How to measure AI adoption and impact in DX

Most AI tool lists 🗒 are noise, but this one is different. One of the biggest challenges I see with AI adoption is distinguishing between tools that are exciting for a week and those that truly become part of daily workflows. That’s why this recent a16z list of favorite AI apps caught my attention. After testing hundreds of products, they highlighted the ones people actually kept using, beyond the initial hype. The list spans everything from workflow automation tools that save hours of repetitive work, to creative apps that unlock entirely new possibilities. In my work with startups and enterprises, I notice a pattern: companies experiment widely, but only a small set of AI tools prove sticky enough to drive real business impact. If you’re exploring how to cut through the noise and implement AI that delivers real value, I’m always open to a conversation. I’d love to hear from you: Which AI apps have become indispensable in your day-to-day work? And which ones looked promising at first but didn’t stick? Credit to Justine, Olivia Moore, and the a16z Consumer team for pulling together such a well-curated resource. (The full interactive version is available on a16z’s website.)

GenAI adoption is mainly about hands-on, practical and tailored sessions to specific roles. Those were mainly my learnings over the past 12-18 months in enabling our region to leverage AI. Over the past 12 months, we ran a series of Lunch & Learn sessions to help our teams explore AI tools like Copilot, Glean, and more. We followed up with two surveys to understand what’s working, what’s not, and where we go from here. Here’s what we discovered: ✅ Daily AI usage is up — over half of respondents now use AI every day ✅ Lunch & Learns drive impact — 92% felt more productive after attending ✅ People want more hands-on practice — bring-your-own-laptop sessions, live demos, and role-specific examples ✅ Tool overwhelm is real — some felt lost in the sea of options But the best part: the appetite to learn is strong. Here's some of the feedback: 💬 “Just practicing more and examples how to use it in my daily work.” 💬 “Continued tips and tricks how to use AI tools with everyday tasks.” 💬 “Explore sessions tailored to roles like sales and customer success, etc.” So what’s next? We’re continuing our AI learning curve, but with a more structured approach incorporated by the feedback received. The ideal goal is to get colleagues to a certain baseline level so that they understand AI, then get 80% of participants to a stage of applying and leveraging AI in their day-to-day, while 20% are going to build and scale agents to boost workflows for others. I'm excited to get started in our new fiscal year and continue to learn about AI and enable others to become proficient at leveraging the technology.

Solo builders don’t need 80-hour weeks anymore. They need AI microtools. ⚡ Build in days ⏱ Save 10+ hrs/week 🎯 Personalize at scale Back in 2023, AI tool usage among small businesses jumped 300%. Now in 2025, the AI market is hitting $391B, with 78% of companies already using AI. This isn’t hype; it’s the new infrastructure. Why every solo builder needs AI microtools: 1️⃣ Speed → go from idea to product in days, not months 2️⃣ Efficiency → automate repetitive work and focus on growth 3️⃣ Personalization → deliver products that adapt to each user Real results today: - 80% of support is automated with chatbots - 50% more content shipped in less time - Full apps built in under 48 hours Start small. Automate one task this week: emails, meeting notes, or create a custom one with ChatAndBuild. By the time others are still planning, you’ll already be shipping. The future of solo building is here, and it’s powered by AI. 📖 Read the full article in the comments.

DigitalOcean’s GenAI Platform: Easy AI Agents for Everyone 🚀🤖 DigitalOcean just dropped its new GenAI Platform, a cool way for businesses and creators to build AI helpers without needing to be AI wizards 🧙♂️. Launched in August 2025, this tool lets you connect powerful AI models with your own data, so the AI actually knows your stuff—like a chatbot that’s read all your documents 📄💡. What’s hot? 🔥 You can plug in your data and use built-in AI models to create smart agents that help with customer support, data insights, or automations. It supports multi-agent setups, meaning bots can team up and tackle complex jobs together. Plus, it has safety guardrails to keep answers on track ✅. Some quirks ⚠️ The interface is straightforward but not fancy, so it feels a bit like building with blocks rather than a polished app. Some advanced features need a bit of learning — not quite plug-and-play for absolute novices. Should you try it? If you want to bring AI into your projects without coding headaches and love experimenting, this platform is a smart playground. But if you crave a flashy, super simple AI, you might want to hold off until it’s more polished 🎯. Basically, DigitalOcean’s GenAI Platform is a solid gateway to AI, mixing power and simplicity for curious users ready to roll their sleeves up! ✨🛠️

MIT recently published a report that 95% of AI projects don't return ROI. Turns out that most AI pilots get stuck at the “cool demo” phase. These AI project forget. They don't assemble or carry context. They never cross into or adapt to real value-add workflows. The headlines from MIT’s report reads negative, but the report itself is really a roadmap for ROI. I turned that checklist into a simple plan for associations. Feel free to check that out here: https://lnkd.in/eSF-visj

Anyone else feeling the tension around the GPT-5 release? 😬 For some, it's an exciting new tool. For others, it's a frustrating change that feels like a downgrade. This isn't just about AI. It's a fundamental challenge in tech: the creators are focused on the shiny new features, while the users are grieving the loss of their favorite workflows. With AI, this gap can feel even bigger as the models we rely on change almost overnight. If we want people to actually adopt new technology, we can't just release it and hope for the best. We need to communicate clearly, discuss the pros and cons honestly, and, most importantly, listen to the users who will be most impacted. If they feel left behind, no amount of new features will make a difference. Speaking of adoption, don't miss our upcoming live session with Adobe to help navigate adoption through enterprise. We'll be addressing real-world hurdles: workflow inefficiencies, organizational inertia, and strategic AI placement. 🔗 It's free to attend: https://lnkd.in/ep7wgvjb

MIT report: 95% of generative AI pilots at companies are failing 📊 I'm seeing, and talking to a lot of professionals that have big lofty goals especially in the customer experience space. (I have big lofty goals too 😉) Most organizations are trying to run before they crawl. They're launching complex AI initiatives without understanding the fundamentals of integration, user adoption, or practical workflow impacts. My Thoughts? Crawl → Walk → Run 🔹 Crawl: Start with simple, low-risk automations that solve real problems 🔹 Walk: Iterate rapidly based on user feedback and measurable outcomes 🔹 Run: Scale what works, kill what doesn't In my experience, the magic happens in rapid iteration cycles. Fail early, fail often, but fail forward. Each failure teaches you what your teams actually need versus what you think they need. The companies succeeding with AI aren't the ones with the biggest budgets or flashiest tools. They're the ones willing to start small, measure relentlessly, and pivot quickly based on real user feedback. What's your approach to AI implementation? Are you crawling, walking, or trying to sprint out of the gate?

Beyond Proof of Concept: Making Generative AI Solutions Production-Ready  Many generative AI projects never make it past the proof-of-concept (POC) stage. The gap between a slick demo and a production-ready solution is often massively underestimated. After working on multiple GenAI initiatives, here are a few tips I’ve learned about designing AI solutions that actually deliver in production: 1️⃣ Start with a real business problem Don’t chase “cool demos.” Anchor your solution to a business-critical problem tied to measurable outcomes (efficiency, accuracy, revenue). 2️⃣ Design for validation from the start GenAI is non-deterministic. Always give business users a way to check the output — whether through citations, context, or confidence scores. 3️⃣ Involve business users early Real feedback > lab assumptions. Get business users into the loop before launch and treat their input as fuel for product evolution — not just bug reports. 4️⃣ Apply the 80/20 rule Prioritize the 20% of features that drive 80% of the value. A simple, reliable solution beats a fragile “feature-complete” one every time. 5️⃣ Build for monitoring + iteration Launch isn’t the finish line. Data shifts, user behavior evolves, and edge cases appear. Bake in monitoring, feedback loops, and retraining processes from day one. 6️⃣ Have responsive AI governance ready Policies and guardrails shouldn’t just exist on paper — they need to adapt as models, regulations, and business use cases evolve. Governance must be agile enough to support innovation and protect against risk. 👉 The key: design for scalability, reliability, and usability from the very beginning. What strategies have you found most effective in taking GenAI from POC to production? #GenerativeAI #AIinProduction #AIProductDevelopment #POCtoProduction

So, you’ve probably heard about generative AI, right? It’s that cool tech that’s changing the game for businesses. Big names are already using it to revolutionize how they operate, and it's pretty exciting to see. Think about it: companies like Google and OpenAI are using AI to create content, automate tasks, and even personalize customer experiences. It’s not just for tech giants though; businesses of all sizes are jumping on board to streamline their processes. For instance, AI can help with marketing strategies, customer service via chatbots, and even product development. Imagine cutting down on your workload and speeding up response times while also saving money. Sounds like a dream come true, right? But here’s the deal: many businesses struggle with operational headaches, like slow responses and high costs. That’s where we come in. At Data Science London, we specialize in automation workflows and AI-powered chatbots. We help businesses achieve faster results, improve accuracy, and relieve some of that heavy workload. If you want to see what generative AI can do for you, check out [Make.com](https://lnkd.in/ecwFCrHp). Let’s turn those dreams into reality! Vikky - datasciencelondon.uk Source: https://lnkd.in/dEnXKf9f #GenerativeAI #Automation #BusinessSolutions #Innovate #TechTrends

In the last 2 weeks, people have asked me a few times about MIT report on the 95% failure rate of AI deployments in enterprise. After all, I've been extremely bullish about AI in every forum I've spoken at, every post I've written. My answer: "I didn't get the time to read it yet, but I am sure there is some nuance to what the 95% or 5% represents, etc. Did you actually read it?" And finally I got time this long weekend to read up on this, and also some articles digging into where the number came from. Turns out this number wasn't what most people interpreted it to be. The success rate of AI deployments were far higher - more like 1/3rd for "build" and 2/3rds of the "buy" deployments of AI made it to production. But when you take some task specific custom AI deployments that were "top down" in nature, which was a smaller subset, the ones that had sustaining ROI and impact were 5%. And why the failure to deliver ROI? Picking the wrong problems, not integrating well, etc. The answer in my head: - Don't try to solve everything internally. Use hackathons, internal initiative to validate, understand, do simple POCs. - Build a business case to see if the problems you are picking are truly worth solving before you go all-in on an initiative - Work with vendors who have solved the problem before or are motivated to "work through the last 20%" with you. Getting POCs to 80% there is easy with internal teams and the 80 to 100 journey is where most often AI is failing today. Fin.