The advent of generative Artificial Intelligence has been a catalyst for change in many sectors. In 2024, organisations will be forced to organise themselves to meet this new challenge, while certain technical transformations (digitalisation, data, etc.) are still underway and the profound changes in environmental and societal responsibilities need to be accelerated. With this in mind, we are publishing the White Paper “AI at the Heart of the Enterprise: Perspectives, Practices and Ethics”.
This document brings together the testimonies of 25 players from major groups and IA specialists from different sectors (Banking, Energy, Transport, Public Sector, Luxury), in order to identify the keys to success and the pitfalls to avoid, using examples and good practice.
AI at the heart of strategic and operational transformation
The last few years, and particularly the last few months, have seen a veritable explosion in the integration of AI systems, across all sectors. This is helping to optimise operational processes, increase efficiency, stimulate innovation and open up the possibility of exploring new sources of revenue. Indeed, the use of Artificial Intelligence makes it possible to achieve certain results more quickly and more efficiently, at every level of the value chain and for all the businesses involved.
The success of AI projects lies in putting in place a clear strategy, detailing coherent objectives, such as reducing processing time or costs, to ensure that the projects meet the initial requirements. Companies that succeed in launching AI pilot projects quickly have invested in data work beforehand, including governance, pre-processing and data quality, as well as technical architecture. This is an essential prerequisite, albeit a long and costly one.
At the same time, it is essential to demystify AI and understand that it is first and foremost a decision-making tool, and not a means of replacing decisions themselves. Indeed, organisations will need to realise that it is a genuine vector for transformation, redefining the rules of the game and offering a path towards a more competitive and dynamic future.
Finally, for companies that are convinced and already operational, the challenges of scaling up should not be underestimated. Data quality and the implementation of scalable, environmentally-friendly platforms are the cornerstones of successful AI integration. These challenges must be anticipated and tackled with the utmost care, as they form the foundation on which the effectiveness and sustainability of the AI solutions deployed rest.
Helping employees to use AI on a day-to-day basis
A consensus is emerging on the crucial need to acculturate and train users in generative AI, while establishing a clear framework for use, to avoid the risks of data leakage and legal implications. Employees must be at the heart of this transformation, as stakeholders and beneficiaries, to ensure a smooth and efficient transition to this new era of Artificial Intelligence. This means putting in place best practices (workshops, videos, tutorials, etc.) to develop their critical view of the results generated by AI, but also to improve decision-making. To achieve this, several organisations have started by drawing up a charter for the professional use of public generative AI, deploying “private” instances based on OpenAI (or other models) or acquiring AI licences integrated into office automation tools such as Copilot.
What’s more, in addition to training in the tools and analysing how they are used, companies are faced with another issue generated by AI: its inexorable impact on business lines. AI can be used to automate certain low value-added tasks, reducing the number of FTEs (full-time equivalents) needed to carry them out and helping employees to move on to higher value-added tasks.
That’s why Human Resources Departments, on the front line, need to fully immerse themselves in the AI revolution, to understand in depth the transformations on the horizon, particularly in terms of job protection and retraining.
Reconciling AI and ethics: a major challenge
Digital technology accounts for 3 to 4% of greenhouse gas emissions worldwide and 2.5% of the national carbon footprint. AI raises a number of concerns, not least environmental concerns, but also about the place of human beings and the new duties incumbent on them. The need to reconcile economic development, innovation and respect for ethical, moral and environmental standards is emerging as an imperative in corporate strategy. This is particularly true of mission-driven companies, which have made it a priority.
To this end, some companies have not waited for the IA Act to explore several avenues and best practices for incorporating ethical criteria into the development of AI. This involves setting up internal controls, such as multidisciplinary committees, to assess and monitor the ethical aspects of AI projects, and incorporating specific criteria into the evaluation grids for AI projects.
Moreover, ethics is not the only problem that needs to be considered. Other aspects such as data provenance and confidentiality, system sovereignty and digital responsibility are also important, and require safeguards to reinforce trust in AI systems. This means that certain areas of AI application need to be considered at the highest level of the business to ensure alignment with the organisation’s values. Specific best practices, such as using the Cloud to optimise resources and avoid purchasing hardware, using local providers for data sovereignty and adopting methodologies to reduce bias, are also recommended.
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