AI Is Enabling the Next Generation of Distributor Workflows
Key Highlights
- Manual processes like quoting and order entry are slow, error-prone and unable to scale with increasing demands and customer expectations.
- AI interprets imprecise customer requests, automates data translation and learns preferences, enabling faster and more accurate order processing.
- Embedding AI into distribution workflows is now a necessity for operational efficiency, competitive advantage and future industry resilience.
Workflows at distributors have been more or less the same for decades. Quoting, order entry, PO tracking and invoicing still rely on emails, spreadsheets and ERPs with manual effort expected to close the gap. That model worked when volumes were lower and expectations were more forgiving, but the world is changing.
Customer expectations have never been greater, and distributors must respond to growing volumes of requests quickly and accurately, often across fragmented inputs and constantly changing information. At the same time, experienced employees are retiring and taking years of institutional knowledge and customer context with them with fewer new hires available to take their place.
Highly manual workflows can’t keep up with this new reality.
AI represents a practical solution. Not as a bolt-on tool, but as a way to change how work gets done — handling the translation between how requests come in and how orders need to be processed. That shift removes the bottleneck at the center of quoting and order entry, allowing teams to respond faster, reduce errors and operate more consistently despite the rising pressure.
Traditional methods are struggling to keep up
Manual processes have served the industry well for decades, but the world around them has changed. The bar for customer service has been set by the instant, digital experiences customers experience everywhere else in their lives, and they’re bringing those expectations onto the jobsite. Requests come in faster, across more channels, with less patience for delays. Labor shortages across both construction and distribution have narrowed the margin of error for those who remain.
Manual processes weren't designed to scale with that combination of pressures.
Quoting and order entry illustrate this most clearly. Customer requests routinely arrive with imprecise or informal product descriptions, missing part numbers and casual references to previous jobs. Before a quote can be built, that request has to be decoded and translated into something an ERP can work with — line by line, item by item, all entered by hand. It's slow, repetitive and prone to error. When mistakes happen, a team that's already stretched thin has to spend their limited time fixing them, pulling focus away from the work that actually moves the business forward.
What changes when AI enters a workflow
AI does not just speed up existing processes, it changes where the work starts. Instead of manually decoding customer requests from zero, teams can rely on AI to interpret imprecise inputs and return accurate product matches. Translating requests into usable data becomes a background process that happens before a request ever reaches a sales rep.
AI also has a level of adaptability that traditional software lacks. It learns over time, picking up on customer preferences and surfacing relevant SKUs and frequently ordered items preemptively. The result is a faster, more accurate quoting process, and reps who are free to spend time with customers instead of spreadsheets.
Transferring knowledge as teams evolve
AI also plays an important role in knowledge transfer, especially as the workforce evolves. The distribution industry is built on long-term relationships, and businesses rely on the expertise of long-tenured employees to know the specific “ins-and-outs” of each customer. When these individuals retire, decades of product knowledge and practical know-how goes with them. For younger workers who grew up with fast, intuitive consumer technology, legacy systems built around manual data entry and steep learning curves are a hard sell.
AI tools can capture best practices from experienced employees and encode them directly into the workflow — things like how to interpret a customer's shorthand or which products are typically ordered together. The knowledge becomes structural, shaping how work gets done regardless of who's doing it. Distributors that thoughtfully deploy AI protect their operational continuity, attract new talent, and give less seasoned employees a faster path to success.
Human oversight remains critical
Customers are expecting more and AI can help distributors deliver, but removing people from the equation is neither possible nor preferable. AI works best when automation and human oversight are combined, not when one substitutes for the other in either direction.
AI handles the tedious, repetitive, time-consuming work that creates bottlenecks and errors. Employees bring the judgment, context and personal customer relationships that no system can replicate. The goal is to take low-value work off the plate of skilled people so they can focus on the customer service that meaningfully differentiates a distributor from the competition.
Looking ahead
The pressures facing distributors aren't new, but they are compounding. Customer expectations, workforce challenges and operational complexity are converging in a way that manual processes can no longer handle.
AI has moved from a competitive advantage to a baseline requirement to keep pace. Distributors that embed it into their core workflows will operate more efficiently, compete more effectively for customers and build an operation that holds up as the industry continues to evolve. Those that don't will find themselves poorly positioned to keep up with a world that isn't slowing down.
Michael Delgado is CEO of Canals www.canals.ai. He graduated from Harvard Law School in 2014 with a doctor of law degree and the University of Miami in 2010 with a bachelor’s of science in neurobiology and neurosciences.