The AI (r)evolution for SMB supply chains

Erwin Hermans, Partner - Operations Practice Lead

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I have always been a believer in people-process-technology.  In that order.   For the past few decades supply chain software firms have made great strides in enabling supply chain operators to be better at managing all aspects of supply chain.  However, hiring the right talent and defining how you want to design the supply chain was a necessary pre-requisite for the software to be leveraged to its greatest potential.  And even then, survey after survey found that the software applications fell short of expectations.

At least large enterprises were able to invest in software and endure the years long implementations.  Small and medium enterprises were completely boxed out for the formative years of their growth (or ever), either because of affordability or simply not having the supply chain talent to take advantage of the features of the software. 

Now that AI is starting to get entrenched in supply chain applications, is this the time that AI is leveling the playing field?  With price points measured in hundreds of dollars per year, not hundreds of thousands or millions, are SMB’s able to take advantage of the advancements in technology and frankly leapfrog some of the entrenched players.

I am afraid that this post is not going reveal a big aha moment.  I still have more questions than answers but thought it would be good to start committing pen to paper on a train of thought that will hopefully help SMBs find their own answers.  I aptly titled the post with a “(r)evolution” – the supply chain discipline is definitely evolving, whether we have a revolution on our hands is yet to be seen.

Why SMB Supply Chains Are Different—and Why That’s an Opportunity

To appreciate if the advances in AI are truly democratizing supply chain capabilities, one needs to understand how SMBs operate under constraints that look very different from the Fortune 500:

• Lean teams, often with a small operations staff juggling multiple roles

• Tight budgets that get consumed by making product or buying inventory, not managing it

• Unpredictable demand, where one big customer order or seasonal swing can make or break the month.

  
  

But these constraints create opportunity. In a small business, incremental improvements compound quickly.  There is no room for a multi-year ERP implementation, best to focus on building capabilities one quarter at the time.  AI doesn’t need to overhaul an entire system to deliver value—it just needs to make existing decisions smarter, faster, and more predictive.

From Reactive to Predictive

Most SMB supply chains are reactive by nature. You discover you’re low on product when a customer is already waiting. You rush an order when a supplier is late. You pay for expedited shipping because you didn’t see a disruption coming.

AI changes the equation. By spotting patterns in data—sales history, supplier lead times, seasonality—it can give SMBs a forward-looking view of their operations. Suddenly, you’re not reacting to problems; you’re anticipating them. And that kind of foresight no longer requires enterprise-grade systems.

Practical Entry Points for SMBs

AI isn’t about building a digital twin of your supply chain. It’s about practical wins that pay off quickly. Let’s look at a few areas where immediate impact can be achieved:

1. Inventory Optimization

   Affordable apps now plug into accounting or e-commerce platforms and automatically recommend reorder points, safety stock levels, and replenishment schedules. That’s the kind of analysis that once required a team of analysts.

   
   

2. Supplier Collaboration

   AI tools can draft clear supplier communications, analyze purchase orders for discrepancies, and flag risks hidden in contracts or invoices. For SMBs that depend on a few key suppliers, these safeguards are invaluable.

   
   

3. Logistics Efficiency

   Route optimizers and shipping comparison tools use AI to identify the fastest or cheapest way to get products to customers. Even shaving a few dollars per shipment can add up quickly.

   
   

4. Demand Sensing

   Beyond internal sales data, AI can pull signals from Google Trends, competitor pricing, or even weather forecasts to anticipate demand swings. For seasonal businesses, this can mean stocking up at the right time instead of getting caught short.

   
   

5. Everyday Efficiency

   Sometimes the simplest use is the most powerful. Upload a messy spreadsheet into an AI assistant, ask for insights—top-selling SKUs, margin erosion, supplier delays—and you suddenly have analyst-level summaries in minutes.

   
   

I am purposely staying away from listing AI applications for each of these opportunity areas.  To stay true to my people-process-technology mantra, the tool is rarely the answer.  You first need a solid foundation of people and process to extract the most value out of any AI application.  I’ll safe the tool specific discussion for a future post.

The New Role of the SMB Leader

Now we get to the people part.  I had the great opportunity to speak with a few professors at Indiana University’s Kelley School of Business and Wisconsin’s Business School.  My hypothesis centered on supply chain students and entry level supply chain professionals being prepared to apply what they learn about AI in their jobs.  I went into the discussion fully expecting a conclusion that students and young professionals are not prepared to take advantage of the tools due to their lack of practical experience.  Instead, they flipped the script on me : supply chain managers are not ready to take advantage of the AI expertise supply chain graduates and young professionals bring to the table.

I was encouraged by this premise and see it as an opportunity for SMBs to unleash the potential of the supply chain talent they hire.  There is no entrenched supply chain dogma (good or bad) that stifles innovation and creativity.

As a founder or executive at an SMB, you play a big role in driving the AI innovation cycle for your business.  AI doesn’t replace leadership; it redefines it. Instead of being the bottleneck for every decision, SMB leaders can lean on AI as an analyst and sounding board.

The mindset shift is subtle but critical: don’t ask, “Do I need AI?” Ask, “How can AI make the decisions I already make more accurate and less stressful?”

The best leaders will learn to blend their intuition—the deep, hard-earned knowledge of their business—with AI’s pattern recognition. Trust, but verify. Use AI as a force multiplier, not an autopilot.  The talent you hire needs to fit that philosophy.

Getting Started Without Overwhelm

Adopting AI doesn’t have to be a moonshot project. The key is to start small:

• Pick one process. Inventory, logistics, supplier collaboration—choose the area with the most pain.

• Test one tool. Many platforms you already use (QuickBooks, Shopify, HubSpot) now embed AI features. Explore what’s available before buying something new.

• Measure one outcome. Fewer stockouts, lower shipping costs, or reduced manual time. Track the win and build momentum.

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Think of AI adoption as iterative. Every small win builds confidence and capability, both in the technology and in your team.

The Democratization of Supply Chain Intelligence

AI is no longer a luxury for the biggest players. It’s a necessity—and an opportunity—for SMBs. Those who embrace it early will not only close the gap with larger competitors, but they may also leapfrog them. Larger organizations move slowly; smaller businesses can experiment, adapt, and scale quickly.

The era of being boxed out is ending. The door is open. The real risk now isn’t that AI is too complex or too expensive—it’s that SMBs wait too long to start.

The future of supply chain intelligence is democratized. The winners will be those who step through the door today.