Despite quickly growing interest and investment in artificial intelligence (AI) tools, a recent study from SAS revealed that nearly 70% of small and midsized businesses (SMBs) are still in the "experimental" or "opportunistic" phases of AI adaptation. While many SMBs are testing AI tools, the majority of them have not integrated AI practices into a cohesive business strategy, revealing a critical disconnect between AI aspirations and organizational readiness.
"To actually make something of their AI strategy, SMBs need to move from disconnected pilots to true alignment of their data, people and resources," said Daniel-Zoe Jimenez, Vice President, Research from IDC. "Experimenting with the technology is one thing. Deploying it strategically and sustainably is quite another."
While today's technology landscape is saturated with stories about the potential of AI, many SMBs lack the data foundation, strategy, skills and governance in place to effectively scale AI within their own business to deliver tangible results. The report highlights obstacles industries such as banking, insurance, government health care and life sciences face in AI implementation ranging from fragmented data and inconsistent execution to regulatory challenges and limited organization-wide adoption that hinder the scaling of AI across their organizations.
For example, government organizations show strong planning and oversight for AI, but legacy systems and data silos continue to slow execution. Similarly, health care is experimenting with AI to improve efficiency, but data complexity, regulation and skills gaps keep adoption at an early stage.
"SMBs don't need more hype. What they need are results that translate into a meaningful return on their AI investments," said John Carey, Senior Vice President of Global Channels at SAS. "This research shows that AI adoption is already widespread, but operationalizing AI at the company level remains a challenge. SAS outlines a path forward with tools and resources that make sense for SMB customers."
The article emphasizes that successful AI implementation requires more than experimentation – it depends on building a clear, organization-wide strategy that aligns data, people and business goals. It recommends that SMBs strengthen data infrastructure, improve employee AI skills and governance and move from isolated pilot programs toward more integrated, scalable AI adoption across the business.
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