The Wall Street Journal recently highlighted something we at Serverless Solutions see every day with our own customers: small businesses have become some of the fastest and most creative adopters of generative AI.

For many organizations, AI isn’t just “nice to have” anymore. It’s part of daily operations—drafting communications, interpreting financial data, visualizing designs, generating code, and automating customer interactions. And according to the U.S. Chamber of Commerce, 58% of small businesses now use generative AI, up from 40% in 2024, and more than double what we saw just two years ago.

This rapid shift isn’t hypothetical. It’s happening in real time, and it’s reshaping how teams work.

But while we celebrate what’s possible, we also want to add an important dose of realism—because the velocity of generative AI adoption can sometimes create more confidence in AI than its current technical maturity warrants.

What Tasks Small Businesses Are Turning Over to Generative AI

Across industries, small business owners are finding that generative AI can meaningfully reduce administrative drag and enhance decision-making. Some of the most common patterns include:

1. Financial and Operational Insights

Business owners like the Chicago café and retail operator in the WSJ article are using AI as a pseudo-CFO—feeding POS data, QuickBooks exports, and operational metrics into LLMs to generate:

• Cost-of-goods reports

• Pricing recommendations

• Cash-flow summaries

• Forecasting scenarios

Tasks that once required hours of manual analysis can now be repeated every few weeks or even on-demand.

2. Customer Service and Content Generation

Tools like Gemini, ChatGPT, and NotebookLM now:

• Interpret customer questions

• Draft email replies

• Generate social-media posts

• Create marketing copy

• Even synthesize podcasts into newsletters or video clips

But as the article points out, this only works after significant human review, iteration, and guardrail-building. Hallucinations still happen—sometimes with real business consequences.

3. Visual and Design Work

Interior designers, construction firms, and consumer-facing businesses increasingly rely on AI to:

• Generate product mockups

• Visualize room layouts or color palettes

• Produce blueprints and job-site mappings

• Assist with merchandising and catalog imagery

These are high-impact, time-saving tasks that previously required outsourced specialists.

4. Lightweight to Moderate Coding Tasks

This might be the most surprising area of rapid adoption. Non-technical employees are now:

• Adjusting website templates

• Writing scripts

• Manipulating data

• Automating workflows

• Prototyping integrations

In the WSJ example, AI helped redesign a retail website’s structure—something that used to require a developer. We’ve seen similar results with our own clients, especially when AI is paired with well-structured cloud environments or serverless architectures.

Why Generative AI Has Exploded in Popularity Over the Last Two Years

The shift from 25% → 40% → 58% adoption didn’t happen because small businesses suddenly became more technical—it happened because the tools got good enough to be used without technical expertise.

A few tailwinds accelerated adoption:

• Models became dramatically more capable.

GPT-4, Gemini, Llama 3, and the new wave of multimodal systems were leaps above early 2020-2023 tools.

• Costs dropped.

Inference became cheaper, making experimentation possible even for new or lean teams.

• Integrations became accessible.

Plug-ins, low-code/no-code layers, and native connectors made AI usable inside tools small businesses already rely on—QuickBooks, POS systems, CRM platforms, and scheduling tools.

• Vendors started embedding AI everywhere.

AI isn’t a standalone product anymore—it’s infused across entire ecosystems (Microsoft, Google, HubSpot, Shopify, and others).

• The ROI for small businesses is immediate.

Unlike enterprises, small businesses feel operational friction acutely.
A single automated task—pricing updates, customer inquiries, scheduling—can transform daily workload.

The result? Adoption curves that look more like consumer tech than enterprise tech.

The Velocity of Adoption Creates a New Risk: Overconfidence

At Serverless Solutions, we are strong advocates for practical, value-driven AI adoption. But we also spend a lot of time tempering assumptions about what AI can do today versus what business owners wish it could do.

One line in the WSJ article captures this tension:

“I don’t foresee ever needing to hire another junior engineer again.”

This sentiment rests on a few risky assumptions:

1. Generative AI does not replace foundational engineering skills.

AI can draft code, but it cannot consistently:

• Architect systems

• Validate patterns

• Handle edge cases

• Understand organizational constraints

• Enforce security or compliance requirements

• Maintain production-grade solutions over time

Bad AI-generated code is still bad code—it’s just produced faster.

2. The more code AI produces, the more senior engineering oversight becomes necessary.

AI accelerates development but also amplifies:

• Hidden bugs

• Architecture drift

• Dependency sprawl

• Security vulnerabilities

This is why many organizations using AI heavily are hiring more senior engineers, not fewer.

3. AI needs guardrails, governance, and continuous evaluation.

Just like the cooking school owner learned, “set it and forget it” doesn’t work.
AI systems evolve, and without oversight:

• They hallucinate

• They misunderstand rare questions

• They introduce risk or inconsistency

• They drift away from business rules

4. The most successful AI programs are collaborative—humans + AI, not humans vs. AI.

AI augments talent. It does not eliminate the need for human judgment, mentorship, creativity, or accountability.

Small businesses have always been masters of efficiency—but relying on AI as a wholesale replacement for early-career roles today is premature and potentially harmful to long-term engineering health.

Where Serverless Solutions Stands

We’ve helped companies of all sizes implement responsible, scalable AI solutions—and the pattern is consistent:

• AI can replace tasks, not roles.

• AI can accelerate output but not discern quality.

• AI can make your team faster but not automatically make your systems better.

• AI needs architecture, governance, and human oversight to remain safe and effective.

We believe the winners in the next phase of AI-enabled business won’t be the organizations who replace humans—it will be the organizations who learn how to pair humans with AI in a structured, secure, cloud-based environment that lets both excel.

Ready to Explore AI Responsibly and Strategically?

Serverless Solutions helps small and mid-sized businesses implement the right AI capabilities—not the most hype-driven ones.
Whether you need:

• Practical use-case identification

• AI workflow automation

• Cloud-hosted RAG and GPT-based systems

• Data governance and security frameworks

• Cost-optimized AI infrastructure

• Or clear guidance on what not to automate

We can help you build an AI strategy that is powerful, safe, and grounded in engineering reality.

If you’re curious about what AI can do for your business—and what it shouldn’t do—let’s start a conversation.