The current AI data center frenzy draws sharp comparisons to the fiberoptic mania of the early 2000s. Millions of miles of glass were buried under the promise of explosive internet growth, only to crash into a devastating debt and supply-demand glut. Today, hundreds of billions flow into GPU clusters and hyperscale facilities. Is history repeating itself, or are the fundamentals different?

#AI is not only about tools and agents.
Any enterprise needs to address the following without the list to be exhaustive:

- Data Readiness: quality and accessibility

- Availability of skillful AI hashtag#talents

- Cross functional collaboration

- Provision of training and development of AI champions

- The right innovation culture (Psychological safety, Experimentation, Leadership support)​

- AI strategy aligned with business goals

- System-wide workflows automation, not silo implementation

and last but not least

- Governance framework (Technical, Operational, Ethical and Compliance

Building an AI agent is a structured process with several key stages:

- Planning and design: Define goals, constraints, and desired outcomes.

- Platform setup and configuration: Select tools, set permissions, and connect data sources.

- Building and testing: Create the agent and verify its performance with sample workflows.

- Refinement and optimization: Adjust logic, improve instructions, and enhance reliability based on test results.

- Deployment: Launch the agent, monitor its behavior, and plan for rollbacks or staged rollouts as needed.

Are you a CEO?
What are the industry facts?

- Many companies think that they have implemented AI because they have an IT department that is using ChatGPT/DeepSeek/other AI tools or many employees are using AI tools or they are implementing 1 or 2 small scale pilots.

- Many company employees are using AI tools without the company knowledge or partial knowledge of the IT department and without any AI governance framework in place they share daily confidential company information with the AI ecosystem.

The 8 DOs are related to Talent, Culture, Data Readiness and Governance and follow the following formula:

AI Effectiveness =Talent X Culture X Data Readiness X Governance

The DOs are about how to initiate, plan, design and implement AI systems.

Clearly defining how AI will impact or transform your business, solving real business challenges with measurable outcomes with ROI and building a complete AI ready organization, starts with pilot projects and then implementing scalable AI solutions across the organization.

Here we will address only culture and data quality as these 2 factors are responsible for the majority of AI implementation failures. As above 70% of AI transformations fail the attached AI pitfalls and challenges must be taken into account in the necessary for success prior change management process.

How do you exploit global opportunities and manage challenges in times of uncertainty?

In 2024, the Nobel Prize in Economics was awarded to Daron Acemoglu, Simon Johnson, and James A. Robinson. In a nutshell, their findings show that countries which promote broad participation, secure rights and the rule of law, are economically more successful longer-term than those who concentrate power and wealth among an elite, hindering development.

Although each of us learns through different ways (Reading, Watching, Listening,..) learning by doing is one of the most effective ways. In this article I describe the leadership and other valuable lessons from a real life complex turnaround and M&A project in the technology industry. It is very relevant for the 21st century business world where many start-ups and established companies collaborate to innovate. For obvious reasons no names or locations will be mentioned.

The post discusses the transformative shift from Software-as-a-Service (SaaS) to Agent-as-a-Service (AaaS), highlighting the impact of AI agents on the SaaS industry. It begins by noting the historical evolution of SaaS, which started as a time-sharing model on mainframes in the late 1960s and became the standard for software use at the beginning of the new millennium. However, the landscape is now changing with the advent of AI agents, which are reshaping the industry.