Eléonore Crespo’s Post

Why AI Alone Doesn’t Sell in Enterprise? AI adoption doesn’t start with an API call. It starts with a human walking into the building, asking the right questions, and tailoring the system to the mess on the ground. In this new blog post, Dayana Marin, Co-Founder at Adasight, explains the consulting playbook driving enterprise adoption: why “dropping an LLM” isn’t enough, how embedding experts makes the difference, and what startups can learn from OpenAI’s approach. 👉 Read the full article here: https://lnkd.in/egqWSDAG

The pace of AI is unlike anything we've seen before—with adoption 4x faster than the desktop internet. For enterprises, this presents a critical choice: adapt or fall behind. The recent "Staying ahead in the age of Al" guide from OpenAI highlights five key principles that deeply resonate with this goal: - Align: Leaders must set the vision and communicate the "why" behind AI adoption. As the guide states, thoughtful communication creates trust and clarity, helping employees see how changes align with their own work and goals. - Activate: Provide employees with the training and support needed to confidently adopt generative AI. Companies that move fast invest in supporting their employees' learning. - Amplify: Break down silos by sharing successful AI use cases and building a culture of shared knowledge. Amplifying progress means turning scattered wins into shared knowledge. - Accelerate: Remove friction by empowering teams with the right tools and clear decision-making processes. Accelerating means removing friction and ensuring good ideas move quickly from pilot to production. - Govern: Implement lightweight, practical guidelines that enable rapid innovation while managing risk. Good governance should support rapid action, not create new roadblocks. This isn't just about implementing new tools; it's about building a new way of working. Applying these principles with focus and discipline empowers businesses to thrive, build resilience, and gain a competitive advantage in a world where AI progress never slows. #AI #BusinessStrategy #Innovation #Leadership #EconomicGrowth #OpenAI

At Bounteous, we believe the future of innovation lies in AI-enabled experiences designed around people. In our latest article, Marty Young and I share how applying experience design principles to AI helps organizations move from experiments to proven experiences, delivering adoption, trust, and value at scale. https://lnkd.in/enw4P3QE

OpenAI and Allied for Startups released “Hacktivate AI,” a 20-point plan to speed AI adoption in Europe, ahead of the EU’s Apply AI Strategy. Ideas focus on skills, sector uptake, and simpler rules, including an Individual AI Learning Account, an AI Champions Network for SMEs, and a European GovAI Hub. OpenAI notes uneven workplace use across industries, urging targeted action, and points to OpenAI Academy and policy work to advance a unified digital market.

💡 AI doesn’t fail because of weak models. It fails because the data isn’t ready. Enterprises are learning that data readiness is the foundation of real AI impact. When governance is manual and data is scattered, decisions slow down. We’ve broken down 3 reasons enterprises are prioritizing data readiness and how it’s helping them: ✓ Make the right decisions ✓ Speed up enterprise operations ✓ Automate governance Because in enterprise AI, readiness comes first. See how data readiness drives confident, scalable AI. 🚀 Meet us at GITEX, Hall 22, Za’abeel Hall 1, Stand A (Upper Level) to see how Emergence AI is powering the next generation of intelligent systems.

Planning an AI tooling budget for 2026? There’s a lot of noise right now around AI adoption. Everyone is wondering if they have the right tools and if those tools are actually helping their developers move faster than the competition. Just giving developers licenses is not enough! Organizations seeing meaningful ROI are investing $500-$2K per developer in training and experiential workshops, intending to embed AI into workflows and ensure utilization sticks long after rollout. 🚀 Here’s the link to our webinar with Abi Noda and Laura Tacho, where they break down our research on how to plan and allocate AI tooling budgets for 2026. ⬇️ https://lnkd.in/gKMwAqgc

🚨NEW VIDEO ALERT🚨 We interviewed Sven Peters (Atlassian AI evangelist), and we go past the buzzwords and into outcomes⬇️ We talked about Rovo, Rovo Agents, and overall about AI powering trusted answers, cross-tool context, and repeatable automations across Atlassian Apps. In this first part of the conversation, you'll learn about which cases AI already saves time, the possibilities to create custom agents, and more. 🎥 Watch & share with your leadership team by clicking the link in the comments 👇🏼 #AI #AtlassianRovo #artificialintelligence

At Bounteous , we believe that driving AI adoption - and hence value - comes from focusing on the experience from the start. In our latest article, Michael Mayton and I share how applying experience design principles to the AI development process helps organizations move from experiments to trusted outcomes faster. https://lnkd.in/eQiykxAf

🚀 From Projects to Platforms: Rethinking AI Strategy for Enterprise Scale AI pilots may win headlines, but they rarely scale. The difference between experimentation and transformation lies in the mindset: ➡️ Projects don’t scale. Platforms do. In this episode, we unpack: 🔹 Why AI pilots stall in silos 🔹 The core attributes of scalable AI platforms 🔹 How governance, MLOps, and data fabric unlock enterprise-wide value 🔹 The leadership shift required to move from isolated wins to long-term transformation For enterprises aiming to compete in the age of agentic and autonomous AI, the message is clear: stop building projects—start building platforms. 🎧 #AI #DigitalTransformation #AIPlatforms #EnterpriseAI #Leadership

Democratizing AI Isn’t Just About Access—It’s About Impact A few years ago, I gave a talk titled 'Everyone’s a Developer Now'—and while I still believe that, I’d probably call it 'Everyone’s an AI User Now' if I gave it today. Reading the new piece from IBM on democratizing AI, what struck me wasn’t just the acceleration of adoption (from drive-thru assistants to perfumers using AI to craft scent profiles), but the three layers that often get overlooked: use, development, and governance. We’ve made amazing strides in putting AI into the hands of more people—but as someone who’s led ML teams and advised on AI platforms across startups and enterprises, I’ve seen what democratization _looks like_ when it stops short: tools that are accessible, but not inclusive. Models that are open, but not transparent. Governance policies that protect companies, but not necessarily communities. The reality is, giving someone access to a model isn’t the same as empowering them to shape that model—or to trust what it’s doing. The thing I keep coming back to: how do we design AI systems that center agency, not just automation? Would love to hear from others leading in this space—what does responsible AI deployment _actually_ look like inside your organization today? #AIgovernance #machinelearning