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Everyone’s talking about agentic artificial intelligence, manufacturers and distributors included. We hear that it will change absolutely everything about your operation. And that’s true — for better or worse.
The very best applications can drive major efficiency gains, enhance visibility and reduce risk. Others are essentially multi-million-dollar mistakes. And in most cases, the tech is the exact same.
It all comes down to a question of approach.
The most important driver of successful agent deployment lies in the use case. The key is deploying the AI to support people — not replace them — so they can dedicate their time and attention to higher-value tasks. The work isn’t changing; the way we accomplish it is.
Of course, the ideal approach will depend on your unique operation. But the following agents tend to be some of the most consistently strong:
Sales order entry. This is one of the best AI applications in manufacturing and distribution use cases. That’s because most manufacturers still pay teams to receive purchase order emails or PDFs, and manually re-key them into the system. The process can take more than 10 minutes per order, even when all the relevant information already exists right within the attachment.
A sales order entry automation agent can instantly understand the request, read attachments, check for issues and apply relevant business policies. This could include automatically addressing all the most common order issues your staff sees again and again, such as someone trying to order something you no longer make, an order improperly requesting 2023 pricing, a too-short delivery window, or part numbers that don’t match and require cross-reference.
And, with an agent handling these time-consuming basics, your team can spend more time on the activities that matter most — say, serving key accounts.
Customer service. These agents are great at responding to product and quote inquiries, which also tend to take too much manpower.
How many times do you think a lighting manufacturer hears, “I need a light with these specs. What fits?” Or, “I’m about to buy a competitor’s bulb. Do you have something similar?”
The answers are often the same, and to be frank, these asks just aren’t worth your employees’ valuable time.
An AI agent can seamlessly process the message, research internally, identify comparable products and help manage the response. Additionally, it can prepare organizational knowledge articles that can be referenced in future responses.
Sourcing. Most procurement teams make daily decisions around price, quality, location, transit time, tariffs and inventory availability. That’s a lot of independent variables for any one worker to weigh, especially when time is of the essence. Sometimes an employee may not notice a change in quality or performance of a supplier, and may lean on the same supplier used for the last purchase to move things along quickly.
AI can instantly factor in all of these data points and recommend the optimal supplier. Then, a human simply needs to approve the PO, and even this step can be automated later.
This way, people can spend their time finding more suppliers, negotiating better rates and experimenting with contingency planning — essentially, becoming proactive instead of reactive.
Production costing. As you’re building and assembling products, you incur costs, both direct and indirect. That includes everything from labor to materials and facility expenses. But if you’re like many manufacturers, cost analysis might only be done on a handful of orders each month.
A production costing agent can work behind the scenes to constantly review production orders as transactions occur. As employees weld, assemble, inspect and rework, the agent notes and evaluates the fluctuations between cost and performance, comparing these with contracts, plans, averages, and estimates.
Then, it can surface anomalies that may uncover issues with machines or operators and identify trends, such as repeated overruns or unacceptable levels of variance. The best agents even understand the production flow and can suggest relevant corrective actions.
This degree of monitoring could mean improved cost control, more accurate quoting and better planning overall.
Agentic AI is pretty incredible, but it isn’t magic.
Failures typically come down to mismatched or unrealistic motivations, expectations or approaches. Agents that encapsulate this problematic thinking include the following:
Customer service replacements. We’ve already covered the fact that customer service agents can be extremely helpful. But they can’t replace 50% of your staff. A recent MIT study found that only 11.7% of the U.S. workforce could technically be replaced by current AI capabilities, and only 2.2% of the labor market has been visibly affected.
The reality is that workers are still needed for high-context questions, product nuances, exception handling and any decisions that require the kind of deep, tribal knowledge that only comes with tenure. Humans provide a level of flexibility that an AI, trained and focused on a particular task, cannot accomplish.
And, if members of your workforce do need to be diverted, customer service employees can become really great quality managers and procurement agents.
Demand forecasting limited by bad data. This is one of the strongest use cases for AI: analyzing historical trends, seasonality, customer patterns, lead times and disruptions. But the underlying data is key. When agents are stuck analyzing nothing but flawed data, the results will be flawed, too.
For example, AI may struggle with the outliers and anomalies that can be easily explained by a human. This could include product launches, marketing campaigns, competitor landscapes changing and supply shortages. In essence, without human insight, the AI is lost.
Production planning without change management. This one of the most cross-functional processes, touching everyone from sales to scheduling to customer service. It’s why there’s such a strong case for optimization.
With so many stakeholders, however, change management becomes critically important. Rollouts that don’t include clear expectations and enthusiastic buy-in from everyone involved are all but destined to fail.
Solutions in this area must show straightforward explicability. In other words, it must be clear why the AI took a particular action, and it might be important to only partially automate the process until everyone is comfortable with the input data and AI output.
The most successful initiatives in agentic AI typically come down to the following:
Proving out a business case. To actually define success, there needs to be a clear problem to be solved. Don’t implement AI just for the sake of it. Instead, identify which specific issues your operation is facing today, or might be facing in the future, and align targeted solutions that drive real impact.
You’ll also need to ensure that an agent is in fact the right fit for each use case. In some instances, such as demand forecasting, predictive analytics (more traditional AI) can be even more impactful.
Work to understand the potential impact of the solution in terms of revenue, cost, time or experience. Ask questions such as, “If we had this” or “If we knew that,” then explore how it would affect the way you operate.
Understanding technical feasibility today. When implementing agentic AI, manufacturing and distribution leaders will need to understand what it can and can’t do today. Trying to predict tariffs is a dead-end goal. No one, agents included, can predict the future.
Instead, think of agents as interns. You might turn to them when you hear conversations like:
- “Too much clicking”
- “We have to check/verify/validate this every time”
- “We must analyze…”
- “Someone needs to follow up on…”
- “We run this report daily and then…”
- “We’re constantly switching between systems”
- “This takes forever to process”
- “We have to remember to…”
Essentially, AI agents excel with repetitive, rules-based or analytical tasks.
Paving a realistic path to scale. Again, don’t try to replace half your staff with AI agents at the very first opportunity.
For example, AI might be technically capable of answering all customer emails. But start with 5%. Prove it out, add another 10%, and expand gradually. Monitor results and ultimately scale in a way that feels comfortable and manageable.
Employing best practices to change management. Remember that rolling out an agent inherently means changing the way the business operates.
For example, a customer service worker who used to conduct most of their job in email every day might now focus instead on large quotes and helping win new business. That can be scary, and people naturally worry about their jobs.
Include your people in the project, and dedicate plenty of time to helping them understand what’s changing, how the changes will benefit them and what exactly they’ll be doing going forward. Then ask them to communicate that to their sphere of influence.
When it comes to agentic AI, manufacturing and distribution leaders should remember to focus on the unique needs of their business, what’s actually possible with agentic AI, and the right approach to managing the massive shift.
Remember: The strongest results happen when people are augmented, not replaced.
Understanding these facts, and applying them with intention, are key to successfully deploying agentic AI across manufacturing and distribution.
Dag Calafell is director of technology innovation at MCA Connect.
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