You have an expert with 30 years of experience. You have them making photocopies.

1. Introduction

My name is Dominique Sodon. I hold a degree in finance and I have more than 25 years of experience in IT and in building complex systems. That double background — finance and software engineering — has deeply shaped the way I approach organizational, strategic, and human questions.

For several years I have been working on the design of platforms that structurally narrow the gap between the strategy that gets decided and the execution that actually happens, by aligning data, decisions, and operational action. LENSX came out of that work: a platform for organizational steering, augmented by artificial intelligence.

Some of these reflections were nourished by conversations with Jean Calvin IBock, an HR director with solid HR experience, whose observations on the realities of the field allowed me to validate and illustrate intuitions I already held about the capacity of AI to objectify human questions.

Working with Freddy Tchala, an expert in strategy and management with long experience as an executive in complex organizations, was likewise essential in testing these models against the realities of the field and the constraints of strategic execution.

2. My experience of artificial intelligence

Artificial intelligence is massively misunderstood today.

For close to 99% of users, AI comes down to a chatbot: you ask a question, you get an answer that is more or less well phrased. That view is reductive and, frankly, largely out of date.

We have already entered a post-LLM era.

Language models are no longer an end in themselves, but one cognitive component among others inside far broader software architectures. The point is not to make AI “talk”, but to let it reason over structures, dependencies, indicators, trajectories.

Our approach rests on a proprietary modelling approach that lets AI reason about the organization as a whole — not as a succession of isolated queries, but as a coherent system of relationships.

I do not deny the usefulness of tools like Copilot or everyday conversational assistants; they are a real help for discrete tasks. But AI becomes genuinely transformative only when it is embedded at the heart of well-designed software platforms, connected to the organization’s real data and able to produce concrete effects on decision and action. That is a difference in kind, not in degree.

And this is exactly where the fundamental misunderstanding about artificial intelligence lies.

Today’s generative AI is, by construction, a vessel of experience: it was trained on decades of human knowledge, practical cases, and expert reasoning across every field. And yet the vast majority of users treat it as a simple repository of knowledge — an improved search engine.

To see how absurd that is, picture an executive with 30 years of international experience, someone who has run major transformations in varied contexts. Now picture handing them exactly the same remit as a 24-year-old graduate, simply because they share a job title.

That is precisely what we do with AI when we settle for asking it simple questions and waiting for formatted answers. We are wasting a capacity for reasoning and structuring that could transform entire organizations.

3. The impact in my own field: organization, strategy, and HR

Jean Calvin IBock’s diagnosis

It was above all through my conversations with Jean Calvin IBock that certain truths imposed themselves.

His reading of the HR landscape let me put words to a reality that is widely shared but rarely formulated this way: the HR function is still too often locked into an essentially administrative approach, sacrificing its real strategic potential.

That situation is not down to a lack of skill among HR professionals, but to a structural gap in the tools available to them.

In finance we have long had instruments for measuring precisely, comparing heterogeneous situations, modelling scenarios, anticipating trajectories. HR, by contrast, has long had to make do with fragmented indicators, qualitative assessments that are hard to compare, and a weak capacity to objectify at scale.

The result: critical decisions about people still rest too often on isolated perceptions, local intuitions, and scattered Excel files.

This is precisely where artificial intelligence changes the terms.

Embedded in coherent platforms, AI finally brings HR what it structurally lacked: a capacity to objectify at scale, coherently and usefully, without reducing human complexity to arbitrary simplifications.

That realization was decisive in the design of LENSX: giving HR the means to play its strategic role in full, with a level of precision and legibility comparable to what finance functions have had for decades.

4. A realization reinforced by field experience

The challenge of integrated modelling, with Freddy Tchala

Another major turning point in this thinking came from my work with Freddy Tchala, an expert in strategy and management with extensive experience as an executive in complex organizations.

Our exchanges brought to light a reality that is often underestimated: modelling strategy, organization, indicators, and human activity simultaneously is a challenge of extreme complexity.

That complexity does not come from a lack of data or of will. It comes from systemic fragmentation: the systems that handle these different aspects have historically been partitioned. ERPs promise integration but create rigid silos. The PowerPoints consultants leave behind after an engagement are not operational. Dashboards aggregate indicators without revealing the causal links. Each function works with its own tools, its own vocabulary, its own timescale.

It was a genuine super-challenge: how do you connect within one system what was never designed to be thought of together?

The answer we built rests on a proprietary architectural approach that connects what traditional systems keep separate: strategy, organization, performance, and human capital. Where the usual route is to laboriously integrate disparate tools — with the costs, the delays, and the loss of information that entails — our platform offers a unified, navigable view from the outset.

That approach makes possible something traditional tools cannot do: steering several major strategic initiatives at once — digital transformation, geographic expansion, reorganization, a sustainability programme — while seeing their crossed impact on every entity in the organization.

A leader can finally see, in one space, not ONE transformation project but the complete strategic portfolio: the dependencies between programmes, the trade-offs available, the real impact on the structure and the teams.

That success confirmed a deep intuition: the future of organizational steering rests neither on isolated dashboards nor on static strategic plans, but on living systems, where indicators, actions, structures, and responsibilities are explicitly connected and navigable.

5. Opportunities and fears

What executives see

When a senior leader watches LENSX operate on their own organization’s data, one reaction comes back every time: the immediate identification of considerable savings.

That is no accident. A chief executive or a CFO knows by heart what they pay for strategic clarity: the time it takes to produce an organizational diagnostic, the variable quality of the deliverables, the dependency created at each new phase of thinking.

When they see a platform generate in a few days what used to take months and substantial budgets, the arithmetic does itself.

The major opportunity of AI is not blind automation, but clarification and strategic autonomy. Clarification of causes, effects, responsibilities, and levers for action. Autonomy to iterate, adjust, and steer without depending on external resources at every step.

My fears

My main fear is not AI itself, but its misuse:

• The illusion of understanding that an eloquent chatbot produces
• The blind delegation of judgement to unstructured systems
• Fascination with the technology at the expense of meaning and impact
• The adoption of tools that produce brilliant answers and few useful decisions

Without serious modelling, without deep integration into well-conceived platforms, AI will remain a conversational gadget.

With rigorous design, it becomes instead a major strategic lever, able to durably transform the way organizations understand themselves, steer themselves, and evolve.

6. Conclusion

We are entering a phase in which those who understand how to exploit the full capacity of AI — not as a chatbot, but as an integrated system of reasoning — will take a decisive advantage over those who go on using it as a simple research assistant.

The real question is no longer whether AI can “understand” a question, but whether it can make visible what was invisible: the dependencies between strategy and execution, the gaps between ambition and organizational capacity, the human levers that can actually be pulled.

It is in that space — between human complexity, analytical rigour, and strategic execution — that the ambition of LENSX sits.

The question is no longer “should we use AI?”. It is “are you ready to trust it with something other than photocopies?”