“The biggest thing is to not chase shiny toys. Any tool you employ has to fit your strategy as a business. You don’t want your business to fit the technology; you want the technology to fit your business.”
— Will Quinn Warehousing and Supply Chain Strategist
then leverage use cases to bring rapid impact to keep momentum and help with self-funding the AI journey.” With the goal set, businesses should prioritize
organization-wide buy-in. Quinn acknowledged that many in the workforce are on edge about AI. Clear, upfront communication can help ease those concerns. “They think companies want to implement it to get rid of people, but it’s a tool,” he said. Just as a forklift moves more freight than a hand truck, AI lets employees crunch a lot more data than if they relied solely on Excel. Helping employees understand that benefit can bring them on board. “Transparency and honesty are the best policy,” Quinn said. “In the absence of information, humans fill in worst-case scenarios. So be transparent — here’s what we’re doing, why, what we’re hoping to accomplish, and how it’s going to help you.”
Messaging starts at the top, Oca added, and these initiatives work best when they are backed by the entire C-suite. Sponsorship from the CFO and COO along with support from the chief information officer or chief development officer illustrates that any automation investment isn’t just a tech roadmap. “If positioned as an IT initiative, it will fail,” he warned. “It has to be coded as the new way of working. Enterprises that run successful transformations put the employee and its value proposition at the forefront, not AI, digital, or tech.”
3. Reskill Employees
Putting employees at the center of AI transformation often requires investment in reskilling. AI may be able to take over much of the grunt work, but supervisors, op- erators, and planners should be trained to interpret the
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output — and rapidly determine whether the machine has generated a nonsensical or inaccurate response. This kind of mistake, known as “AI hallucination,” occurs when systems fill in gaps with nonexistent or misinter- preted data. The result might look plausible, but closer inspection can reveal underlying flaws. When it comes to acting on an AI’s analysis or recommendation, humans are needed to validate the information and make the call. “They still have power and need to make decisions,” Oca said. “What’s changing is on what and how.” Some companies may choose to rely heavily on
third-party services to implement AI solutions, but those looking to invest internally may need to adjust their talent strategy. “Bring in data scientists, data engineers, and machine learning (ML) engineers,” Oca said. The sup- ply chain practitioners already in the organization may also need training to better collaborate with these new specialists. “You will need supply chain people who can translate the needs for the scientists and engineers so that they can code the agents, algorithms, and work- flows,” he added. Once the organizational groundwork is in place, dis- tributors can begin applying AI to specific warehouse and inventory processes.
4. Improve Forecast Accuracy Inventory management is one of the most impactful
components of distribution performance — too few items in stock can create service failures, and too many can constrain capital and clutter operations. Manually reviewing and adjusting SKUs may work well in some situations, but as assortments grow, that approach can quickly become inefficient. Here, too, AI offers a solution. Masked language models (MLMs) help strike a more disciplined balance by analyzing historical demand, seasonality, lead-time variability, and demand segmentation with greater precision than traditional planning spreadsheets.
“Planners now need to shift to trust the outputs and make minimal overrides,” Oca said. “Meaning AI does the work and they focus on the exemptions. In situations where they manage hundreds of SKUs, they have no choice but to trust some of these outputs for low-priority SKUs.”
Quinn recalled a distributor that launched a new branch and decided to rely almost entirely on an AI system to drive demand planning for the location. The warehouse racks were often empty enough that workers could cleanly throw footballs through aisles of shelving, but the company never missed any orders. “The potential
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