Processes, AI, mathematics: our ideas, without the jargon.

In September 2025, Adama Ouattara, then co-founder and CEO of Gencovery, published a white paper for business leaders: Digital Leadership Mastery (DLM). Its starting question was simple: how do you activate the value of your data? The method it describes grew out of many conversations with leaders, especially in the life sciences and healthcare, and twenty years of experience across research and industry.

Terminus 2π takes up these principles today and extends them with one conviction: data is born in processes. It is by digitizing and automating processes that data finally becomes an asset. This article presents the white paper in full, then what we do with it.

The starting point: ever more tools, ever more fragmented data

The white paper starts from an observation. For twenty years, information technology has been changing at an accelerating pace, especially since the COVID-19 pandemic and the rise of generative AI. The health crisis exposed how unable our systems are to handle fragmented, siloed and poorly interoperable data, in companies as in healthcare organizations.

A study published in 2018 already listed more than 20,000 digital tools and databases created between 1990 and 2017 in bioinformatics alone (Clément et al.). With generative AI, fragmentation deepens: everyone builds their own agent or assistant, often outside any structured framework, which makes validation and integration harder. For a leadership team, building a calm, coherent digital strategy becomes extremely difficult.

Active digitalization: making your data work

In the usual sense, digitalization means using digital technology to improve processes. But an improvement is not necessarily profitable: the return on investment of every digital project must be measured. The white paper therefore proposes a more demanding definition.

Active digitalization is the art of making your data work for your business: it must become a lever for growth, innovation and profitability.

Data is a liability when it is produced to deliver a service, then stored, often poorly, and left dormant: it costs money and, over time, is less and less likely to be reused. It becomes an asset when it is:

• stored intelligently;
• annotated, and structured when needed, so it is accessible and usable;
• continuously reused to deliver new services.

And all of this autonomously, without extra effort each time. That is exactly what process automation makes possible.

The four stages of value creation

What creates the most value in a company? The white paper answers with a four-stage cycle. Your people develop your knowledge and know-how. That know-how materializes as information, in other words data: protocols, methods, procedures, results, models. This information is the value you bring to your clients. Your clients, in return, enrich your processes and your data.

Hence the order of priority it proposes to leaders: invest in and protect their people first, then their knowledge (intellectual capital), their data (digital capital) and finally their clients. These clients are not only external: a department that builds a technology for other teams has "internal clients", a lever still too rarely used to activate data in a measurable way.

When data belongs to your clients, as patient data does, it can still be put to use with their consent, in line with regulations and a fair sharing of value. Trying to keep everything for yourself is usually a losing strategy.

Annotate before you structure

Do you need to structure your data to get value from it? The white paper is clear: no. Structuring is neither necessary nor sufficient. It assumes the use cases are defined in advance and often produces rigid systems that are costly to maintain.

The advice is to start by annotating and harmonizing: enriching data with labels and metadata, like tagged items in a warehouse, without locking it into a rigid schema. It is cheaper, faster, and the return on investment is much higher.

The Active Digitalization Quadrant

At the heart of the method, the quadrant lets a company place itself and see the path that turns its data into a performance lever. It describes four postures.

• User (U): you work with your data by hand and get little value from it. This is the starting point.
• Builder (B): you systematically try to build your own tools. Data stays passive, operations get heavier and efficiency drops, because this isn't your core business.
• Accelerator (A): you rely on external expert platforms and gain productivity, but you do not yet capture the full value of your data, often scattered across platforms.
• Master (M): you master your data and turn it into value without extra effort. This is the goal: continuous profitability and recurring revenue.

On the left of the quadrant (U and B), data is a burden; on the right (A and M), it is an asset. The fundamental difference: in U, B and A, the company remains a user of digital tools; in M, it has built a viable business model around its data. The same company can sit in several quadrants depending on its market segments.

The Builder trap

Quadrant B is the do-it-yourselfers' quadrant. Its tools are often solid, built by talented in-house experts. The problem lies elsewhere: a digital product is never finished. It has to be maintained, updated and adapted in an ecosystem that never stops changing. The effort goes into the tools, and the Builder never manages to automate what would make its data work.

The white paper also calls it the "Skeptics' Quadrant": companies there defend a digital empire built in-house and struggle to stop, without emotion, a project that is not working. Paradoxically, it is also the quadrant that often has the best resources to progress toward M, provided it decides to make its data work for it, and not the other way around.

Why digitalization projects fail

For the white paper, there is a single cause: the strategic vision does not take the real value of data into account. Leadership teams not trained in the economic value of data make poor digital decisions, and teams lack the tools to measure return on investment. Success therefore starts with raising awareness among leaders, before training operational teams.

One story illustrates it. In September 2024, three executives were looking for an AI training course. Instead of training, a thirty-minute conversation about the role of data in their company was enough: they repositioned themselves toward quadrants A and M, avoided the B trap, then won funding for a digitalization project, which they entrusted to the author.

The virtuous growth cycle, and the role of automation

The virtuous cycle is only possible in M: clients generate information in return that continuously and effortlessly improves the company's services. It requires seeing your data as a product or service in its own right, usable as is.

The key condition: value must be created without extra effort at each step. Repetitive processes must therefore be automated. And you can only optimize what you measure: you need tools that measure processes, automate them and improve them continuously. Well-annotated processes can then be consolidated, traced and optimized.

The three pillars of active digitalization

The white paper distinguishes digitization, moving from paper to digital to improve processes, from digitalization, which also makes digital assets produce value. The latter does not rest on the IT department alone: IT, data included, must sit at the heart of innovation and value creation, aligned with the company's vision. Three pillars carry it:

• vision, carried by executive leadership;
• innovation, carried by R&D leadership;
• revenue, carried by financial leadership.

Three stories

• Company 1, a provider of biological sample analyses, delivered its results as PDFs through an in-house tool. With support, it automated part of its analysis workflows and built a recurring-revenue model with its clients.
• Company 2 has been building its tools in-house for years, with dedicated teams. It manages its data well but struggles to turn it into value and to steer complex projects: a typical Builder.
• Organization 3, a non-governmental organization, holds ten years of data on tens of thousands of patients. It chose an ethical use of that data, respectful of consent, to better serve local populations.

What Terminus 2π does with it today

The white paper set the course: activate the value of data. Terminus 2π turns it into an operational path, starting where data is born: processes. That is what our signature means, "Digital process first. AI at the core."

• Digitize processes end to end, so information is entered once and traced by design: data stops being a liability.
• Embed AI agents at the core of processes to automate what is repetitive: value arrives without extra effort, the condition for the virtuous cycle.
• Measure every cycle (hours saved, errors avoided, costs cut) to steer return on investment, as the white paper recommends.
• Build the company's agentic digital twin step by step: the model on which agents simulate and propose, and the road to quadrant M.

For a company, relying on the Terminus 2π platform and team means following the path the white paper recommends: accelerate with an expert partner rather than building everything yourself, then turn your data into a real asset. The self-assessment, built on this quadrant, will soon place you in 3 minutes.