The Misconceptions of AI: Are Business Leaders Expecting Too Much?

The hype around Artificial Intelligence (AI) has businesses abuzz with the potential for overnight transformation. Beneath the surface of this enthusiasm, however, lies a pressing question: Are leaders harbouring unrealistic expectations about what AI can truly deliver? Or are the misconceptions about what this innovation can achieve undermining their ability to fully capitalise on its transformative potential?

To get a deeper insight into these pressing issues, we reached out to industry experts who shared with us the most common pitfalls and challenges companies face, as well as the strategies that can help bridge the gap between expectation and reality.

Mirage of Gold: The Trap of Over-Promising

John Young, Principal at Hendrick & Struggles, pinpoints a significant misconception, “With 80% of executives convinced that automation can be applied to any business decision, business leaders see AI as a solution that will yield immediate and transformative outcomes,” he said.

But he went on to explain the other side of this perspective, “However, they often overlook the foundational work required to unlock its true value, such as adequate infrastructure and ethical use policies.” This is the heart of the issue—executives expect miracles but fail to invest in the groundwork that AI demands.

Young also highlights that in the rush to “catch the AI wave”, many organisations dive into adoption without a coherent strategy. As a result, they struggle with integration efforts. He pulled out a Deloitte survey which echoes this sentiment, revealing that 68% of respondents admitted their organisations have moved fewer than 30% of generative AI projects into full-scale productions. This gap between ambition and reality is clear evidence that businesses are expecting too much, too soon.

Artificial Intelligence implementations, Young argues, should start with smaller, manageable use cases. “Ultimately, CIOs play a key role in identifying and prioritising the right projects that will reap maximum benefits from the use of AI,” he notes. In other words, businesses need to take a measured approach, identifying high-impact projects and scaling up only once they’ve proven successful.

Unrealistic Expectations: A Symptom of Inadequate Planning?

While the issue of over-promising is clear, Rashid Khan, CPO and Co-Founder of Yellow.ai, believes the problem isn’t just about unrealistic expectations. According to Khan, the underlying issue is “inadequate planning and groundwork”. Artificial Intelligence cannot be expected to deliver results without a well-defined strategy.

Khan advocates for a more strategic approach to AI adoption, where businesses take the time to carefully define their AI ambitions. “It’s not enough to simply want AI to solve your problems – you need a strategy, a plan of action,” he explains. CIOs must determine where AI will be deployed and, equally important, where it won’t be. Defining these boundaries is crucial for setting realistic goals and preventing disappointment.

Instead of trying to apply AI across the board, organisations need to focus on specific areas where AI can genuinely drive value. Khan provides a compelling example—in customer service automation, businesses should first assess their current infrastructure to pinpoint areas where AI could add the most value, whether it’s customer-facing operations, back-end processes, or workforce automation.

All in all, the key to success, according to Khan lies in deploying this game-changing innovation with a purpose, rather than hoping it can solve every issue.

Leadership: The Missing Piece of the Puzzle

One of the most overlooked elements in AI adoption is leadership. As Young highlights, “Many companies in Asia are adopting AI driven by fear of being left behind, often without a clear strategy or purpose.” But the problem doesn’t end there. Young also points to the shortage of AI leaders who possess both technical expertise and strategic business acumen.

Artificial Intelligence leaders are the lynchpins of successful projects. Without individuals who understand the technology and can align it with broader business goals, even the most promising AI initiatives can falter. Companies often opt for technically skilled individuals without the leadership experience necessary to make strategic decisions, leading to ineffective AI adoption.

Young suggests that organisations should evaluate their “level of data analytics maturity” to better position themselves for AI success. By moving from siloed data to an integrated approach where data analytics informs decision-making, businesses can lay the groundwork for AI to thrive.

Recalibrating Expectations: The Path to Success

So how can businesses recalibrate their expectations and approach AI projects in a more grounded manner? Khan outlines a three-pronged approach:

1. Value Proposition. Clearly define what the business aims to achieve with AI—whether it’s optimising internal processes, enhancing customer experiences, or creating new revenue streams.
2. Deployment Strategy. Decide whether to use pre-built models, adapt existing ones, or build custom solutions from scratch.
3. Risk Management. Establish a clear risk tolerance and address concerns such as data privacy and compliance.

By focusing on specific, measurable outcomes, businesses can avoid the disappointment of unmet expectations. It’s equally important to recognise that not every problem requires an AI solution, and leaders must be selective in where and how they implement it.

Baby Steps: Looking Beyond the AI Hype

The hype around AI is undeniable, with technology vendors often selling it as a panacea for all business challenges. Yet, as John Young and Rashid Khan have both pointed out, the key to success is managing expectations. AI is not a silver bullet. It’s a powerful tool that requires careful implementation, strategic leadership, and clear goals.

As Young advocates in this feature, start small, and build up areas where it is manageable, and scale the necessity to develop it further over time. Business leaders need to adopt this mindset if they want to see tangible results from their AI investments. Rather than expecting this technology to deliver immediate and transformative outcomes, companies should focus on setting realistic goals and building the right foundations.

In the end, AI’s success lies not in grand expectations, but in careful strategic planning. The question remains; are business leaders ready to do the work necessary, or are they simply expecting too much?