How to Transition to AI-Driven Workflows

I see many people struggling or confused when switching into AI. Don’t jump straight into frameworks like LangChain or LangGraph. Frameworks are accelerators, not starting points. Without foundations, you’ll end up building fragile demos instead of production-grade systems. Here’s a step-by-step path to transition your career into Generative AI: 1. Build Core Foundations --Python (APIs, JSON, virtual envs, packaging) --Git, Docker, Linux basics --Databases: Postgres + pgvector, or FAISS for embeddings 2. Learn Just Enough Math & Data --Vectors, cosine similarity, probability --Tokenization, chunking, normalization 3. Understand LLM Basics --How transformers work at a high level --Different types of models: base vs. instruct, hosted vs. local --Prompt engineering patterns (instruction, few-shot, tool-use) 4. Get Hands-on with RAG (without frameworks first) --Ingest → chunk → embed → store → retrieve → re-rank → generate --Add logging, caching, retries --Evaluate outputs with ground-truth sets 5. Learn Evaluation & Safety --Handle hallucination, PII, toxicity --Define and track metrics (accuracy, latency, cost) 6. Explore Reliability & MLOps --CI/CD for prompts/config --Observability, tracing, cost dashboards --Error handling and fallbacks 7. Then Explore Agents --Start simple: one-tool agents --Add planning and memory only when metrics prove value 8. Finally → Use Frameworks Wisely --Adopt LangChain, LangGraph, or LlamaIndex as orchestration layers --Keep your core logic framework-agnostic 9. Showcase Projects --Document QA system with metrics --Structured extraction pipeline with redaction --A small but reliable agent automating a real workflow 10. Be Interview-Ready --Explain RAG pipelines on a whiteboard --Compare models and providers --Justify design choices (chunking, caching, re-ranking) Learn the primitives first. Frameworks make you faster after you understand what’s under the hood. That’s how you build systems that last.

𝘓𝘦𝘵’𝘴 𝘣𝘦 𝘳𝘦𝘢𝘭: Instructional Design is evolving—fast. AI isn’t just a tool anymore. It’s a collaborator. If you're still designing static courses in Storyline or obsessing over ADDIE without integrating AI, you're stuck in the old L&D model. That model is 𝘥𝘦𝘢𝘥. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗳𝘂𝘁𝘂𝗿𝗲-𝗽𝗿𝗼𝗼𝗳 𝘆𝗼𝘂𝗿 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗮 𝗛𝘂𝗺𝗮𝗻-𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: 1️⃣ 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗩𝗮𝗹𝘂𝗲 Stop thinking like a content creator. Start thinking like a 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦𝘳. Ask: “How can I use AI to close performance gaps in real time?” 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗗𝗲𝗲𝗽𝗹𝘆) Don't just “play” with ChatGPT, Copilot, Gemini, and Claude. Master how to: ▪️Structure prompts ▪️Chain prompts ▪️Design AI workflows ▪️Generate data-driven learning assets in seconds 3️⃣ 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗶𝗻 𝗣𝘂𝗯𝗹𝗶𝗰 Share what you’re building. Post your AI-powered learning experiences on LinkedIn. Turn your process into 𝘱𝘳𝘰𝘰𝘧 of skill. 4️⃣ 𝗥𝗲𝗽𝗹𝗮𝗰𝗲 “𝗖𝗼𝘂𝗿𝘀𝗲𝘀” 𝘄𝗶𝘁𝗵 “𝗦𝘆𝘀𝘁𝗲𝗺𝘀” Employees don’t need more content. They need performance systems: ▪️AI copilots ▪️Embedded nudges ▪️Just-in-time guidance You design the systems. AI delivers the scale. 5️⃣ 𝗔𝘂𝗱𝗶𝘁 𝗘𝘃𝗲𝗿𝘆 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗳𝗼𝗿 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁 Go task-by-task through your ID process. Ask: “𝘊𝘢𝘯 𝘢 𝘮𝘰𝘥𝘦𝘭 𝘥𝘰 𝘵𝘩𝘪𝘴 𝘧𝘢𝘴𝘵𝘦𝘳, 𝘣𝘦𝘵𝘵𝘦𝘳, 𝘰𝘳 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴𝘭𝘺?” If yes—build the automation. You’re not just an Instructional Designer anymore. You’re the architect of 𝗛𝘂𝗺𝗮𝗻-𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. Make the leap. Or risk being automated out of the equation. What part of your current workflow do you think AI could take over tomorrow? Drop it below. Let’s dissect it together.

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.

Most people use AI like this at work: • “Summarize this doc” • “Write this email” • “Give me ideas” • “Explain this topic” That’s fine. But that’s level 1. If you want to get ahead, you need to move from using AI for tasks → using AI to design how your work gets done. Here are 10 specific, actionable ways to do that…with real examples: 1/ Build a reusable update generator ↳ Prompt: “Act as a program manager. Turn this input into: 1. What changed 2. Why it matters 3. Risks 4. Next steps with owners” ↳ Example: Paste messy notes → get a clean exec update in 30 seconds No more rewriting updates every week. 2/ Turn every meeting into a system ↳ Workflow: Transcript → summary → action items → follow-up email ↳ Example: Zoom call ends → paste transcript → instantly get: • 5 bullet summary • action items • draft email Meetings become outputs. 3/ Create a decision brief generator ↳ Prompt: “Summarize this into: problem, 2 options, tradeoffs, recommendation” ↳ Example: Instead of a long Slack message, you send: • Option A vs B • Clear recommendation Now leadership can decide fast. 4/ Build a “thinking partner” loop ↳ Prompt: “What’s weak in this plan? What would leadership challenge?” ↳ Example: Paste your plan → AI flags missing risks + gaps You fix it before review. 5/ Generate stakeholder-specific comms ↳ Prompt: “Rewrite this for: exec, team, and Slack” ↳ Example: Same content → • Exec = 3 bullets • Team = detail • Slack = 1 line No rewriting needed. 6/ Turn notes into structured artifacts ↳ Prompt: “Convert this into decisions, risks, owners, next steps” ↳ Example: Messy notes → • Decision • Risk • Owner Clarity in seconds. 7/ Run a weekly risk detector ↳ Prompt: “What risks are hidden here?” ↳ Example: Paste your update → AI flags dependencies or timeline gaps You catch issues early. 8/ Build a mini-agent workflow ↳ Chain: Notes → summary → tasks → email ↳ Example: Paste notes → everything generated That’s an agent. 9/ Simulate stakeholder pushback ↳ Prompt: “Act as a skeptical VP. What’s wrong?” ↳ Example: Paste your plan → AI surfaces objections You tighten before the meeting. 10/ Use AI to cut low-value work ↳ Prompt: “Which tasks can be automated or removed?” ↳ Example: Paste your to-do list → AI suggests what to drop You reclaim hours. Here’s the shift: Most people use AI to go faster. The people who win use AI to eliminate, restructure, and redesign work. 📬 I write weekly about AI, execution, and operating at a higher level in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc Which one are you trying first?

𝐇𝐨𝐰 𝐝𝐨 𝐈 𝐬𝐭𝐚𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚? The question I keep getting from professionals across every function — engineering, marketing, finance, operations: "What should I be doing right now to enhance my chances of keeping and flourishing in my job?" Having watched this shift play out across our portfolio companies, here is how I think about it. 𝐁𝐮𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐨𝐧𝐞 𝐡𝐚𝐫𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧. Before you re-skill, ask whether the company you work for has a future in the AI era. If your company's core product is being replaced by AI — not enhanced, not contested, but replaced — reskilling inside that company may not be enough. Getting out early is not disloyalty. It is career survival. Assuming you are in the right place — three things, in order. 𝐒𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫 𝐭𝐨 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫. Your value is no longer in doing the work — it is in knowing what work to do, why, and whether the output is right. The person who can break a problem down, delegate to AI, and judge the result is more valuable than the person who can execute a single step perfectly. This is a fundamental shift in identity — from "I am good at X" to "I know when X is done well." 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐟𝐥𝐮𝐞𝐧𝐜𝐲 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐝𝐚𝐢𝐥𝐲 𝐮𝐬𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐮𝐫𝐬𝐞𝐬. Stop taking "AI for professionals" courses. Start using AI tools in your actual work, every day. Draft with it, analyze with it, review with it. Fluency comes from repetition, not theory. The people pulling ahead are the ones who integrated AI into their daily workflow six months ago. 𝐃𝐞𝐞𝐩𝐞𝐧 𝐲𝐨𝐮𝐫 𝐝𝐨𝐦𝐚𝐢𝐧, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐭𝐨𝐨𝐥𝐬. AI commoditizes execution. What it cannot replicate is your understanding of why things work the way they do in your industry — the exceptions, the judgment calls, the context. When you can see the full picture of how outcomes are produced, you start thinking in terms of improving those outcomes, decreasing cycle times, and removing friction. That is where AI becomes a force multiplier — not on isolated tasks, but across workflows. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Ask the hard question about your company first. Then shift your mindset from executor to orchestrator. Build AI fluency through daily use, not courses. And deepen the domain expertise that no model can replace. The window to build these habits is now — not next year. What has worked for you in re-skilling for AI? Would love to hear.

Enterprises leaders, if you want to embed AI workflows into your system but are overwhelmed with all the information out there, here’s what you should focus on first. Forget all the questions about which model you should pick, the safest vendor and which use case is impressive. Your first challenge should be simpler and more operational: get your organization to use AI in a way that produces reliable work, instead of more noise. If teams do not know how to frame tasks, set constraints, and evaluate outputs, AI becomes a tax. People generate faster drafts, but managers spend more time reviewing, correcting, and reworking. The organization concludes “AI is not ready,” blaming it on model capability when the missing piece is human capability. This is why AI literacy is human capital strategy. It determines whether your organization builds a workforce that can direct AI effectively, or a workforce that uses AI for surface-level speed and creates downstream clean-up. If you want a practical way to build this capability, here is a simple 5-step starting loop you can run this week: 1/ Pick one workflow that repeats weekly (customer responses, internal reporting, onboarding, policy questions). 2/ Write a one-page “good output” rubric for that workflow (what must be true, what must not happen, what needs citation, what requires escalation). 3/ Have the team run AI on the same input, then do a 30-minute review where you grade outputs against the rubric. 4/ Promote the best version to a shared template, and document the failure modes you saw so the next iteration is sharper. 5/ Repeat weekly for four weeks across one workflow at a time, and you will feel the capability shift. If an enterprise wants AI to stick, it cannot just buy tools. It has to build the muscle to use them well. Remember that AI is not a spectator sport, you have to be in it, willing to sweat every single time to have the results you’re aiming for.

🌊 AI won’t sink your boat. But denying the leak? That will. Most AI failures don’t start with broken tools. They start with leaders who stop asking hard questions because the dashboards look “fine.” I see it everywhere right now: Teams are overwhelmed. Workflows are cracked. Adoption is shallow. But everyone is busy reassuring each other that “it’s normal” even when it’s not. AI doesn’t destroy organizations. Avoidance does. Especially when people are too afraid to say, “This is slowing us down.” If you’re leading AI adoption, or any transformation, your real job isn’t to push the boat faster. It’s to find the leaks before they drown your people. Here’s what that looks like from a Change Manager’s lens ⬇️ ✅ Do an AI Workflow Audit (the right way) Not a temperature check. Not a vanity dashboard. A real diagnostic. 1. Audit the Work, Not the Tool Look at where work actually flows and where it actually jams. 2. Map Friction Points Where are people creating workarounds? Where does AI create double work? Where is trust lowest? 3. Surface the Unsaid Ask: “What part of this feels heavier because of AI?” People hide pain when they feel pressure to “embrace the change.” 4. Validate Impact with Behavior, Not Opinions Are people actually using the tool? Has the workflow actually changed? Behavior tells the truth. 5. Fix the leak before scaling the system Small problems become cultural problems when ignored. Slow adoption becomes resistance. Resistance becomes failure. AI doesn’t require perfection, it requires awareness. A team that speaks up early stays afloat. A team that minimizes problems sinks quietly. So here’s the one question every leader should ask this week: “Where is AI making work harder and what leak do we need to fix before we row faster?” ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez