Evaluating Workflows for Efficiency

HR doesn’t need more dashboards. It needs better listening. Most people teams measure what’s easy…like engagement scores or turnover. But the best teams? They build feedback loops that help them predict problems, not just react to them. This post gives you 11 of the most useful, often-overlooked loops you can implement across the employee lifecycle: 🟢 Week 2 new hire check-ins (capture early impressions) 🟠 Post-interview surveys (from both sides) 🔵 Onboarding reviews (day 90 is your goldmine) 🟡 Skip-level 1:1s (cross-level truth-telling) 🟣 Quarterly team health check-ins (lightweight, manager-led) …and 7 more. 📌 Save this if: • You’re building a modern HR function • You want fewer “We should’ve seen this coming” moments • You believe listening is strategy Which feedback loop is missing in your company?

Harvard report: 71% of meetings are unproductive. 65% keep people from doing real work. The best leaders don’t run more meetings. They run the ones that matter. The goal: Keep teams aligned, focused, and moving fast. Without wasting time. Here’s how to run the meetings that actually move the business forward: Weekly 1:1 ↳ Let them drive the agenda ↳ Listen first, coach second ↳ End with 2–3 clear next steps Leadership Team Meeting ↳ Review key metrics in 10 minutes ↳ Focus on 2–3 high-impact issues ↳ End with clear decisions and owners Weekly Operating Review ↳ Review KPIs by exception ↳ Flag risks in revenue, churn, or ops ↳ Assign fixes with owners and due dates Quarterly Planning Session ↳ Review last quarter’s goals ↳ Debate and choose top 3 priorities ↳ Assign clear owners and resources Voice-of-Customer Session ↳ Bring 3 real pain points ↳ Let customers talk 70% of the time ↳ Follow up within 30 days Board or Investor Update ↳ Share the hard stuff first ↳ Highlight 1–2 metrics that matter ↳ Ask for help with specific challenges All-Hands ↳ Explain the why, not just the what ↳ Take live, unscripted questions ↳ End with one clear message You may not need all of these. Some might add a daily standup. But chances are, your company doesn’t need half the meetings on the calendar now. Use this list to audit what’s working. Cut what’s not. Your team will thank you for their time back. Better meetings = faster decisions, sharper focus, and real momentum. P.S. Does your company have too many meetings or just right? ♻️ Repost to help a leader in your network. Follow Eric Partaker for more productivity insights. — 📌 Want PDFs of this and 100+ free leadership resources? Get them here: https://lnkd.in/ekhxjakK

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

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 👇

Once, we built a machine learning model that was expected to drive a 15% lift in conversions. The result? A shocking 0.01%. What went wrong? The model worked perfectly, but the business process behind it was too long and complex. By the time the offer reached the clients, most leads were lost. And the kicker? The business case was literally giving money to the clients! This experience taught us a crucial lesson: even the best machine learning model can fail without an aligned, efficient business process. The model had identified high-value leads, but the operational workflow to turn those leads into conversions was cumbersome and slow. It involved multiple handoffs, redundant steps, and delays that made it nearly impossible for the offer to reach the client in time. In this case, the problem wasn’t technical—it was systemic. The gap between predictive insights and actionable outcomes created friction that nullified the model's value. When we revisited the process, we streamlined the journey from the model’s output to client interaction. By reducing the time and steps involved, we saw significant improvements—not just in conversion rates but also in the trust clients placed in the business. This is why aligning AI models with business operations is just as critical as building accurate models. Are your machine learning projects driving real business impact, or are they stuck in the pipeline? Let’s discuss strategies to close the gap and unlock the full potential of your AI investments. Share your thoughts or experiences below!

Are 80% of your meetings effective? Do people have at least four 2+ hour blocks of focus time every week? Scaling effective meetings, asynchronous collaboration and time for "deep work" across thousands of employees is challenging. Too many leaders shrug and give up: "it's just the way things are." ⭐ It might be hard, but it's totally possible to scale better use of time: 📅 Dropbox employees say 69% of meetings are effective, impressive vs research showing both executives and employees told Future Forum that ~50% of all meetings should be eliminated entirely. 🕖 Dropbox also got to >80% compliance with core collaboration hours around the globe -- a massive win, especially when you realize "one size doesn't fit all" on almost any work practice. 💪 Atlassian saw a 31% increase in progress against weekly goals when combining better calendar management with weekly goal-setting. 🔎 Slack got to 85% of employees saying Focus Fridays and No Meeting Weeks were a significant benefit to them -- higher than many monetary or services benefits. What's the secret sauce? 1️⃣ Aligned Executives: in both cases, the executive suite from CEO on down understood that excessive meetings and a lack of time for deep work were leading to burnout and lower quality work. 2️⃣ Pilot then Expand: We experimented with No Meeting Weeks in the Product, Design & Eng team at Slack, refined it, then partnered with functional leaders to translate specific meeting types and workflows in order to roll it out. 3️⃣ Measure Progress: A quarterly pulse survey with results by function and Spotify's meetings cost calculator are examples of pretty straightforward ways to measure progress. Tools like Microsoft Viva also help! 4️⃣ Reinforce Regularly: Discuss survey results in exec staff quarterly, build reinforcement into leadership conversations, All Hands meetings and comms. A cross-functional task force can bring ownership closer to functions. ❓ What practices have you scaled in your organization? Where have you seen programs fail to take hold? 🏗️ Dig deeper: 🔗 Links to Atlassian's time boxing and goal setting experiments by Molly Sands, PhD and team, Dropbox's virtual-first toolkit by Allison Vendt, Melanie Rosenwasser and Alastair Simpson and the Slack Focus Friday and Maker Week content I did with Christina Janzer and Kristen Swanson in comments. Would also recommend Kasia Triantafelo's collection of insights from the Running Remote community, linked as well. This is Part 2 of a series on 2025 Resolution: Make Better Use of Time. Thanks Karrah Phillips, Dave O'Neill, the folks listed above and Kevin Delaney, Tim Glowa (IBDC.D, GCB.D) & Nick Petrie for inspiring me to pick this back up! #Meetings #Productivity #Focus #DeepWork #FocusTime #Collaboration #Leadership #ChangeManagement #EmployeeExperience #EX

The Four Places Enterprise AI Breaks Down ...And Why Most Teams Miss Them After reviewing dozens of AI initiatives, I’ve noticed something consistent. Enterprise AI rarely fails randomly. It fails in the same four places over and over again. 1. Ownership & Workflow Breakdown (The People and Process Gap) This is the most common failure. The model produces outputs, but - No one owns the decision - No workflow actually changes - We continue working the same way as before AI takes the side seat instead of a decision driver. If no one is accountable for acting on the output the system will be ignored no matter how good it is. 2. Data & System Fragility (The Foundation Problem) Teams often think the hard part is modeling. In reality, the biggest blockers are - Unreliable or restricted data access - Manual data pulls - Legacy systems that can’t support continuous operation - No plan for drift or data change and most leaders don't have a clue what it is When data pipelines aren’t production grade, AI becomes expensive to maintain. 3. Value Definition Failure (The KPI vs Outcome Trap) Many teams optimize what’s easy to measure - Accuracy - Precision - Engagement - Usage But they never answer - Which business decision is changing? - What cost, risk, or time is actually reduced? - How will success be measured after the decision? This is how organizations end up with impressive metrics and no ROI. 4. Risk & Control Blind Spots (The Governance Reality Check) Enterprise AI doesn’t operate in a vacuum. Security, legal, compliance, audit, and risk teams eventually get involved and when they do late surprises kill momentum - No audit trail - No explainability - No guardrails - No incident response plan Projects don’t fail here. They get paused, scoped down, or quietly shelved. Why These Failures Are Easy to Miss Each is often owned by a different group - Business - Data/Engineering - Product - Risk/IT/Security Everyone thinks they’re doing their part. But AI value only appears when all four zones align at the same time. A Better Way to Judge AI Progress Before celebrating accuracy or dashboard trend check is - Has a real business decision shifted? - Is there a named owner accountable for that decision? - Can the impact be measured after the decision, not just before it? - Would the business notice if the AI were switched off? If the answer is probably NOT then you’re looking at check box activity not value creation. If you design explicitly for all four components mentioned earlier the odds of success change dramatically. Far Side Of AI #AI #FarSideOfAI

Slow progress isn’t always a competence problem. Often, it’s a systems problem hiding behind good intentions. Here’s what usually causes the drag: 1. Too many priorities at once; When everything is important, nothing moves decisively. 2. Decisions without clear owners: Consensus feels safe, but it quietly delays action. 3. Unspoken friction: Small blockers go unaddressed until they compound into major slowdowns. 4. Fear of breaking what’s working: Past success can make teams overly cautious about change. 5. Execution without a reset: Without recovery and reflection, teams stay busy but lose momentum. Hard truth: Good teams don’t stall because they lack talent. They stall because speed requires clarity, trust, and designed systems. Fix the system and pace follows. #Leadership #TeamPerformance #BusinessOperations #OperationalExcellence #ScalingBusinesses

I automated my entire team's workflow—and then THIS happened. Ever wonder what would happen if your team could complete a week's worth of work in a single day? Sounds like a dream, right? Well, that's exactly what we achieved. A few months back, I noticed my team was bogged down with repetitive tasks. Brilliant minds were spending hours on mundane activities. So, I decided to take a bold step. We invested in automating these tasks. The initial push was challenging: - Learning new tools - Changing long-standing processes - Overcoming resistance to change But the payoff was incredible. Results: - Productivity skyrocketed: We accomplished more in less time. - Stress levels dropped: The team felt less overwhelmed. - Innovation flourished: Free time led to creative solutions. - Employee satisfaction increased: Work became more fulfilling. The most surprising outcome? Our team cohesion strengthened. With less time on grunt work, we collaborated more on strategic projects. The takeaway? Automation isn't about replacing people. It's about freeing them to do what they do best. Embrace technology to unlock your team's true potential. Have you implemented automation in your work?