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Every technology-vendor pitch seems to include the phrase “AI-powered” or “leveraging artificial intelligence (AI).” But the uncomfortable truth is this: a large portion of what is labelled “AI” isn’t really AI, or at least not in a meaningful, business-impacting way.
What Is AI-Washing?
AI washing is when a company markets a product, feature, or process as “AI” when it isn’t. In practice, it looks like relabeling simple rules or scripts as “AI,” implying autonomy where there’s none. It wastes time and budget on the wrong solutions, confuses buyers and users, and erodes trust making people skeptical.
Reality Gap
A recent report from Massachusetts Institute of Technology (MIT) found that around 95% of enterprise generative-AI pilots fail to deliver measurable profit-and-loss impact – Forbes
What this suggests is: simply adding “AI” to your product or process doesn’t magically make it transformative.
What “Not-Really-AI” Looks Like
Here are a few patterns you’ll see:
- A vendor claims “AI-driven” when the solution is actually a traditional rule-based automation or scripting with little or no machine learning or adaptive behaviour.
- Marketing materials highlight “AI” but the deployment lacks data infrastructure, clear use case or integration with business workflow — and so it doesn’t deliver value.
- The term “AI” is used loosely to attract investment, press or customers, while the actual technology is minimal or superficial. The phenomenon is often called “AI washing.” – Bernard Marr
- The vendor or organisation lacks transparency about what is AI vs what is legacy software.
If “AI” just means “some algorithm” or “some automation” rather than something that adapts, learns, or meaningfully augments decision making, it may not deserve the label.
More Than Just Annoying
More than just annoying, AI-washing carries real costs. Organizations chase “AI” because of the buzz and get trapped in endless pilots that consume time, talent, and budget without ever delivering results. As every vendor slaps an AI label on ordinary software, buyers and users struggle to discern what actually creates value; the signal-to-noise ratio collapses and decision-making slows.
Repeated disappointments erode trust: employees, clients, and stakeholders grow skeptical and start rejecting even the tools that could genuinely help. Meanwhile, the opportunity cost mounts: resources poured into overhyped or mis-labeled initiatives could have funded straightforward digital improvements or, better yet, the focused data work and integration required to make real AI succeed.
Why People Should Care About Real AI
Despite the hype and false alarms, true AI has the potential to be a game-changer when done right. The key is to focus on use case + data + process integration + measurable impact. For example, when you find a specific business problem, ensure that you have the right data, the right people, the right workflow, and a clear metric.
As one article puts it: asking “What business problem are we solving?” is the right starting point – CIO
Also, understanding that many “AI projects fail” doesn’t mean AI is useless—it often means the foundations weren’t ready.
AI Washing Signs
Here are some red flags:
- “AI” is used as a catch-all buzzword rather than being clearly defined.
- Claims of “AI” but no explanation of how it learns, adapts or integrates into workflows.
- The vendor has little evidence of production-scale deployments or measurable outcomes.
- The solution lacks clear data-governance, lack of clarity on how the model is trained or maintained.
- The project is framed as “let’s try AI” with vague goals rather than “we have this measurable target, AI is the tool” approach.
Reports like the one from CFA Institute describe “AI washing: signs, symptoms, and suggested solutions” for investors and users alike.
What You Can Do
Be candid about your readiness. Real results depend on clean data, well-integrated workflows, committed stakeholders, and basic governance. If these foundations are missing, the initiative is likely premature. Clarify the term “AI” as well. Ask vendors to explain precisely what powers the solution.
Determine whether it uses machine learning, deep learning, or adaptive recommendation systems, or if it is simply rule-based automation with a new label. Transparent answers prevent confusion and disappointment.
Make a plan for what happens after the pilot. If the project does not deliver against the metrics you set, pivot quickly or stop and reallocate resources to what works.
Continuing to fund an underperforming experiment wastes time and erodes trust. Focus on demonstrable outcomes, and you will protect your team’s attention while building credibility for AI where it truly adds value.
Conclusion
AI-washing is a short-term risk that already slows teams down. It wastes time in pilots, clouds expectations, and erodes trust just when clear wins are needed. That is why we’re concerned now.
Representative24 delivers customer experience (CX) agents that do more than chat. They connect to your systems and perform real tasks: booking appointments, qualifying leads, updating CRM records, opening tickets, and scheduling meetings – across web, WhatsApp, Facebook, and via API. The focus is action, integration, and multichannel reach, so outcomes are tangible, not cosmetic.