Growing organizations are turning to generative AI to improve efficiency, productivity, and innovation without dramatically increasing headcount or costs.

Recent research from Enterprise Strategy Group (ESG) highlights how small and mid-sized businesses (SMBs) are approaching this:

• 33% of SMBs already have generative AI in production (vs. 42% of enterprises).
• 68% believe generative AI delivers or will deliver transformative or significant value in areas like efficiency, productivity, and creativity.
• 30% expect their generative AI budget to significantly increase over the next 12 months.

Organizations are primarily looking to generative AI to:

• Boost employee productivity and output quality (a top ROI metric for 50% of SMBs).
• Improve customer satisfaction, retention, and acquisition (tracked by 46% of SMBs).
• Reduce operational costs and overhead (important for 45% of SMBs).
• Capture time and cost savings from automation (also cited by 45%).

In short, companies your size are using generative AI to reimagine how work gets done—automating routine tasks, speeding up analysis, and enabling smaller teams to operate with the agility and insight typically associated with larger enterprises.

Many growing organizations share the same set of hurdles when they try to move from AI experimentation to day-to-day use. ESG research with SMBs surfaces several consistent challenges:

• Skills and expertise gaps – 42% of SMBs say lack of employee expertise or skill is a top challenge. Smaller teams often can’t hire dedicated AI specialists and struggle to keep up with fast-moving tools and frameworks.
• Data quality and access – 36% cite poor data quality as a major barrier. Data is frequently incomplete, inconsistent, siloed, or locked in legacy systems, which limits the accuracy and usefulness of AI outputs.
• Immature or hard-to-fit solutions – 33% point to solution immaturity, and 31% struggle to integrate AI with existing and legacy systems. Many current environments weren’t designed with AI in mind.
• Ethical, legal, and compliance concerns – 32% are concerned about bias, fairness, and legal implications. On top of that, 72% say data privacy and security are among their biggest generative AI risks.
• Infrastructure constraints – Running modern AI models often requires high-performance compute, scalable storage, and modern architectures. For many growing organizations, upgrading or replacing infrastructure is complex and costly.

Because of these challenges, SMBs say they need to invest in:

• Training and employee skills (45%).
• Information management, data science, and ML capabilities (40%).
• Data privacy, compliance, and risk management (38%).
• Cybersecurity (38%).
• Cloud, infrastructure, and operations (34%).

This is why many organizations look for an AI partner that can help them clarify strategy, right-size infrastructure, and provide built-in security and compliance rather than trying to solve everything in-house.

For growing organizations, the most effective starting point is to focus on a few targeted, high-impact use cases that map directly to business goals. ESG research shows SMBs are prioritizing:

• Data analysis and insights (64%)
  Use AI to analyze customer behavior, sales trends, website activity, or operational data. Generative AI can surface patterns, generate summaries, and suggest actions—without needing a full data science team. This helps smaller organizations make faster, data-driven decisions.
• Content creation (44%)
  Automate parts of marketing and communications: blog drafts, social posts, email campaigns, product descriptions, and personalized messages. This is especially valuable when you don’t have a large content or creative team and need to keep up with ongoing content demands.
• Content summarization (41%)
  Condense long reports, emails, meeting notes, and feedback into concise summaries. This saves time for small teams juggling multiple roles and improves internal communication by making dense information easier to act on.
• Data augmentation and data generation (29%)
  Use synthetic data to supplement limited real-world data—e.g., for training models, testing scenarios, or prototyping new services. This is useful in areas like e-commerce (product images or descriptions), predictive modeling, and scenario planning.

A practical approach is to:

1. Start with one or two use cases that clearly tie to metrics you already track (e.g., productivity, customer satisfaction, or cost savings).
2. Use low-code or managed solutions to reduce the need for deep in-house AI expertise.
3. Iterate based on results, then expand into more complex or customized applications once you’ve proven value and learned from early deployments.

Partners like Dell, through the Dell AI Factory approach, are designed to support this path—helping organizations identify the right first use cases, align infrastructure, and protect critical data while they scale AI adoption.