Artificial intelligence is no longer a promise of the future\u2014it is a present reality. Every week, new tools and platforms make headlines, and executive boards ask: \u201cAnd us\u2014when do we start?\u201d The pressure to adopt is real, but it hides a structural problem that is rarely acknowledged: most projects fail not for technological reasons, but due to a lack of strategic focus.<\/p>\n
It is worth recalling here the warning of economist Daron Acemoglu: \u201cautomation does not create value per se.\u201d Value depends on whether technology complements human capabilities or replaces tasks that do not generate value. Keeping this distinction in mind radically changes how projects are approached.<\/p>\n
At Infini, we propose considering six key factors for applying AI in business:<\/p>\n
Factor 1 \u2013 Purpose before technology.<\/strong> Factor 2 \u2013 Tacit knowledge is indispensable.<\/strong> Factor 3 \u2013 Analyze before automating.<\/strong> Factor 4 \u2013 The right technology for the problem, not the other way around.<\/strong><\/p>\n Whether it is RPA, a custom development, or a SaaS solution, the choice must stem from a deep understanding of the challenge. Many organizations implement LLMs when a simple decision tree would have solved the problem at lower cost and with greater control. Many have already purchased Copilot licenses without knowing why they need them; staff do not use them, and IT departments restrict them for privacy and security reasons.<\/p>\n Factor 5 \u2013 Data is not the new oil\u2014it is asbestos.<\/strong> Factor 6 \u2013 Bridges between business and IT.<\/strong> Seventy percent of digital transformation projects fail due to a lack of effective communication between these areas. Mixed teams, shared goals, and a common language are not optional best practices\u2014they ultimately determine success.<\/p>\n The six factors described are not a checklist\u2014they are a way of thinking. And like any real change, they begin with an honest question: are we ready to do this properly, or are we simply in a hurry to say we\u2019ve done it?<\/p>\n AI is not a solution in search of problems. It is a powerful resource that, when properly directed, can transform how companies operate. Misguided, it consumes budgets, generates frustration, and leaves behind unused licenses and abandoned projects.<\/p>\n From our perspective, the organizations that will succeed will not necessarily be those that adopt the most technology, but those that ask the right questions before adopting it. In an environment where everyone has access to the same tools, the difference lies in the quality of prior thinking: understanding the problem, listening to people, cleaning up data, and building internal bridges.<\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence is no longer a promise of the future\u2014it is a present reality. Every week, new tools and platforms make headlines, and executive boards […]<\/p>\n","protected":false},"author":17,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[74],"tags":[],"class_list":["post-10980","post","type-post","status-publish","format-standard","hentry","category-blog-en"],"acf":[],"yoast_head":"\n
\nStarting the conversation with the solution instead of the problem is the most common mistake. \u201cWe need to do something with AI\u201d is not a strategic objective. It is essential to define the desired business outcomes and how success will be measured. Delegating this decision to IT dooms the project from the outset: it is a business decision, not a technological one.<\/p>\n
\nWork teams know better than anyone where real processes fail. Without their involvement, even the most sophisticated solution will not be adopted. For example, a clinic that automated appointment confirmations via email \u201cbecause everyone does it\u201d later discovered that 40% of its patients preferred phone contact. Absenteeism increased instead of decreasing.<\/p>\n
\nAutomating an inefficient operation only magnifies inefficiency. Often, the best solution is to redesign or even eliminate the process. A logistics company that wanted to automate claims management discovered, after mapping the real workflow, that 60% of incidents stemmed from a preventable labeling error. The solution was not AI\u2014it was a procedural change that cost zero euros.<\/p>\n
\nWithout reliable data, no AI model will work. But poorly managed data creates more liabilities than benefits: systemic bias, incorrect decisions, and legal risks. Data quality and governance are not secondary technical issues; they are prerequisites.<\/p>\n