Artificial Intelligence Is Forcing Small Businesses To Rethink Their Data Strategies

How AI Is Changing Data Strategies for SMBs

AI is often easiest to launch in the cloud, particularly for organizations without modern on-prem infrastructure. Cloud platforms offer fast access to computing resources, making them ideal for proofs of concept and early experimentation.

But when AI workloads move into production, the economics can change quickly.

“AI outcomes have an intense resource requirement,” Brodsky says. “When you look at cloud consumption models, it isn’t always the most financially effective way to run those workloads long-term.”

For small businesses watching every line item, unpredictable cloud costs can become a concern as AI use scales. Running steady, data-intensive workloads on-prem can provide more predictable expenses over time.

Performance is another factor. AI workloads such as inference, analytics and data processing often benefit from low latency. “If timing is critical, you want your data as close to your business as possible,” Brodsky says. “That’s where cloud isn’t always the ideal fit.”

READ MORE: Get the strategic pros and cons of cloud computing for SMBs.

On-Prem Hardware’s Role in a Hybrid SMB Environment

Data governance is also influencing these decisions. Keeping sensitive or proprietary data on-premises can make it easier for small IT teams to maintain visibility and control — especially in industries with compliance or customer privacy requirements.

“When it’s on-prem, you have control,” Brodsky says. “But you also have the risk.”

That trade-off matters. Rashid Rodriguez, a consulting enterprise strategist at CDW, notes that cloud platforms include built-in protections such as geographic redundancy and disaster recovery — features that don’t automatically exist when workloads move back on-premises.

“In the cloud, disaster recovery is largely baked in,” Rodriguez says. “When customers bring workloads back, they need to make sure they’re not losing the level of protection they previously relied on.”

For small businesses, that often means taking a closer look at backup and recovery strategies, including immutable backups and more automated recovery processes that don’t require constant hands-on management.

Repatriation Requires Planning, Not a Simple Reversal

One misconception about cloud repatriation is that it’s just a matter of moving workloads back where they came from. In reality, AI workloads can quickly exceed the capabilities of older hardware.

“Servers that were procured three years ago may not be able to handle what these applications require,” Brodsky says.

As a result, even limited repatriation efforts often involve modernization — whether that’s refreshing hardware, improving storage performance or rethinking security tooling. Before making those investments, IT leaders need a clear understanding of what their current environment can support.

“You have to evaluate whether your on-prem environment can actually ingest and protect what you’re bringing down from the cloud,” Rodriguez says.

For small businesses, that evaluation is often best done in phases. Some start with high-level assessments to guide strategy, while others focus on specific workloads where performance or cost concerns are most pressing.

Despite renewed interest in on-prem infrastructure, this shift doesn’t represent a move away from cloud computing. Instead, it reflects a more mature, practical approach to hybrid IT.

“Very few organizations end up 100%cloud,” Brodsky says. “Most find they get the best results by mixing cloud flexibility with on-prem performance and predictability.”

For small IT teams navigating AI adoption, those decisions are rarely binary. They’re consultative, interconnected — and increasingly essential for making AI sustainable at scale.