Training employees in AI is not just about responding to a current trend, it's about supporting the transformation of our businesses. All businesses are and will be impacted by AI. It is therefore essential to explain and demystify artificial intelligence before taking any steps.
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Training in AI to break out of an overly pessimistic (or optimistic) vision
Artificial intelligence (AI) has been raising expectations in companies for several years now. This trend has recently accelerated with the emergence of Generative AI (AI Gen), which reinforces the idea that AI is capable of providing solutions to all the problems they face, while enabling them to boost productivity and cut costs.
This optimistic view is tempered, however, by the many fears about the impact of AI on data security, compliance with various regulations, corporate reputation and, more generally, on the work and place of employees.
To avoid the erroneous perception of AI as a technological solution, to make its use more secure, and to allay fears about replacing people in their jobs, acculturation and training are more important than ever, as they help explain how AI works, and point up its benefits, constraints and risks.
Acculturation to artificial intelligence: a risk mitigation strategy
The first objective of acculturation is to clear up any confusion surrounding AI itself. Indeed, the term has often been overused, and it's not always easy to distinguish between the tools offered in companies and used daily by teams, and the image of an omnipotent machine on the way to replacing humans.
The case of "common" AI
AI, which has spread massively throughout organizations in recent years, is in fact a set of techniques most often based on the application of predefined rules (chatbots, RPAetc.), visual data analysis (dashboards) or predictive analysis via machine learning (Machine Learning) or deep learning.
Use cases based on these techniques are now numerous, and were often launched several years ago. The digital maturity of teams using these tools, although heterogeneous, is therefore satisfactory, and the numerous feedbacks available to companies today enable them to better manage the associated risks.
However, there are still plenty of mistakes to be made, and there are a few things you can do to reduce the risk of failure.
Defining an AI use case
This is perhaps the most important aspect of training, as many business players ask for AI applications without always understanding the implications of these technologies. A better understanding of concepts, types of AI and associated constraints is the first step in reducing the failure rate of deployments. It then enables us to define relevant use cases and better assess their impact.
How to integrate and monitor AI usage in the company
The use of AI can lead to the processing of large quantities of data, which are often poorly controlled and of poor quality, significantly degrading performance. In our workshops, we focus on understanding this interdependence, and show how to implement quality remediation and model performance monitoring measures as early as possible.
At iQo, AI acculturation for all our consultants
At iQo, we promote the hybridization of our consultants' skills of our consultants, placing AI at the heart of their skills. Our approach "AI Readiness approach is based on the following four objectives.
- Train iQers in the technical, regulatory, ethical and safety issues surrounding generative AI, so that they can use it responsibly.
- Enable iQers to write high-performance queries that quickly deliver the results they want.
- Create augmented consultants, assisted by AI in their daily tasks to improve their productivity.
- Create an internal community to share best practices (collaborative prompt library) and feedback.
The case of Generative AI
Generative AI: what exactly are we talking about?
Technically speaking, Generative AI is a Deep Learning tool which, provided it has access to huge amounts of data and computing power, can create text, images, videos, sound, etc. It can be specialized or multimodal, and this ability to generate realistic content raises a number of ethical, regulatory, security, legal, environmental and, more generally, societal issues. It can be specialized or multimodal, and this ability to generate realistic content raises a number of ethical, regulatory, security, legal, environmental and, more generally, societal issues. Indeed, it promises, for example, to profoundly change our relationship with work.
This is particularly true for Large Language Models (LLMs), of which ChatGPT is a part, which enable easy, near-human exchanges with AIs, and which can already support companies in tasks as diverse as document synthesis, internal and external documentation search and structuring, and Python code generation.
Massive (but disorganized) deployment of generative AI in many professions
We recently gave the case of the Contract Management professions and the impact of Generative AI (download our free white paper). Although the techniques on which AI Gen is based are older, the issue of its use in businesses emerged with the launch of OpenAI's ChatGPT in November 2022, and its massive spread with a celerity never seen before.
For IA Gen use cases, the issues are quite different. As feedback is still scarce, and the risks - notably data leakage - much greater, acculturation and training become an inevitable prerequisite before embarking on enterprise deployment, even on a small scale.
What are the priority training courses for Generative AI?
- Prompt creation training: getting the most out of an LLM requires not only an understanding of how it works and how to write prompts, but also a critical eye for the results. Without support in getting to grips with them, LLMs quickly fall by the wayside, as users fail to see the value they can bring them.
- LLM and risk management workshop : the implementation of an LLM entails a risk of data leakage that it is important to take into account. It will differ according to the deployment solution chosen, and will provide the keys to guiding your choices.
How can iQo help you learn about AI?
These training courses are one of the first steps in launching an AI initiative, enabling us to better target our actions and improve our understanding of the business.
Over and above their primary benefit, they also represent a launching pad for more structuring initiatives, such as the creation of a prompts library, or data governance and quality initiatives.
iQo has a training offer that can be adapted to different corporate contexts and will give you a better understanding of the issues, whether before launching an AI initiative, or to support the transformation of an AI usage.
Jérome PRIOUZEAU
iQo Partner
LinkedIn
Farid RAHOUI
Data Science Senior Manager iQo
his LinkedIn profile
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