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 𝗵𝗼𝗹𝗶𝘀𝘁𝗶���, 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 👇

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.

This is how you measure your AI system as an AI Engineer 👇 For regular software you would track metrics like uptime, error rate, p95 latency. However, they say little about whether the system is fast where users feel it, affordable at scale or correct. Here are the metrics we track when building LLM systems. It is useful to group them by the question they answer: 𝟭. 𝗜𝘀 𝗶𝘁 𝗳𝗮𝘀𝘁? (𝗟𝗮𝘁𝗲𝗻𝗰𝘆) ➡️ Time to first token (TTFT): how long the user is exposed to a blank screen, the number that defines perceived latency. ➡️ Inter-token latency (ITL): how smoothly tokens stream after the first one. ➡️ End-to-end latency at p50 / p95 / p99, dominated by output length, track it per use case rather than globally. 𝟮. 𝗖𝗮𝗻 𝗶𝘁 𝘀𝗰𝗮𝗹𝗲? (𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁) ➡️ Tokens per second per user vs total system throughput, the two trade off against each other on the same hardware. ➡️ Input and output tokens per request to measure your unit economics. ➡️ Cache hit rate - prompt caching is often the technique that reduces cost the most. ➡️ Cost per successful task, not cost per request, a cheap request that fails is a waste. 𝟯. 𝗜𝘀 𝗶𝘁 𝗰𝗼𝗿𝗿𝗲𝗰𝘁? (𝗤𝘂𝗮𝗹𝗶𝘁𝘆) ➡️ Task success rate on a labeled eval set, re-run on every prompt or model change. ➡️ Groundedness for RAG - is the answer supported by the retrieved context. ➡️ Retrieval precision@k and recall@k - generation cannot fix what retrieval never surfaced. ➡️ LLM-as-judge scores over time, calibrated against human labels. ➡️ User feedback signals: thumbs, edits to generated output, free form feedback. 𝟰. 𝗗𝗼𝗲𝘀 𝗶𝘁 𝗵𝗼𝗹𝗱 𝘂𝗽? (𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆) ➡️ Error, timeout and rate-limit rates per provider. ➡️ Retry and fallback rate - how often you silently switch to a backup model. ➡️ Guardrail trigger and refusal rates. 𝟱. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗲? (𝗔𝗴𝗲𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀) ➡️ Tool-call error rate. ➡️ Steps and tokens per completed task - drift here means cost is rising while accuracy remains the same ➡️ Context window utilization - the early warning for compaction and truncation issues. Read more about this in my newsletter: https://lnkd.in/dhiscYbm ❗️ Latency and reliability show up on day one because standard infra emits them. Quality, cost per task, and agent behavior need deliberate instrumentation, and they are where AI systems fail in production. Which metric caught a real problem for you that the standard dashboards missed? 👇

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

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?

Most companies are still measuring agentic AI like a software rollout. Adoption rates. Usage logs. Sentiment scores. That helps explain why 56% of CEOs say their AI investment has not produced a meaningful revenue or cost benefit and only 12% can point to both. (PwC 2026 Global CEO Survey) The gap is not just talent or technology. It is what gets measured, and what gets ignored. Here is the framework I’d bring to the next board meeting: 1/ Stop reporting adoption & start reporting outcomes → “X% of employees are using the agent” is a usage metric, not an ROI metric. → Adoption only matters when it is tied to business value: cost reduced, revenue increased, margin improved, or risk lowered. 2/ Pick one outcome metric per agent → Every agent should have a measurable job. Cost per resolved ticket. Cost per qualified lead. Cost per contract reviewed. Cost per incident closed. → If you cannot name the unit of work, you cannot price it, compare it, govern it, or defend it in front of the CFO. 3/ Treat token spend as a line item → Token costs are visible, but they are not the full cost of agentic AI. → Compute, retrieval, orchestration, monitoring, exception handling, and failure recovery all add cost. 4/ Build the harness budget into the business case → Guardrails are not overhead. → Evals, monitoring, permissions, audit trails, escalation paths, and human review are the cost of making agents safe enough to use. 5/ Set the baseline before deployment. → Before the agent goes live, capture the human-only process: cycle time, error rate, cost per task, rework rate, escalation rate, and customer or employee impact. → Without a documented baseline, every “improvement” is just a story. 6/ Watch the foundation metrics → Data quality, governance maturity, integration depth, workflow readiness, and ownership clarity are leading indicators of whether AI will produce financial returns. → The output metrics tell you whether the agent worked. The foundation metrics tell you whether it can scale. 7/ Retire vanity benchmarks → Generic “hours saved” claims and headline ROI percentages will not survive the CFO’s first follow-up question. → The real question is “What did this do to revenue, margin, cost, risk, or cash flow?” If the metric does not tie to P&L, it is directional at best. The CXOs who can defend AI ROI will not be the ones running the most pilots. They will be the ones who decided, before they started: → What they would measure. → What they would ignore. → What success would look like. → And what would make them shut a project down. Save this for future reference.

Most enterprise AI KPI lists track activity. Almost none track value. The real work is knowing which numbers actually predict whether your AI program is working. I have sat in enough board reviews to know how this fails. Teams report twenty metrics. Leadership feels informed. Six months later the program is over budget with nothing in production. The dashboard was full. The signal was missing. Here are the five KPIs from this map that actually predict success. And the threshold that tells you whether each one is a green light or a red flag. 1. Pilot to Production Rate. The single most honest number in enterprise AI. How many of your pilots actually made it into production. Under 30%, you do not have an AI program. You have an experiment budget. 2. Time to Value. Days from project start to first measurable business outcome. Not first demo. Not first deployment. First actual outcome. Over 180 days, your operating model is built for slides. Under 90 days, it is built for speed. 3. Reusability Rate. How many components from past AI projects are being reused. The closest thing enterprise AI has to compounding interest. Under 20%, your team is rebuilding from scratch every project. Over 40%, you are building a platform, not a portfolio. 4. AI Risk Coverage. The percentage of your AI systems with active governance. Not policies on paper. Active controls in production. Under 70%, this is the number a regulator will ask you about. And the one you will not be able to answer. 5. Change Resistance Index. The level of pushback inside your organization. Escalations and opt-outs from AI tools. The most underrated KPI on this entire map. Rising resistance is the leading indicator that adoption is about to stall. Most teams measure adoption. Few measure why it is failing. Here is what this map does not say. A great KPI dashboard makes you feel in control. The right five make you actually in control. If you brief your board this quarter, structure the dashboard in three rows. Outcomes at the top. Pilot to Production Rate. Time to Value. Capability in the middle. Reusability Rate. Trust at the bottom. AI Risk Coverage. Change Resistance Index. What I call the AI Value Capture System™ has five components. Identify. Prioritize. Architect. Measure. Scale. The Measure layer is where most enterprise AI programs quietly lose. Not because they are not measuring. Because they are measuring everything. The right five turn measurement from a reporting exercise into a strategic asset. Pick the five. Drop the rest from the headline view. Lead with what predicts success. 💾 Save this so you have the value-predicting KPIs ready before your next board update ♻️ Repost so the leaders in your network can stop reporting activity and start reporting outcomes 🔔 Follow Gabriel Millien for AI transformation insights that turn strategy into execution Image Credit: Vaibhav Aggarwal

Bayesian and entropy-based metrics are becoming important because AI products do not behave like traditional software. Entropy-based metrics help us understand uncertainty in the AI output. Bayesian metrics help us understand how much confidence we should place in the evaluation result itself. For LLM products, uncertainty is part of the experience. The same user can ask the same question twice and receive different answers. A task can be completed successfully while the user still leaves with the wrong level of trust. AI UX therefore needs metrics that capture uncertainty, variability, reliability, and calibration. Entropy-based metrics are useful because they focus on output-level uncertainty. Token-level entropy looks at uncertainty over the words or tokens the model generates, but that is often too shallow for UX. Users do not experience tokens; they experience meaning. Semantic entropy is more useful because it compares whether multiple generated answers converge on the same meaning or diverge into different interpretations. Five different phrasings of the same answer are very different from five answers that imply different conclusions. Kernel language entropy captures degrees of semantic similarity instead of forcing answers into simple “same” or “different” clusters. Semantic entropy probes try to estimate meaning-level uncertainty more cheaply, which could matter for real-time products. When an answer is semantically unstable, the product should probably not present it with the same confidence. It may need to ask a clarification question, retrieve more evidence, show uncertainty, or escalate the case to a human. Conformal methods are useful because they turn uncertainty into a product rule. Conformal abstention helps decide whether the AI should answer, refuse, or escalate. Conformal factuality control goes deeper by identifying which claims inside an answer are reliable enough to keep. This is important because an AI response is rarely cleanly right or wrong. Some claims may be safe, while others carry more risk. From a UX perspective, this turns reliability from a hidden backend score into visible product behavior. Bayesian methods address uncertainty at the evaluation layer. They help teams avoid overclaiming from noisy comparisons. In AI product evaluation, teams often compare models, prompts, or product versions using win rates, judge scores, benchmarks, or preference labels. Raw win rates can be misleading because the evaluator may be biased, the benchmark gap may be small, or the ranking may be unstable. Bayesian evaluator calibration and Bayesian benchmark reporting make that uncertainty explicit. A Bayesian framing changes the conclusion from “Model A is better” to something more decision-useful: “Model A is probably better, but the uncertainty is still large,” or “The difference is not strong enough to justify a deployment decision.”