An effective strategy for adopting Generative AI in the enterprise is based on two complementary components: responding immediately to concrete uses with the right tools, and acculturating teams to Generative AI to enable them to take hold of it tomorrow.
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Many companies want to take advantage ofGenerative AIbut the needs expressed by employees are often related to other solutions (automation, process optimization). An effective strategy is based on two complementary components: responding immediately to concrete uses with the right tools, and acculturating teams to Generative AI to enable them to make the most of it tomorrow. This dual approach, supported by iQo, promotes a realistic, useful and sustainable transformation.
Taking advantage of Generative AI in business, is as much about meeting concrete business needs as it is about opening up a new field of possibilities. A two-pronged approach, lucid and progressive.
When the promise of Generative AI meets reality in the field
There were about ten of them around the table. Managers, business experts, operational managers. The aim of the workshop: to identify use cases for integrating Generative AI into their activities.
The first exchanges were rife: "automate reporting", "make better use of our data", "respond faster to internal requests"...
The technological promise is there. But as the discussions progress, one thing becomes clear: behind the enthusiasm for AI, very concrete needs are emerging. Repetitive tasks. Perfectible processes. Scattered data.
And above all: very few of these cases actually require Generative AI.
This observation does not disqualify interest in technology. Rather, it repositioned it. It shows that the key challenge is not to impose an innovation, but to understand its exact place in the existing ecosystem. From there, we can build a two-pronged approach:
- On the one hand, respond immediately to business irritants with the right tools - whether AI or not.
- On the other hand, to create fertile ground for our teams to understand, test and appropriate the real uses of Generative AI. And from there, build a two-pronged strategy.
Responding to identified business uses: start with concrete solutions
Generative AI use cases rooted in everyday life
The requests expressed by the teams are clear. They concern recurring tasks, tedious handling and information flows that could be better orchestrated.
In many cases, it's not a matter of generating text or conversing with a conversational agent. It's about interfacing, automating and informing decision-making with well-structured data.
Don't force AI where it's not needed
It would be tempting, when faced with a dynamic of innovation, to systematically resort to Generative AI to meet these needs. This would be a mistake. The right answer is not always AI, but sometimes a no-code solution, well-configured automation, a rethought interface with tools already available (Power Automate, Make, n8n...).
At iQo, this technological lucidity is a principle:
"You don't put a hammer where you need pliers."
Acculturating to Generative AI: opening up a new field of uses
Generative AI is making headway, but it has yet to be fully embraced
52% of employees in France claim to be using Generative AI in 2024, compared with just 20% a year earlier (source: BCG, "The Future of Work Is Generative", 2024).
This dazzling progress conceals major disparities in usage, understanding and mastery. For many organizations, Generative AI remains a vague, poorly mapped and sometimes fantasized continent.
Acculturation doesn't happen through talk - it happens through practice.
That's why our approach doesn't start with slides, but with exchanges. Demonstrations. Trial and error, iterations.
- Understanding what an LLM is
- See what Copilot can - and cannot - do in a business context
- Learn how to write useful prompts
- Identify where automatic generation of text or summaries can really enhance a profession
A dual methodological rationale to support the adoption of Generative AI
At iQo, we have designed an approach structured around two complementary operational axes, designed to be activated in parallel and in synergy.
Focus 1:
Acculturation to Generative AI
and emergence of new uses
- Diagnosis of maturity and targeted awareness-raising: a positioning MCQ enables content to be adapted to mastery levels and expectations. You can't talk about LLM in the same way to a data expert as to an HR manager.
- Concrete ideation workshops based on business irritants: instead of projecting AI in an abstract way, teams start from their daily lives to identify areas of potential value.
- Qualification of use cases with high AI potential: not all use cases fall within the scope of Generative AI. We help you distinguish between what can really be augmented via LLM and other levers such as automation.
- Prototyping AI demonstrators: integrated with existing tools (such as Copilot, customized GPTs or on-board assistants), prototypes are designed to illustrate added value in a real-life context.
- Hands-on sessions: beyond the demo, the aim is to enable employees to use these use cases themselves, with clear support and operating procedures.
Axis 2:
Automate and make reliable
existing use cases
- Targeted review of expressed use cases: the aim is to start from reported needs (internal forms, manual tracking, manual reporting, etc.) and to functionally re-qualify them.
- Analysis of available automation levers: each case is analyzed from the point of view of flows, data, and tools already in place within the organization (e.g. M365, Power Platform, or in-house solutions).
- Choice and development of the most pragmatic solutions: the objective is not sophistication, but reliability and replicability. Simple, documented and interoperable solutions are preferred.
- Formalization of use cases and transfer to teams: each solution is accompanied by a scoping sheet, documentation, and a replication approach to enable its extension to other areas.
Coherence between the two is essential. It is in this linkage that the transformation takes place: acting now on what can be done, while at the same time while preparing the company to take advantage of the higher-value AI uses that will be of greater value tomorrow.
The Big Day: a key event to connect the two dynamics around Generative AI
Where the two axes converge, the Big Day is a half-day of exchange and showcasing of results. Prototyped use cases are presented, feedback from the teams is valorized, and the dynamic of multiplication is established. It's a time for feedback, federating, and projecting the next steps: governance, ambassadors, roadmap.
The link between these two axes is essential. It demonstrates that Generative AI is not a technological mirage, but a concrete lever, provided it is anchored in real uses and made intelligible to those who will bring it to life.
What this approach means for employees
- They see tangible results on their daily irritants. Use cases don't fall from the sky: they are identified by the teams themselves. As a result, the demonstrators or automations activated respond directly to real-life problems - time-consuming tasks, double data entry, manual extractions, etc. - and provide an immediate source of satisfaction. This generates immediate satisfaction and feeds a positive dynamic around the transformation.
- They gain skills in tools they will actually use. The proposed acculturation is not abstract. It is based on the tools in their work environment (e.g. Copilot in Microsoft 365) and takes them from initiation to progressive mastery: formulation of useful prompts, understanding how templates work, best practices in formulation and use.
- They understand the complementarities between technologies. By seeing that certain requests can be met with automation rather than generative AI - and vice versa - employees develop a finer vision of the solutions available. They become able to choose the right approach according to need, which reinforces their technological autonomy.
- They become players in the transformation rather than receivers of change. The ownership effect is decisive. By being involved in the ideation, testing and demonstration phases, employees are not subjected to an imposed innovation. They are the architects. This facilitates adoption, stimulates curiosity and encourages the spread of uses within teams.
Conditions for successful acculturation to Generative AI: points of vigilance
- Don't confuse AI, automation and classic digitization. Many of the requests we receive concern the simplification of processes or the orchestration of workflows, without the need for a generative model. It is essential to make a clear diagnosis for each use case, to avoid unnecessary investments or broken promises.
- Target demonstrable, useful and replicable use cases. Exemplarity is key. A good use case is not only relevant to one team: it can inspire other functions, demonstrate the feasibility of a tool or create a trigger. Well-chosen quick wins serve as the foundation for a broader dynamic.
- Anticipate technical and organizational constraints. Access to tools (licenses, rights, security), the availability of staff and the test environment are all conditions that need to be defined from the outset. A well-prepared approach avoids logistical bottlenecks and helps maintain the pace of adoption.
- Structure governance around use cases. Who proposes? Who arbitrates? Who develops? Who maintains? Formalizing a clear use-case management circuit - even a light one - helps to avoid dispersion, to capitalize on learning, and to make the effort sustainable.
- Valuing results to anchor momentum. A well-designed Big Day plays a structuring role. It's not just a time for feedback, but a founding act: demonstrators and feedback are shared, sponsors are mobilized, and teams are encouraged to continue exploring.
The Big Day: a key event to connect the two dynamics around Generative AI
Acculturating people to Generative AI is not about forcing them to use it. It's about giving people the opportunity to see, understand and try. It's about showing what the technology can do - but also where other tools can do it better.
Last but not least, it means helping our teams to become more autonomous, so that in the future they can choose the tool best suited to their needs.
At iQo, we believe in a transformation rooted in reality, driven by those who do. Generative AI won't change jobs for them. But if properly guided, it can breathe new life into them.
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