Using AI For Task Management

You're a #CTO. Your board asks: "What's our ROI on AI coding tools?" Your answer: "40% of our code is AI-generated!" They respond: "So what? Are we shipping faster? Are customers happier?" Most CTOs are measuring AI impact completely wrong. Here's what some are tracking: - Percentage of AI-generated code - Developer hours saved per week - Lines of code produced - AI tool adoption rates These metrics are like measuring how fast your assembly line workers attach parts while ignoring whether your cars actually start. Here's what you SHOULD measure instead: 1. Delivered business value 2. Customer cycle time 3. Development throughput 4. Quality and reliability 5. Total cost of delivery (not just development) 6. Team satisfaction Software development isn't a typing competition—it's a complex system. If AI makes your developers 30% faster but your deployment takes 2 weeks and QA adds another week, your customer delivery improves by maybe 7%. You've speed up the wrong part. The solution: A/B test your teams. Give half your teams AI tools, measure business outcomes over 2-3 release cycles. Track what customers actually experience, not how much developers produce. Companies that measure business impact from AI will pull ahead. Those measuring vanity metrics will wonder why their expensive tools aren't moving the needle. Stop measuring how much code AI generates. Start measuring how much faster you deliver value to customers. What are you actually measuring? And is it moving your business forward? -> Follow me for more about building great tech organizations at scale. More insights in my book "All Hands on Tech"

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

This productivity tool saved me 20 hours per week: The Eisenhower Matrix. Most people confuse being busy with being productive. But activity isn't achievement. Progress is. I spent years in reactive mode—fighting fires, handling "urgent" tasks, wondering why I never made real progress on what mattered. Then I discovered this: Not all tasks are created equal. The breakthrough came from separating urgent from important. The system is simple: Draw a 2x2 matrix and categorize every task: • Important & Urgent → Do Now • Important & Not Urgent → Decide (schedule it) • Not Important & Urgent → Delegate • Not Important & Not Urgent → Delete Track your tasks for one week. At the end, ask yourself: • Which quadrant consumed most of your time? • Which quadrant holds most of your tasks? The gap between these answers reveals everything. I discovered I was spending 70% of my time on "urgent but not important" tasks—other people's priorities disguised as emergencies. The shift was simple: I started saying no to fake urgencies and scheduling deep work for what actually mattered. You can't eliminate all urgent tasks. But when you spend most of your time on important non-urgent work, you build the life you want instead of reacting to the life you have. Watch the full 3-minute breakdown to implement this system today.

Project AI Assistants are the secret weapon to 10x your productivity. They're one of my favorite ways to use AI. Here's how to build one in minutes You can use ChatGPT Projects, Claude Projects, or Gemini Gems for your Project AI assistant. You create a separate project assistant to manage each major outcome you're accountable for, e.g., grow demand by 30%, double weekly active users, use AI to increase closed-won deals by 50% etc. For each Project AI assistant 1. Give it all the context: People don't understand how amazing AI is at holding all the context for you. Give it: - All the project's strategic documents. - All the project's meeting transcripts - Bonus: use a meeting app like 'Fellow' to attend meetings on your behalf and grab the meeting notes; now your assistant has context across all meetings, even if you're not in them. - Loom transcripts. Have the team send updates in Looms; it's a huge unlock. - External Deep Research: pairing external research with internal is powerful 2. Instructions: Provide your project assistant with clear instructions on how to work with you. Below is just a tiny sample from mine. a. Be clear and concise: Get to the point, but add context where needed. Prioritize clarity without losing important nuance. b. Use evidence: Cite sources (e.g., "2024 Q3 GTM Strategy Doc") and include relevant excerpts when making recommendations. c. Surface blind spots: Go beyond the prompt. Flag risks, missed opportunities, or second-order effects. d. Challenge respectfully: If you disagree, explain why with logic and evidence — constructively. [I'm doing a complete breakdown of my Project AI Assistants for my newsletter subscribers, signup for full instructions & templates. Signup link on LinkedIn profile page] 3. Templates Give the Project AI assistant templates of frequent asks you'll have; examples I use: - Executive Memo Template: a 6-page memo template on progress, challenges, blockers, opportunities - Weekly Blockers Template: surfaces the biggest blockers to solve that week - Bi-weekly Momentum Template: surfaces what's been shipped the past two weeks and what's planned for the next two weeks - Monthly Status Template: writes a monthly summary of what results to drive accountability across the team - Opportunities Researcher Template: Identify the biggest missed opportunities the team should pay more attention to. There's so much fluff in all the AI demos you'll see on social media that people forget about the less flashy but more impactful use cases for AI.

how to measure AI impact the right way: (don’t get duped by shiny new tools!) most teams track AI the wrong way (counting tools, prompts, experiments). none of that shows actual impact. the only metrics that matter are simple: 𝘁𝗶𝗺𝗲 𝗿𝗲𝗰𝗹𝗮𝗶𝗺𝗲𝗱 and 𝗼𝘂𝘁𝗽𝘂𝘁 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱. but here’s how to measure them properly: 𝟭. 𝘁𝗶𝗺𝗲 𝗿𝗲𝗰𝗹𝗮𝗶𝗺𝗲𝗱 start by tracking how many hours AI actually removes from your workflow. not “time saved in theory”, but real reclaimed time, meaning you’ve replaced the task, not just sped it up. example: if AI drafts 80% of client reports and your team only edits you didn’t save 10 minutes, you reclaimed the whole drafting process. 𝟮. 𝗼𝘂𝘁𝗽𝘂𝘁 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 this is your leverage metric. how much more work can your team produce with the same headcount? example: if your content team goes from 4 videos a month to 12, w/o adding people, that’s AI working as an engine, not a shortcut. 𝟯. 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝗼𝗿 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 this is the guardrail. AI’s gains only count if the output stays at or above your previous quality bar. 𝘁𝗵𝗲 𝗳𝗼𝗿𝗺𝘂𝗹𝗮: (ai impact) = (time reclaimed × output increased) × quality/consistency ai isn’t about speed. it’s about scalability. when you measure that, you’ll stop chasing new tools and start building real leverage.

"A Multifaceted Vision of the Human-AI Collaboration: A Comprehensive Review" provides some interesting and useful insights into effective Humans + AI work, drawn from across the literature. Some of the specifics insights in the paper: 🧭 Use the five-cluster framework to tailor collaboration depth. The framework defines five types of human-AI collaboration: (1) Humans as optional tools, (2) Consensus-based coordination, (3) Asynchronous collaboration, (4) Humans and AI as co-agents, and (5) Humans directing AI. Choose the type based on your task: use cluster 1 for personalization (e.g. recommender systems), cluster 2 for group decision-making, clusters 3 and 4 for task co-execution, and cluster 5 when human judgment must lead the process. 🧠 Let humans steer the learning loop. Design workflows where human feedback isn't just collected but actively changes the model. Show users how their input influences outcomes, and ensure systems update based on their corrections—failing to do so erodes trust and engagement fast. 🔄 Support iterative improvement through clear feedback cycles. Let users provide input at multiple points in the workflow—before, during, and after AI output. Use real-time feedback, editable suggestions, and memory-based personalization (e.g., saving past preferences) to refine collaboration with each loop. 📣 Grant users communication initiative. Don’t restrict user interaction to predefined prompts—enable them to ask questions, challenge decisions, or suggest new directions. This increases user autonomy, supports trust, and improves performance in both individual and group collaboration. 🛠️ Customize AI outputs to user-specific contexts. Embed features that allow tailoring of recommendations, predictions, or decisions to individual preferences or needs. For example, let users tweak rehabilitation goals in health tools or input content preferences in recommender systems. 🤖 Use AI as an impartial coordinator in group settings. In scenarios with multiple human participants—such as disaster planning or multi-user workflows—deploy AI to synthesize input, allocate tasks, and reduce bias. Ensure the system is transparent and users can reject or adjust AI decisions. 🔐 Prioritize human-centered design values. Build systems that are transparent (explain why outputs were generated), trustworthy (learn from user feedback), accessible (usable by non-experts), and empowering (give users control over high-level behavior). These are essential for lasting, ethical collaboration.

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐃𝐫𝐢𝐯𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞 AI metrics should drive Business Outcomes, not just Measure Performance.  Here is the Framework that aligns AI Metrics with Real-World value: 1. THE BLUEPRINT Three pillars: Decision Impact + Operational Reliability + Human Trust. Example: A claims agent that approves low-risk claims, escalates edge cases, and keeps humans in control. 2. NORTH STAR METRIC Pick one metric that captures value in production. • Net value per decision ↳ Fraud agent prevents $25 loss per case, costs $4 to run/review. Net value = $21. • Regret rate (% of decisions reversed) ↳ Out of 10,000 recommendations, 800 are changed by humans. Regret rate = 8%. • Revenue impact ↳ AI routing lifts conversion from 2.0% to 2.3% on 1M visits (3,000 extra conversions). • Cost per correct action ↳ Monthly run cost $200K / 400K correct actions = $0.50 per action. 3. DATA Leverage post-launch signals to understand behavior. • Decisions & outcomes ↳ Tracking "Approve claim" vs. whether it later became a chargeback. • Overrides & appeals ↳ Agent rejects refund → customer appeals → human approves. (Log this loop!) • Latency & failures ↳ P95 latency spikes during peak hours causing tool call timeouts. 4. CONSTRAINTS Constraints define what is sustainable at scale. Internal: • Review capacity: Your team can review 500 escalations/day. If the model sends 1,200, you bottleneck. • Infra cost: A "better" model doubles quality but triples cost per case. ROI drops. • Latency: Agent assist must respond under 800 ms to be usable. External: • Market behavior: Fraud patterns shift after you deploy. • User adaptation: Reps stop trusting suggestions after two bad calls, even if accuracy is high. 5. IDEATION + PRIORITIZATION Generate metric-driven improvements. • Impact vs risk: Automate low-risk approvals first. Keep high-risk human-led. • Regret frequency: 60% of overrides come from document parsing? Fix that first. • Drift severity: Regret rate rises from 6% to 11%? Roll back or retrain. • Cost vs value: Add a retrieval step that costs $0.02 but cuts regret by 20%. 6. EXPERIMENTATION Run controlled changes on: • Thresholds: Raise confidence threshold so fewer cases auto-approve. • Escalation rules: Escalate when the model disagrees with policy rules. • Model versions: A/B test smaller model vs larger model on "cost per correct action." MY RECOMMENDATION AI metrics aren't about model performance, they're about business value. Measure what drives decisions, not what's easy to measure. Track regret, not just accuracy.  Track value, not just speed.  Track adoption, not just deployment. Which metric are you tracking that does not drive business value? PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #EnterpriseAI #AgenticAI

In every conversation with project/procurement leaders, the same frustration arises: 𝐍𝐨 𝐨𝐧𝐞 𝐬𝐭𝐢𝐜𝐤𝐬 𝐭𝐨 𝐭𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐬𝐮𝐟𝐟𝐞𝐫. I’ve seen this happen firsthand—delays don’t happen in isolation. It’s never just the vendor, the client, or the procurement team. It’s one of those collective contributions! Some of the many reasons: - Albeit under pressure, Vendors commit to terms without 100% clarity. - Low focus on planning at MSMEs adds to the noise. - Vendors portray on-ground situations much better than they really might be. - Any mid-way changes by the clients, shifting expectations and complicating the problem statement for the vendors further. - Vendors scramble with last-minute acceleration and resource constraints. - Internal teams juggle misalignments, leading to reactive decisions. In project procurement from MSME vendors, in my view, the biggest aspect that leads to delays is a lack of transparency and visibility of how the work is progressing on the vendor side. For instance, on the vendor side— any gaps in planning for the procurement of raw materials and bought-out items lead to chaos at the last minute. Inefficiencies in capturing real inputs in current formats—spreadsheets, emails, scattered approvals—only add to the chaos. Further, the lack of authentic data makes it difficult to address real issues. What happens next? 𝐅𝐢𝐫𝐞𝐟𝐢𝐠𝐡𝐭𝐢𝐧𝐠, 𝐜𝐨𝐬𝐭 𝐨𝐯𝐞𝐫𝐫𝐮𝐧𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐥𝐚𝐲𝐬 𝐭𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐞𝐝 𝐟𝐨𝐫! At Venwiz, we are leveraging technology and have developed a Milestone Management Tool (MMT) to capture real-time information and reduce human dependency, tracking jobs at multiple vendor locations. The on-ground team is responsible for capturing raw data from different sites. However, all the metrics used for project tracking are calculated using our Milestone Management Tool (MMT)—which adds to the authenticity and reliability of the data. Our core focus is on actively preventing (and reducing) delays by understanding the root causes. In my opinion, the best procurement leaders don’t just manage vendors—they orchestrate the entire project ecosystem with data and transparency. How do you tackle shifting timelines in your projects? #Manufacturing #CapEx #Procurement #VendorManagement #Automation

Everyone obsesses over AI benchmarks. Smart people track what actually matters. I analyzed 200+ AI deployments to find the metrics that predict real-world success. The crowd obsesses with: ❌ MMLU scores (academic tests) ❌ Parameter counts (bigger = better myth) ❌ Training FLOPs (vanity metrics) ❌ Benchmark leaderboards (gaming contests) Smart people track: ✅ Token efficiency ratios ✅ Hallucination consistency patterns ✅ Real-world failure rates ✅ Cost per useful output The data is shocking: GPT-4: 92% MMLU score, 34% real-world task completion Claude-3: 88% MMLU score, 67% real-world task completion Why benchmarks lie: → Test contamination in training data → Optimized for specific question formats → Zero real-world complexity → Gaming beats genuine capability The 4 metrics that actually predict success: 1. Hallucination Consistency → Does it fail the same way twice? → Predictable failures > random excellence 2. Token Efficiency → Value delivered per token consumed → Concise accuracy > verbose mediocrity 3. Edge Case Handling → Performance on 1% outlier scenarios → Robustness > average performance 4. Human Preference Alignment → Do people actually choose its outputs? → Usage retention > initial impressions Real example: Company A: Chose model with highest MMLU score → 67% user abandonment in 30 days Company B: Chose model with best token efficiency → 89% user retention, 3x engagement The insight: Benchmarks measure what's easy to test. Reality measures what's hard to fake. What hidden metric have you discovered matters most?