Let’s start with what is at least relatively obvious. Cybersecurity analytics, intelligence and response are undergoing significant advances, driven primarily by AI automation. These technologies are revolutionizing threat detection, incident response, improved malware analysis, and overall security posture.  

This enables organizations to proactively defend against sophisticated cyberattacks. Quick examples? One is reduced false positives. AI algorithms can differentiate between legitimate and malicious activates, reducing the number of time-consuming false alarms. Another is increased automation and efficiency.  Specifically, AI-powered automation streamlines security processes, freeing security teams to focus on more strategic tasks.

Yet another example is advanced security, which uses AI to detect and block phishing attacks. And, too, there is advanced malware analysis, which identifies new strains before they cause trouble.

What gets lost in the shuffle, however, is that these strengths are far more common in large companies than in medium and small businesses. Some cyber pros view small business as a borderline afterthought – i.e., the small business version of comedian Rodney Dangerfield, who complained of having “no respect.”

While it’s true that AI today is more accessible to small businesses than ever, small businesses nevertheless face several challenges when adopting and implementing AI. This includes high costs, lack of expertise, data privacy concerns, and integration issues. In addition, many small businesses also lack the resources to invest in the necessary infrastructure, training, and personnel.

This is problematic. According to the US Small Business Administration roughly 45% of all private sector employees in the US work for small businesses, and many suffer cyber breaches. Another survey by MasterCard in March found that 46% of small and medium-sized business (SMBs) owners experienced a cyberattack in their current business. An additional study by the National Federation of Independent Business (NFIB) in June found that only 24% of small business owners said they were using AI. Other sources place this number higher, although it never reaches a majority of small businesses.

For many years, SMBs commonly maintained a false sense of security due to the relative obscurity of their business. Their reasoning was that a hacker would not target, say, a small doctor’s office when they could just as easily infiltrate the network of a major hospital. Today, however, it has become much harder to take down large, prominent targets.

Consequently, many hackers have set their sights elsewhere, including the SMB market. Also, in the mix are SMBs that are third-party purveyors for big companies – i.e., a target to break into the big company as well.

Small businesses are also troubled by internal issues. Most small businesses have relatively few employees to accomplish their work. As a result, the owner or top manager wears a number of different hats. Do they really have the time to learn complex new systems or the budget to gamble on solutions that might not pay off right away? The bottom line: AI can easily slide to the bottom of the priority list.

Among the challenges that small businesses face regarding the adoption and effective use of AI are cost and budget constraints, lack of expertise and talent, data-related challenges, and integration and implementation complexities. Let’s examine each of them. All are victims of budgetary or technical constraints or both.

• Cost and budget constraints: The implantation of AI solutions can easily require significant investment in specialized hardware and infrastructure. AI tools also come with recurring subscription costs and maintenance fees. And, too, profit-based small businesses may struggle to see a clear and immediate return on AI.
• Lack of expertise and talent: There is a global shortage of data scientists, AI engineers and machine learning experts. Small businesses often can’t compete with larger corporations for highly sought-after talent. Budget constraints also typically lead to limited internal training. Sometimes, they don’t fully understand what AI can do for their specific business.
• Data-related challenges: AI models require large volumes of clean, well-structured, and relevant data to be effective. Too often, small businesses often have disparate or poorly organized data and sometimes don’t have enough historical data to train robust AI models.
• Integration and implementation complexities: Many small businesses rely on older, disparate legacy systems not easily integrated with modern AI tools. In addition, deploying and maintaining AI solutions requires technical expertise that might exceed internal capabilities.

Despite all the issues undermining small businesses and AI, there is yet another: Strained budgets, overstretched teams, and a rise in sophisticated threats is causing small business security confidence to erode. The World Economic Forum's Cybersecurity Outlook 2025 report has noted that 71% of cyber leaders say small organizations have already reached a critical tipping point that can no longer adequately secure themselves.

As grim as all this may seem, hopes of improvement are widespread. It need not be hard, either. In plenty of situations, it would be a big plus if small businesses simply made a point of not starting with the technology but rather with the business’s security problems. Why are customers leaving the ship? What tasks consume too much of the company’s time?

After identifying these pain points and others, small businesses should seek established AI solutions and other issues that specifically address them. Meanwhile, generative AI enables small companies to scale their output with fewer resources, including the use of AI tools, for example, to draft marketing copy and social media posts. This reduces staff workload, enabling employees to concentrate on more important challenges. 

Before long, AI will be a necessity, not a luxury. With better instincts and some additional training, there is no good reason why small business cannot at least mitigate its headaches.