Has the AI hype cycle come back down to earth?

Artificial intelligence (AI) has been one of the most hyped technologies in recent years, promising to revolutionise all industries from healthcare to finance. However, despite its enormous potential, many are struggling to get underway with implementation. In fact, research indicates that just 17% of organisations are expected to fully integrate AI by 2030, far lower than the early expectations that once fuelled the AI buzz.

While the journey towards widespread AI adoption may take time, the possibilities remain vast. To reach those transformative outcomes, businesses must first focus on laying the right foundations. The key to unlocking AI’s true potential lies not in expecting overnight success, but in making the necessary investments in infrastructure, talent, and strategy. With a solid foundation, the most impactful AI innovations are well within reach.

Modernising infrastructure to unlock AI’s potential

Many organisations face barriers such as outdated legacy systems, fragmented data silos, and a lack of the necessary infrastructure to support advanced AI applications. Moreover, regulatory concerns, ethical questions about the responsible use of AI, and technical limitations have slowed adoption. These issues highlight the need for more practical approaches to AI implementation, moving beyond the initial excitement toward a deeper understanding of the steps required for success.

Investing in specialised data architecture, such as a data intelligence platform, revolutionises data management by leveraging AI to automatically assess data usage across the enterprise. These platforms allow users to input queries in natural language and receive highly relevant responses. This approach not only enhances efficiency but also empowers employees at all levels.

The importance of AI-ready talent

While it's essential that everyone across the organisation has access to data and AI tools, it's equally important to build AI-capable teams who can guide the development and deployment of these technologies. While advanced AI technologies can offer transformative opportunities, they cannot achieve this without the right people in place to guide their development, implementation, and integration into business processes. Technology alone isn’t enough to drive meaningful change. With 40% of CDOs, CTOs and CIOs stating that the biggest difficulty they face with their data and AI platforms is the training and upskilling of staff, it is essential that businesses invest in their teams. Upskilling is particularly important, as many employees will need to learn new skills in data science, machine learning, and AI-driven automation to work

effectively with these technologies. However, AI upskilling should also happen across the entire organisation, as every role can benefit from using this technology.

In addition to upskilling, organisations should also create specialised roles that are dedicated to AI development and implementation. These roles might include data scientists and AI engineers, who together form the backbone of an AI-capable workforce. The technical expertise and knowledge these individuals can bring is critical for managing AI projects, from initial development through to deployment and long-term maintenance.

Moreover, AI systems require ongoing monitoring and refinement, meaning teams need to stay up-to-date on evolving technologies. Establishing cross-functional teams that combine AI specialists with domain experts helps ensure that AI is deployed in ways that meet both technical and business objectives.

Building a resilient AI strategy

Although this downward phase in the AI hype cycle may seem like a setback, it actually represents an important turning point. As companies work through these obstacles, they begin to develop more grounded strategies, focusing on realistic use cases that align with their current technological maturity. Rather than rushing to implement AI for the sake of innovation, organisations are now emphasising the importance of clear roadmaps to prevent over-implementation and avoid duplicating efforts. A well structured strategy starts with identifying specific areas where AI can deliver measurable value and ensuring that resources are focused there.

Organisations should also invest in robust data governance and quality. AI models are only as good as the data they are trained on, and poor quality data can lead to inaccurate predictions and costly errors. This also safeguards compliance with regulatory requirements, particularly for industries that rely heavily on data integrity.

By managing data and AI together in a controlled, strategic manner, companies can set a strong foundation for trustworthy, scalable AI deployment.

Preparing for AI in 2030

Laying a strong foundation is essential for organisations to harness the full potential of AI. Without this solid groundwork, managing the demands of data processing while maintaining the quality of governance can become increasingly challenging. Although the pace of AI adoption may not be as rapid as initially predicted, the organisations that focus on getting the foundations right will be best positioned to harness its power in the years ahead.

The organisations that commit to fundamentals will enable them to navigate AI implementation and transcend the initial hype surrounding this new technology.