L'Generative Artificial Intelligence (AI) has undergone rapid development in recent years, profoundly transforming the technological landscape and practices within companies in all sectors.
EVOLUTION OF GENERATIVE AI, A RAPID RISE
- 2022 : Generative AI is becoming accessible to the general public, marking the beginning of its democratization.
- 2023 : Diversification of players and applications, with increasing adoption in various sectors.
- 2024 : Emergence of multimodality and interoperability of systems, with dedicated AI* agents interacting with specific industrial tools.
*AI Agent : An autonomous programme capable of perceiving its environment, analysing data and acting to achieve a given objective.It can interact with users (chatbots, voice assistants), act on tools (ERP, CRM, etc.), automate decisions (finance, logistics) or drive intelligent systems (autonomous vehicles, robots).
Today, generative AI is opening up to more complex and interdisciplinary applications, revolutionizing entire sectors.
DEPLOYMENT OF GENERATIVE AI IN BUSINESS: KEY STEPS
Integrating generative AI into large enterprises requires a structured approach, tailored to each organization's constraints and strategy. Key steps include:
- Deployment of the basic model(s): Implementation of generative AI models within the enterprise infrastructure.
- Adaptation of models and integration of documents: Customization of models according to specific business needs and integration of internal and external data.
- Application development: Creation of applications using generative AI to meet identified use cases.
STAKES AND CHALLENGES FOR LARGE COMPANIES
Despite its potential, generative AI has limitations that must be considered:
- Large-scale combinatorial optimization problems: For some complex tasks, generative AI acts primarily as an assistant, although its reasoning capabilities are continually improving.
- Risks in critical operations: The use of generative AI in sensitive operations remains risky due to possible errors or “hallucinations.”
- Need for traditional AI tools: Classical algorithms remain essential for optimizing complex problems, such as operations optimization.
- Data quality: The performance of generative AI depends heavily on data quality.
- Costs: The financial and environmental costs of Generative AI increase with the level of model performance and the complexity of integration. Models need to be adapted to control costs and "not crush a fly with a hammer."
EMERGING TRENDS AND PERSPECTIVES
Three major trends are emerging:
- Large Language Models (LLMs): Increasingly sophisticated models capable of breaking down problems and “reasoning”.
- Compound AI systems: Using multiple AI models, adapting and integrating data sources to create more robust and versatile solutions.
- AI Agents: Development of intelligent agents dedicated to specific use cases, capable of interacting with specific business tools.
This evolution leads to the emergence of vertical AI agents, designed for specific uses in each industry, marking a new era where software becomes a service in itself.
SPRINT'S EXPERTISEPROJECT
At SprintProject, we support large companies in the selection and integration of generative AI, emphasizing innovation and adaptation to the specific needs of each organization.
Innovation is a powerful lever for bottom-line optimization, thus choosing the right innovation partners can set you apart from the competition and turn these initiatives into sustainable competitive advantages
As part of our targeted monitoring, we identify start-ups specializing in generative AI on a global scale, assessing their technological and market maturity.
Our approach focuses on actionable, in-depth data and analysis, identifying relevant use cases and selecting the right innovative solutions.
To learn more about the global panel of these startups and discover how generative AI can transform your business, contact us.
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