By Chandan Sharma
Artificial intelligence has become the business world’s favourite label. I will not be surprised if I see ‘AI powered’ on the packet of my washing machine powder. Every platform, every app, every new tool seems to carry the tag AI-powered. However, upon closer examination, much of it isn’t truly AI at all. What many companies are working with today is better described as pseudo-AI—systems that appear intelligent from the outside (or are described as such) but don’t actually learn, adapt, or think in the way true AI does.
And that’s a problem. Because if organisations can’t tell the difference, they risk spending big money on technology that sounds transformative but delivers little more than automation dressed up in shiny branding.
What pseudo-AI really means
For instance, a chatbot that claims to ‘understand’ customers but really redirects them to a knowledge base or a human support agent is pseudo-AI. Similarly, a recommendation engine that pushes out suggestions based on static rules rather than live user data is another example of pseudo-AI in action.
It’s not that these tools are useless. They can save time, cut costs & improve efficiency. But they aren’t AI in the truest sense. They don’t get better with use. They don’t adapt to new contexts. They just follow instructions someone programmed in.
Why there’s so much pseudo-AI around
There are several reasons why pseudo-AI is prevalent right now. Part of it is the hype. Companies know that adding “AI” to their product description makes it more attractive to investors, clients & even employees.
Another reason is cost. Building genuine AI—machine learning systems that can process large volumes of data and adapt their outputs—requires significant investment in infrastructure, data quality & talent. Many businesses aren’t ready to make that leap.
And then there’s the knowledge gap. Not every leader can spot the difference between an algorithm that truly learns and one that looks clever in a demo. That lack of literacy makes it easy for vendors to blur the lines.
The risks of relying on pseudo-AI
The problem isn’t that pseudo-AI exists—it’s that companies often rely on it without realising what it is. Over time, that can lead to some costly outcomes.
First, there’s the risk of eroding trust. Employees expect AI to be smart, and customers expect it to solve problems quickly. When it doesn’t, confidence in the system drops fast.
Second, there’s wasted investment. Organisations may spend millions on platforms that can’t scale or deliver transformative value, locking them into tools that soon feel outdated.
And finally, there’s the regulatory angle. With AI regulation advancing in markets like the EU, labelling something as AI when it’s really just automation could invite scrutiny.
What real AI looks like
So how do you tell the difference? Real AI learns. It improves when it sees more data. It adapts to new circumstances without requiring a human to rewrite rules in the background.
Think of the recommendation systems used by global streaming platforms. They don’t just show you the same genres every time—you see new suggestions because the system adapts based on your behaviour. That’s genuine AI.
Pseudo AI, on the other hand, tends to need more manual input as complexity increases. It looks slick until you push it outside of its preset boundaries.
Moving from pseudo to real
The automation vs AI fight is real. The path forward isn’t simple, but it’s clear. Organisations that want to truly harness the power of AI need to move beyond pseudo-AI. This requires the right people with the right skills, solid data foundations, and responsible governance. It’s a challenging journey, but the potential benefits of real AI, such as new business models, enhanced customer experiences, and productivity gains at scale, make it a path worth pursuing.
- Invest in people with the right skills—data scientists, ML engineers & business experts who can connect the dots.
- Strengthen data foundations. Clean, reliable & accessible data is what makes AI work. Without it, even the smartest algorithm fails.
- AI Governance. With new regulations on the horizon, ethical and transparent frameworks are non-negotiable.
Partnerships can also help. Few organisations can do it all in-house, so collaborating with research institutions or AI specialists is often the smarter move to build an enterprise AI adoption.
A realistic perspective
Here’s the reality: pseudo-AI isn’t going away anytime soon. In fact, for many businesses, it’s a useful stepping stone. It helps them experiment, get employees comfortable with automation & build early wins. But leaders should be very clear-eyed about its limits.
True AI is where the real competitive advantage lies. It’s what enables new business models, new customer experiences & productivity gains at scale. Getting there requires patience, investment & above all, honesty about where you stand today.
Final thoughts
The phrase “AI-powered” has been stretched far beyond its real meaning. In most cases, what’s being sold is pseudo-AI—good for efficiency, yes, but not the game-changer it’s often claimed to be.
The challenge for leaders is to separate hype from reality. It’s crucial to know when you’re buying automation and when you’re investing in intelligence. Because in the long run, only genuine AI has the power to reshape industries. This knowledge empowers you to make informed decisions about your AI strategy.
And that’s the decision point every organisation faces: stay comfortable with pseudo-AI, or commit to the harder path that leads to the real thing.
The author is General Manager- Digital Media, Adani Group. Views are personal.
