As we continue to share our takeaways on artificial intelligence within small to mid-sized organizations, much like our own, we are focusing on this tool as a business process improvement opportunity that uses some cool tech to make businesses work more efficiently. In our first blog, we shared takeaways from a cutting-edge managed services and technology conference last fall. In this blog, we're talking about suggestions for early use cases for small businesses that you might want to try in your organization. This approach enables you to experiment with AI in a limited way, focusing on proven areas where AI shines. Nearly all revolve around data analytics, one of the strongest, most straightforward uses for AI.
Key Takeaways
- AI delivers the strongest ROI in repetitive, data-driven, low-risk business processes.
- Governance and guardrails are essential to mitigate shadow AI and compliance risks.
- SMBs that pilot AI intentionally — with clear goals and human validation — gain measurable productivity advantages.
Thinking About AI? Focus On Safe, Proven Use Cases
Here's your reality check. Research shows that employees are moving faster than "official" businesses. According to research from Microsoft, nearly 78% of artificial intelligence users bring their own tools to work (BYOAI is a thing!), and it's even more common at small and medium-sized companies (80%). So while leadership is wringing its hands about Copilot licensing, many teams are taking things into their own hands. Often. A U.S. Gallup Workforce survey in November 2025 found 12% use artificial intelligence daily, and about one-quarter use it a few times a week or more.
Now that we have your attention, let's talk about how to get started using AI for business process improvement—officially. Like many new tools, AI should be piloted thoughtfully, with a small team, and include not just testing of the AI tools you are considering, but also policy creation, guardrails for usage, consideration of any compliance standards, and more.
Common use cases that you may want to consider as a practical starting point:
- Analysis of customer service ticketing data
- AI automation of invoice processing and basic financial workflows
- Sales and marketing operations support (not strategy)
- Security (or IoT) monitoring and anomaly detection (with human oversight)
As a reminder, every use case example includes human validation — no fully autonomous decisions and no operations in a vacuum.
If none of the above fit your business, our tip: Pick a process that is repetitive, text-heavy, and low-risk if the first try isn't perfect.
Where Does AI Deliver Real ROI For Small Businesses Today?
AI is a tool, so let's rein in the expectations of magical transformations and focus on actionable improvements. Some examples:
- In a controlled study of professional writing tasks conducted by researchers at Massachusetts Institute of Technology and Stanford University, participants given access to ChatGPT completed mid-level professional writing tasks significantly faster than those without access. Their work quality was also rated higher by experienced evaluators who were blind to whether ChatGPT was used. Specifically, time to complete tasks decreased (roughly 40% faster) and quality increased (about 18% higher on average) for those with ChatGPT assistance.
- In real workplace deployment of AI for customer support, followed by the National Bureau of Economic Research, a generative AI conversational assistant used in various customer service environments increased productivity by 14% on average, with 34% improvement for novice and low-skilled workers, showing where artificial intelligence can help most: repeatable work with guidance and coaching baked in.
Steps for Deploying Test Cases in Your SMB
If you see an opportunity for testing AI in your organization, we have some suggestions from experts on how to best get started and see the most impactful, successful outcomes.
Each use case should have certain foundational elements: what you want to automate (be specific); what guardrails you are putting in place, such as human review or approval, etc.; documentation; a hypothesis on the outcome (what do you want to achieve); and a timeline for rollout, experimentation, revisions, and evaluation.
Here are some examples:
Customer Service Knowledge Library
- Automate: Convert resolved tickets with shared pain points into draft knowledge articles; summarize long procedures into simple "self-help" steps
- Goal: Reduce the number of service tickets about issues that users can resolve themselves with proper guidance by identifying those opportunities through data analysis and drafting articles for human review
- Guardrails: Technical and content review required before publication
- Example: After resolving "password reset," AI drafts a knowledge article with cause, fix, and preventive tips that is shared in a customer knowledge repository
Accounts payable and invoice processing
- Automate: Extract invoice fields, match to purchase orders, flag discrepancies
- Goal: Reduce manual time spent on initial reconciliation tasks
- Guardrails: Approval remains human
- Example: Invoices over a threshold or missing a purchase order are auto-flagged for finance review
Sales operations and customer communications (assist, don't "replace")
- Automate: First drafts, meeting summaries, follow-up emails, proposal section drafts
- Guardrails: Ban confidential uploads into unapproved tools; require final human edit and sign off
- Example: Meeting notes become a clean recap with action items and owners
What Guardrails Should Businesses Implement Before Adopting AI
You may be wondering if the benefits of testing AI use cases are important right now – and here is your answer. Microsoft and LinkedIn found that employees are already using AI to resolve business challenges, often without approval, which carries the same threat as the use of other Shadow IT tools. These unauthorized or unsupported apps at work inside your business can silently compromise security, among other risks. Research from IBM reveals 80% of U.S. office workers use AI at work, but only about 22% rely on sanctioned tools. Risks of shadow IT, in particular unsanctioned AI tools, include data leakage, compliance gaps, and loss of auditability. Shadow AI tools only expand the threat surface. Gartner warns that 40% of businesses could experience a breach tied to shadow AI by 2030, with many organizations already suspecting or detecting unsanctioned tool usage.
Bottom line: Even if your leadership doesn't completely buy into the value of AI within your organization, responsible businesses must address the use of AI by employees, and the most successful way to do that is provide an outlet for that creativity and innovation to pull AI usage under your governance and control. One of the often overlooked keys to success with AI is finding the right champions. Rarely is it your tech team; rather, most early adopters with AI are process thinkers. Those employees who are constantly looking for a better way to do better work more quickly. They think more along the lines of project managers than IT geeks.
The Future Belongs to Intentional Investors
AI is moving fast. But speed isn't the point. The businesses that succeed won't be the ones chasing every new tool—we've said that before. Instead, winning organizations look at technology as a business investment—and like it or not, AI is part of that stable of tech tools that can have an incredible impact on your business.
AI is a business process improvement lever — and the organizations that learn how to pull it thoughtfully will build lasting advantage.
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