Gone are the days when a warehouse manager at a wholesale distribution company would have to pull up a contract on a tablet, a security alert on a second screen and a revenue recognition summary on a third. Artificial intelligence now assists with many of those tasks at roughly three in four wholesale firms. The technology is becoming a core part of the infrastructure.
That shift is creating a divide among enterprises. Some organizations are spreading AI across a broad range of functions and building repeatable operating models around it. Others are concentrating AI in a handful of use cases, pursuing narrower deployments while they evaluate where the technology delivers the greatest value.
Across sub-industries of the goods sector, firms are spending more on the technology. Executives see AI as a tool for strengthening core operations, improving profitability and supporting more informed decision-making. At the same time, they’re keeping a close watch on how much authority those systems should have, placing human oversight at the center of their AI strategies.
Wholesale firms present a particularly revealing case study in AI at scale. They have woven the technology into more business functions than their peers, developed stronger alignment on where it should be used, and have shown a greater willingness to pursue long-term returns on their investments.
“Wholesale Writes the AI Playbook: How Goods Firms Are Scaling Intelligence Across the Enterprise,” the latest edition of the Enterprise AI Benchmark Report, is based on a survey of 60 senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue, fielded in May 2026. It examines how goods-focused companies (spanning retail, eCommerce and marketplaces, construction and manufacturing, and wholesale, fleets and goods distribution) are deploying AI across 75 distinct tasks in eight business functions. The data reveals three distinct strategies that happen to share the same budget line.
Wholesale’s AI Leadership
Wholesale’s use of AI is both the most extensive of all goods sub-industries and the most consistent.
It may seem that goods firms are relatively uniform in their AI use across sub-industries. A typical firm in retail or construction uses AI for roughly 31 of 75 tasks tracked in the survey underpinning this report. The average wholesale firm uses it for 35 tasks. That gap might seem small and suggest that the three sub-industries are essentially keeping pace with one another.
However, delving into those headline numbers reveals that wholesale firms’ usage is far more concentrated. In retail, at least half of all firms in the sub-industry use AI in only 20 of the 75 tasks. Construction firms use AI in just 22 tasks. But wholesale firms use AI in 35 tasks, the same number as the typical firm’s total task count. That means wholesale firms are using AI consistently. It’s not a random smattering of deployments so much as a shared playbook.
When a majority of firms in a sector agree on which AI tasks are worth using, the technology becomes a baseline. Wholesale firms that haven’t yet adopted those 35 tasks lag the new operational standard.
Wholesale leads in AI use for six of eight business functions.
Of all sub-industries in six of the eight functions the survey tracked, including corporate and strategy, data and technology, risk and compliance and payments and finance, wholesale firms showed the highest rates of majority AI adoption. Retail and construction firms, by contrast, use AI for marketing and sales more often than wholesale enterprises. All three sub-industries use it about equally for supply chain and fulfillment functions.
The three most widely adopted AI tasks in wholesale illustrate how broadly those firms are using the technology. Three-quarters of wholesale firms use AI for contract and proposal generation (i.e., to produce or assist with the documents that govern their commercial relationships). The same share uses the technology for security monitoring and threat detection, indicating that wholesale firms are applying AI not only to revenue-generating functions but also to the infrastructure that supports them. Similarly, 70% are using AI for revenue recognition and close support (the process of determining when income is officially recorded).
These tasks aren’t flashy consumer-facing applications but core business processes (legal, financial and security) that have historically required specialized human expertise. The fact that a considerable majority of wholesale firms have standardized AI use in these areas suggests a level of institutional confidence that retail and construction have yet to reach.
AI Governance and Control
Wholesale firms want to increase AI use in the next few years, but they are keeping humans in charge for now.
Wholesale firms may have the most mature AI program in the goods economy, but they’re also holding the most rigid boundaries around how much autonomy that AI should have. Regarding how much authority it would grant an AI agent today, every single wholesale firm surveyed said it would allow look-up access only. In other words, the agent can retrieve information but cannot act on it.
Retail firms show a similar sentiment. Nine in 10 would limit an agent to look-up access, with 5% allowing some execution with human approval (the greatest share of any sub-industry) and 5% preferring no access at all. Construction firms are the most conservative, with 85% willing to grant look-up access only and 15% preferring no access whatsoever.
The picture shows AI use in the goods economy functioning as a sophisticated research and reporting tool. It surfaces information, flags issues and generates documents. Humans are the ones deciding what to do with the output.
AI’s role will grow in the years ahead, but it won’t take full control.
Looking ahead, executives in the goods sector are prepared to extend considerably more authority to AI systems. No executive surveyed expects fully autonomous AI in the next five years.
Instead, the goods sector splits between two visions. Many retail and construction firms expect AI to augment human decisions, providing recommendations that people can act on. Most executives expect it to be semi-autonomous with oversight. AI will be able to take certain actions independently, with humans monitoring and retaining the ability to intervene.
Wholesale firms are the most willing to grant this kind of access: Eighty-five percent say their primary five-year goal is semi-autonomous AI with oversight, compared with 60% of retail firms and 55% of construction firms.
Today, wholesale firms grant AI look-up access only, without exception, and their peers are nearly as restrictive. In the next five years. AI may be semi-autonomous, but humans will still be keeping watch.
AI Spending and ROI
All goods firms are spending more on AI, but each sub-industry is betting on different timelines to see ROI.
All three goods sub-industries plan to invest more in AI in the next year. Across the entire survey, zero firms plan to reduce AI spending over the next 12 months. Among wholesale and retail firms, 65% are actively increasing their budgets, with the remainder holding steady. Construction firms are the most aggressive, with 75% increasing spending.
Where there’s less consensus is in the expected timeline for those investments to pay off.
Retail firms are the most optimistic. Forty percent expect a return on investment (ROI) within one to two years. Another 35% expect returns in three to four years, and only 25% are prepared to wait five or more years.
Wholesale firms, meanwhile, are taking a longer view. Only 10% expect a return within two years. Another 20% anticipate returns in three to four years. The remaining 70%, which marks a considerable majority, expect to wait five or more years for their AI investments to deliver meaningful returns.
Construction firms are in the middle: 20% expect a short-horizon return, 55% expect a medium-horizon return in three to four years and 25% expect to wait five or more years.
Wholesale firms are willing to wait for returns.
The long payoff horizon in wholesale likely relates to the specific areas where firms in that sector are directing their next wave of AI adoption. Eighty-five percent of wholesale firms say they are focusing their upcoming AI projects on margin improvement and profitability.
By contrast, many retail firms are investing more in near-term efficiency. Improving margins through AI typically means renegotiating supplier contracts with better data, optimizing distribution routes or restructuring pricing models. These are structural changes to the business. They take a long time to design, longer to implement and longer still to show up in the financial records.
Construction spreads its investments across risk management as well as experimental applications that don’t have clear, expected financial outcomes or returns on investment. This diversified approach may reflect the sector’s broader uncertainty about where AI delivers the most value.
Wholesale firms have already standardized AI across more tasks than any other goods sub-industry. They have the strongest consensus on a semi-autonomous five-year vision. Now, they are investing in the hardest, highest-value problem in their business (i.e., profitability), while accepting that the payoff will come slowly. That combination of ambition, control and long-term patience is unusual in a technology adoption cycle typically defined by immediate ROI pressures.
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Methodology
The latest edition of the Enterprise AI Benchmark Report, a PYMNTS Intelligence exclusive series, is based on a survey of 60 senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue, fielded in May 2026. This report examines how goods-focused enterprises are deploying and scaling artificial intelligence across 75 distinct AI-supported tasks spanning eight business functions. Respondents are primary decision-makers or the most knowledgeable individuals in their organizations on AI strategy, adoption and operations. The sample is divided equally across three sub-industries: retail, eCommerce and marketplaces; construction and manufacturing and wholesale, fleets and goods distribution. Each sub-industry contained 20 respondents. Revenue distribution: 68.3% of firms reported $1 billion to $5 billion in annual revenue, 26.7% reported $5 billion to $25 billion and 5.0% reported $25 billion or more. Figures are directional given sample sizes.
