AI Agents Will Filter Down to SMBs in 2026

AI and agentic AI were hot technology topics in 2025—at least for larger enterprises that were scrambling to build and implement the latest AI tools and capabilities. But for small- and medium-sized businesses (SMBs), AI continued to be a tougher technology to integrate and use due to smaller budgets, a lack of IT staff, and other daily tasks that were higher priorities.

In 2026, however, SMBs could begin to expand their use of AI, particularly when it comes to AI agents that could help them automate processes for increased efficiency, said Andy Sweet, vice president of enterprise AI solutions at AI analytics vendor AnswerRocket.

“In the final months of 2025, the right conditions converged to support widespread AI agent adoption among SMBs,” Sweet told ChannelE2E. “They stand to benefit from AI tools that can enable the productivity they need to automate critical workflows and go head-to-head with major brands. Obstacles such as financial investment, technical skills gaps, and limited opportunities to build useful AI agent implementations beyond personal productivity have impeded this potential.”

Helping to bring about these changes in 2026 is a proliferation of tools that are democratizing the technology, including AI-powered coding tools and agent builders, Sweet said.

In addition, Sweet said he is seeing early signs that major consumer LLMs (large language models) may open their interfaces to third-party apps, which would help break down technical barriers that are slowing SMB implementation of these technologies.

“But more importantly, AI in small business is evolving from individual or point solutions to address more critical workflows,” Sweet said. “This frees up smaller workforces to apply their creativity to higher-order tasks, potentially opening new lines of business they had not previously been able to offer customers. Due to the flexibility of SMBs and the deep understanding they have of their value to customers, they may be better positioned than larger enterprises to harness these opportunities to reshape their daily operations.”

The Great AI Sprawl

Arvind Nithrakashyap, co-founder and chief technology officer at security and AI operations vendor, Rubrik, told ChannelE2E that he sees 2026 as the year organizations fully grasp the need to carefully control and manage all of the AI agents and use cases they are deploying.

“The proliferation of AI agents is creating the great AI sprawl, forcing IT and security teams to reconcile rapid deployment with system control,” Nithrakashyap said. “This dynamic will necessitate a governance renaissance in 2026 and immediate, focused investment to bring agents into production safely and at scale.”

Companies will find that to achieve production-grade agent deployment, they must rapidly implement monitoring and governance controls to ensure visibility into how AI agents are accessing applications and data, and to ensure adherence to corporate policies, he said. “Inevitably, agents will make mistakes, and organizations will need remediation strategies in place. In 2026, heavy investment in security and governance systems will be essential to monitor, control, and remediate agent output.”

An AI Reality Check Is Looming

Luis Blando, chief product and technology officer at AI-powered low-code platform vendor OutSystems, said he sees 2026 as the year of the “AI reality check.”

“The widely circulated promise that AI will fundamentally change everything has fueled unsustainable market hype,” Blando said. “We are quickly moving past the speculative, trillion-dollar dreams built on fragile revenue streams. In 2026, real impact will come from agentic systems in production that improve customer operations, improve accuracy, eliminate repetitive tasks, or streamline data quality assurance processes.”

“To make this happen, AI must be focused, trained, and deployed specifically to solve existing, high-value business problems,” he said.

“In 2026, AI development will be all about specialization instead of general-purpose use cases,” Blando said. “Solutions will focus on specific workloads that deliver faster, more accurate results for specific business functions. Expect the AI conversation to move away from hype about a single ‘best model’ toward thoughtful selection and integration.”

Also growing in importance will be conversations about “vertical AI,” which uses models trained on industry-specific language, workflows, and data to solve problems that arise when using generic AI, he said. “Companies that ensure these solutions are robust and can handle variations in real-world data relevant to their specific tasks are poised to succeed in 2026.”