Riding the AI Wave: The How is What Matters

The hype surrounding Artificial Intelligence (AI) is bigexpansive—and rightly so. What we are witnessing with AI today is reminiscent of the “Internet moment” towards the end of the 20^th century. Curiosity is high and, the possibilities seem endless, and yet there is a sense of uncertainty. Discussions about AI often focus on technical issues that, while important, are not the main drivers of the coming revolution.

A critical choice for businesses

AI is an extremely disruptive technology with enormous potential, with the global AI market expected to grow from $124 billion in 2024 to USD 826 billion by 2030. Generative AI has the potential to create winners and losers in the business world. Companies that ignore AI may slide into irrelevance as we saw with the Internet and digitalization. Those who effectively leveraged the new technology gained significant advantages, while those who resisted faced obsolescence.

The key question for businesses seeking to leverage AI is: Where should the transformation lead, and how can it be achieved? The importance of AI’s development is apparent, but the ‘how’ of the implementation is not. This uncertainty is also reflected in the Wavestone Global Technology & Data Leaders Survey, where 86 percent of respondents agreed that they want to first see what works and what doesn’t before adopting generative AI.

$826 bn is the expected growth of the global AI market by 2030.

Change management in the spotlight to engage people

Adapting and deploying AI is less about the technology itself and more about smart and effective change management with a strong “people-centricity.” Technology evolves and is a smaller challenge compared to managing the transformation and integration of technology into the business. This also requires a change of corporate culture. AI needs to be integrated seamlessly across the organization, rather than being a proof point of a technology that isn’t relevant to daily operations.

Achieving this through smart change management requires the active involvement of employees. This includes clear communication and transparency, fostering employee engagement and participation, and providing ample opportunities for training and development. The successful adoption of AI hinges on employees understanding how to leverage AI to add value to their organizations. According to a McKinsey study, 72 percent of surveyed organizations now leverage AI in at least one business function. This highlights the critical role that effective change management plays in ensuring the successful integration of AI technologies.

Maintain focus: Quality over quantity

First, successful business cases show how AI can transform processes, produce tangible results, and offer competitive advantage. These cases are characterized by the fact that, from the outset, the big questions were asked: How can we harness AI and generative AI to reinvent our business? Equally crucial is another key point: the focus on a few, but the right cases. AI doesn’t need to be everywhere. It should be applied strategically, where it can truly add value, where it is genuinely needed, and where it can effectively drive impact. Through a selective approach to concentrate on areas with the highest potential for AI to enhance efficiency, reduce costs, or create new revenue streams, businesses can avoid the pitfalls of overextending resources and instead foster sustainable, impactful development and innovation.

New attention to a known problem: Keep data quality as the basis

The hype around generative AI has led to increased interest in AI solutions overall. Other forms of AI, such as machine learning, predictive maintenance and robotic process automation, are gaining more attention from businesses. This interest and the vast possibilities of AI highlight an old weakness and simultaneously motivate companies to finally address it: data quality. The success or failure of (generative) AI depends on data quality. Many companies have struggled for decades with incomplete, poorly analyzed, or unreliable data. According to the Wavestone Global Technology & Data Leaders Survey, 31 percent of respondents indicate that the reliability or quality of data is a barrier to projects involving generative AI. Now is the time to resolve these issues once and for all. Without quality data, there can be no Artificial Intelligence.

Use AI responsibly

Despite the hype and opportunities, responsibility must not be neglected. Responsible AI is the foundation for creating and using AI systems that are not only powerful and efficient but also trustworthy, fair, and transparent. AI must align with our societal values and legal norms. The EU’s AI Act is creating a pioneering legal framework to meet this significant challenge. However, development should not be stifled by excessive regulation. Good guidelines, governance, and standards for AI are essential.
This also applies to the use of AI in businesses. A Deloitte survey reveals that 62 percent of respondents prioritize balancing innovation with regulation in AI development and deployment. Additionally, 76 percent indicate that their organization offers ethical AI training for their employees.

Embrace frugality: AI and the Environment

Another critical challenge that rarely comes to the forefront of AI discussions is sustainability. The environmental impact of AI is a complex and multifaceted issue. AI is both an environmental villain and a beacon of hope. Today, the use of Generative AI has a considerable negative impact on a company’s carbon footprint, with direct environmental effects. A fact that many are reluctant to acknowledge. While 46 percent of respondents in the Wavestone Global Technology & Data Leaders Survey claim that the environmental impacts of generative AI are taken into account, our experience in the field strongly contradicts this.

Generative AI will not deliver its promises unless companies take care of its many aspects.

Chadi Hantouche, AI Partner at Wavestone

Yet, AI holds great potential to drive sustainable transformation in businesses and the economy. Companies can use AI to optimize their energy consumption. Combined with the ability to predict energy demand and weather patterns, resources and renewable energy can be deployed and used more efficiently. In addition, AI can help companies optimize their production and supply chains, leading to reduced waste and increased recycling of materials, thus advancing the circular economy. AI can also accelerate the development of innovative technologies, such as the creation of new, more sustainable materials. By analyzing large data sets, AI can potentially identify environmentally friendly materials that require fewer resources and are easier to recycle.

Surf the AI wave

The possibilities AI offers are vast and hard to grasp. AI will change our world. However, its true impact will unfold more slowly than many wish. Consider the Internet moment of 1998—it took years and decades to understand what the Internet truly meant. With AI, we have before us a technology of enormous, scarcely estimable potential. Now is the time to unlock that potential.