Roughly half of the articles published on the internet today are estimated to be AI-generated. At the same time, content about AI itself has grown exponentially. For many professionals—and especially for those of us studying AI in a business context—it can feel overwhelming, as if we are faced with an endless stream of AI-related insights, opinions, and essays.
Writing about my own AI journey sometimes feels like adding to that noise. Yet, after reading extensively, I find it difficult not to note down and reflect on the ideas that genuinely resonate with me.
Over the past two weeks, while traveling, I also had the opportunity to revisit suggested readings and capture some personal reflections. What stands out most is that across industries, we are witnessing a wave of experimentation and investment in AI. At the same time, we continue to hear about failing projects and limited business impact.
This gap is not surprising. Research consistently shows that implementing AI on top of legacy workflows is rarely sufficient to create value. McKinsey emphasizes that value creation increasingly comes from reshaping offerings, business models, and even market structures. Similarly, BCG argues that organizations must design themselves around AI, rather than simply adding AI into existing structures.
Agentic AI is a particularly powerful force in this transformation. Unlike traditional tools, these systems can plan, act, and learn autonomously, often behaving more like coworkers than tools. This fundamentally challenges how organizations have traditionally operated. Workflows, operating models, decision rights, and even organizational culture must be reimagined.
Such transformation is especially difficult in hierarchical and siloed organizations. AI-enabled ways of working thrive on cross-functional collaboration, fluid decision-making, and integrated data flows—conditions that many organizations still lack.
One of the most commonly cited challenges is data readiness. Despite strong ambition, only a small percentage of enterprises report that their data is fully ready for AI. Organizations continue to face obstacles such as siloed data, data integration challenges, lack of clear data strategy, and data quality or bias issues. As we repeatedly hear in practice and in class: AI is only as powerful as the data behind it.
Beyond data, integration with existing systems and resistance to change remain significant barriers. These challenges highlight that AI transformation is not only technological—it is deeply organizational.
Several questions from the Artificial Intelligence and Business Strategy research project have stayed with me:
- How can organizations design processes to effectively supervise an agent that also works autonomously?
- What should organizational structures look like when humans and agents work side by side?
- How do we manage artificial colleagues that we own like equipment but must supervise like people, and that depreciate like machinery but learn like humans?
These questions capture the essence of the transformation ahead. They point to a future where the boundaries between roles of employees and AI agents become increasingly fluid, and where traditional management paradigms no longer fully apply.
During EY AI Week, leaders emphasized the importance of keeping humanity at the center of future organizations. The focus was on empathy, imagination, and ethical judgment—qualities that remain uniquely human. The vision is not one of replacement, but augmentation: AI enhancing human capabilities with the right mindset, skills, and tools.
Ultimately, the challenge is not whether organizations will adopt AI, but how they will evolve alongside it. As AI continues to reshape the business landscape, competitive advantage will increasingly depend on organizational design rather than technological access. Companies that succeed will be those that invest in data and operating model transformation, and deliberately redefine the human–AI relationship—moving toward what EY describes as “collective intelligence.”
In this context, becoming truly AI-enabled is less about adopting new tools and more about redefining the structures, capabilities, and culture required to create sustained value.
Initially published on Linkedin.
