SFM at the Age/Edge of AI

By Jean-François Thiriet

Success Factor Modeling™ (SFM™) and generative AI present fascinating parallels that

deserve exploration. These two distinct fields share fundamental principles that can

enrich our understanding of success and innovation.

In the Beginning Was the Data: The Modeled

SFM focuses on identifying and applying critical success factors of entrepreneurs and

high-performing teams and leaders. SFM models these factors by observing and

analyzing the data: behaviors, beliefs, and strategies of successful individuals and teams.

Similarly, generative AI relies on vast datasets to learn and generate new content. In both

cases, the goal is to extract patterns and structures from existing examples to create

something new and effective.

Then Came the Algorithm : The Strategy/The Model

SFM models are structured around deep structures which act as an "algorithm" for

understanding and replicating success. In generative AI, the algorithm corresponds to the

model architecture that is being created from data and is able to generate new content.

Both approaches aim to create a systematic framework for producing exceptional results.

The Learning and Application Process

SFM models go through a T.O.T.E. model (Test-Operate-Test-Exit). It involves a loop

between observation, practical applications and feedback. Entrepreneurs learn to develop

a specific entrepreneurial Mindset, as sequences that guide their actions until they achieve

the level of performance they want. Generative AI, on the other hand, goes through

phases of training, adjustment, and application, progressively learning to generate

increasingly sophisticated content.

The Importance of Ethics and Responsibility

SFM emphasizes success that is not only economic but also humane and ecological,

highlighting the importance of an ethical and responsible approach to entrepreneurship.

In the field of generative AI, there is also an increasing emphasis on ethics and

responsibility, with guiding principles to ensure fair, transparent, and beneficial use of

technology.

Conclusion

In conclusion, the parallel between SFM and generative AI highlights how these two

fields seek to model and reproduce success, whether in the entrepreneurial context or in

AI-generated content creation. Both approaches rely on the aim to generate new results,

while emphasizing the importance of ethical and responsible implementation. This

comparison opens up interesting perspectives for the cross-application of SFM principles

in the development of generative AI, and vice versa, thus offering new avenues for

innovation and success in various fields.