Francis Dinha is CEO and cofounder of OpenVPN Inc., a leading enterprise network security company.
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Artificial intelligence (AI) is no longer a technology reserved for organizations with deep pockets and dedicated research divisions. According to McKinsey, as of 2025, 88% of companies have adopted AI in at least one business function, and the gap between large enterprises and smaller operations is closing faster than most expected. The tools are more accessible. The costs have come down. The question for most business leaders today is not whether to adopt AI but how to do it in a way that genuinely serves their organization rather than simply adding to its complexity.
That answer looks different depending on the size of your business. A 10-person company and a 10,000-person enterprise are not just different in scale. They carry different infrastructure, different risk profiles and fundamentally different immediate needs. Applying AI strategically begins with an honest understanding of your company's needs.
For small businesses, the most powerful application of AI is not transformation—it’s relief. The owners and teams running small operations are typically stretched across customer service, marketing, operations and finance all at once, often without enough hours to do any of it as well as they would like. AI addresses that directly.
Start with the tasks that consume the most time and yield the least strategic value: drafting routine communications, answering common customer inquiries, summarizing documents. Tools like AI writing assistants, customer-facing chatbots and automated scheduling platforms are widely available, affordable and require no technical expertise to deploy. Research shows that 96% of small business owners plan to adopt emerging technologies, including AI, and those that act thoughtfully rather than reactively tend to see the clearest returns.
The priority at this stage is selecting tools that integrate with what you already use rather than overhauling systems that are working. A small business does not need a custom AI model. It needs a well-configured, accessible tool that gives its people back time each week.
Mid-size organizations tend to have mores established workflows and enough accumulated data to start doing something interesting with it. This is where AI can operate not just as a convenience, but as a source of competitive intelligence.
At this level, the more valuable applications involve using AI to analyze patterns and surface insights that humans would not catch at speed: identifying which customers are most likely to churn, flagging anomalies in financial data, optimizing inventory and supply chain decisions or sharpening how sales teams prioritize and personalize their outreach. These are not incremental efficiency gains. Executed well, they are durable competitive advantages.
Mid-size businesses should also begin establishing AI governance at this stage. Who has access to which tools? What data are those tools ingesting? How are outputs being reviewed before they influence decisions? These questions matter more as AI becomes embedded in more processes, and organizations that develop clear policies early avoid the disorder that tends to follow unmanaged adoption across departments.
For large enterprises, 87% of which are now implementing AI in some form, the central challenge has shifted from adoption to orchestration. The tools are already in place. The more pressing question is whether they are working in concert and whether the value they generate is being measured effectively.
At enterprise scale, the highest-leverage applications of AI tend to live at the infrastructure layer: automating security monitoring and policy enforcement, managing identity and access at scale, accelerating software development cycles and deploying AI agents to absorb the operational coordination work that has historically required layers of middle management. Organizations formally measuring AI ROI are finding that 3 out of 4 leaders see positive returns, with gains emerging most clearly where AI is woven into existing workflows rather than positioned alongside them.
The risk for large organizations is fragmentation. AI tools acquired by different departments, operating on different data, enforcing different standards, have a way of recreating the very tool sprawl problem that has complicated enterprise security for years. At this scale, centralized governance and a unified data strategy are foundational to success. Those who put it off do so at their organization's peril.
Regardless of where your organization sits on this spectrum, the single most important factor in a successful AI implementation is security. According to Stanford's 2025 AI Index Report, AI-related security and privacy incidents rose 56.4% in a single year, and 69% of business leaders now cite AI data privacy as a top concern. The pace of adoption is outrunning the governance structures designed to contain its risks.
Several principles apply universally. First, know precisely what data your AI tools can access and enforce firm limits around it. Sensitive customer data, financial records and proprietary information should never flow into a third-party AI system without a thorough review of that vendor's data handling practices. Second, establish access controls so that employees interact only with the tools relevant to their roles. Shadow AI, where staff reach for unapproved tools and feed them company data, is one of the most underestimated exposure points in organizations of every size.
Third, and critically, ensure that all AI-related activity on your network travels through a secure, encrypted connection. A business VPN provides the foundational layer that keeps data private in transit, restricts network access to verified users and devices and gives IT teams genuine visibility into how the network is being used. As AI tools multiply and more sensitive data moves through cloud platforms, remote environments and third-party APIs, that encrypted layer will become the baseline from which everything else is built.
AI will define the next era of business competitiveness. The organizations that approach it with clarity about their size, their needs and their security obligations will be the ones that capture what it offers—and preserve what they have worked to build.
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