Christine Coudert’s Post

Are you AI-agile? Do you know what being agile with AI means, and what it takes to get there? 🧁 AI-agility is the key ingredient in leveraging AI flexibly. 🔵 It requires superior data management and governance practices. 🔵 The AI-agility message is simple: no quality data, no flexibility, no effective AI. I’m always telling people that AI can’t work without good data, and real AI-agility is good data done well. Finding those AI applications that provide real business value for you is going to be hard without it. So, let’s look at how to reach AI-agility in 6 steps 👇 #AI #Data #AIAgility #Lumilinks

#DidYouKnow❓️ 💡 It takes a lot of effort and processing to make #data valuable for an AI deployment. 💡 A simple question-and-answer pair within an #AI application currently #costs around $3. 💡 The AI landscape is moving so quickly that many businesses jumping on the ‘latest thing’, find themselves with a tech stack that is outdated six months later and is expensive to replace. 👉 In this article we uncover the real costs of AI implementation: https://lnkd.in/d5crZbxc 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀. 𝗗𝗼𝗶𝗻𝗴 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁, 𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀!

These days, adopting #AI solutions is a must. But one thing to consider is will your data ecosystem play nicely with them or do you need to modernize first? See how experts are approaching it here. #DataAnalytics #CDWExecutiveSummIT

These days, adopting #AI solutions is a must. But one thing to consider is will your data ecosystem play nicely with them or do you need to modernize first? See how experts are approaching it here. #DataAnalytics #CDWExecutiveSummIT

These days, adopting #AI solutions is a must. But one thing to consider is will your data ecosystem play nicely with them or do you need to modernize first? See how experts are approaching it here. #DataAnalytics #CDWExecutiveSummIT

'AI first' is basically another way of saying that the solution has priority over thinking through the problem. Expressions like 'AI first' or 'Data first' should reflect the intent to integrate AI and data into the solution development process. But to the AI or data novice it can mean this: → Don't bother understanding the problem. AI solves it. → AI and data are the responsibilities of IT folks. → Decisions are data-driven with no influence from human views. → I am wrong and AI/data is right. No wonder many digital transformations are failing. You need engagement and buy-in from regular non-data people, not ambiguous terminology. #ai #datadriven #decisionmaking #projectmanagement #digitaltransformation

The key to successful adoption of AI in any company is not a simple equation of Tech + Money + Tech. Instead, it's a bit more complex. According to one expert, the right formula is Use Cases + Skills + Data + Tech + Organization. By focusing on these five crucial components, companies can create a solid foundation for AI adoption that will set them up for success. Let's work together to make AI work for us! #AIadoption #futureofwork #ai #saudibusiness #d365

Data-driven success is no longer a challenge for MandM! 📊 Explore how Dataiku & #GCP fueled the online retailer's growth, delivering lightning-fast models and #Personalized experiences. From silos to seamless operations, witness the transformation! 🌟 #DataScience #AI #ROAI | https://bit.ly/4dlHPyF |

Launching Data-Driven AI Products: A New Paradigm The question of when to launch a data-driven AI product – before or after extensive data training – is a common puzzle. My perspective? The traditional product lifecycle is no longer the best fit for AI-powered solutions. Embrace the MVP Mindset Launch an MVP (Minimum Viable Product) trained on a smaller, focused dataset. This allows the product to live within the user ecosystem, gather real-world data, feedback and iteratively improve. Users must understand these early models are dynamic – expect frequent refinements instead of "alpha" and "beta" stages. Wall Street, A Note for You AI products are not static software. They learn and evolve. Initial biases are inevitable on the road to maturity. A cancel culture will stifle progress. Let's treat AI systems with the same understanding we give humans – they can and will learn from their mistakes. If AI has to approach the complexity of human intelligence, it's essential to allow it the space to make mistakes and learn from them. #AI #datadrivenproducts #productlaunch #MVP #innovation

AI is too complex to implement: Agree or Disagree? We believe AI can be simplified for non-technical users with user-friendly platforms. Share your perspective on AI's complexity! #AIComplexity #AI #Automation #Productivity #TimeFreedom #Efficiency #AIbots #RPA #GrowthHacking #BusinessAutomation #TechSolutions #SuccessStories #EffortlessWork #InnovateToday