Maturity of Artificial Intelligence: An Organizational Guide for AI-Driven Transformation.

As the transformative power of generative artificial intelligence reaches new heights, this technology is significantly changing the way practically all organizations around the world operate, collaborate, and innovate. However, adoption and progress are not uniform, leading to the emergence of two distinct groups: those organizations that have slowed their implementation of artificial intelligence tools and those that are advancing toward leveraging the opportunities it offers. A recent study reveals that only 10% of organizations are scaling AI across one or more business functions, while 40% have yet to take any action in this regard.

The study also indicates that the advantages are clear, as organizations that have progressed in AI adoption see a 2.6 times increase in revenue growth and a 38% increase in EBIT growth over a three-year period, along with significant increases in market share and customer satisfaction. There are various ways in which AI can simplify and optimize daily processes, as well as support content creation and solving complex problems. To truly leverage these new capabilities, organizations need to rethink traditional ways of operating and adapt to new paradigms.

Through conversations with leading enterprise clients, briefing sessions with analysts, and our own independent research, we have identified a common set of characteristics that organizations exhibit as they mature in their understanding and adoption of AI. By understanding the indicators, cultivators, and inhibitors, organizations can accelerate their path toward realizing the benefits of AI. Below are the phases to enhance how organizations utilize AI.

Phase 1: Exploration

Known as the initial phase, where organizations begin to understand what AI is and how it can be applied in their context. This phase is characterized by an entrepreneurial and opportunistic approach. It is essential to establish a solid foundation that includes educating the team on the fundamentals of AI and machine learning, being strategic with existing IT infrastructure and data, and evaluating current policies to ensure that AI integration aligns with data security and ethical protocols.

Phase 2: Experimentation

At this stage, organizations start experimenting with AI technologies to empower their teams. It is advisable to conduct pilot projects and proof of concepts, promoting focused use of AI to address specific opportunities. Communication is essential at this phase to ensure that everyone is aligned with the project's central vision.

Phase 3: Innovation

The innovation phase is probably the most exciting, as the results of the tests are implemented. Organizations may consider establishing new AI roles, retraining team members, and enhancing existing infrastructure. A comprehensive training program is crucial to empower employees.

Phase 4: Realization

Finally, this phase focuses on fully integrating AI into decision-making processes and operations to unlock new opportunities, drive innovation, and maintain a strong competitive position. It is important that all employees are equipped with the necessary technological and leadership skills to leverage AI.

In summary, when adopting artificial intelligence at the enterprise level, it is essential to remember that it's not just about doing things faster and more efficiently, but about leading with empathy and understanding, creating work environments where both people and technology can thrive.