Global Blog

In the evolving landscape of enterprise AI, the rise of agentic systems marks a pivotal shift. AI agents are autonomous applications powered by large language models (LLMs) capable of reasoning, memory, and action. They are no longer passive responders to user input; they are active decision-makers influencing business processes in real time. But with autonomy comes risk, and with risk comes the need for structured supervision. In this new paradigm, supervision of tech systems is no longer optional, it is foundational.

As search progressively shifts toward conversational engines such as ChatGPT, Gemini, Le Chat, Claude or Perplexity, brands must fundamentally rethink how they exist online. Visibility becomes central: it is no longer about pleasing Google’s algorithms but about appearing within generated, synthesized, and contextualized answers.

Generative AI has compressed delivery times: what took eight hours now takes three. But where do the remaining five hours go?

Something subtle but seismic is happening in enterprise software. For years, SaaS thrived on UX—well-crafted interfaces designed to guide humans through increasingly complex workflows.

For decades, consulting thrived by delivering polished slide decks filled with frameworks and benchmarks.

Brands have long envied the troves of first‑party data sitting inside retailer systems. The average shopper enrols in roughly 13 loyalty programmes but stays active in barely half of them, so the “relationship” often stops at the cash register.

The Sales and Operations Planning (S&OP) process aims to align sales, operations, and finance departments around an integrated and feasible plan. More than a monthly agenda, S&OP is a continuous cycle that seeks to anticipate the future and prepare the organization to meet it efficiently. Today, Artificial Intelligence (AI) is already a reality in parts of this process — especially in demand planning. But its potential goes further: as we evolve in analytical maturity and in the integration between teams and data, AI can become a central gear for faster, more accurate decisions aligned with business reality.

As the future of advertising rapidly evolves, so too does the way we understand and reach consumers. Google is ushering in a new era of search metrics that deliver more granular, actionable insights for marketers. Two new metrics, User Searches and Ad Opportunities are at the forefront of this transformation, powered by advancements in AI and multimodal search capabilities.

The rapid ascent of Generative AI (GenAI), powered by sophisticated Large Language Models (LLMs), has captured global attention, demonstrating remarkable capabilities in content creation, summarization, and interaction. Businesses are actively exploring and integrating these tools to enhance productivity and unlock new avenues for communication. However, focusing solely on GenAI overlooks the next seismic shift in artificial intelligence: Agentic AI.

In today's relentlessly competitive business landscape, agility and informed decision-making are paramount. The ability to swiftly access and leverage an organization's collective intelligence can be the critical differentiator between thriving and merely surviving. Yet, beneath the surface of many enterprises lies a significant, often underestimated drain on productivity and innovation: the pervasive challenge of fragmented and inaccessible knowledge.

At the recent Artefact Data & AI Summit in Dubai, Vincent Luciani, Co-founder and Executive Chairman of Artefact, shared his insights on the evolving landscape of artificial intelligence. This article covers the main points from Vincent’s session, including the commoditization of AI, its impact on the workforce, the associated challenges and opportunities, and strategic recommendations for organizations.

Imagine a scenario where every operational data point of an industry is continuously captured, organized, scalable, and free from cyber threats. This is the concept of IT/OT (Information Technology/Operational Technology) convergence, an evolution where the synergy between data and technology optimizes efficiency, security, and innovation in manufacturing, enabling high-quality data and robust security that support reliable and strategic decisions.

The Artefact Research Center's mission is to foster an innovation ecosystem around AI and data by encouraging collaboration between university professors and representatives of international partner companies. The goal: cutting-edge research driven by data and the industrial applications of its partner companies.

Vincent Luciani, co-founder and Executive Chairman of Artefact, emphasizes that artificial intelligence (AI) must be concretely integrated into a company's collective processes, if it is to deliver real productivity gains. The entrepreneur will be taking part in the MKIA, an event organized by Maddyness and dedicated to AI, at the Salle Gaveau in Paris, on Tuesday April 29.

No strategy is effective without measurement! Choosing projects, evaluating the performance of AI agents or the hallucinations of the machine: detailed explanations of the governance you need to put in place to stand out from the crowd thanks to artificial intelligence.

In July 2024, Robin was promoted to CTO of Artefact France for his deep technology expertise and management skills. He now leads a large team of data scientists, software engineers and frontend developers, ensuring that his team is at the forefront of technology, with training and curiosity at the heart of his approach.

AI is rapidly evolving toward a future where users no longer think about models, modes, or tools. Instead, systems are becoming dynamically adaptive—automatically adjusting reasoning depth, selecting the right tools, and managing resources in real time based on task complexity.

As AI agents increasingly take over tasks like email drafting or resolving customer service issues, we face a pivotal challenge: how do these agents communicate effectively, not just with humans but with the software ecosystems they rely on?

Generative AI is profoundly reshaping the way we work, introducing tools that automate a wide range of tasks—from coding and data analysis to content creation. These tools don’t replace human expertise; they redefine it. Experienced professionals—whether engineers, analysts, or creatives—are becoming indispensable, not for execution, but for supervising, refining, and integrating the outputs produced by these technologies.

The journey from simple AI assistants to powerful, cross-system agents reflects a profound shift in how businesses leverage generative AI. In 2023, the rise of embedded GenAI promised seamless integration into daily workflows, enabling users to adopt advanced capabilities without disrupting habits. Yet, high costs per user and limited customization left many questioning the value proposition of these black-box solutions.