Making AI Work for SMBs in a Risk-Adjusted manner — ATB Technology - July, 2, 2025
By Staff Reports - July 2nd, 2025
As artificial intelligence reshapes the enterprise landscape, small and midsize businesses (SMBs) find themselves at a crossroads: innovate or risk falling behind. For many, the challenge isn’t awareness—it’s execution. AI tools such as Microsoft Copilot and generative chat applications are already on the radar. What’s less clear is how to align these capabilities with business goals, safeguard sensitive data, and balance opportunity with risk.
Chris Miller, Vice President of Sales and Marketing at ATB Technologies, brings firsthand perspective on how SMBs are navigating this transformation. As a managed services provider (MSP) with deep roots in manufacturing, engineering, and nonprofit sectors, ATB is working on the front lines to help organizations adopt AI in ways that are responsible, practical, and tailored to their specific needs. In this Q&A, we explore strategic, operational, financial, and technological dimensions of AI adoption—what’s working, what to watch out for, and what comes next.
NOTE: The feature below has been organized into the strategic, operational, financial contexts that emerged in the interview.
Here is what he had to say:
STRATEGIC CONSIDERATIONS
BTR: How are SMBs thinking about AI at the strategic level?
Chris Miller: Most SMB leaders know they need to modernize, but they’re looking for guidance on how to align AI with their goals. The misconception is that AI is a futuristic or enterprise-only tool—but many AI functions today are very practical and attainable. Leaders want to know: how can we grow, serve customers better, and remove friction from our day-to-day work?
For many, AI isn’t about headcount reduction—it’s about productivity. They’re asking how AI can reduce repetitive tasks, enhance decision-making, and help employees focus on the work that drives innovation. In that way, AI becomes a strategic enabler, not a threat. Forward-looking SMBs are starting to treat AI not as a siloed initiative but as something that touches HR, sales, operations, and customer engagement.
BTR: Are AI strategies evolving beyond general productivity to include vertical-specific use cases?
Miller: They are—but slowly. Right now, most of what we see are horizontal use cases like HR screening, marketing automation, or summarizing internal communications. But the next evolution is definitely vertical integration. In manufacturing, for example, we worked with a client in the liquid processing industry that had remote vats at customer sites. By integrating sensors and using AI to predict refills, they improved delivery logistics, customer satisfaction, and cost efficiency.
These opportunities are everywhere—in logistics, field services, distribution. The challenge is helping SMBs define the problem clearly and then map AI onto the solution. That’s where partners like MSPs come in—we can bring context from other clients and help design use cases that work within real-world constraints.
OPERATIONAL CONSIDERATIONS
BTR: How ready are SMBs to implement AI operationally?
Miller: Most are just getting started. The AI conversation has moved from curiosity to action, but execution is still early-stage. We see adoption beginning with off-the-shelf tools like Microsoft Copilot or industry-specific SaaS features. These tools offer immediate value: they help teams triage emails, generate reports, or draft proposals faster. It’s tangible, and it builds confidence.
Operational readiness varies widely. Some clients have in-house IT; others rely entirely on MSPs like us. Many don’t have formal data governance, which becomes an issue once employees start interacting with AI tools. There’s often no policy around what data can be shared, which applications are allowed, or how outputs are verified.
BTR: What are the top risks you help clients mitigate?
Miller: The big one is data leakage. Many employees don’t realize that when they paste proprietary information into a public AI tool, they may be making that data accessible to unknown third parties. We’ve seen cases where sensitive financial data or customer insights were input without any controls.
We help organizations create AI use policies that define what tools can be used, for what purposes, and under what supervision. Data governance might sound like a big-enterprise concern, but for SMBs it’s just common sense. You wouldn’t let your sales team export your entire customer database to an unsecured flash drive, and yet people are pasting similar data into AI tools every day.
The other key operational issue is shadow IT. Employees download AI apps on their own and start using them without IT oversight. This opens the door to malware, phishing, and compliance issues. Education and policy must go hand in hand.
FINANCIAL CONSIDERATIONS
BTR: What’s the business case for AI among SMBs that can’t afford large investments?
Miller: AI for SMBs isn’t about massive upfront investments. It’s about incremental value. Leaders want to know: Can I save my team an hour a day? Can I eliminate the need for outsourced services? Can I generate better marketing results with less effort?
We’ve seen strong ROI from use cases like automatic meeting summarization, proposal drafting, or coding simple apps. For example, a small engineering firm we work with had no developers on staff. With the help of generative AI, one of their project managers created a basic scheduling app using natural language prompts. That would’ve cost thousands with an outside vendor, but here it was nearly free.
We advise clients to start with packaged tools—things like Copilot or embedded AI features in their existing software. These allow you to track value early and keep costs under control. The key is to avoid overspending before you understand the impact.
BTR: Are there cost risks SMBs should be aware of?
Miller: Yes—and they’re often hidden. Many AI tools charge based on tokens or compute cycles. A casual user may rack up charges quickly without realizing it. On top of that, some apps pull large volumes of data from APIs, which can have downstream pricing implications.
There’s also a larger macroeconomic issue. AI consumes a lot of power. As AI usage grows, it may start impacting electricity costs—even for non-AI workloads. Small manufacturers already feel the pinch of energy expenses. If AI data centers cause grid strain or new surcharges, SMBs could see indirect cost increases.
We help clients monitor AI-related costs and recommend budgeting frameworks that focus on short-term wins while remaining mindful of long-term implications.
TECHNOLOGICAL CONSIDERATIONS
BTR: What AI tools are SMBs adopting first?
Miller: Microsoft Copilot is probably the most common starting point. It’s integrated into the Microsoft 365 environment, which most SMBs already use. It helps with writing emails, summarizing documents, and preparing presentations. From there, we see adoption of other tools like Grammarly, Jasper, ChatGPT, or embedded AI in CRM and ERP systems.
AI’s power comes from reducing friction. An HR director using AI to score job interviews, a marketing lead using it to write campaign copy, or a sales rep using it to summarize meetings—all of these are real-world examples where SMBs are seeing value today.
BTR: How do infrastructure demands affect AI adoption in this segment?
Miller: For now, most SMBs don’t need to worry about infrastructure. They’re consuming AI as a service—via the cloud, not through on-premise deployments. They’re not buying GPUs or building data lakes. But that doesn’t mean infrastructure isn’t relevant.
Behind the scenes, AI tools are incredibly energy-intensive. Some estimates suggest that a single data center for an AI supercomputer consumes the equivalent of a small town’s power usage. If these costs rise or are passed downstream, SMBs may need to rethink how and when they use AI.
Another concern is that AI tools evolve rapidly. Unlike traditional software, AI models can update frequently—changing outputs, accuracy, and even behavior. That creates a moving target. You don’t buy AI once; you engage with it continuously. It’s software as a service—but also service as software.
BTR: How can MSPs help SMBs manage this complexity?
Miller: MSPs are in a unique position. We operate like a virtual IT department for many SMBs, so we’re part of their strategic and operational planning. The best MSPs don’t just keep the lights on—they help identify business challenges and match them with emerging technologies.
In AI, that means curating tools, setting policy, training staff, and monitoring usage. It also means co-creating use cases. We recently worked with a client to create a smart routing system for delivery trucks using AI to optimize fuel usage and customer delivery windows. That kind of solution would’ve been unthinkable for them even two years ago.
We always tell clients: Bring us to the table early. Don’t wait until AI is a problem. Let us help shape the strategy, test the tools, and ensure that your use of AI aligns with your goals and risk tolerance.
BTR Conclusion:
AI has moved from hype to practical reality for small and midsize businesses. As Chris Miller of ATB Technologies explains, the question is no longer whether SMBs will engage with AI—it’s how effectively they’ll do it. The winners will be those who treat AI as a strategic asset, start small, and partner with trusted experts to guide implementation and policy.
Whether it’s writing better job descriptions, optimizing delivery routes, or safeguarding sensitive data, AI has clear value. But it must be approached with intention, clarity, and oversight. With the right roadmap, SMBs can harness AI not just to keep pace—but to lead.