The AI summit in Paris has marked a decisive turning point for both businesses and for society.
The AI summit in Paris marked a decisive turning point, emphasizing massive investments, controlled risk management and the race for innovation to maximize AI profits, both for businesses and for the company. A strong signal which should encourage the leaders of the manufacturing sector to refine their investment strategy in terms of AI, despite the economic challenges, the instability of demand and geopolitical tensions.
The “EU AI Champions” initiative launched at the summit, benefits from the support of several companies and a financial commitment of 150 billion euros, and 50 billion euros from the EU for the development of AI in Europe. In parallel, France plans to invest 109 billion euros to strengthen its IA ecosystem, while the United Kingdom unveils a new IA growth plan, with 14 billion investment books and the creation of more than 13,000 jobs by technological companies, in addition to the 25 billion pounds already announced.
The data challenge for industry
The president of the European Commission, Ursula von der Leyen, said: “AI in Europe focuses on its adoption in complex applications, based on our industrial data and our unique know-how”. Indeed, use cases in the pharmacy, automotive or food sectors require more advanced AI solutions, such as Deep Learning and 3D digitization software, to be able to manage them.
However, these ambitions are based above all on access to high quality and high added, essential data in many industrial processes. “Industries will be able to collaborate and share their data. We therefore create a secure environment, because AI needs both competition and collaboration. “Added Ursula von der Leyen. In addition, a letter signed by founders and CEOs of companies pleads for the provision of public data with high value in a secure framework and respectful of confidentiality. These initiatives highlight a major challenge for the manufacturing industry: how to effectively use data within companies? According to a recent study, almost 20% of artificial vision experts in the automotive sector in Germany and the United Kingdom estimate that their AI systems could be more efficient. To maximize the potential of AI, data management is a central issue.
The data generated on the outskirts of operations (Edge Computing) can be transformed to create value. They are used to train and test AI models or provide information back to optimize manufacturing and quality control processes. Once integrated, the AI and integrated data facilitate the automation of processes thanks to intelligent cameras, sensors and robots guided by vision. This allows managers to redeploy their field teams on higher strategic value tasks.
However, factories often operate in silos, with little or sharing data, even between sites using similar processes. The diversity of experiences and availability of teams complicate more usable data, a challenge accentuated by the difficulty in recruiting talents with the skills and the expertise necessary.
The data must be stored, annotated and used in a consistent manner for the learning of models, while being supplemented by other data sets for the test phases. Keep this isolated data obstructs the optimization of AI performance and reduces its effectiveness.
Solutions accessible for a more efficient AI
The AI offers solutions already proven to optimize the performance of the industrial sector, whatever the case of use. Today there are software, cameras and specialized sensors, which allow you to optimize in particular: the inspection of electric batteries, the sorting of fresh food products, the conformity and the quality of the packaging, the reading of standard and characters or the detection of defects on automotive parts and finished products.
For AI to be more efficient, it is essential to measure its impact with specific indicators, especially in terms of return on investment and data quality. In addition to the transformation of data management, intelligent automation requires suitable implementation, team training and operational adjustments. Certain AI solutions adopt a low-code or no-code approach, ready to use, allowing faster return on investment.
The strategic asset of intermediate management
A recent study by McKinsey reveals that Millennials aged 35 to 44 are increasingly occupying managers and team leaders in their companies. They are on the front line, in contact with the teams in the field, while ensuring the link with management. These intermediate managers are distinguished by their experience and their enthusiasm from AI. Moreover, 62 % of employees in this age group say they have advanced expertise in the matter, making them key players in digital transformation.
In addition, another study shows that only 30 % of business leaders (CEO, presidents, executive executives, vice-presidents, etc.) increase the resources allocated to growth initiatives in all sectors. And only 29 % said they devote more than 30 % to it.
Intermediate management represents a pool of key talent that managers must exploit to meet strategic issues: how can IA in particular industrial vision based on Deep Learning accelerate transformation in a complex market, stimulate long-term growth, increase productivity, automate processes and improve quality? In times of uncertainty, where instinct pushes to slow down investment projects, it could be wise to rethink their role and mobilize them to accelerate the integration of AI and industrial vision.
The time has come to adopt a more daring approach and to bet on intermediate leaders to make AI a lever for growth and competitiveness.
