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.

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.

Your AI Strategy Is Probably Already Obsolete!

Most companies are still asking:

How do we implement AI?

Wrong question!

The correct question is:

How should our organization work when AI keeps getting better?

AI is not another technology upgrade. It changes how work gets done, how decisions are made, how people develop expertise, how managers manage and potentially how the business creates value.

Yet many organizations are approaching AI as a collection of pilots, productivity tools and technology deployments.

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.

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.

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.

Deepseek was founded approx 2 years ago, has 200 employees, and has invested approx $5 million to develop their innovation. In contrast, OpenAI was founded 10 years ago, has around 4,500 employees, and has raised $6.6 billion in capital. Deepseek is a customer of OpenAI and could scrape their data.
Deepseek disclosed that they used a process called distillation using the Llama open source model to develop their model by asking hundreds of thousands of questions and analyzing the answers.

Many LinkedIn members follow the wind. For 2 years Generative AI would take the world was the motto.

The last few weeks or months the wind has changed and there are clouds in the horizon.

The question is what is the truth of the matter?

Until the 1970/80s the hashtag#research was done in the corporate labs (AT&T, Bell Labs, Dupont, IBM, hashtag#Philips (In the 80s, 6000 PhDs and Scientists in research), Siemens,..), government labs e.g. DARPA in the hashtag#USA and they created innovations like, hashtag#Semiconductors, Radar, hashtag#Nuclear Power, New materials, hashtag#Internet, hashtag#Software, LED, Telco & hashtag#Mobile revolution resulting in companies like Intel, Apple, Microsoft, Amazon, Alphabet, NVidia, ASML, Tencent, Alibaba, that are still driving the economic growth.

How do you Know a Company is Ready for AI?

What are the Steps to Become One?

As AI/Generative AI is the talk of businesses, universities, schools and daily life, many companies are asking:

A. Am I ready for AI and what do I need to have in place for success?

To answer this question please consider the following:

0. THE MOST IMPORTANT: Every company needs to FIRST identify Important Business Problems that AI can enable Solutions easier, cheaper, more efficiently or increase of revenues faster/better than manual or less automated processes. You do not apply AI for the sake of AI.

Large Language Models (LLM) where generative AI belongs are trying to model the human brain with benefits increasing productivity of analyzing or writing text or creating pictures and/or video. Goldman Sachs believes that it will eliminate 300 million workers.

There are many applications (see picture) and the question arises how does it work?

1. Everything starts from a Big Purpose: Generative AI can help you to find the right purpose for you and your company.

2: Compose the Right Team with complimentary competences, commitment and good chemistry among the team members.

3. Work to build a Business Model suitable for the Purpose

How to Build Great Startups the Coming 10 Years

The article is focusing on 2 very important stages of the Startup journey:

1. Customer Discovery

and

2. Customer Validation

It describes step by step all necessary planning and execution activities of a startup journey to ensure the startup belongs to the 5-10% of the startups with successful exits and impact. It also stresses what are the Key principles of success.

Do you have the Culture of Fast Scaling?

Successful leadership follows a number of principles that are the same and independent of where they are applied. Indeed, leadership is about people and leaders need to understand well key aspects of human skills and behaviors. It is people that either create or destroy value and it is essential to know how to lead to create value.

CHATGPT Benefits & Energy Consumption

As the whole technology world is talking about CHATGPT the attached file explains what it is and it's benefits. Companies need to join and integrate such platforms otherwise they will be left behind to put it mildly. What we need to solve with the current AI applications that are using deep learning technology, is that they consume too much energy. This needs to change otherwise we boycott the planet earth sustainability.

This is the 1st post and more will follow:

While we all need to support various pro environment actions like:

- Reuse more

- Recycle more

- Change to electric car

- Eat less or stop eating meat and switch to vegetable based diet

- Reduce food waste

- Support companies with Sustainability driven business models

the sad story is that the 2050 net zero target or max rise in temperature of 1.5 degrees C can never be achieved. Why ?