5 points to remember
- The use of AI improves productivity as it drives innovation, increases work efficiency, and guarantees instant reaction to demand.
- Artificial intelligence (AI) is the lever of the third economic transformation at the service of business.
- The other side of AI’s power is its propensity to make mistakes… in a different way to humans.
- The added value of AI is based on humans.
- AI becomes a factor in transforming corporate governance and the skills of its employees.
Introduction
The wave of AI is sweeping across all domains. Making rapid progress, it abruptly opens the field of possibilities, offering remarkable transformative potential in the business world. At the business level, AI reveals new operational modes. It allows companies to rethink decisions and redefine customer interactions. It helps maintain competitiveness in a constantly changing environment.
The deployment of AI is already underway. Companies embrace its promises while preparing for associated challenges. We will explore the main opportunities for innovation, efficiency, and commercial success. Then, we will address the principal challenges to better overcome them.
Opportunities for AI systems, and more specifically generative AI systems
Driving innovation
Increased collaboration between humans and artificial intelligence is leading to revolutionary advances in various fields.
From the discovery of a new antibiotic to the possibility of detecting the authenticity of a wine with the aid of AI, not forgetting a tool capable of analyzing the behavior of farm animals or the discovery of a new recipe for Coca-Cola: there are plenty of headlines defending and ratifying the advances made possible by AI.
Automating processes
By automating processes, employees can free themselves from repetitive tasks. They can then focus on strategic, complex activities with higher added value.
For example, in online retail, chatbots handle customer queries immediately. They process product returns and manage orders based on customer preferences. In the restaurant sector, AI can automate responses to private messages on social networks, improve the management of customer reviews and write posts more quickly.
Advanced data analysis
AI is not just about analyzing data, it is also about harnessing immense volumes of it, in real time. In the age of data-driven decision-making, AI can predict stock market trends or generate personalized content for users.
Capable to project themselves into an explicit environment, businesses can adapt in real time. For instance, in the restaurant sector, it is possible to identify customer preferences based on their behavior: predicting affluence, avoiding waste, or even triggering promotional offers at the right moment.
Predicting failures and forecasting demand
Companies can use AI to predict failures and the maintenance that will follow. They can also anticipate product demand for fluctuating and unpredictable markets, like financial markets, where conditions change in milliseconds.
However, these opportunities come with challenges. Companies must address ethical, legal, and governance issues, data management, and adapt to complex regulations. Embracing AI on solid footing requires focusing on Trustworthy AI.
The challenges ahead
The adage « garbage in, garbage out » holds true in the field of AI. When models are designed or fed with incomplete, biased, or obsolete information, their predictive results are also impacted.
Ethical issues: the example of recruitment
AI technologies, which exploit machine learning and algorithms to analyze vast sets of data, are increasingly being integrated into various HR processes. From candidate selection and predicting employee attrition to talent management and succession planning, these technologies offer time-saving and predictive capabilities.
Diversity, fairness and inclusion are essential components of the recruitment tool: for example, the US Equal Employment Opportunity Commission condemned a company for hiring discrimination when it allegedly programmed its recruitment software to exclude older candidates.
Legal issues: copyright protection: provisions subject to change
As we mentioned in our article, Confrontation and Convergence: Copyright in the face of artificial intelligence, the scope of European regulations regarding intellectual property was one of the stumbling blocks during the negotiations in the various trialogues leading up to the provisional text adopted by Coreper on 13 February. Even today, the link between copyright and AI data training is still being debated.Despite the AI Act’s inclusion of copyright, the French Ministry of Economics and Finance wants to reopen the Copyright Directive. The Ministry argues that generative AI is revolutionizing copyright. AI developers don’t acquire protected works; they rent them to train their data. The committee of experts on generative AI, whose report is expected in March, is expected to comment on this issue.
In the United States, after the New York Times filed a lawsuit against Open AI, the major cloud and AI providers, notably Amazon, Google and Microsoft, say they will assume the legal risk of a copyright claim arising from the use of the LLMs they offer. But these protections come with important caveats.
Governance issues: leaders « expected at the turning point » of AI
For entrepreneurs in the sector, and in particular Sam Altman, CEO of OpenAI, there is no doubt about it: « Jobs are definitely going to go away, full stop. » So, is employment the adjustment variable for the use of AI? The OECD stated in 2023, 27% of jobs will be at risk as a result of automation. However, the outlook for its deployment seems more optimistic, since in a survey conducted in October 2023, only 17% of managers believe that AI will replace their employees.
Often initiated by employees as part of their routine, the use of generative AI needs supervision. It must be properly integrated into the corporate culture. This integration greatly impacts the adoption of the technology. Once accepted, management must balance employees’ human intuition with the machine’s capabilities. AI, which has its own instabilities, can be managed by training employees to master it.
Generative AI is considered as a top-three priority by 89% of business leaders. However, only a tiny proportion of them have been able to train their employees, and almost half have not yet established a framework for its use, even though it is essential for several reasons.
The risks of Generative AI
Data leakage
Like Apple and Spotify, several companies have regulated generative AI use due to data leakage risks. Employees sometimes send sensitive data, such as lines of code or meeting minutes, to AI systems. Such supervision is crucial, especially since the Fishbowl survey reveals that 68% of the 43% of employees using AI do so without informing their superiors.
Deepfakes
OpenAI’s new tool named Sora, capable of creating ultra-realistic videos from text, could democratize the use of deepfakes in the short term. Used for identity theft, it could prove to be a formidable tool, multiplying situations of deepfakes. This type of deception is no fiction, as demonstrated by the recent case of a Hong Kong bank employee who transferred 25 million dollars to criminals posing as members of his management team via a video call.
Hallucinations linked to the use of Generative AI
Selected as « Word of the Year 2023 » by the Cambridge dictionary, hallucination occurs when generative AI models provide unexpected results or results that are not based on reality. Let us take an example from the field of legal practice: researchers at Standford RegLab have shown that in response to specific legal queries, the hallucination rate is around 70%, and that the more complex the task, the higher the hallucination rate. Lawyers and their New York-based law firm have been sanctioned for having, in all good faith, presented to the judge their conclusions generated by ChatGPT, conclusions based on purely fictional cases.
When driven by context-specific, real-time data, a model can generate more relevant content, enabling the company to derive high added value.
Regulatory shock
The « 50 shades of regulation » that have emerged to date from the latest version of the European AI Act, combined with those of the United States Executive Order and the Chinese IMMGAI (Interim Measures for the Management of Generative Artificial Intelligence Services), cannot be understood without the help of a management tool. Transformed into concrete, explicit actions, the various provisions of current and future legislation become essential compliance tools for taming the regulatory framework.
Conclusion
Artificial intelligence, at the heart of business innovation, is undergoing a perpetually accelerating evolution that no one can slow down. This movement can only be seen as a virtuous circle within the framework of a trustworthy AI ecosystem that complies with the standards laid down and respects the ethical principles to which companies must adhere.