These days, it feels like AI is everywhere, whether you’re hearing about it, reading about it, or seeing products labeled as “AI-enabled.” From AI-powered HVAC systems to self-driving cars and software solutions, the term is being attached to nearly everything. This is especially true in the technology sector, where vendors across cybersecurity, network solutions, and cloud computing are racing to brand their offerings with the AI label. It’s enough to make you wonder: what isn’t AI-enabled? Is AI just a buzzword?
In some cases, yes. Not everything marketed as “AI” truly uses artificial intelligence. Many rely on clever programming or rule-based automation. As a result, AI has increasingly become more of a marketing label than an accurate description. While real investments and advancements have been made in AI, it’s not always as groundbreaking as some might believe.
Navigating the "AI Washing" Wave
I’ve seen many organizations rush to embrace AI without first defining what they hope to achieve or taking the time to understand how AI could actually support their goals, or whether it’s even the right fit for their needs. In one recent meeting, I was with a client and a manufacturer when the manufacturer confidently stated, “Our product is AI-enabled.” The client, who oversees a complex environment with significant investments in their data center and cloud infrastructure, looked at us, then back at the manufacturer, and said bluntly, “Oh, AI is a bad word here.” It was a clear reminder that without purpose, context, or clarity, AI can quickly go from a promise to a punchline.
This reaction isn’t surprising. The trend of “AI washing”—where companies exaggerate or fabricate their AI capabilities—has led to widespread skepticism. When every piece of procurement software or security tool claims to be revolutionary, the term loses its meaning. It creates a credibility gap that makes it difficult for legitimate cybersecurity companies and innovators to stand out. For leaders trying to navigate complex decisions around their datacenter architecture or digital transformation, this noise is more of a hindrance than a help.
Not All AI is Created Equal
To have an informed conversation about AI, it’s important to understand the main types and how they differ. This clarity is crucial whether you’re evaluating a new cybersecurity solution or considering a vendor for managed IT support.
- Artificial Intelligence (AI) refers to systems that mimic human intelligence by analyzing data, recognizing patterns, and making decisions or predictions. A true AI system learns and adapts over time, making it a powerful tool for complex tasks like threat detection in network security or optimizing workloads in a hybrid cloud environment. For instance, a genuine AI could analyze cooling efficiency, power usage, and server loads to dynamically adjust a data center’s environment for optimal performance, something a static script could never do.
- Generative AI is designed to create new content like text, images, or code. This is typically done by learning patterns from large datasets or data models. We’re seeing this applied in innovative ways, from generating incident response reports to creating code for customized network solutions. It can even be used to draft initial marketing copy for new cloud hosting services, freeing up teams to focus on strategy.
- Fake AI is typically a tool marketed as AI but is actually scripted automation using basic rules, logic trees, or macros to simulate intelligence. These systems don’t learn or adapt; they simply follow predefined instructions without true decision-making capability. This is often seen in basic automation that might be part of a larger software suite, but it lacks the dynamic learning capability of true AI. Think of a chatbot that can only respond to a very specific set of keywords versus one that understands conversational nuances.
Asking the Right Questions Before You Leap
Knowing the differences above matters. Before engaging in any serious IT procurement discussions for an “AI-powered” platform, it’s essential to dig deeper. Asking thoughtful questions like these before making the leap ensures that AI is applied with purpose and precision—maximizing its value while avoiding wasted investment and hype-driven decisions:
- What problem are we trying to solve? Is this a challenge that requires predictive analytics, or is it a repetitive task suitable for automation?
- Do we truly need AI, or would automation meet the need more efficiently?
- What data will the AI use, and do we own or control that data? Where will this data reside—in our on-premise data center, in public cloud storage, or both? What are the data security and compliance implications?
- How will we measure success, and how does it align with the business? Will the ROI be measured in cost savings, improved cybersecurity posture, or faster delivery of cloud services?
I’ve explored these ideas further in previous blogs such as “Should Your Company Have an AI Strategy?” and “Are AI-Enabled Networks Enough?“.
The Human Element: Why AI Still Needs Us
A common misconception is that the goal of AI is to completely remove humans from the equation. In reality, the most effective AI strategies embrace a “human-in-the-loop” approach. AI is brilliant at processing vast amounts of data at speeds no human could ever achieve, but it often lacks context, ethical judgment, and the ability to handle unforeseen edge cases. This is where human expertise becomes irreplaceable.
For example, an AI-powered cybersecurity platform might flag thousands of potential threats a day. It’s the human analyst, perhaps from your internal team or a managed security service provider, who investigates the most critical alerts, understands the business context of a potential breach, and orchestrates the response. Similarly, when tackling the challenges with cloud migration, an AI tool might suggest an optimal path, but it takes an experienced IT consulting firm to navigate the organizational politics, retrain staff, and manage the project to completion. The AI provides the data; the humans provide the wisdom.
The Slow Road to Success
Can AI help your organization improve? Absolutely. However, this is often a slow, methodical process—one that requires learning and adapting along the way. For example, in the recent article “IBM Lays Off 8,000 Employees for AI Automation, Only to Rehire Just as Many Soon After Because Of…“, IBM acknowledged that while AI enhanced efficiency, the cost savings created opportunities to reinvest in roles that require uniquely human skills, like creative problem-solving, strategic planning, and nuanced client management, ultimately resulting in new hires.
Similarly, another organization I’m familiar with has been steadily enabling its business units with AI. Their approach begins with pilot programs and active collaboration, ensuring each initiative is grounded in a human-centered use case that preserves human judgment while delivering excellent service. They started with a targeted project to improve their network security solutions, using an AI platform to analyze traffic patterns and identify anomalies that human analysts might miss. This wasn’t a complete overhaul but a focused enhancement. Along the way, they’ve learned valuable lessons. They actively promote the benefits of AI but also emphasize the importance of managing risks related to data quality, process safety, and potential cybersecurity vulnerabilities introduced by new integrations. They often work with their managed security service provider to assess these risks, encouraging a balanced and responsible adoption strategy.
From Buzzword to Business Value
Has AI become a bad word? That depends on how we use it. If we treat it as a marketing badge, it probably will. If we approach it as a strategic enabler, built on human judgment, strong governance, and clear business value, it can be one of the most powerful tools in our arsenal. The key is to put in the work: start small, make targeted investments, learn from pilot programs, and build momentum over time. This isn’t just about buying the latest technology; it’s about fundamentally rethinking processes, from hardware and software procurement to customer service. Whether you are undergoing a large-scale cloud migration or simply looking to improve your internal processes, this steady, intentional approach can transform AI from a buzzword into a trusted driver of innovation and measurable results. If you’re ready to move from concept to action, our infographic, “How to Get Started with AI Adoption”, offers a practical, step-by-step guide to making AI work for you.
About the Author:
Chris Price is an experienced executive deeply committed to nurturing and empowering team members to realize their fullest potential. My passion lies in technology thought leadership, and my career has been dedicated to providing guidance and leadership in aligning technology with business objectives. In recent years, we’ve observed a significant evolution in technology, particularly in digital solutions, which have the potential to differentiate businesses and confer a competitive advantage in their respective industries. In this new era of digital business, organizations must embrace transformation. Within my team, we possess the expertise to guide organizations through the disruptions brought by digital innovations, offering innovative ideas and state-of-the-art technology to navigate these changes effectively.
