From hype to reality: Separating 5 AI myths from facts

There was a time when artificial intelligence was seen as the distant future, a shiny, mysterious technology that would one day “take over the world.” But today, we’re not just living with AI; we’re living in it. Conversational AI evolved from smart assistants to healthcare bots, from hyper-personalised recommendations to real-time multilingual translation. AI has not just moved from fiction to functionality; it has become the backbone of digital transformation globally.

The greatest concern of people isn't whether the technology would succeed; it is whether it would replace humans altogether.

Myth 1: AI will replace humans entirely

Real-world applications convey a very different message. AI isn't meant to replace human intelligence; it is meant to augment it—“AI augmentation vs replacement”. In multiple public service and enterprise use cases I’ve worked on, conversational AI systems didn’t reduce jobs; they redefined them, i.e. “conversational AI in enterprise”.

For instance, when a state government rolled out an AI-driven virtual assistant to provide citizen answers automatically, it did not render call centre agents redundant. Rather, it automated repetitive, low-complexity questions, emancipating human agents to tackle more complex issues that needed empathy, judgement, or creativity.

Myth 2: AI knows everything

One of the biggest myths on the ground is the assumption that AI is an “all-knowing brain.” In reality, AI systems are only as good as the data they are trained on and the context they’re designed to understand.

In a healthcare pilot, an AI assistant trained on structured FAQs could answer medical questions only up to a point. When patients spoke local languages and phrased symptoms in non-standard terms, the system crashed, because it hadn't yet learned that pattern of language. This wasn't "AI failing humanity"; it was just a reminder that AI isn't magic, it's mathematics, algorithms, and training by human beings. Even agentic AI agents, which can autonomously reason and act across multiple tools or data systems, depend heavily on human context-setting and curated knowledge bases to function effectively

Unlike human intelligence, which can reason outside of present knowledge, AI is limited by its dataset. That's why human-in-the-loop models, where humans guide, curate, and situate AI answers within context, are so crucial in constructing reliable systems

Myth 3: AI is biased by nature

AI is not inherently biased; it acquires bias from humans and data. In the early days of our AI rollouts, we observed how models replicated patterns of exclusion that were inadvertently baked into training data. A digital assistant that was trained in only English, for example, would not be able to serve non-English speakers fairly.

By meticulous data curation, varied training inputs, and extensive testing, bias can be greatly diminished. Regulatory schemes and responsible AI framework principles are surfacing worldwide to drive this further. The 2024 OECD AI policy brief, for example, advocates increased transparency and accountability in sources of training data.

Myth 4: Generative AI is a silver bullet

From writing essays, code, and marketing materials, the possibilities seem endless. But in practice, GenAI works best when paired with domain-specific intelligence rather than being applied universally. During a financial services deployment, a generic large language model produced beautifully structured but factually inaccurate responses to policy queries.

It wasn't until domain-specific AI models were added on top of the generative foundation that the outputs were fluent and consistent.

The take-home: GenAI is mighty, but context-less and ungoverned, it will hallucinate. True impact is achieved by mixing GenAI with domain knowledge, human guidance, and robust architectures.

Myth 5: AI adoption is a one-time project

Many organisations approach AI like a one-time product launch, but it is far from it. AI systems are dynamic systems. They need ongoing learning, model retuning, security patches, and user feedback incorporation to remain up-to-date.

Projects fail not because the model is weak, but because the maintenance strategy was missing.

The real story: AI is an enabler, not a hero

The truth is that AI is neither a mythical energy nor a silver bullet. It's human-driven and shaped by responsibility. When used wisely, through human-focused design, domain-specific LLMs, secure GenAI, and composite AI architectures, it has the potential to boost productivity, enhance citizen services, and bring knowledge to everyone.

A McKinsey & Company estimate in 2024 puts the potential that AI can contribute to the global economy at $4.4 trillion every year.

Conclusion

The future of AI isn't machines becoming human; it's humans becoming more powerful with AI. Every deployment, every model, and every decision shapes public perception.

When AI is designed with humans, for humans, the hype gives way to tangible impact. The emergence of agentic AI, where AI agents collaborate, learn, and act alongside humans, marks the next phase of this evolution: from assistance to autonomy, without losing human oversight.

(Ankush Sabharwal is the CEO of CoRover, a human-centric conversational AI platform.)