But what happens when the tool has no predefined use? When it is neither specialized, vertical, nor prescriptive? When it doesn’t solve a specific problem, but instead offers a wide range of possibilities to explore? This is the case for many tools that could be described as freeform tools or freeform technologies (sorry for the neologism, but I couldn’t find a better term). This is a diverse category that includes platforms such as Notion, Miro, and Coda, as well as collaborative and social tools and, more recently, AI tools such as ChatGPT, which impose no structure and leave it up to the user to decide what to do with them.
These tools are powerful and flexible, but they defy traditional use cases, and that’s the problem. Trying to adopt them using old methods risks missing out on their value or, worse, limiting them to overly narrow uses.
In short:
- The traditional approach to use cases is unsuitable for “freeform” technologies, which do not meet a specific need but offer a wide range of possible uses to be invented and structured by users.
- Freeform tools are flexible, with no imposed structure, and encourage emerging, often informal, personalized, and evolving uses.
- Adopting these technologies requires a change in attitude: encouraging exploration, creating communities for exchange, offering flexible frameworks, and documenting real-world uses.
- Scaling up poses risks: too much standardization can stifle innovation, but a lack of coordination can lead to disorder; the goal should be to achieve gradual consistency based on effective practices.
- The role of support functions is evolving toward facilitation, legitimization, and light supervision of uses: flexible but clear governance is essential to prevent abuses while promoting autonomy.
Freeform tools: tools for unstructured uses
Unlike traditional business software, these platforms do not offer predefined uses: they are malleable, modular, and require users to give them structure and meaning. This is characteristic of freeform technologies, which are neither rigid nor prescriptive, but simply environments that users can organize themselves.
Even traditional suites (Microsoft 365, Google Workspace) are moving in this direction, blurring the line between office automation, collaboration, and information management, and opening up to more creative and cross-functional uses.
The “use case” reflex: a legacy of “old” IT
Historically, software adoption has been structured around a use case: a specific need is identified, software capable of meeting that need is selected, it is deployed, training is provided, and ROI is measured. This approach is effective for standardized functional tools (ERP, CRM, payroll, etc.).
However, with freeform tools, there is no fixed functional scope: usage depends on individuals, teams, or contexts, and a free tool cannot be encapsulated in a single use case.
Use cases are unsuitable for freeform technologies
The use case approach is difficult to apply to such tools because their uses are virtually unlimited and sometimes even exceed the intentions of their designers.
The same tool can be used to organize projects, manage contacts, keep a journal, or generate content with AI. There is no single use case, but rather a box of Lego bricks that can be used to build whatever you want.
Then there is the fact that we are talking about individualized workflows.
Each person creates their own, according to their role or preferences. A salesperson, a lawyer, and a manager will never use the same tool in the same way. Worse still, uses differ even within the same team.
Finally, because these tools enable what are known as emerging uses.
These tools are discovered by using them and observing the uses that emerge. Often, unexpected uses emerge, far beyond the initial intentions.
A key issue for AI
I am revisiting this topic because it is coming back to haunt us with the advent of AI. On this subject, I recommend reading an excellent article by Frédéric Cavazza: [FR] The adoption of generative AI will not happen through politics or use cases.
His analysis is based on the observation that businesses are seeking to channel generative AI through top-down strategies (charters, usage frameworks, committees) or by identifying “priority” use cases, whereas these approaches, although essential at some point, are ineffective as a starting point.
Generative AI does not fit naturally into a “need-solution” logic. On the contrary, it is exploratory, malleable, and adaptable. It creates value through unforeseen, often informal, sometimes improvised uses that emerge from practice rather than planning.
Frédéric also emphasizes the technological culture gap between those who explore these tools on a daily basis, as individuals, and those who seek to regulate them without really understanding them.
In this regard, he advocates adoption in the field, based on actual uses, rather than through top-down mandates.
Applied to freeform tools, this framework is particularly relevant. These tools, like generative AI, do not need a use case to start creating value.
They need a space for appropriation, legitimization of trial and error, and support that follows usage but does not precede it.
What approach should be taken to freeform tools?
To successfully adopt freeform technologies, it would be better to:
- Explore: give time and space, encourage experimentation.
- Bring communities together: promote discussion, sharing, and best practices.
- Provide inspiring templates: reusable templates that can be modified.
- Document actual usage: rather than predefining how something should be used, observe what happens and promote it.
- Redefine management: move from software deployment to organizational design, including governance, spaces for exchange, and usage indicators.
Blind spots that should not be overlooked
However, it is important to be careful: we are talking about the start of a process, but at some point it will be important to structure things.
These technologies shine in their ability to quickly produce local, adapted, and often highly effective uses. But when it comes time to scale up, two risks emerge:
- wanting to standardize too early and killing the richness of emerging uses,
- or allowing a myriad of incompatible practices to coexist, generating organizational debt, silos, or confusion.
The challenge is not to standardize at all costs, but to allow organic consistency to emerge by consolidating uses that prove effective.
This raises the question of who is in charge of the process. Here, the role of managers, IT, and even HR is key.
These tools cannot be adopted without explicit sponsorship, legitimization of experimentation, community engagement, and sometimes light supervision.
The role of support functions is evolving: it is no longer a matter of “deploying” a tool, but of supporting the creation of new workspaces, in collaboration with users.
Finally, there is the question of governance. The greater the freedom, the greater the risk of abuse.
Duplicated databases, scattered sensitive data: digital disorder threatens if the organization does not define even minimal standards or a reference canvas.
Bottom line
It is not up to tools to adapt to our old methods, but rather up to us to review our approaches and support to allow value to emerge.
The malleable or flexible nature of tools is not the problem, but it does reveal a gap between promises of autonomy and organizations that are still too rigid in their ways of adopting, managing, and standardizing.
Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)
Related articles:
The limits of technology-driven transformation
Are you familiar with Amara’s law on the short- and long-term impact of technology?
Driving viral change is more than good communication
Generative AI: your organization is worth more than your tools
Where’s the digital in the digital transformation
The digital transformation is not the future of your business
