Ryan Johnson is the chief product officer at CallRail, an AI-powered lead engagement platform that serves over 225,000 businesses worldwide.
The conversation around AI has shifted from how we use it to how we incorporate it into key business processes to drive measurable revenue outcomes. For most SMBs navigating this transition, fully operationalizing AI at scale has been a challenge.
Recent research from SAS and IDC supports this trend. Of the 1,600 SMB leaders surveyed, approximately 70% remain “in the early stages” of AI maturity, and “37% are still experimenting” with AI in isolated ways compared to the 9% that have “fully embedded” AI into their operational framework.
Bridging this gap in AI maturity ultimately requires a fundamental shift in how SMBs think about AI implementation. Too often, the approach is technology-first: What can AI do? When the thinking should be outcome-first: How can AI facilitate revenue growth?
What Most SMBs Get Wrong About AI
When it comes to integrating AI into core revenue workflows, the problem for SMBs is multifaceted. On the one hand, most try to boil the ocean. The approach is to do everything at once, launching multiple pilot programs with no clear goals, rather than starting with one key business objective and defined success metrics.
On the other hand, there’s a tendency to underestimate AI’s power as a revenue driver. AI is often seen as a way to speed up processes and improve productivity, rather than as a capability for acquiring more customers, driving demand and capturing more leads at scale. However, solutions like AI voice agents are changing that.
A year ago, booking an after-hours appointment with an AI agent may have seemed impossible. But now, SMBs across home services, healthcare, automotive and real estate, to name a few, can use it to help ensure that no leads slip by.
Finally, there’s the perception that customers are averse to AI. Some SMBs fear that using it in customer-facing interactions can lead to lost business, but depending on where and how it’s used in the customer journey, the opposite may be true.
For instance, in 2025, Domino’s Pizza rolled out its updated AI assistant to improve the order intake process from inbound calls. By using regional accents to make the interaction more relatable and natural, Domino’s Pizza improved customer acceptance compared to when it first started using AI.
Embedding AI: Start With Goals
Successfully operationalizing AI starts with goals, specifically those tied to business priorities. To achieve that, SMBs should define goals based on the outcomes they hope to achieve by implementing AI, such as reducing appointment booking time or improving lead intake.
Having the goal(s) defined is important because it enables SMBs to move beyond the experimentation loop, serving as a foundation for integrating AI into revenue-generating processes. Likewise, aligning with business priorities creates buy-in from key stakeholders by providing greater clarity into intent and expected outcomes.
Identify One Core Revenue-Generating Workflow
After the goal is defined, selecting which revenue workflow to reimagine is easier. The key is to start with one workflow, test and measure it, and then use the results to build a foundation for approaching other AI use cases more effectively.
For example, a law firm client wanted to improve the lead-capture workflow for its overflow and after-hours calls. The company used an AI voice agent to collect call data and reduce inconsistencies in its third-party answering service, improving inbound call conversions and providing a consistent customer experience.
By replacing their third-party service with AI, the law firm client now has a consolidated system for every call, with attribution, lead and conversion data on a single dashboard. These insights have also enabled the law firm to maintain a consistent, bilingual caller experience while helping ensure that every marketing dollar is well spent.
Plan, Test, Measure And Expand
Next, develop a phased rollout plan that organizes the identified workflow into individual steps. It could be taking an after-hours call workflow, like the prior law firm example, and breaking it down into determining lead quality, routing and appointment booking depending on the identified criteria.
Once those steps have been identified, the goal is to start with one, defining what business outcome matters most, the rollout owner and how success will be measured. When selecting success metrics, it’s important to select those aligned with business outcomes.
Measuring the number of appointments booked, leads converted or response time, for instance, provides more actionable insights than tracking AI use or prompting. In the case of the after-hours call workflow, the success metric could be testing AI’s lead scoring accuracy in relation to lead quality and its impact on bookings.
Depending on the result, additional CRM context, training or prompts may be needed to further improve AI’s lead scoring accuracy before tackling the next step in the workflow: routing. As each step is refined, SMBs can apply those lessons to other revenue-generating workflows.
Moving Upstream: Achieving Revenue Impact
Getting the most out of AI investments today means focusing on the business outcomes we want to drive. SMBs are uniquely positioned to capitalize on this due to their size. Because there are fewer levels of complexity, they can move faster compared to larger organizations.
Although SMBs are at different points along the AI maturity spectrum, from experimentation to full integration, what matters most is starting with a single workflow and then compounding efforts over time. This is what differentiates SMBs who stay stuck in pilot mode from those who gain a competitive advantage with AI.
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