AI washing: Surviving ‘AI-powered’ vendor fatigue
Remember the ‘cloud-native’ gold rush? Every vendor slapped that label on their products regardless of the actual underlying architecture. History is repeating itself with artificial intelligence, except AI claims are more difficult to verify at face value.
Unlike cloud washing, where you could eventually spot a traditional server setup, AI requires deeper technical evaluation—and 54% of companies are betting their budget dollars on getting it right.
AI is already delivering meaningful value for many use cases. But as an IT pro, you need to understand which vendor claims represent genuine improvements versus opportunistic marketing. Here’s how to tell the difference between fanciful AI washing and actual AI innovation.
What AI washing looks like in practice
When your network monitoring vendor can’t explain how their “AI-powered alerting” differs from the threshold-based rules you’ve been using for years, you’re probably looking at a rebrand, not an upgrade. To separate genuine AI from marketing spin, you need to understand where vendor claims fall on the spectrum. Not every AI assertion deserves the same level of skepticism.
Legitimate AI implementations involve systems that learn patterns from your actual data and adapt accordingly. For example, an email security gateway that gets better at catching phishing attempts by analyzing your organization’s specific patterns uses real AI. The system should show how its performance improves over time and explain why it flagged specific items.
AI-adjacent features benefit from AI research but don’t fundamentally change how software operates. Natural language search in your documentation system or improved spam filtering falls here. These add convenience but aren’t revolutionary.
Pure AI washing happens when vendors rebrand existing rule-based automation as “AI-powered.” Your monitoring system triggering alerts when CPU usage exceeds 80% isn’t AI—that’s a conditional statement from the 1970s. The giveaway is complete lack of learning or adaptation.
Say you’re looking at an ‘AI-powered network optimization’ tool that amounts to standard quality of service (QoS) rules with a dashboard makeover. When you ask how the AI component works, the sales engineer can’t explain what data the system learned from or how recommendations changed over time. That’s hype over substance.
Practical AI evaluation for busy IT directors
Once you understand the different types of AI claims, you need practical ways to evaluate them during vendor meetings. Fortunately, you don’t need deep subject matter expertise to spot legitimate AI implementations. Here’s how to identify the solutions that are worth your time:
Focus on before-and-after scenarios. Ask vendors to walk through specific examples of how their AI handles situations your current tools struggle with. If they can’t provide concrete scenarios where AI performs differently than traditional automation, you’re likely looking at marketing spin.
Test with your actual data. Real AI should behave differently with different data sets. Ask for trials using your organization’s actual network logs, help desk tickets, or security events. If vendors insist on using demo data or can’t accommodate testing with your environment, that’s concerning.
Look for learning and adaptation over time. Real AI needs time to learn your environment’s patterns, then continues adapting as it encounters new data. Ask to see how the system’s behavior changes over weeks or months. If vendors promise immediate AI benefits from day one with no ongoing learning, they’re almost certainly using pre-configured rules rather than adaptive intelligence.
Evaluate the feedback mechanism. Can you correct the system when it makes mistakes? Real AI should incorporate your corrections to improve future performance. If there’s no way to “teach” the system based on your organization’s specific needs, it’s probably not learning anything.
Red flags that indicate AI washing
Beyond the evaluation framework, certain warning signs consistently indicate when vendors are overselling their AI capabilities. Watch for these red flags during vendor demonstrations:
Vague technical explanations. When vendors describe AI using phrases like “proprietary algorithms” or “advanced machine learning” without specifics, they’re usually describing traditional programming with marketing language. Legitimate AI vendors can clearly articulate their approach in plain English.
Perfect accuracy claims. Real AI systems have confidence levels and error rates. Vendors claiming 100% accuracy are either describing deterministic rule-based systems or touting their capabilities in ways that should make you nervous about their technical competence.
Making AI work with limited resources
Enterprise IT pros are 1.5 times as likely to believe AI is worth investing in compared to those working in small businesses. Large companies have dedicated funding for extensive pilot programs and AI governance frameworks. Your AI budget may not be that generous, but you can still secure a decent ROI from your AI investments.
Start with AI features in tools you’re already using rather than shopping for dedicated AI platforms. Your existing security tools, monitoring systems, or helpdesk platforms might be adding legitimate AI capabilities that provide incremental value without requiring new budget or extensive evaluation processes.
Focus on AI implementations that reduce your manual workload rather than requiring additional management overhead. The most valuable AI features handle routine pattern recognition and basic decision-making while leaving complex judgment calls to you.
Consider the total cost of ownership. Some AI features require additional licensing, training data preparation, or ongoing maintenance that might not make sense for smaller organizations. Make sure you understand the full implementation cost, not just the initial price tag.
AI integration without the enterprise overhead
You might not have the resources to implement enterprise-scale AI governance frameworks, but you should still consider how AI-enabled tools fit within your existing security and operational practices. 21% of organizations choose not to use AI-powered features due to security concerns, and those reservations are often valid.
Ask vendors about data handling practices and where AI processing occurs. Some AI features process data locally, while others send information to cloud-based machine learning services. Make sure you understand what data leaves your environment and how it’s protected.
While you’re at it, plan for monitoring AI-enabled features just like any other system component. AI systems can behave unexpectedly as they encounter new data patterns, so you’ll need processes to catch and correct issues before they impact operations.
The practical reality of vendors’ AI claims
Most “AI-powered” features won’t transform your IT operations overnight. The technology is legitimate and valuable in specific use cases, but it’s still in a state of massive transition and often oversold by vendors eager to capture AI budget dollars.
The vendors making the boldest transformation promises often have the least mature technology. So, focus on incremental improvements that solve specific pain points in your current operations rather than vendors trying to sell you completely new approaches to familiar challenges.
Good technology solves real problems, regardless of whether it uses AI or traditional programming to get there. Your job is distinguishing between the two, and now you have the evaluation framework to do it.
