Artificial intelligence has reached a point where most small and midsize businesses (SMBs) no longer need to ask whether AI can deliver meaningful capabilities. They face a more practical question: Where can AI create meaningful business value, and how much responsibility should they trust it to assume?
Agentic AI raises the stakes. These systems can reason through multi-step tasks, interact with business applications, retrieve information from multiple sources, and execute actions with limited human intervention. For most SMBs, however, the challenge is not building AI. It is adopting it wisely.
Large enterprises can absorb more experimentation and custom development. SMBs typically have less room for costly mistakes, but they also have an advantage: they rarely need to build their own AI. Business applications increasingly include AI assistants and agentic capabilities. The challenge lies in determining whether those capabilities fit the work and whether the organization can trust them with it.
That challenge led me to develop an Agentic AI Adoption Framework for small and midsize organizations. It starts with four questions.
1. Can we?
Choose the right work to automate.
Agentic AI works best when tasks follow clear rules, require multiple connected steps, and rely on accessible, reliable data. A good example involves researching a prospective customer, gathering information from several applications, drafting a response, and recording the results in a CRM.
Ambiguous work, poorly documented processes, and tasks that require substantial human judgment make weaker candidates. When organizations struggle here, AI may not represent the underlying problem. The process itself may need attention.
Standardize before you automate.
2. Should we?
Capability does not equal business value.
AI agents still require oversight. APIs change, workflows evolve, and organizations need to refine processes. The resulting gains must justify that effort.
Organizations must also consider the consequences of failure. If an incorrect action could affect customers, finances, security, or compliance, AI may still provide significant value—but organizations should retain appropriate human review for critical decisions.
The goal should not simply be to automate more work. AI should measurably improve outcomes while reducing effort, cost, or risk.
3. How do we?
Start with the technology you already own.
Many SaaS platforms now include native AI and agentic capabilities. These tools can provide a faster path to value while limiting integration complexity. If existing applications cannot meet the need, organizations can consider low-code orchestration platforms or experienced implementation partners before pursuing custom development.
For most SMBs, building an AI agent from scratch should remain the exception, not the starting point.
4. How do we keep it safe?
AI should earn its authority. Organizations should not move AI directly from recommendation to autonomous execution. Instead, they should expand AI's authority through demonstrated performance.
Start with limited authority. Let AI recommend actions or operate in shadow mode while people retain execution responsibility. As the technology demonstrates reliability, organizations can expand its authority while maintaining governance, auditing, and appropriate oversight.
EMA's 2025 ServiceOps research illustrates the challenge. Sixty-three percent of organizations still require human approval before acting on AI recommendations. That finding highlights an important distinction: organizations must decide not only where AI can create value, but also how much authority they are prepared to give it.
Operational readiness, governance, trusted data, and disciplined processes will therefore matter as much as AI capabilities themselves. Technology can enable autonomous action. Organizations determine whether they can trust that action.
This framework marks the beginning of a broader research effort into how organizations evaluate AI readiness, establish trust, and expand autonomy over time.
The organizations that succeed will not necessarily adopt AI the fastest. They will know when AI has earned greater authority—and when it has not.
