Overhype and Under-Delivery: When AI Promises More Than It Can Deliver

AI has been hailed as a transformative force, capable of driving efficiency, unlocking innovation, and reshaping industries. But while the promise of AI is enormous, the reality for many businesses is far less impressive. Too often, AI projects fail to deliver on their potential—not because the technology isn’t powerful, but because expectations are set too high, too soon, without a clear strategy for success.

In the rush to adopt AI, businesses can fall into the trap of chasing trends instead of solving real problems. They invest in pilots that don’t scale, chase use cases that don’t align with core business goals and end up with AI solutions that underwhelm stakeholders. The result is wasted time, sunk costs, and disappointment that erodes trust in the very idea of AI.

The Gap Between AI Hype and Reality

The hype around AI is hard to ignore. Vendors promise rapid ROI, analysts predict multi-billion-dollar markets, and headlines paint a picture of AI as the key to future success. But many businesses discover the reality is more complex.

AI isn’t magic. It can’t fix broken processes, create data where none exists, or instantly transform operations. Yet the pressure to “do something with AI” often leads organisations to jump into projects without fully understanding the challenges.

Teams may choose flashy use cases like chatbots or predictive models because they sound innovative, rather than because they address a specific business need. They may start pilots without a clear success metric or invest in tools that don’t integrate with existing systems. The result is often a proof-of-concept that never scales, a model that works in theory but not in practice, or a solution that creates more complexity than it resolves. 

Without alignment between AI initiatives and business strategy, even the most advanced AI tools won’t deliver meaningful value.

Setting Realistic AI Goals

To avoid the trap of overhype and under-delivery, businesses need to approach AI like any other strategic investment by starting with a clear problem, not a technology trend.

The first step is defining the business objective. What is the specific challenge AI can help solve? Is it reducing customer churn, improving operational efficiency, or enhancing fraud detection? The goal must be concrete, measurable, and aligned with broader business priorities.

Once the objective is clear, businesses can evaluate whether AI is the right tool for the job. Not every problem needs an AI solution. If the task involves clear rules, stable processes, and predictable outcomes, traditional software may be more effective. AI shines when the problem involves patterns too complex for manual coding, requires adaptation over time, or benefits from learning from data.

Success in AI isn’t just about building a model—it’s about deploying it into the real world and achieving measurable outcomes. That means setting realistic timelines, defining clear KPIs, and having a plan for integrating AI into existing workflows. It also means acknowledging the effort required such as cleaning and preparing data, monitoring model performance, and retraining as conditions change.

Measuring What Matters

One of the reasons AI projects under-deliver is a lack of clear metrics. Success is often defined too broadly like “improve customer experience” without tying it to specific outcomes.

Measuring AI success requires identifying the right metrics. For a customer service chatbot, is it about reducing response times, improving resolution rates, or increasing customer satisfaction scores? For a predictive maintenance model, is it about reducing unplanned downtime, lowering maintenance costs, or extending asset life?

AI metrics should be tied to business impact, not just technical performance. A model’s accuracy or precision score is important, but if it doesn’t translate into real business value, it’s not enough.

AI as a Long-Term Strategy, Not a Quick Win

Businesses that succeed with AI understand it’s a long-term journey, not a short-term fix. They invest in the foundations like data quality, governance, and cross-functional collaboration and set realistic expectations about what AI can and can’t do.

AI projects that deliver value start small, prove impact, and scale deliberately. They focus on the right problems, align with business goals, and embed AI into existing processes in a way that enhances, rather than disrupts, operations.

The key is clarity. AI isn’t a silver bullet, and it’s not the solution to every problem. It’s a tool and one that, when used wisely, can drive significant results.

Turning AI Hype into Real Business Outcomes

AI can create incredible value, but only when approached with realistic expectations and a clear strategy. Overhyped promises lead to underwhelming results, but a grounded, business-focused approach unlocks the true potential of AI.

The businesses that succeed are the ones that start with the problem, not the technology—set measurable goals, choose the right use cases, and commit to the long game.

Join us on 26^th June 2025 at Tattersall’s Club in Brisbane as we bring business and technology leaders together to unpack what it truly takes to implement AI effectively.