However, this kind of arms race is a double-edged sword. Many organizations are prioritizing speed over methodological soundness when implementing AI in their processes, which inevitably leads to medium- and long-term issues if the proper foundations are not established to sustain that progress over time.
Approximately 88% of companies already use AI in at least one task within their organization, according to McKinsey estimates for 2025. However, the vast majority remains stuck in an experimentation phase that rarely translates into real financial impact at the corporate level.
In fact, more than half of CEOs (56%) surveyed by PwC this year admitted that implementing AI tools has not resulted in economic benefits or cost reductions for their companies.
This happens because organizations are automating processes with AI instead of redesigning them to leverage the true strengths of generative intelligence. Rushing to implement a chatbot or coding tool simply because “everyone else is doing it,” without a clear methodology or planning, leads to what management expert Steve Blank calls “innovation theater”: activities that appear innovative but, due to how they are implemented, generate no tangible value.
Data shows that a small group of companies (1 in 8) is actually increasing revenue and reducing costs through AI. They achieve this by focusing on three key methodological pillars:
- Workflow redesign: high-performing organizations are three times more likely to fundamentally redesign their workflows instead of simply applying AI to legacy processes to do the same tasks with digital rather than human labor.
- Data architecture and quality: the critical point remains data cleaning and standardization. Without high-quality information, AI models are ineffective and risky, as they lack the interpretive flexibility of an experienced human employee.
- Governance and trust: 66% of CEOs surveyed by PwC admitted facing shareholder distrust regarding their AI strategies in 2025. A solid AI integration methodology requires clear processes and human validation (“human-in-the-loop”) to ensure accuracy and ethical alignment within the organization.
The urgency to “be part of the revolution” is even greater now as we enter the era of agentic AI, with systems capable of reasoning, coordinating, and executing complex workflows autonomously on behalf of organizations.
This is not just another wave of automation; it is a structural shift in enterprise technology. Capturing the potential of agents requires modernizing IT architecture to enable interoperability and real-time data access. If a company has not been able to scale its generative AI initiatives, the complexity of agentic AI will simply multiply its technical debt and operational risks.
What must be clear for any organization considering the inevitable implementation of AI solutions is that achieving real usefulness requires strong discipline in process design and governance. This ensures that the transformation becomes a virtuous cycle of modernization and competitive acceleration. Organizations that move decisively—redesigning both their architecture and ways of working—will be the only ones capable of fully leveraging AI in the long term.
