{"id":137376,"date":"2026-07-01T07:32:22","date_gmt":"2026-07-01T06:32:22","guid":{"rendered":"https:\/\/univga.org\/intelligence-artificielle-decision-management\/"},"modified":"2026-07-01T07:32:22","modified_gmt":"2026-07-01T06:32:22","slug":"artificial-intelligence-decision-management","status":"publish","type":"post","link":"https:\/\/univga.org\/en\/intelligence-artificielle-decision-management\/","title":{"rendered":"Artificial intelligence at work: who's really in charge?"},"content":{"rendered":"<div class=\"veille-intro\">\n<p>Imagine you run a small distribution company in Libreville. An artificial intelligence (AI) tool alerts you: in three weeks, a high-demand product is at risk of running out. Should you order more, increase the price, look for another supplier, or accept a temporary stockout? The answer isn't just about technology.<\/p>\n<p>Another scene: in a hospital, the procurement team receives an automated proposal to replace a soon-to-be unavailable medication. The idea seems useful. But who approves it: the pharmacy, the financial department, senior management, or compliance? That's the real issue for managers.<\/p>\n<\/div>\n<aside class=\"veille-oneminute\">\n<p class=\"veille-oneminute__label\">What to remember in 1 minute<\/p>\n<ul>\n<li>Gartner names AI capable of acting in stages as a major 2026 supply chain trend.<\/li>\n<li>These tools are no longer limited to responding; they plan, compare scenarios, and recommend actions.<\/li>\n<li>The sensitive point becomes governance: who decides, who validates, who is accountable in case of error?<\/li>\n<li>Leadership, strategy, team management, and commercial performance are directly concerned.<\/li>\n<\/ul>\n<\/aside>\n<h2>What is it, concretely?<\/h2>\n<p>An agent AI is an artificial intelligence system that follows an objective and chains multiple steps to help a team act. Instead of just providing an answer, it can analyze sales, inventory, supplier lead times, margins, and risks, then propose a plan. For a manager, it's comparable to a control assistant: it shows several options with their possible consequences. For example, ordering more reduces the risk of stockouts but ties up cash. The main challenge, therefore, is not to replace the decision-maker but to organize clear rules. With human validation, reliable data, and written logs, the tool becomes a support, not an autopilot.<\/p>\n<h2>Concrete case: what to do and what not to do<\/h2>\n<aside class=\"veille-case-study\">\n<p class=\"veille-case-study__intro\">Let's take a hypothetical case study: Libreville Medical Services, a small and medium-sized enterprise (SME) in Gabon that distributes medical equipment to private clinics and pharmacies. It sells gloves, syringes, blood pressure monitors, and small equipment. Its problem is concrete: stockouts are recurring, import lead times often change, and the sales department is losing sales.<\/p>\n<p class=\"veille-case-study__intro\">The temptation is strong to install an AI agent that monitors sales, inventory, and suppliers, then automatically proposes orders. The idea can improve responsiveness. But rules are still missing: who approves, what data is allowed, how are errors tracked, and who is responsible if a recommendation is expensive for the company?<\/p>\n<div class=\"veille-case-study__grid\">\n<div class=\"veille-case-study__col veille-case-study__col--do\">\n<p class=\"veille-case-study__label\">What can be done<\/p>\n<ul>\n<li>Limit the tool to recommendations: it prepares an order, but does not send it alone.<\/li>\n<li>Name a business manager according to the subject: purchasing, finance, sales, or general management.<\/li>\n<li>Set simple thresholds, for example, purchase validation at 500,000 FCFA and management for amounts over 5 million FCFA.<\/li>\n<li>Keep track of each recommendation: data used, proposed scenario, person who validated.<\/li>\n<li>Train teams to question the tool, especially when the numbers seem inconsistent.<\/li>\n<\/ul>\n<\/div>\n<div class=\"veille-case-study__col veille-case-study__col--dont\">\n<p class=\"veille-case-study__label\">What not to do<\/p>\n<ul>\n<li>Let AI automatically command without written procedure.<\/li>\n<li>Connect all company data without sorting sensitive information.<\/li>\n<li>Confusing speed with quality: a quick response may rely on incomplete data.<\/li>\n<li>Remove the manager's role, even though they remain responsible for the final decision.<\/li>\n<li>Measure only the time saved, without tracking avoided downtime, errors, and cash flow.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/aside>\n<h2>Questions to Ask Before Acting<\/h2>\n<ul>\n<li>Which decision do we want to improve: inventory, pricing, recruitment, scheduling, budget, or customer relations?<\/li>\n<li>Should AI recommend, prepare, or execute an action?<\/li>\n<li>Who validates the final decision if it has a financial, human, or legal impact?<\/li>\n<li>Are the data used reliable, recent, and authorized?<\/li>\n<li>Can we simply explain why the tool proposes this decision?<\/li>\n<li>What do we do in case of an error: correction, alert, suspension, or audit?<\/li>\n<li>Are teams able to challenge a recommendation instead of accepting it reflexively?<\/li>\n<\/ul>\n<h2>UNIVGA Viewpoint<\/h2>\n<aside class=\"veille-univga-voice\">\n<p>For Gabon and Francophone Africa, the issue goes beyond purchasing software. UNESCO describes an AI adoption that is still emerging in Gabon, with progress in the private and quasipublic sectors. This is precisely the time to train managers capable of governing these tools, before their use becomes widespread without a method. Reading a recommendation, asking for proof, arbitrating between cost, timeline, risk, and human impact: these skills are becoming central to management.<\/p>\n<p>Data sovereignty must also become a management reflex. A bank, university, SME, or hospital cannot transmit client, patient, student, or supplier data to any service. Hosting, transfers, confidentiality, and individual rights must be verified. In Gabon, the National Commission for the Protection of Personal Data (CNPDCP) and law no. 001-2011 provide a basis to be aware of.<\/p>\n<p>UNIVGA's position is simple: no AI without human responsibility. A good leader does not delegate blindly. They organize validation, document choices, train teams, and are willing to slow down a decision when risk demands it. This is how AI can support business performance without undermining trust.<\/p>\n<\/aside>\n<aside class=\"veille-univga-encart\">\n<p class=\"veille-univga-encart__label\">Training at UNIVGA<\/p>\n<p>UNIVGA offers professional certifications in many fields. Browse the complete catalog of our schools and training programs to find the path that suits your project.<\/p>\n<p><a href=\"https:\/\/univga.org\/en\/our-training-courses\/\">Discover our training programs<\/a><\/aside>\n<h2>Sources<\/h2>\n<ol>\n<li><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-06-30-gartner-identifies-top-supply-chain-technology-trends-for-2026\">Gartner Identifies Top Supply Chain Technology Trends for 2026<\/a><\/li>\n<li><a href=\"https:\/\/www.board.com\/news\/agentic-continuous-planning-supply-chain-merchandiser-ai-agents\">The Future of Planning Isn\u2019t Another Chatbot: Board Introduces Supply Chain and Merchandiser Agents for Agentic Continuous Planning<\/a><\/li>\n<li><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-04-23-gartner-survey-reveals-80-percent-of-ceos-say-artificial-intelligence-will-force-operational-capability-overhauls\">Gartner Survey Reveals That 80% of CEOs Say AI Will Force Overhauls of Operational Capabilities<\/a><\/li>\n<li><a href=\"https:\/\/newsroom.ibm.com\/2026-05-04-ibm-study-ceos-are-reshaping-c-suite-roles-for-the-ai-era?asPDF=1\">IBM Study: CEOs Are Reshaping C-suite Roles for the AI Era<\/a><\/li>\n<li><a href=\"https:\/\/www.unesco.org\/ethics-ai\/en\/gabon\">Gabon | Global AI Ethics and Governance Observatory<\/a><\/li>\n<li><a href=\"https:\/\/www.rapdp.org\/en\/node\/64\">Gabon \u2013 National Commission for the Protection of Personal Data (CNPDCP)<\/a><\/li>\n<\/ol>","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence agents are arriving in planning and purchasing. For managers, the challenge is to decide quickly without losing control.<\/p>","protected":false},"author":6945,"featured_media":137377,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1969],"tags":[1982,1980,2001,2000,2002],"offerexpiration":[],"class_list":["post-137376","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-veille-technologique","tag-gouvernance","tag-intelligence-artificielle","tag-leadership","tag-management","tag-supply-chain"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.9 (Yoast SEO v27.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Intelligence artificielle au travail : qui d\u00e9cide vraiment ?<\/title>\n<meta name=\"description\" content=\"Des agents d\u2019intelligence artificielle peuvent recommander achats, prix ou plannings. 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