Key Takeaway: AI washing is widespread in the MSP vendor channel. The questions that cut through the hype are specific: what model, what data, what measurable outcome, and who has achieved it.

AI washing in the MSP channel occurs when vendors exaggerate AI capabilities while lacking real substance, and you can spot it by demanding concrete evidence, checking for specific use cases, and verifying measurable outcomes rather than accepting marketing claims at face value. True AI solutions provide transparent methodologies, verifiable results, and clear limitations, while AI washing relies on vague promises and buzzwords without substance.

Ford Motor Company laid off hundreds of experienced quality engineers. Then it replaced them with AI-powered cameras and automated inspection systems. The AI missed things the veterans would have caught. Defects slipped through. So Ford did what any honest company would do: it admitted the mistake and rehired more than 300 of those inspectors. ¹

That story should hit every MSP owner like a truck. You are selling AI-powered solutions to clients who trust you to separate real innovation from expensive theater. When your vendor’s AI misses what a human expert would catch, it is not innovation, it is a liability waiting to happen.

What AI Washing Looks Like in the MSP Channel

AI washing follows the same pattern as greenwashing or virtue signaling: take a real concept (artificial intelligence), strip it of meaningful substance, and sell the husk at a premium. In the MSP world, this shows up as:

• Vague promises: “Our AI-powered platform revolutionizes IT management” with zero details on how.
• Buzzword bingo: Slapping “AI” on everything from ticket routing to patch management without changing the underlying mechanics.
• No transparency: Black-box systems that won’t show you their training data, error rates, or failure modes.
• Human replacement theater: Claims that AI will eliminate the need for skilled technicians while actually creating more work fixing AI mistakes.

The Ford example is not an outlier, it is a warning. When you replace experienced humans with unproven AI, you trade proven judgment for statistical guesses. In IT, where mistakes cascade into downtime, data loss, and security breaches, that trade is often catastrophic.

How to Spot AI Washing Before You Recommend It

Before you put your reputation behind an AI-powered product, demand answers to these questions:

• What specific problem does it solve? If they cannot name a concrete use case with measurable outcomes, walk away.
• What data was it trained on? If they won’t share or say “proprietary,” assume it is insufficient or biased.
• What is the error rate? Every AI system fails sometimes. If they cannot give you false positive/negative rates, they are hiding something.
• Can it show its work? Explainable AI is not optional, it is a requirement for trust and accountability.
• What happens when it is wrong? If the answer is “we will retrain it,” you are signing up for indefinite liability.
• Who maintains it? AI is not set-and-forget. It needs continuous monitoring, retraining, and oversight.

Real AI vendors welcome these questions. AI washing vendors deflect, obfuscate, or get angry.

The Human in the Loop Is Not Optional

Experienced IT professionals are not interchangeable parts. They bring pattern recognition, contextual understanding, and judgment that no current AI can replicate. The Ford case shows what happens when you ignore that: expensive systems that create more problems than they solve.

When evaluating AI-powered MSP tools, ask:

• Where does the human fit in? If the answer is “nowhere,” it is a red flag.
• What decisions can it make autonomously? Be wary of systems claiming full autonomy in complex environments.
• How do you handle edge cases? Real-world IT is 90% edge cases.

The goal is not to eliminate humans, it is to augment them with tools that handle repetitive tasks while leaving critical judgment to experienced professionals.

Building a Real AI Strategy for Your MSP

If you want to offer genuine AI-powered services, start here:

• Start small and prove value: Pick one well-defined, high-volume task where AI clearly outperforms manual processes.
• Insist on transparency: Demand access to model cards, training data summaries, and performance metrics.
• Plan for human oversight: Design workflows where AI handles the routine and humans handle the exceptional.
• Measure everything: Track accuracy, false positives, time saved, and user satisfaction.
• Have an escape hatch: Know how to revert to manual processes if the AI fails or becomes too expensive.

Real AI adoption is a marathon, not a sprint. It requires investment in training, infrastructure, and change management, not just buying the shiniest new tool.

Where This Leaves You

AI washing is not going away, it will get more sophisticated as the hype cycle continues. Your defense is skepticism, due diligence, and a commitment to your clients’ actual outcomes over vendor promises.

The next time a vendor tells you their AI will “revolutionize” your service delivery, ask for the evidence. If they cannot provide it, you are not looking at innovation, you are looking at a liability waiting for the right moment to cause harm.

Your clients trust you to be their technology advisor. Earn that trust by refusing to sell AI theater and insisting on real solutions that deliver real value.

Frequently Asked Questions

What questions cut through AI washing?

What model does the product use, and is it proprietary or a wrapper around a public API? What data does the model train on, and does client data contribute to that training? What is the specific use case, and what is the measurable outcome? Can the vendor provide a reference client who has achieved that outcome?

How do I explain AI washing to clients?

Ask the vendor to demonstrate the specific AI capability in a live environment with real data. Vendors who cannot demonstrate their AI claims in a live environment are selling marketing, not capability.

Sources

¹ BBC News, “Ford brings back human inspectors after AI vision system fails,” bbc.com/news/articles/cgrkd41n2v9o, 2024.

About Brent Lacy: Brent Lacy has been in the IT industry since 1997. He moved into the managed services world around 2015 and was doing vCIO work before the title even existed. He writes about the operational discipline, trust-based relationships, and strategic thinking that separate MSPs built to last from those built to bill. He is the author of Rewired MSP: Mastery, Scalability & Performance, vCIO Rewired: Virtually Conquering IT Obstacles, and Near Miss: Preventable IT Failures Threatening Your Business Security.

This article is part of the AI for MSPs Hub, everything Rewired MSP has published on AI strategy, governance, and the AI revenue gap.

Related Reading: AI Washing in the MSP World | AI Agents for MSPs | AI Acceptable Use Policy Template