How AI Empowers Non-Developers

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

ChatGPT 03 Mini-high is A Warning Shot for Non-Coding Professionals. It can take an abstract gaming concept - like a Dungeons & Dragons battle map - consider variations, implement working Python code, and even visualize the scene with graphical output. One simple prompt to ChatGPT 03mini. For many non-coding professionals, this is an early sign of how AI is going to reshape workflows in 2025. Think about it: the ability to translate ideas into functioning code, adjust based on user feedback, and generate visual outputs used to be the domain of skilled developers. Now, AI can bridge that gap. This isn't just about gaming. Imagine an architect sketching out a building design in natural language and having an AI generate the 3D model. Or a financial analyst requesting a custom data dashboard without touching a single line of code. The barriers between technical and non-technical roles are fading. AI isn’t just an assistant anymore - it’s becoming an active collaborator in creative and technical fields alike. 2025 will be the year when professionals across industries realize: If you can describe it, AI can probably build it. Are we ready for this shift? How do we prepare for a world where knowing what to ask is more important than knowing how to code? #FutureOfWork #ArtificialIntelligence #Coding #Innovation

𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗼𝗱𝗲: 𝗛𝗼𝘄 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝘀 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗔𝗽𝗽𝘀 Software development is no longer limited to engineers and coders. For all the technological democratization that generative AI has brought about, vibe coding is in its own league when it comes to bringing about real change in business applications. Simply put, vibe coding is the process of generating and refining software, including code, design, and functionality, by prompting LLMs. In other words, vibe coding allows technical and non-technical folks to build apps or tools in seconds from natural language. As Andrej Karpathy said, English is the hottest new coding language! An interesting use case for vibe coding that we are exploring at Egnyte is by our product managers, who now use AI for UI prototyping using tools such as v0.dev. It can be used to quickly iterate and explore different design alternatives. For example, a Figma, Balsamiq, or even a hand-drawn design could be used as a base to create an interactive application. Further prompts could be used to design UI elements such as filtering functionality, location maps, and more. All this with reasonable test data. Beyond the workplace, vibe coding tools in the hands of non-technical people hold some interesting promise. While students can use AI to solve all assignments for them, it can also help educators move away from memorization-based learning to using tools that directly assist with teaching the material at hand. I like these second-order effects where teachers in non-STEM disciplines are experimenting with vibe-coded games that teach and test subjects such as economics and historical periods. The fact is that AI learns from people as people learn from AI, and the mutual learning of behavior through prompts is going to impact the maturity of LLMs as much as it affects the output they generate. Vibe coding is, therefore, a significant step in how AI codes and will impact the lives of people beyond tech.  So, as a techie at heart, does this type of technology make me fear for the future of coders? The answer is resolutely no. While these tools will surely cause structural unemployment, we will create many more new opportunities in the economy. This is because while these tools can enable faster ideation and improve the quality of the output, there is more to delivering useful applications and services than just programming. A majority of an engineer’s time does (and should) go more towards ferreting out functional requirements, optimizing usability aspects, and overall delivering solutions that fit naturally into user processes. These tools, with their ability to deliver rapid prototypes, will help ensure better product-market fit sooner.

AI-native isn't a tech stack. It's a mindset shift. Reading Sarah Tavel's interview with Rekki's CTO hit different than the usual "AI will change everything" content flooding my feed. Here's what caught me: Borislav Nikolov didn't just give his team AI coding tools. He fundamentally restructured his role from task executor to infrastructure provider—like becoming an internal AWS for his entire company. The breakthrough wasn't technical. It was psychological. The real barrier wasn't capability—it was conditioning. His operations team was conditioned to believe they "couldn't code." His engineers were conditioned to be gatekeepers. Both sides had to unlearn decades of role definitions. This mirrors what I'm seeing across product teams right now. Traditional lines between “technical” and “non-technical” are dissolving—but most orgs are still clinging to pre-AI operating models. Three patterns emerging from companies making this transition: ✅ Domain experts become builders—when given the right guardrails ✅ Engineers shift from doers to enablers ✅ The real unlock isn’t the model—it’s expanding what people believe they’re allowed to do This hit especially close to home. We’ve assembled a tiger team of 20 AI builders—most of us non-coders—and we’re already halfway through a 6-week sprint to build a companion app to productize and scale the impact of our AI community. Most of us aren’t coders. But we’re doing it—with scrappy tools, shared vision, and just enough infrastructure to move fast and learn together. One bold move from Rekki: They required everyone to understand LLMs—50+ hours of study. Not “prompt hacks.” Backpropagation by hand. Most companies are skipping this step. They’re installing tools without upgrading mindsets. Are we preparing our teams for expanded capability, or just adding new tools to old processes? Read Sarah Tavel’s article: https://lnkd.in/eUR4M3mz — 👋 This is Shyvee Shi — former LinkedIn product leader, now building the AI Community Learning Program, powered by Microsoft Teams. If you're curious about building and upskilling with AI, you can join our AI Community and get access to curated resources, tools, programs and events via aka.ms/AICommunityProgram. ♻️ Repost to help someone learn, build, and grow in the AI era. #AI #ProductManagement #FutureofWork