply chain transactions across more than 15 active workflows.

Continental serves markets ranging from passenger and heavy-duty vehicles to powersports, marine, recreational vehicles, lawn and garden equipment and industrial applications.

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Wholesale distributors are starting to build an artificial intelligence workforce, creating new jobs, and adding AI responsibilities to existing positions as the technology moves deeper into sales, inventory management, ecommerce, and operations.

A review of current distributor job openings shows AI-related hiring stretching from industry giants Ferguson and Wesco to privately held McMaster-Carr and other distributors and business units focused on industrial automation.

The jobs vary considerably. Ferguson is hiring employees to build and deploy AI agents. Wesco is recruiting executives to capitalize on demand for AI infrastructure. McMaster-Carr is putting AI into management and software engineering positions, while Olympus Controls, part of Applied Industrial Technologies, is seeking a sales specialist focused on physical AI and robotics.

Some of the jobs pay well into six figures.

The hiring activity offers a more concrete measure of AI adoption than corporate announcements about experimentation and pilot projects. Distributors are beginning to decide which AI capabilities they need to own, which business problems they expect AI to solve and what they are willing to pay for the people who can make that happen.

Ferguson Builds an AI Team

Ferguson offers one of the clearest examples.

The plumbing, HVAC and industrial distributor is hiring a Lead Builder for its Artificial Intelligence Studio, part of the company’s AI Center of Excellence.

The position is not primarily a research job. Ferguson wants someone who can turn AI concepts into working business applications.

The employee will design, build, and deploy AI agents, automated workflows and conversational applications using platforms including Microsoft Copilot Studio and Google’s Gemini Enterprise. The job also involves technologies such as retrieval-augmented generation, semantic search, and workflow automation.

Ferguson wants the employee to work with business units to identify problems that can be automated and then move those applications into production. The person also will work with the company’s legal, information security, privacy, and risk teams to establish boundaries around what individual AI agents can do.

The company lists compensation of $9,700 to $15,517 per month, or about $116,000 to $186,000 annually, before potential incentive compensation.

Ferguson’s job description also points toward a broader deployment model. The lead builder will help employees in other business units create their own AI applications under guidelines established by the company’s AI Center of Excellence.

That suggests Ferguson is preparing for AI development to spread beyond a centralized technology team.

The company has approximately 36,000 employees across 1,700 locations.

Ferguson’s hiring is significant because it shows the distributor moving beyond buying AI software. It is developing internal expertise to determine how AI agents are designed, governed, and deployed across the company.

AI Becomes a Sales Opportunity at Wesco

Wesco is approaching AI from another direction.

The electrical, communications and utility distributor is recruiting a director of advanced infrastructure solutions specializing in artificial intelligence and high-performance computing.

The position combines technical expertise with customer-facing responsibilities around accelerated computing, advanced data centers, and AI infrastructure.

Wesco also has been recruiting a sales director for its Global Enterprise Data Center AI business.

Taken together, the jobs show Wesco treating AI as more than an internal productivity technology. It is also a market the distributor intends to sell into.

That distinction is becoming increasingly important as companies invest billions of dollars in data centers, computing capacity, networking, and electrical infrastructure needed to support artificial intelligence.

For distributors serving electrical, communications and data center markets, AI spending can translate directly into demand for products and services.

Wesco’s hiring suggests the company builds technical and sales expertise specifically around that opportunity.

McMaster-Carr Pushes AI Into Management

McMaster-Carr is taking a different approach by putting AI responsibilities into jobs that do not necessarily carry AI titles.

The industrial distributor’s current management recruiting program says employees may use large language models to automate routine work, analyze customer feedback, and improve the process of creating product information.

Other assignments can include warehouse automation, fulfillment and improving the order-to-payment process. That makes AI one of the tools McMaster-Carr expects future managers to use rather than a capability reserved exclusively for software engineers or data scientists.

The compensation underscores the value the company places on those employees. McMaster-Carr has advertised starting base compensation of about $133,000 for some management-track positions, with total cash compensation reaching $165,000 to $180,000, including profit sharing.

The company recruits candidates from a range of academic backgrounds rather than limiting the program to computer science graduates.

McMaster-Carr’s current career site also shows openings across technology leadership, automation, operations and management, including software engineering and leadership positions tied to customer service, fulfillment, and automation.

The strategy points toward what could become a larger shift in distributor employment: AI expertise becoming part of traditional management jobs rather than remaining a separate technical specialty.

AI Moves onto the Distributor Sales Floor

Applied Industrial Technologies provides another example through Olympus Controls, its automation business. Olympus has been recruiting a Physical AI and Robotics Sales Specialist in Fremont, California. The position combines technical expertise with sales responsibilities and is aimed at customers developing robotics and advanced automation applications.

The employee is expected to work with technologies including robotics, machine vision and motion control while helping customers design and deploy production automation systems. Compensation has been listed at $95,000 to $145,000, depending on experience and incentives.

The job represents a potentially important development for industrial distribution. AI does not have to be an internal cost-saving initiative to generate returns for distributors. Companies that already sell automation, controls, electrical equipment, and other industrial technology can turn AI investment by their customers into additional revenue.

That could create demand for a new type of distributor employee who combines the skills of an application engineer, automation specialist, and technical salesperson.

CDW Applies AI Directly to the Sales Pipeline

CDW is taking the concept a step further by applying AI to its own revenue operations.

The technology distributor recently posted an opening for an AI Opportunity Routing Specialist supporting AI Central, which CDW describes as its central hub for activating AI sales opportunities.

The employee will manage the intake, qualification and routing of AI-related sales leads and help develop an agentic system capable of automating portions of that process.

The system is intended to automate lead intake, qualification, routing recommendations, and customer relationship management updates while maintaining human review for selected decisions.

CDW estimates human intervention eventually could be required for less than 15% of opportunities handled through part of the routing process.

The job also includes maintaining sales dashboards, tracking pipeline performance, and measuring how quickly AI opportunities move from initial contact to the appropriate sales or technical team.

CDW lists annual compensation of $87,725 to $122,745, plus a quarterly bonus target of 10%. CDW’s approach is notable because the employee is not being hired simply to develop AI software. The job connects AI directly to the distributor’s sales process and revenue pipeline.

Builders FirstSource Expands AI Investment

Builders FirstSource also is expanding its use of AI across its digital operations. The building materials distributor has been hiring across business analytics and data functions while simultaneously increasing its investment in AI-enabled technology.

In August, Builders FirstSource announced a strategic partnership with Digs, an AI platform for homebuilders and homeowners. Builders FirstSource is leading a $25.3 million Series A investment in Digs and entered into a five-year commercial agreement with the company.

The companies plan to use AI to connect plans, specifications, product information, approvals, warranties, and other construction data while automating workflows stretching from estimating and procurement through construction and homeowner service.

Builders FirstSource said the technology is intended to reduce manual work, accelerate decisions and improve productivity for its more than 140,000 customers.

The company also said earlier this year that 120 engineers had been trained on AI-native development workflows as part of another technology initiative.

That combination of hiring, employee training and outside investment illustrates another way distributors are assembling AI capabilities. They do not necessarily need to build everything internally. They can develop internal expertise while investing in or partnering with specialized AI companies.

Smaller Distributors Target Specific Problems

The hiring strategy becomes more targeted farther down the distributor market. Rather than creating large AI organizations, smaller and mid-size distributors are more likely to seek employees who can apply AI, machine learning, and advanced analytics to a specific operating problem.

Inventory is one of the most obvious targets. For a distributor carrying tens of thousands of stock-keeping units across multiple distribution centers, small improvements in demand forecasting and inventory allocation can reduce working capital while improving product availability.

Customer service and product discovery are another target. Generative AI can potentially interpret complicated customer requests, search large product catalogs, and automate routine inquiries that previously required employee intervention.

Warehouse operations provide another opportunity as AI increasingly intersects with robotics, machine vision, and traditional industrial automation.

The emerging jobs therefore look less like the positions created during previous technology cycles and more like hybrids. A distributor may need an inventory expert who understands machine learning, a salesperson who understands robotics, a software engineer who understands product data or a manager who knows when an AI agent can safely automate a business process.

Six-Figure Salaries Raise the Stakes

Those skills are not inexpensive. Current distributor postings reviewed by Distribution Strategy Group show AI-related positions ranging from $88,000 at the lower end to more than $180,000 in annual compensation for some senior positions, depending on experience, location, incentives, and profit sharing.

Ferguson’s AI Studio position can reach $186,000 in annual base compensation. Applied Industrial Technologies’ Olympus Controls business has advertised total compensation approaching $145,000 for its physical AI and robotics sales position. CDW’s AI opportunity position reaches about $123,000 before its targeted bonus.

McMaster-Carr’s management positions can reach $165,000 to $180,000 in total cash compensation. That puts distributors into direct competition for talent with software companies, manufacturers, consulting firms, and technology providers.

The challenge could be particularly acute for smaller distributors that cannot match the salaries or career opportunities available at multibillion-dollar companies. Instead of building large internal teams, those companies may hire a small number of employees capable of connecting AI technology supplied by outside vendors with the distributor’s data, workflows, and industry expertise.

AI Starts Becoming Part of the Job

The bigger workforce change may be visible in jobs that do not have “AI” in their titles. McMaster-Carr’s management recruiting is one example. Builders FirstSource’s training of engineers on AI-native workflows is another.

The implication is that distributors eventually may not count AI employees separately any more than they count employees who know how to use spreadsheets or customer relationship management software.

AI could become another expected business skill. That transition is still in its preliminary stages, but current hiring provides evidence that it has begun.

Distributors spent much of the initial generative AI boom testing tools, launching pilots and determining where the technology might fit. The next phase requires people who can connect those tools to actual distribution processes.

That means deciding how much inventory to buy, helping customers find products, automating sales workflows, improving warehouse operations, and selling increasingly sophisticated technology to customers.

The job postings show distributors beginning to put salaries and responsibilities behind those ambitions. For an industry that has spent the past several years talking about what AI might eventually do, the hiring market provides a more tangible signal. Distributors are starting to pay people to do it.

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Mountainland Supply Co. is deploying an artificial intelligence-powered inventory system across 42 branches to reduce dead stock and improve how products are positioned across its distribution network.

The Orem, Utah-based distributor selected Thrive Technologies’ Thermostock Rebalance platform to analyze inventory and identify products that can be moved from locations where demand has slowed to branches where they are more likely to sell, the companies announced Sept. 2.

The technology will integrate with Mountainland’s Epicor Eclipse enterprise resource planning system, allowing the distributor to add AI-based inventory analysis without replacing its core ERP platform.

For Mountainland, the objective is straightforward: use data already generated across its branch network to find inventory that is no longer selling in one market but remains in demand somewhere else.

That addresses a fundamental challenge for multi-branch distributors. Companies need enough inventory locally to maintain product availability and fill rates, but carrying too much stock at individual branches ties up working capital and increases the risk that products eventually become dead inventory.

Mountainland carries products across plumbing, heating, ventilation, and air conditioning, hydronics, waterworks and other categories. Its 42 branches span six states, creating a large pool of inventory that can potentially be repositioned before additional products need to be purchased.

Thrive’s system analyzes inventory and customer purchasing data to identify transfer opportunities. It evaluates demand patterns, including the breadth and recency of customer purchases, to determine where slow-moving products may have a better chance of selling.

Buyers can then prioritize transfers based on factors such as proximity and customer demand.

“We are really excited about our ability to quickly reduce our dead stock,” Matt Scott, Mountainland’s director of inventory, said in a statement.

Scott said Mountainland is already seeing early returns from reducing dead stock, although the company did not disclose specific financial results or inventory reductions.

The deployment represents a practical application of AI in distribution operations, where the technology is increasingly being used to analyze large amounts of transactional data and recommend actions rather than simply generate content or answer employee questions.

In Mountainland’s case, AI is being applied directly to inventory decisions that affect working capital, product availability, and profitability.

The system is designed to identify inventory problems across the network that would be difficult for buyers to continuously analyze manually. Multi-branch distributors can carry tens of thousands of stock-keeping units, making it difficult for purchasing teams to regularly determine whether individual products should remain at a location, be transferred elsewhere or eventually be written down.

Mountainland’s approach also illustrates another emerging model for distributor AI adoption: adding specialized AI applications on top of existing enterprise systems rather than undertaking a major technology replacement.

By integrating with Epicor Eclipse, Mountainland can use its existing inventory and transaction data while applying another analytical layer to determine where products should be positioned.

That turns AI into an operating tool with a measurable objective. Instead of testing a general-purpose AI application, Mountainland is targeting a specific financial problem: reducing the amount of cash tied up in inventory that is unlikely to sell where it currently sits.

If the system works as intended, the payoff is not simply less dead stock. Moving existing inventory to locations where customers are buying it could also reduce unnecessary replenishment purchases, improve inventory turns, and put working capital back to use elsewhere in the business.

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The Descartes Systems Group has acquired Extensiv for approximately $120 million, expanding its warehouse management and fulfillment capabilities as the logistics technology company builds out its artificial intelligence portfolio.

Descartes has acquired California-based Extensiv with cash on hand. Extensiv develops warehouse management, inventory, order fulfillment, and billing technology used by third-party logistics providers, or 3PLs, and the brands they serve.

The acquisition gives Descartes more than another warehouse management platform. Extensiv connects inventory, orders and B2B and B2C fulfillment across sales channels, ecommerce platforms, online marketplaces, and carriers, generating operational data that Descartes plans to use to support AI applications.

Extensiv already uses AI to help warehouse operators analyze information, make decisions, and reduce manual work. Descartes said adding Extensiv to its Global Logistics Network will increase the amount of fulfillment data available across its logistics technology platform.

“3PLs are under constant pressure to fulfill faster, scale flexibly, and support the evolving needs of modern brands,” Mikel Richardson, general manager of ecommerce operations at Descartes, said in a statement.

Richardson said Extensiv will add participants, operational data, and fulfillment intelligence to the Descartes network.

The deal follows Descartes’ Aug. 24 acquisition of Tai, which provides AI-powered transportation management software for freight brokers. The two transactions extend Descartes’ technology further into two major parts of logistics operations: transportation and warehousing.

The acquisitions also illustrate how Descartes is building its AI strategy around operational data. Extensiv brings information generated by inventory, orders, and fulfillment, while Tai adds data and technology tied to transportation management and freight brokerage.

That combination could give Descartes more opportunities to embed AI directly into logistics workflows rather than offer it as a separate application. Warehouse and transportation data can be used to identify operating patterns, automate routine tasks, and provide recommendations as orders and shipments move through the supply chain.

For distributors and 3PLs, the deal also reflects a broader shift in logistics technology. AI increasingly is being incorporated into the systems companies already use to manage inventory, warehouses, transportation, and fulfillment, putting a premium on technology platforms with access to large volumes of operational data.

Descartes is also positioning itself to offer more of those capabilities through a single technology provider. Its existing portfolio includes transportation management, shipment visibility, trade intelligence, customs compliance and last-mile delivery.

Scott Sangster, general manager of logistics services providers at Descartes, said combining Extensiv with those capabilities could allow logistics providers to scale their operations without relying on a patchwork of technology vendors.

The Extensiv acquisition moves Descartes further inside the warehouse while adding another source of operational data for its AI strategy. As logistics software providers compete to automate more supply chain decisions, the ability to connect data across inventory, fulfillment and transportation is becoming an increasingly important part of that race.

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