“The growing AI adoption gap” can be described as the tale of the two wildly different starting lines.

On one side, there are large companies, which already have standardized software stacks, in-house teams for IT, security, legal, and budgets for spending resources on upgrading infra, investing in Enterprise AI solutions, and/or hiring experts. On the other side, there are small to mid-sized companies, which have limited resources, less time, and tighter budget flows, thereby making it difficult, even with strong leadership vision, for them to execute their vision of AI adoption.

That’s why a Forbes Research-published “snapshot” (based on a 2025 survey of 1,075 C-suite level executives) paints the following contrasting picture: While 73% of large businesses surveyed have committed to making enterprise-wide use of AI, 22% of small businesses have done so.

The same Forbes Research article also indicates that some small businesses are possibly still using AI, either in some capacity that has not been applied to their whole business, or that has not been applied on a full scale to their organization.

Following is an in-depth look at what exactly the gap represents, why the gap is occurring, and how small companies can bridge the gap without requiring a “Silicon Valley budget.”

1) What “AI adoption” means and why the phraseology is important

When media reports ‘Company X implements AI,’ what comes to mind are robots taking over everything. What happens in actual companies when they adapt AI technology is:

Experimentation (personal use): Employees will use ChatGPT for writing, summarizing, and very basic analysis.

Team-level pilots: “A department will test an application with a workflow such as customer service, sales emails, invoices, hiring, etc.”

Operational integration – AI is incorporated into key business processes (CRM, ERP, customer service system). Data is shared automatically. Outcomes are measurable.

Enterprise-Wide Deployment: Several functions apply AI on an ongoing basis with governance, security controls, training, or budgeted programs.
This typical “gap” for a Forbes magazine approach is almost entirely concerned with the final two tiers: from isolated use to organized use throughout an organization.

So it’s not that small businesses are “anti-AI.” It’s that “enterprise-wide” is a heavy lift when you don’t have enterprise resources.

2) Why big companies move faster: the structural advantage

A) Bigger budgets can absorb the “hidden costs”
N/A

“AI is not just the price you pay each month. The cost quietly includes:”

Data cleaning and organization

Upgrading existing systems to allow connectivity through APIs

Security reviews, Compliance, or Vendor risk evaluations

Employee training and workflow redesign

Monitoring quality (Hallucinations, Errors, Bias, Safety)

This expense can be better absorbed by large corporations because it appears as a small portion of their revenues and employees. Small businesses are pinched for every extra gun, consultant, and hand “not pulling today’s weight.”

B) Mature IT infrastructure

Companies may have:

Standardized Identity Solutions (Single Sign-On)

Data warehouses/RW Lakehouses
Data lakes

Cloud contracts with major vendors
However, if the

Cybersecurity capabilities

“Procurement” is the acquisition and purchasing process

“This ‘foundation’ helps explain why it’s easier to insert the AI. Without this, AI is duct tape applied to messy systems—and messy systems produce messy answers.”

It is a theme that emerges in other surveys as well: adoption is increasing, but it is difficult to scale. Take the example of the survey carried out by McKinsey: there have been indications of an increase in the usage of AI, although many companies are finding it difficult to unlock the value that can be gained.

McKinsey & Company

C) Specific talent and leadership

Larger corporations are more likely to:

Data engineers and Machine Learning Engineers

security architects

Legal counsel on privacy/ip

Change management teams
Other

Internal audit/compliance resources

This means that small businesses would have one IT person or outsourcer handling everything. This is not to say that small businesses cannot perform AI. They simply require simpler models for implementation.

D) Vendor leverage and partnerships

Big companies can acquire the following

Improved pricing

EARLY ACCESS TO FEATURES
In recent

Dedicated support teams
Some companies

Specific contracts and indemnities

Small businesses click on “buy now” and hope it works. Then they just use the generic tech support. That’s a different risk profile.

3) The “adoption gap” is also a “deployment gap”

Important in that regard—small businesses may be using AI, but are certainly not implementing it in a manner that is:

repeatable
measurable,

secure,

and incorporated into daily activities.

This, precisely, is what makes “enterprise-wide adoption” such a significant measure—and why big companies appear so far ahead in surveys, such as the ones summarized for Forbes Research.

“Think of it like fitness:”

Doing workouts willy-nilly = experimentation.

A weekly routine = pilot

A personalized approach – coaching, nutrition, tracking = operational integration

Lifestyle change involving the entire household = enterprise-wide deployment

Large firms are more likely to be in “program mode.” Small businesses are more likely to be in “trying things when time allows.”

4) What’s driving the divide at the moment, the four key barriers facing small businesses

Despite this survey conducted by Forbes, evidence points toward other similar blockers.

1) Skills and time

Small businesses tend to mention a lack of skills and an inability to spare time. Looking at a national picture of Italy, the most common reason for non-AI adopters was a lack of skills (regulatory uncertainty, data protection, and costs considered together).

It appears that the same phenomenon plays out in every country: ability and potential are

2) Cost and ROI Uncertainty

Small business doesn’t just say, ‘Can we buy it?’ It says, ‘Will it pay back quickly?’

Uncertainty surrounding ROI can also be noted at an enterprise level. In a UBS-survey quoted in Barron’s, it was seen that a certain level of ROI uncertainty was the key barrier for most businesses, and very few had implemented AI “at scale.”

If the return on investment is a problem for large companies, it will be even more severe for the smaller companies because of the limited payback period.

3) Data readiness

The data used by the AI needs to be:

accessible (not trapped in spreadsheets on someone’s laptop),

consistent (Same customer name spelled same way),

and controlled (clear guidelines on who sees what).

Smaller companies typically store information scattered across a variety of tools: WhatsApp messages, spreadsheets, email inboxes, a simple billing app, potentially a CRM system that’s not used systemically.
AI cannot organize a mess.

4) Risk: privacy, security, compliance, and reputation

Small business can be devastated by just one major mistake or lawsuit. However, small business has fewer employees who determine risk.

This generates a ‘risk paradox’.

They cannot be scaled up effectively without effective governance,

However, they cannot institute effective governance on their own without resources,

and hence remain relegated to conservative usage only.

5) Where large corporations are using AI first, (and why it’s easier for them)

Large companies are more likely to use AI where they are already active:

many digital workflows,

large volumes of text/data,

clear metrics,

and budget ownership.

. Common enterprise deployment zones:

A) Customer Service and Contact Centers

AI chat and agent-assist tools can:
– Offer

reducing average handling time,

improve resolution rates,

support multilingual service,

and enables faster retrieval of knowledge.

Organizations commonly possess ticket-based systems and knowledge bases, which are ideal AI feeding grounds.
Enterprises are always dealing with tickets; some

B) Employee productivity suites

Large companies launch AI copilots for emails/docs/spreadsheets because:

it’s a controlled environment,

identity access is managed,

and they can train their staff.

C. Software engineering &IT ops

Artificial intelligence coding assistants, incident summarization, log analysis, and knowledge retrieval systems can all contribute to mitigating potential bottlenecks within large I.T. departments.

D) Analytics, Forecasting, and Finance Ops

Automation of variance explanations, billing, fraud analysis, and spend analysis is most effective when the systems are standardized.

Large businesses can quickly move into these regions because the “pipes” are already in place.

6) The human side of the gap: Change Management and Training

That the corporations “move faster” as a result of the fact that they can execute change programs:

training paths,

usage policies,

champions networks,

internal communities,

measurement dashboards,

and incentives.

Small businesses rarely possess a training department. However, training is important because AI value also derives not directly from its acquisition but from its use.

This is where the small firm can get a huge advantage by focusing on one thing: a 20-person organization can change its behavior before a 200,000-person organization—if the leadership decides to do so.

7) The risk of a widening gap: why this matters beyond tech

“If large companies adopt AI at a faster pace, they can acquire an edge whose advantages will be cumulative: i)

A) Productivity Compounding

The speed of repetitive tasks improves with AI: draft, summarize, research, basic analysis, customer response, writing. When you multiply those improvements for thousands of employees, it adds up.

B) Better customer experience

Faster service, more personalized experience, and round-the-clock support—customers begin to demand it. smaller players, with a less appealing experience, risk losing a sale even with a superior product.

A) Business development

Well-qualified employees are seeking more advanced tools. There may be an advantage to companies that use AI-enhanced processes.

D) Market Power and Prices

If costs of operation for big companies are reduced by AI, big companies would compete even more on price, hurting smaller companies.

It’s not simply a matter of “tech adoption.” It can change competitiveness in whole industries.

8) The good news: small firms can close the gap with different tactics

Smaller companies shouldn’t pattern their AI strategy after larger companies. They can capitalize on these factors of a small company:

faster decisions,

shorter communication lines,

fewer legacy layers,

and the ability to recreate business processes.

This is a way that works in the real world.

Step 1: Identify 1-2 use cases to address the immediate ROI

Make sure your goals are not too general, such as “integrate AI into marketing.” Instead, aim for outcomes that can be

Reduce customer response time from 6 hours to 1 hour

Cut the time devoted to quotations by 40%

Increase lead qualification speed

Cut processing time for invoices by 50%

Automatically generate weekly performance reports

“If you can’t measure it, you can’t prove ROI—and without ROI, small businesses will stop.”

Step 2: Use “AI as a feature,” not “AI as a project”
Link:
AI as

Rather than relying on the development of new models, leverage the power of tools that integrate AI:

Customer support platforms with AI agent assist

CRMs with AI capabilities for email writing and summarization

Whether or

Accounting software with auto-categorization functionalities

Conclusion

Scheduling/email solutions combined with AI-powered follow-up

E-commerce solutions including product description & advertisement via Artificial Intelligence

This makes IT security and integration simpler.

Step 3: Clean up Data Sanitization to the Smallest Possible Scope
In this final

Do not “clean all data.” Clean just what you are using.

Figure:

If your use case is faster quoting, you simply need:

accurate product catalog,

pricing rules,

and customer contact information.

Beginning with that.

4. Add a lightweight governance checklist.

You don’t need a enterprise governance committee. You need a simple checklist:

  • Does the system change address a

What types of data are permitted in the tool? (Customer PII data? Financial account numbers?)

Who approves new tools?

Where do outputs get reviewed before they can be distributed to customers?

How do you log issues and create better prompts/templates?

“This avoids the most egregious errors while incurring little overhead.”

Step 5: Train through templates, not theory
If you’re

Small businesses cannot spare time for lengthy training courses. The most effective method of training is:

“These are 10 questions we have for our business purposes:”

“Here’s how we check AI output”

“Here’s our policy: what not to paste into AI”
Whether the AI is meant

“Here’s the workflow: draft → review → send”

Step 6: Report on 2–3 metrics a

Examples:

Time saved per task

Tickets closed per day

Lead to meeting conversion rate

Average response time
Inert

Cost per acquisition (for marketing use cases )

That is how you leverage AI from “cool tool” status to “business muscle” status. 9) Why small businesses are still optimistic (and why their optimism isn’t so crazy) Firstly, although small businesses are lagging in enterprise-wide adoption, a positive outlook is justified because: AI toolkit development is becoming cheaper and more accessible (more cloud-based/SaaS, less custom development). Product-led adoption is a reality, with employees informally adopting tools at work, and their adoption being formalized at a later stage Many high-value activities in small business involve high use of language (sales, service, administration), precisely what modern AI systems excel in. This goes along with the idea from the Forbes Research study, which describes how smaller companies might trail larger ones in overall adoption but still recognize potential returns. Forbes 10) A realistic bottom line The “AI adoption gap” that Forbes talks about doesn’t so much relate to whom they believe about AI. But it relates to whom they can apply it to: Enterprises are more apt to have access to money, people, and resources to safely deploy on a large scale–thus the vast difference in “enterprise-wide” rates of adoption. Forbes Small businesses usually evolve adoption in a piecemeal fashion, followed by scaling, as they require rapid ROI, simplicity, and light governance