A flourishing ecosystem of prebuilt, customizable artificial intelligence agents is making the technology accessible to businesses of all sizes.
Though these agents and foundational AI models have strong general knowledge, they know nothing about your small or midsized business and thus are only as effective as the proprietary data they’re fed.
A small to medium-sized business that fails to generate AI insights with its data risks becoming little more than an “LLM wrapper,” incapable of continuously improving their unique offerings, David Friedberg, CEO of Ohalo Genetics, told Google Cloud for its 2025 survey of startups. Quality data means the difference between AI being a powerful tool or simply another toy for SMBs to play with.
“AI presents a big opportunity for SMBs to gain a competitive advantage, but only if they have the right fuel,” says Jeff Hollan, head of Cortex AI apps and agents at Snowflake. “Without unified, well-governed data infrastructure, even the most advanced models can fall short.”
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Consolidating Data on a Single Platform for AI Agents
A misconception exists that AI agents are purely for large enterprises with vast budgets and deep benches of engineering talent.
“Agents can provide the same transformative benefits to small and midsized businesses that they do for large enterprises: significant productivity gains across HR, IT, sales, procurement and other domains,” says Suzanne Livingston, vice president of watsonx Orchestrate at IBM.
Before investing heavily in an AI agent, SMBs should consolidate their data, ensure strong governance and select platforms that allow them to run models where the data lives. This reduces fragmentation and the cost of moving data while laying a foundation for scalable, efficient AI adoption, Livingston says.
The best way to obtain the diverse, high-quality data needed to train faster, more accurate AI models is to unify structured, semistructured and unstructured data on a single platform.
“Fortunately, there are a number of tools that allow small and midsized businesses to harness their unique company data without hiring a small army of data scientists,” Livingston says. “Data lakehouse technology enables businesses to ingest, govern and retrieve unstructured data — like emails, decks and videos — combine it with structured data, and then seamlessly connect it to their agents.”
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AI Needs SMBs’ Proprietary Data for Continuous Improvement
SMBs should be feeding their AI models and agents valuable data on customer service interactions, sales history and product usage patterns, for starters.
“It’s the context that makes an AI model useful for your day-to-day roles,” Hollan says. “When AI models are trained on proprietary data, they can deliver insights unique to a specific organization, generate responses with a higher degree of accuracy and excel at tasks like personalizing customer experiences.”
Proprietary data further creates a flywheel effect: As customers use the AI solution, they generate more proprietary data such as clicks, purchases and queries.
“This new data is then used to refine and improve the AI model, which in turn improves the product, attracts more users and generates even more data,” Hollan says. “This cycle creates a continuously improving AI product.”
