The State of AI in Distribution

Artificial intelligence has moved beyond the realm of experimentation and into the daily operations of businesses across the global economy. According to recent research from McKinsey & Company, AI adoption has surged over the past two years, with a growing share of organizations reporting measurable impact on revenue, cost reduction and customer engagement. Similarly, Deloitte notes that companies are shifting from pilot programs to enterprise-wide deployment, particularly in sales and service functions.

Yet within the PHCP-PVF distribution channel, the reality is more nuanced. AI is everywhere in conversation, dominating industry events, LinkedIn feeds and boardroom discussions. But on the ground, adoption often feels fragmented. Many distributors are still in evaluation mode, unsure where to begin or how to translate broad potential into practical application.

That tension between urgency and uncertainty is not unique to distribution. Research from Anthropic suggests that while AI tools are widely accessible, their actual use remains concentrated in a relatively narrow set of tasks. In its 2026 economic index, the firm found that most organizations are using AI to assist with specific workflows rather than fundamentally transform operations, highlighting a gap between capability and execution.

At the same time, the technology itself is evolving at a pace that is difficult to ignore. Research from Model Evaluation & Threat Research shows that the “time horizon” of AI systems, also known as the length and complexity of tasks they can complete, is expanding rapidly. Systems that once handled simple prompts are now capable of executing multi-step workflows, a shift that signals a move from assistive tools to more autonomous systems in the near future.

The technology is powerful, but still accessible and the cost of entry is lower than ever. And perhaps most importantly, the competitive landscape has not yet fully reset.

“The organizations that are going to be able to take this on and be really successful are not necessarily those that have the biggest budgets,” says Brooks Hamilton, principal at AI Strategy Advisors. “It is going to be the organizations who can make decisions quickly and then have the staying power to follow through.”

This shift from scale to speed may prove to be one of the most important changes the distribution model has seen in decades.

From hype to reality: where AI actually stands today

Despite the intensity of the conversation, AI adoption is still in its early stages in terms of depth. The Anthropic research underscores this reality, showing that most usage today centers on augmenting human tasks rather than replacing them. AI is helping employees write, analyze, summarize and respond faster, but it is not yet running businesses end-to-end.

This aligns with broader findings from Stanford Institute for Human-Centered Artificial Intelligence, which notes that AI’s near-term impact is most pronounced in knowledge work and customer-facing roles, where it enhances productivity rather than eliminates jobs.

The distribution industry is inherently relationship-driven, built on trust, responsiveness and product expertise. AI, at least in its current form, is not replacing those dynamics. Instead, it is beginning to reshape how efficiently they are delivered. Hamilton sees this clearly in the conversations he has with distributors across the country.

“The amount that we’re bombarded by this in the news and on LinkedIn makes it seem like everybody has figured out all the AI things,” he says. “That’s maybe not entirely accurate.”

In reality, most organizations are still experimenting. The difference is that experimentation is no longer optional. It is quickly becoming a prerequisite for staying competitive.

The biggest misconception: waiting for perfect data

One of the most persistent barriers to adoption is the belief that AI requires clean, structured, well-governed data before it can deliver value. For many distributors, that requirement has effectively delayed action.

Hamilton argues that this mindset is outdated.

“That’s become a really comfortable response for both vendors and customers,” he says. “For distributors, it’s a way to delay doing anything on the AI side.”

Historically, advanced analytics and machine learning models did depend heavily on structured data. Projects like pricing optimization or inventory modeling required extensive data cleansing before they could be deployed. But the current generation of AI tools operates differently.

“These technologies allow us to work across data that is much fuzzier than what we were working with before,” Hamilton explains.

That means distributors can begin using AI with the data they already have, including emails, quote requests and customer interactions. More importantly, the process of using AI can actually improve data quality over time, creating what Hamilton describes as a “virtuous cycle” of learning and refinement. The starting point for AI is not perfection, it’s simply taking action.

Speed over scale

For decades, competitive advantage in distribution has been defined by scale. Larger organizations had greater purchasing power, broader geographic reach and the financial resources to invest in technology. Smaller distributors, while often more agile, faced structural limitations. AI is beginning to disrupt that equation.

“The type of projects that small organizations can now take on have become much more accessible,” Hamilton says.

Capabilities that once required six- or seven-figure investments like advanced quoting systems, pricing analysis, routing optimization, are now available at a fraction of the cost. In some cases, they can be deployed with little more than a subscription to an AI platform.

According to Hamilton, this shift is not just about cost. It is about timing. “We’re going to go through a period of intense transformation over the next five years,” he says.

During that period, the ability to make decisions quickly and to act on them consistently may matter more than traditional measures of scale. Large organizations still have advantages, but they also carry complexity. Smaller distributors, by contrast, can often move faster.

That dynamic is already playing out in other sectors. Economic analysis from the World Economic Forum suggests that AI-driven productivity gains are likely to be unevenly distributed, favoring organizations that adopt early and integrate effectively.

Where AI is delivering value today

While long-term possibilities dominate headlines, the most meaningful impact is coming from practical, near-term applications. Across distribution, these use cases tend to center on sales, quoting and customer experience.

Hamilton points to sales enablement as one of the most immediate opportunities. AI can help sales teams identify prospects, prepare for meetings and access product knowledge more quickly, particularly for newer employees who may not yet have decades of experience.

“It makes it so much clearer what I’m going to say when I get there,” he explains.

Another high-impact area is quote acceleration. In a competitive bidding environment, speed matters. “If we’re able to produce a quote that is fairly reasonable and we’re the first ones back, our likelihood of being able to win is a lot better,” Hamilton says.

AI can streamline this process by analyzing incoming quote requests, identifying inconsistencies and structuring responses quickly. Notably, these capabilities are available today without complex integrations or large investments.

“Without spending more than $20 a month per user, there is already a lot to be gained,” Hamilton notes.

These applications improve speed and consistency without disrupting the relationships that define the business.

Growth vs. cost-cutting: where the real ROI lies

Much of the public conversation around AI has focused on automation and labor reduction. While efficiency gains are real, Hamilton argues that the most significant returns come from growth-oriented use cases.

“If you put $50,000 toward improving your sales process and receive a 1% increase in sales, versus using that same $50,000 to automate customer service, it is about a 50x difference in margin return,” he says.

That distinction is critical for distributors operating in margin-sensitive environments. Investments that drive revenue growth through improved win rates, faster response times and better customer engagement can have a disproportionate impact on profitability.

Broader market analysis supports this view. Research cited by firms such as Morgan Stanley indicates that companies are increasingly focusing AI investments on revenue generation rather than purely cost reduction, particularly in sales and marketing functions. This aligns closely with core business priorities; the goal is not to replace people, but to make them more effective.

The next phase: from copilots to autonomous systems

While today’s AI tools are largely assistive, the next phase of development is already emerging. Hamilton points to the rise of autonomous agents and systems capable of executing tasks independently as a key trend to watch.

“We’ve all been working within a frame of chatbots,” he says. “But that’s starting to change.”

These systems can pursue objectives, adapt to challenges and complete multi-step workflows with limited human input. While still early, their development aligns with METR’s findings on expanding AI task horizons.

The implication on PHCP distribution is not immediate disruption, but accelerating capability. Tasks that currently require manual coordination may soon be handled more seamlessly by AI systems, further increasing the importance of early adoption.

Hamilton offers a cautionary note. “2026 may be the last year that AI feels understandable,” he says.

That does not mean adoption becomes impossible. But it does suggest that the learning curve may steepen over time.

The real risk: standing still

If there is a central risk for distributors, it is not adopting the wrong technology or moving too quickly; it’s failing to move at all.

Anthropic’s research highlights that AI benefits are not evenly distributed. Organizations that adopt early and build internal expertise are more likely to capture outsized gains, while others fall behind.

Within distribution, where competitive advantages are often incremental, those gaps can compound quickly. The industry has seen similar shifts before, from ERP systems to e-commerce platforms. In each case, early adopters gained ground that was difficult to recover. AI may follow a similar trajectory, but at a faster pace.

Practical steps for distributors

For many distributors, the question is not whether to adopt AI, but where to begin. The answer, according to Hamilton, is simpler than it may seem.

Start with small, practical use cases. Focus on areas that improve speed, consistency and customer experience. Use existing tools rather than waiting for large-scale implementations. “Those are really some pretty affordable, quick turnaround activities that distributors can engage in,” Hamilton says.

AI is an active force reshaping how work gets done, how customers are served and how competition unfolds. The technology is still accessible. The barriers are still relatively low. But those conditions may not last indefinitely.

The path forward does not require a complete transformation overnight. It requires a willingness to act, to experiment, and to build capability over time. The companies that do so will not just adapt to the next era of distribution, they will help define it.