AI in Knowledge Work Productivity

Not all AI agents are created equal — and the framework you choose shapes your system's intelligence, adaptability, and real-world value. As we transition from monolithic LLM apps to 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, developers and organizations are seeking frameworks that can support 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, and 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘁𝗮𝘀𝗸 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. I created this 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 to help you navigate the rapidly growing ecosystem. It outlines the 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗶𝗱𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 of the leading platforms — including LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and more. Here’s what stood out during my analysis: ↳ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 is emerging as the go-to for 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — perfect for self-improving, traceable AI pipelines.  ↳ 𝗖𝗿𝗲𝘄𝗔𝗜 stands out for 𝘁𝗲𝗮𝗺-𝗯𝗮𝘀𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, useful in project management, healthcare, and creative strategy.  ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 quietly brings 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 to the agent conversation — a key need for regulated industries.    ↳ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 simplifies the build-out of 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀 through robust context handling and custom roles.  ↳ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 is refreshingly light — ideal for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗺𝗮𝗹𝗹-𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀.  ↳ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 continues to shine as a sandbox for 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and open experimentation. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗵𝘆𝗽𝗲 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀: - Are you building enterprise software with strict compliance needs?   - Do you need agents to collaborate like cross-functional teams?   - Are you optimizing for memory, modularity, or speed to market? This visual guide is built to help you and your team 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. Curious what you're building — and which framework you're betting on?

"Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach. Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations. From our interviews, surveys, and analysis of 300 public implementations, four patterns emerged that define the GenAI Divide: • Limited disruption: Only 2 of 8 major sectors show meaningful structural change • Enterprise paradox: Big firms lead in pilot volume but lag in scale-up • Investment bias: Budgets favor visible, top-line functions over high-ROI back office • Implementation advantage: External partnerships see twice the success rate of internal builds The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time."

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

There’s more to AI than ChatGPT and DeepSeek… Here are 6 AI productivity tools I can’t stop using: 1. Perplexity (Personal Researcher) When I want in-depth answers to urgent questions, I use Perplexity more than Google these days. It’s like having your own 24/7 research assistant — I use it to do industry research, competitor analysis, fact-finding, and much more. https://www.perplexity.ai/ 2. Substrata (Dealmaking Assistant) High-stakes dealmaking can get complex, making it hard to have a clear understanding of how things are going. Substrata solves this by carefully evaluating all the signals (across your calls and emails) to understand who has the upper hand in a deal — and how to get it if you don’t. My company closed two massive deals this year (both Fortune 500 firms), and I used this tool a ton. https://www.substrata.me/ 3. Gamma (AI-Powered Presentations) Create infinite presentations, websites, and more in seconds with AI. It’s saved me hundreds of hours already, and the end results always look great. https://gamma.app/ 4. Claude (Idea Generator) I use Claude 90% of the time, and ChatGPT just 10%. Why? Claude’s writing sounds more human and is really good at giving easy-to-understand concepts. I use it to get ideas for carousels/infographics and improve my LinkedIn content. https://claude.ai/ 5. NotebookLM (Infinite Knowledgebase) This is the most underrated AI tool right now… You can combine all of your knowledge (PDFs, recordings, blog posts, etc) on a given subject in a single place and get instant hallucination-free answers when you search it. The best part? It’s 100% free (from Google). https://lnkd.in/gAfYp_Kb 6. Tango (Easy SOPs) Creating walkthroughs and SOPs for new hires is incredibly important—but equally tedious and time-consuming. This is by far the best tool for doing that (and creating any kind of how-to) that I’ve found. https://www.tango.ai/ … Those are my favorites. Which would you add?

Everyone talks about AI agents. But few actually show useful workflows. In today's episode, Harish Mukhami actually builds an AI employee: He builds an AI CS agent in just 62 minutes. 📌 Watch here: https://lnkd.in/eKbay8tu Also available on: Apple: https://lnkd.in/eAEVwr3u Spotify: https://lnkd.in/eyt7agKj Newsletter: https://lnkd.in/e6KUXi_z Harish is the former CPO at LeafLink (valued at $760M) and Head of Product at Siri. Now, he is the CEO and founder of GibsonAI, which built the scalable database behind our AI agent. Here were my favorite takeaways: 1: Building an AI employee just took 62 minutes. Harish demonstrated creating a fully functional customer success agent using ChatGPT O3 Mini, Gibson AI, Cursor, and Crew AI. The system analyzes data, identifies churn risks, sends emails, and creates Jira tickets—all production-ready. 2: Follow a three-stage evolution for maximum adoption success. Start with dashboards for insights, move to AI recommendations with human approval, then progress to full automation. This builds organizational confidence while gradually removing humans from routine tasks. 3: Architecture planning upfront prevents weeks of technical debt later. Use reasoning models like O3 Mini to define data models and business logic before coding. This ensures clean integration with existing tools rather than building isolated prototypes. 4: Production infrastructure is becoming accessible to non-technical teams. AI-powered databases auto-provision environments, generate APIs, and handle scaling without DevOps knowledge. Gibson deployed production-grade infrastructure in <3 mins. 5: MCP protocols eliminate the need to context-switch between tools. Model Context Protocol connects databases to code editors, letting you manage everything through natural language. Complex workflows across multiple tools become simple prompts. 6: Multi-agent frameworks make sophisticated automation accessible to PMs. Crew AI abstracts complexity that normally requires engineering expertise. Define specialized agents and orchestrate them like managing a human team with clear handoffs. 7: Any information worker role can now be automated. The same framework applies to SDRs, recruiters, and executive assistants. If your job involves data analysis and action-taking, it's automatable. 8: The PM skillset is evolving faster than most teams realize. Product managers who can architect agent workflows and design human-AI handoffs will have exponential impact. Natural language is becoming the primary interface for building software. 9: Development timelines have compressed from quarters to hours. The combination of reasoning models, AI infrastructure, and agent frameworks represents the biggest productivity shift since cloud computing for resource-constrained product teams.

In just a few minutes, here’s one thing you can do to make AI outputs 10x sharper. One of the most common reasons that prompts fail is not because they are too long, but because they lack personal context. And the fastest fix is to dictate your context. Speak for five to ten minutes about the problem, your audience, and the outcome you want, then paste the transcript into your prompt. Next, add your intent and your boundaries in plain language. For example: “I want to advocate for personal healthcare. Keep the tone empowering, not invasive. Do not encourage oversharing. Help people feel supported in the doctor’s office without implying that all responsibility sits on them.” Lastly, tell the model exactly what to produce. You might say: “Draft the first 400 words, include a clear call to action, and give me three title options.” Here’s a mini template: → State who you are and who this is for → Describe your stance and what to emphasize → Add guardrails for tone, privacy, and any “don’ts” → Set constraints like length, format, and voice → Specify the deliverable you want next Until AI memory reliably holds your details, you are responsible for supplying them. Feed the model your story - no need to include PII - to turn generic responses into work that sounds like you.

You can start using AI this week. Here's how: 𝟭/ 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹, 𝗺𝗮𝗻𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: – Writing proposals or RFPs – Reviewing contracts or compliance docs – Summarizing research, meetings, or reports – Drafting job descriptions or performance reviews – Answering repetitive customer or internal questions – Creating slide decks or campaign briefs – Brainstorming campaign ideas or product names – Exploring competitor strategy or market insights – Drafting internal comms or executive summaries – Filling out forms, invoices, or compliance templates 𝟮/ 𝗨𝘀𝗲 𝗮𝗻 𝗼𝗳𝗳-𝘁𝗵𝗲-𝘀𝗵𝗲𝗹𝗳 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼𝗼𝗹 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗱𝗮𝘁𝗮 & 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. 𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: – What steps can AI automate or assist? – What if AI drafted the first version every time? – What if humans only reviewed and approved? 𝟯/ 𝗥𝘂𝗻 𝗮 𝟱-𝗱𝗮𝘆 𝘁𝗲𝘀𝘁: 𝘂𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝘁𝗮𝘀𝗸, 𝗰𝗼𝗺𝗽𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗱𝗶𝗱 𝗶𝘁 𝗯𝗲𝗳𝗼𝗿𝗲. 𝟰/ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀: – Time saved per task – Error reduction or quality improvement – % of task handled by AI – User trust: would you ship this? Share the results. If it works, scale. AI is a tool; grab it and prove value fast.

I've received a few questions on this, so thought I'd share 5 ways I'm using AI in my day-to-day at work to boost productivity, insight, and strategic clarity: Benchmarking: I use AI daily to quickly validate metrics and performance benchmarks. For instance, when reviewing email open rates, I ask ChatGPT (and other LLMs) for industry benchmarks segmented by email types, industries and content. This provides immediate clarity on performance against the rest, and I can see if we're good, great, or have work to do. This information was hard to find or non-existent before and instantly helps builld context. Thought Partner: LLMs elevate my strategic thinking. Whether analyzing competitors or drafting new strategies, I leverage AI to rapidly identify gaps, assess my thoughts against frameworks like "Seven Powers," and run game theory on them with competitive response and market players. It uplevels my thinking and leads to more comprhensive considerations. Deepening Customer Insights: By processing sales call transcripts and meeting notes through AI, I can surface customer pain points and uncover new insights, which improves my understanding of customer needs, sales blockers and messaging that otherwise would be hard to come by. Writing Partner: I use AI to power my writing process—from refining documents to constructing logical, concise, and compelling arguments. It helps draft outlines, provides examples and proof-points to reinforce my assertions, and streamlines my writing. All-in it makes my writing better and faster. Automating Daily Tasks: I use AI-powered tools daily to track competitors, monitor market trends, and check-in on things I care about. It never stops working and so I always have this information available as needed. Today, AI is integral to about half of my workday. And this is just the beginning—there's even more potential to unlock with automations such as reviewing and drafting replies for my emails, prioritizing which documents to review next, and automated meeting prep. How are you integrating AI into your workflow? I'd love to hear what's worked for you.

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?

You’re doing it. I’m doing it. Your friends are doing it. Even the leaders who deny it are doing it. Everyone’s experimenting with AI. But I keep hearing the same complaint: “It’s not as game-changing as I thought.” If AI is so powerful, why isn’t it doing more of your work? The #1 obstacle keeping you and your team from getting more out of AI? You're not bossing it around enough. AI doesn’t get tired and it doesn't push back. It doesn’t give you a side-eye when at 11:45 pm you demand seven rewrite options to compare while snacking in your bathrobe. Yet most people give it maybe one round of feedback—then complain it’s “meh.” The best AI users? They iterate. They refine. They make AI work for them. Here’s how: 1. Tweak AI's basic setting so it sounds like you AI-generated text can feel robotic or too formal. Fix that by teaching it your style from the start. Prompt: “Analyze the writing style below—tone, sentence structure, and word choice—and use it for all future responses.” (Paste a few of your own posts or emails.) Then, take the response and add it to Settings → Personalization → Custom Instructions. 2. Strip Out the Jargon Don’t let AI spew corporate-speak. Prompt: “Rewrite this so a smart high schooler could understand it—no buzzwords, no filler, just clear, compelling language.” or “Use human, ultra-clear language that’s straightforward and passes an AI detection test.” 3. Give It a Solid Outline AI thrives on structure. Instead of “Write me a whitepaper,” start with bullet points or a rough outline. Prompt: “Here’s my outline. Turn it into a first draft with strong examples, a compelling narrative, and clear takeaways.” Even better? Record yourself explaining your idea; paste the transcript so AI can capture your authentic voice. 4. Be Brutally Honest If the output feels off, don’t sugarcoat it. Prompt: “You’re too cheesy. Make this sound like a Fortune 500 executive wrote it.” or “Identify all weak, repetitive, or unclear text in this post and suggest stronger alternatives.” 5. Give it a tough crowd Polished isn’t enough—sometimes you need pushback. Prompt: “Pretend you’re a skeptical CFO who thinks this idea is a waste of money. Rewrite it to persuade them.” or “Act as a no-nonsense VC who doesn’t buy this pitch. Ask 5 hard questions that make me rethink my strategy.” 6. Flip the Script—AI Interviews You Sometimes the best answers come from sharper questions. Prompt: “You’re a seasoned journalist interviewing me on this topic. Ask thoughtful follow-ups to surface my best thinking.” This back-and-forth helps refine your ideas before you even start writing. The Bottom Line: AI isn’t the bottleneck—we are. If you don’t push it, you’ll keep getting mediocrity. But if you treat AI like a tireless assistant that thrives on feedback? You’ll unlock content and insights that truly move the needle. Once you work this way, there’s no going back.