{"id":1065839,"date":"2025-10-22T14:20:15","date_gmt":"2025-10-22T12:20:15","guid":{"rendered":"https:\/\/babelgroup.com\/?p=1065839"},"modified":"2025-10-22T14:20:19","modified_gmt":"2025-10-22T12:20:19","slug":"how-to-achieve-real-roi-with-generative-ai-6-key-learnings","status":"publish","type":"post","link":"https:\/\/babelgroup.com\/en\/how-to-achieve-real-roi-with-generative-ai-6-key-learnings\/","title":{"rendered":"How to Achieve Real ROI with Generative AI: 6 Key Learnings"},"content":{"rendered":"
\n

A recent MIT study has put numbers to what many organizations had already sensed: only 5% of investments in Generative Artificial Intelligence deliver a real and measurable return. Billions of euros have been allocated in recent years to deploy AI solutions, yet most projects have stalled at the pilot phase, never scaling or integrating into core business processes.<\/p>\n\n\n\n

In many cases, AI adoption has been driven by the urgency to innovate rather than by a structured strategy. And when AI is implemented without a clear methodological framework, the gap between promise and impact inevitably widens. It\u2019s not enough to incorporate the technology \u2014 it must be designed into operations in a way that creates sustainable value.<\/p>\n\n\n\n

The difference between experimenting with AI and achieving tangible results comes down to one essential principle: discipline in execution<\/strong>.<\/p>\n\n\n\n

From Internal Adoption to a Proven Transformation Model<\/h4>\n\n\n\n

At Babel<\/strong>, we decided to start with ourselves. Over the past year, we\u2019ve developed and implemented our own methodology for Generative AI adoption, applying it transversally across corporate areas.<\/p>\n\n\n\n

Our goal was not just to incorporate technology but to rethink how we work, make decisions, and generate knowledge.<\/p>\n\n\n\n

Through this process, we achieved four key outcomes:<\/p>\n\n\n\n

    \n
  • Integrating AI into real business operations, not just innovation labs.<\/li>\n\n\n\n
  • Ensuring data quality, consistency, and traceability so every model trains on reliable, contextualized information.<\/li>\n\n\n\n
  • Measuring tangible impact in efficiency, time savings, and reduced external service costs.<\/li>\n\n\n\n
  • Scaling adoption in a controlled manner, ensuring strategic alignment and governance.<\/li>\n<\/ul>\n\n\n\n

    The lessons from this internal process allowed us to refine a framework that now serves as a tested model for other organizations seeking sustainable results with Generative AI.<\/p>\n\n\n\n

    Our Methodology: Six Tips for Creating Real Impact<\/h4>\n\n\n\n

    From that hands-on experience, six core principles emerged \u2014 not just as advice, but as the foundation of a methodology built on value, sustainability, and culture.<\/p>\n\n\n\n

      \n
    1. Computational Thinking: Structure Change Before Accelerating It<\/strong> One of the biggest mistakes in AI adoption is starting with the tool. The key is to think like engineers and decide like strategists. Before deploying models, processes must be decomposed into logical components: which tasks are repetitive, which decisions are data-driven, and where AI can provide true leverage. This computational thinking turns organizational complexity into operational architecture. AI then doesn\u2019t add noise \u2014 it rewrites workflows with precision and purpose.<\/li>\n\n\n\n
    2. Value Strategy: Every Use Case Must Move the Business<\/strong> Generative AI is not an end in itself; it\u2019s a means to create measurable strategic value. Every use case must answer one question: Does it improve a metric the business already considers critical?<\/em> Only when the answer is yes should implementation proceed. This approach avoids the \u201clab effect\u201d and connects AI to real outcomes: faster bids, operational savings, improved decision quality, and greater knowledge traceability. The learning: prioritize by impact, not novelty<\/strong>.<\/li>\n\n\n\n
    3. Progressive Credibility: From Prototype to Operating Model<\/strong> Implementing AI without visible results breeds skepticism. That\u2019s why we must work within a framework of progressive credibility<\/em>: start with quick, measurable, and communicable wins that serve as proof of concept for scaling. Each validated success is documented, measured, and communicated internally \u2014 building legitimacy step by step. The result: a culture that doesn\u2019t fear AI \u2014 it demands it.<\/li>\n\n\n\n
    4. Contextual Intelligence: Train AI in the Language of the Business<\/strong> Generic models may impress, but they don\u2019t transform. The real leap occurs when AI understands internal context \u2014 from terminology to operational logic. To achieve that, data must be structured to be reliable, traceable, and representative of real business dynamics. An AI that speaks the organization\u2019s language fosters trust, precision, and adoption.<\/li>\n\n\n\n
    5. Measurable Impact: Identify the True ROI of AI<\/strong> Measuring AI adoption by the number of prompts or users is meaningless. What matters is what changed in the business. Clear metrics must be defined from the start: hours saved, decisions automated, external costs reduced, execution speed increased. This measurement framework makes it possible to demonstrate return in under six months. The takeaway: AI maturity is measured by evidence, not enthusiasm<\/strong>.<\/li>\n\n\n\n
    6. Augmented Leadership: AI Doesn\u2019t Replace \u2014 It Amplifies<\/strong> Technology only scales when culture supports it. Teams must be trained not only to use AI but to think<\/em> with it. This means developing continuous learning environments and internal certification programs that turn curiosity into competence. That is the essence of augmented leadership \u2014 leaders who combine human intuition with AI-assisted thinking. The final lesson: it\u2019s not about automating talent, but multiplying it<\/strong>.<\/li>\n<\/ol>\n\n\n\n

      From Hype to Impact: Method Matters<\/h4>\n\n\n\n

      The MIT statistic should not be seen as a warning sign, but as a call for discipline. Most companies don\u2019t fail for lack of technology, but for lack of method.<\/p>\n\n\n\n

      The difference between the 95% that struggle and the 5% that succeed lies in the ability to execute rigorously, measure precisely, and scale intelligently.<\/p>\n\n\n\n

      At Babel, we\u2019ve proven internally that it\u2019s possible to turn AI into an operational asset \u2014 not an experiment.<\/p>\n\n\n\n

      Generative AI applied to areas such as PMO and Management Control has transformed decision speed and quality. In bid management, teams can now automatically analyze each opportunity\u2019s potential \u2014 evaluating effort, profitability, and strategic fit in seconds \u2014 enabling data-driven rather than intuition-driven decisions. Likewise, in Management Control, AI instantly answers key questions such as \u201cWhich countries have grown the most in business volume?\u201d<\/em> turning complex financial data into actionable insight.<\/p>\n\n\n\n

      The result: faster, more objective, and strategically aligned decisions.<\/p>\n\n\n\n

      Combined with more than 100 internal use cases, this has saved us over 1,000 hours of operational work \u2014 allowing our talent to focus on higher-value tasks.<\/p>\n\n\n\n

      Today, we apply that same experience to help organizations make the same leap \u2014 from initial enthusiasm to sustainable impact.<\/p>\n\n\n\n

      Building AI with Real Results<\/h4>\n\n\n\n

      AI is not a destination; it\u2019s an ongoing process of transformation. And like any strategic process, it requires vision, method, and leadership.<\/p>\n\n\n\n

      At Babel<\/strong>, we help you move from pilot to real impact \u2014 with a proven, results-oriented methodology backed by our own experience in adoption.<\/p>\n\n\n\n

      Discover how we turn AI into measurable business impact.<\/strong><\/p>\n\n\n\n

      Explore our AI services.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

      A recent MIT study has put numbers to what many organizations had already sensed: only 5% of investments in Generative Artificial Intelligence deliver a real and measurable return. Billions of euros have been allocated in recent years to deploy AI solutions, yet most projects have stalled at the pilot phase, never scaling or integrating into…<\/p>\n","protected":false},"author":7,"featured_media":1060926,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1065839","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categorizar"],"acf":[],"_links":{"self":[{"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/posts\/1065839","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/comments?post=1065839"}],"version-history":[{"count":1,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/posts\/1065839\/revisions"}],"predecessor-version":[{"id":1065850,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/posts\/1065839\/revisions\/1065850"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/media\/1060926"}],"wp:attachment":[{"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/media?parent=1065839"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/categories?post=1065839"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babelgroup.com\/en\/wp-json\/wp\/v2\/tags?post=1065839"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}