Cloud companies are continuing to ramp up their investments in artificial intelligence (AI), even as some of their customers are beginning to question the value of their own AI-related spending. While this sense of disillusionment is understandable, it may be short-lived. Amazon CEO Andy Jassy suggests that as AI evolves, it will drive enterprises to transition more of their workloads to the cloud. He believes that “the ability to use AI more effectively” will be one of the main factors encouraging businesses to embrace cloud solutions, marking a key shift in how applications and infrastructure are handled in the coming years.
The urgency around AI is evident, with cloud giants and companies like Meta collectively spending over $100 billion this year on AI infrastructure, and signaling even more capital investments. Sundar Pichai, CEO of Alphabet/Google, highlights the risk of underinvesting during such a transformative period, emphasizing that “the risk of underinvesting is dramatically higher than overinvesting.” While it’s encouraging that the clouds are investing heavily in the infrastructure and services needed to support AI, the real challenge lies in providing practical guidance to customers on how to leverage AI effectively. The hype surrounding AI’s potential is often overwhelming, but there is still a significant gap when it comes to tangible, actionable insights for enterprises.
To bridge this gap, enterprises need clear, practical approaches to developing their AI capabilities. Dario Maisto, a senior analyst at Forrester, points out that “there is still an issue of translating this technology into real, tangible economic benefit.” In his experience, the focus should be on working closely with developers to turn AI’s promises into achievable results. This approach contrasts with the common practice of grandiose statements on corporate earnings calls, where executives hype the transformative potential of AI without delving into how it will impact day-to-day operations. Instead, enterprises should focus on practical applications of AI that align with their specific needs and capabilities.
One of the best ways for businesses to begin their AI journey is through smaller-scale investments in technologies like retrieval-augmented generation (RAG). These types of projects are ideal for building up AI expertise gradually, especially given the relative newness of AI in many sectors. However, companies need to remember that successful AI deployment requires not just the right technology, but also skilled employees. AI specialists are in high demand, and while many job seekers may position themselves as AI experts, the reality is often different. Building an AI workforce takes time, and enterprises should not be discouraged if they don’t see immediate returns. As a Deloitte study reveals, companies new to AI see only minimal returns—around 0.2%—on their initial investments. Nonetheless, starting now, even with modest efforts, is essential to ensure long-term success in AI adoption.