How to Update Your AI Tooling Practices

My Service Design team at LinkedIn has been actively wokring to experiment with and learn about the new AI tools. So far we've learned that: 🔹 We've got to try the tool to know the tool 🔹 We should document everything - prompts used, tools tried, use cases applied, everything 🔹 We should spend more time writing better prompts to get better results 🔹 We've got to set aside time for learning, for experimentation, for reflection It's now been over a month of intensive experimentation and testing of new AI tools for myself and my team. Some people have been experimenting far longer than that. This post is not for you (though if you have words of wisdom to share in the comments, please do!). If you haven't started trying out the new AI Tools, or you're early in your journey, read on. I wanted to share an early version of a framework for thinking about how to get a handle on the emerging AI Tools landscape: 1️⃣ Learning about new tools - prompt the LLMs, listen, read and watch There are so many new tools to potentially test out. Whatever the use case, I always start by checking with Claude about which tools it would recommend and why. But I also want serendipity and so I am listening to and watching podcasts - Dive Club, Lenny's, How I AI, Beyond the Prompt are my go-tos for now - and reading sources like Lenny's Newsletter, and the Listed AI newsletter to learn about new tools and new use cases. I'm sure there are many other great sources. ❓ What are you go-to sources for learning about AI? 2️⃣ Documenting and evaluating everything that we do to help with tool selection Not only do you have to sift through a growing mountain of new tools, but you also have to match tools with use cases. With that in mind, my team and I keep a running list of problems or opportunities we want to test out AI tools on. In addition, I'm documenting the steps in our workflow, what tools we use today, and where we might integrate new tools to save us time, or improve the quality of our work. This makes it easier to match potential tools with use cases and parts of our workflow. 💡 Clearly documenting what you do and where you might apply new AI tools makes it far easier to move past the paradox of choice with all the new tools, and select a subset to try out. 3️⃣ Set aside time to experiment with the tools - learning the tools does require an investment of time. We meet regularly as a team to experiment and test out new tools. The possibilities of productivity improvements, and quality improvements are real enough that it makes sense for us to devote time to this. I believe it will pay off significantly in the long term. ⏲️ You aren't getting more time in your week, and I'm guessing you were already busy, so you have to make a conscious choice to repurpose some existing time for testing out the new tools. ❓How are you approaching the new AI tools? #AI #Vibecoding #LIPostingDayJune

Most companies aren’t failing at AI adoption because of the tech. They’re failing because employees are afraid to use it. Tools are rolling out fast. But usage? Still stuck in pilot mode. 52% of employees using AI are afraid to admit it. And when managers don’t model usage themselves, team adoption stalls. One thing is clear: AI adoption doesn’t just happen. You have to design for it. Here are 10 strategies that actually work: 1. Track adoption and set goals. Measure usage patterns and benchmark performance across teams. Make AI part of your performance conversations, like Shopify does. 2. Engage managers. If they use AI, their teams are 2 to 5x more likely to follow. Enable them, train them, and let them lead by example. 3. Normalize usage. More than half of AI users hide it. Reframe the narrative. AI isn’t cheating, it’s table stakes. 4. Clarify policies. Without clear guidelines, people freeze. Spell out what’s allowed and what’s not. 5. Promote early wins. A great prompt that saves hours? Share it. Celebrate it. Build momentum. 6. Share best practices. Run prompt-a-thons. Create internal libraries. Make experimentation part of the culture. 7. Deploy AI agents strategically. Use ONA to spot high-friction workflows. Insert agents where they’ll have the biggest impact. 8. Balance experimentation with safe tooling. Watch what tools employees are adopting organically. Then invest in enterprise-grade tools your teams already want. 9. Customize by role and domain. Sales, HR, engineering, each needs a tailored strategy. Design workflows that reflect the reality of each team. 10. Benchmark yourself. How does your AI usage compare to peers? Track maturity, share progress, and stay competitive. From our work at Worklytics, these are the tactics that move organizations from pilot mode to performance. You can find the full AI Adoption report in the comments below. Which of these 10 is your org already doing and what’s next on your roadmap? #FutureOfWork #PeopleAnalytics #AI #Leadership #WorkplaceInnovation

ChatGPT is the new Excel. Here’s your first step toward AI. Many companies are racing to adopt AI, but the biggest opportunity often goes unnoticed: empowering your team with AI tools. It’s not just about building new AI products; it’s about integrating AI into the daily workflow of your employees. The tools—like ChatGPT, Claude, and Perplexity—are available, but the knowledge gap is significant. While people experiment with these tools, few companies provide the right training to maximize their value. A well-trained workforce using AI effectively is a game changer. This skill set not only accelerates daily tasks but also builds the foundation for larger AI initiatives. Companies that fail to build this muscle now are not only leaving productivity gains on the table but also signaling to their most innovative employees that they’re not serious about AI. The wrong step? Banning AI tools like ChatGPT. The right step? Training employees on their effective use. Here’s what you need to be doing: 1 — Align on an AI Assistant (ChatGPT, Perplexity, Claude, etc.) Start by choosing one of the key AI assistants—whether it’s ChatGPT, Claude, or Perplexity—or a combination of them. The great news is that all of these now offer enterprise-grade plans that help you manage your teams efficiently. Plus, they come with major certifications like SOC 2, GDPR, CCPA, and CSA Star, ensuring compliance and security for your business. 2 — Make AI Part of Your Team’s Daily Toolkit Make it clear across your company: just as everyone uses a computer, email, PowerPoint, or Excel daily, AI assistants are going to be a prerequisite for everyday work. Part of becoming an AI-powered organization is ensuring these tools are integrated into everyone’s daily routine. 3 — Organize Structured Training Set up a comprehensive training program that teaches your employees how to work effectively with these tools. Focus on prompt engineering, real use cases, and practical examples. Just as important, provide clear guidelines on what not to do—such as entering sensitive IP or customer/employee information—to ensure proper usage and avoid risks. There’s a lot of FOMO out there, and many companies are rushing to figure out how to implement AI projects. But a prerequisite to all of this is having your workforce turbocharged and powered by AI assistants. Whether or not you end up building your own AI-powered features, this will help boost your team’s overall productivity. It will also build the familiarity and intuition your team will need for working with AI-powered services—or vendors who are leveraging this technology. All in all, it’s a win-win: a low-effort, low-cost, easy way to get started with AI adoption and transformation.

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker