Reviewing Progress Regularly

I just watched an AE lose a $1.2M deal after running a "successful" product trial that the prospect LOVED. After 8 weeks of work, the CFO killed it with five words: "Let's try our current vendor." After analyzing 200+ enterprise sales cycles at companies including Salesforce, HubSpot, Thomson Reuters, and Workday, I've identified the exact framework that separates 80%+ trial conversion rates from the industry average of 30%. The psychological shift required… Stop treating trials as product demos and start treating them as RISK ELIMINATION EXERCISES. After being promoted 12 times and hitting #1 in every role before leading a 110-person team to $190M+ annually, I've developed a framework that's transformed how top companies run trials. THE 5 POINT TRIAL QUALIFICATION SYSTEM: 1. 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝗩𝗔𝗟𝗜𝗗𝗔𝗧𝗜𝗢𝗡 Ask these 3 questions before any trial: → "What happens if you don't solve this in 90 days?" (quantify impact)  → "How have you tried solving this before?" (establishes solution gap)  → "Who else is affected?" (identifies stakeholders) These eliminate 68% of unqualified trials before they start. 2. 𝗦𝗨𝗖𝗖𝗘𝗦𝗦 𝗗𝗘𝗙𝗜𝗡𝗜𝗧𝗜𝗢𝗡 Document these 4 criteria: → Technical requirements (features that must work)  → Business metrics (quantifiable outcomes)  → Timeline requirements (implementation speed)  → User adoption requirements (usage patterns) Get confirmation: "If we demonstrate [criteria], you'd move forward with purchase by [date]. Correct?" 3. 𝗦𝗧𝗔𝗞𝗘𝗛𝗢𝗟𝗗𝗘𝗥 𝗠𝗔𝗣𝗣𝗜𝗡𝗚 Create a "Decision Matrix" for: → Technical buyers (every trial user)  → Economic buyers (CFO/budget holder)  → Political influencers (who can kill it)  → Current solution advocates (status quo beneficiaries) Document each person's personal win/loss if change happens. 4. 𝗣𝗥𝗘-𝗧𝗥𝗜𝗔𝗟 𝗔𝗚𝗥𝗘𝗘𝗠𝗘𝗡𝗧 Have legal review BEFORE starting: "We typically have legal review the agreement structure ahead of time so there are no surprises and to save us both time so we can hit the deadline of December 1st you set. Would you be open to this during the trial?" 5. 𝗖𝗨𝗥𝗥𝗘𝗡𝗧 𝗩𝗘𝗡𝗗𝗢𝗥 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 Ask: → "Have you discussed these challenges with your current vendor?"  → "What was their response?"  → "What specific capabilities do they lack?" Document these to prevent the "let's try our current vendor" objection. RESULTS from this framework: ✅ Trial conversion: 32% to 83% in 60 days  ✅ Average deal size: +40%  ✅ Sales cycle: -37%  ✅ Forecast accuracy: +92%  ✅ Time on unsuccessful trials: -43% — Hey Sales Leaders! Want to see how we can install these kinds of results into your org? Go here: https://lnkd.in/ghh8VCaf

It’s the time of the year for performance reviews. Every year, I remind myself that giving feedback comes down to this: “radical candor” plus “radical compassion.” If you are too candid/direct, you will make your team feel defensive. But if you soften your feedback too much (which I have seen too many leaders do), your message will not be clear. The net effect if you don't get the balance right is that your team will not grow. It’s a difficult balance to strike. We’ve all had moments where we’ve held back the feedback we planned to give because we don’t want to hurt someone’s feelings. But the truth is, when you deliver feedback from a place of wanting to help someone reach their potential, that actually builds trust. I always start there - I make sure that my team knows that I am deeply committed to their growth. So this performance review season, don’t be afraid to be direct. But remember: being direct does not mean being harsh. Show the person you care about their growth and then follow it up with a plan to help them develop. “Radical candor” plus “radical compassion” is the feedback formula that works! What mindset are you taking into this performance review season?

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

“Show outcomes, not outputs!” I’ve given (and received) this feedback more times than I can count while helping organizations tell their impact stories. And listen, it’s technically right…but it can also feel completely unfair. We love to say things like: ✅ 100 teachers trained ✅ 10,000 learners reached ✅ 500 handwashing stations installed But funders (and most payers) want to know: 𝘞𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘤𝘩𝘢𝘯𝘨𝘦𝘥 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝘢𝘭𝘭 𝘵𝘩𝘢𝘵? That’s the outcomes vs outputs gap: ➡️ Output: 100 teachers trained ➡️ Outcome: Teachers who received training scored 15% higher on evaluations than those who didn’t The second tells a story of change. But measuring outcomes can be 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. It’s easy to count the number of people who showed up. It’s costly to prove their lives got better because of it. And that creates a brutal inequality. Well-funded organizations with substantial M&E budgets continue to win. Meanwhile, incredible community-led organizations get sidelined for not having “evidence”- even when the change is happening right in front of us. So what can organizations with limited resources do? 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵: That study from Daystar University showing teacher training improved learning by 10% in India? Use it. If your intervention is similar, cite their methodology and results as supporting evidence. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 𝘀𝘁𝘂𝗱𝗶𝗲𝘀: Baseline and end-line surveys aren't perfect, but they're better than nothing. Self-reported confidence levels have limitations, but "85% of teachers reported feeling significantly more confident in their teaching abilities," tells a story. 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝗹𝗼𝗰𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀: Universities need research projects. Find one studying similar interventions and collaborate. Share costs, share data, share credit. 𝗨𝘀𝗲 𝗽𝗿𝗼𝘅𝘆 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Can't afford a 5-year longitudinal study? Track intermediate outcomes that research shows correlate with long-term impact. 𝗧𝗿𝘆 𝗽𝗮𝗿𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗼𝗿𝘆 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Let beneficiaries help design and conduct evaluations. It's cost-effective and often reveals insights that traditional methods miss. For example, train teachers to interview each other about your training program. And funders? Y’all have homework too. Some are already offering evaluation support (bless you). But let’s make it the rule, not the exception. What if 10-15% of every grant was earmarked for outcome measurement? What if we moved beyond gold-standard-only thinking? 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗮 𝗰𝗲𝗿𝘁𝗮𝗶𝗻 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 “𝗻𝗼𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹”. We need outcomes. But we also need equity. How are you navigating this tension? What creative ways have you used to show impact without burning out your team or budget? #internationaldevelopment #FundingAfrica #fundraising #NonprofitLeadership #nonprofitafrica

Unlock the Power of High-Quality Performance Reviews 'Tis the season for annual performance reviews. They are dreaded by some (both managers and direct reports alike), but a GOLDEN opportunity for growth, alignment and acceleration when done right! When I became a people manager for the first time I had no formal training on how to do a formal performance evaluation which made it more an intimidating and time consuming process than effective. It took me a while to develop some best practices which I still use today. Here are some actionable tips for how to make these conversations transformative instead of transactional: Best Practices for Managers: 1️⃣ Make it a Dialogue, Not a Monologue: Listen as much as you speak. Performance reviews should be a two-way street. 2️⃣ Focus on Specifics: Give actionable, evidence-based feedback tied to clear examples—not vague generalizations. 3️⃣ Balance Praise with Growth Opportunities: Celebrate wins but also highlight areas for improvement with a clear path forward. 4️⃣ Set Goals, Not Just Grades: Use reviews to align on SMART goals for the future. 5️⃣ Document & Follow Up: Don’t let feedback vanish post-meeting. Document outcomes and revisit them regularly. Common Mistakes to Avoid: 🚫 Waiting Until Review Time: Feedback should be ongoing—not a once-a-year surprise. 🚫 Being Too General: Saying "Good job" or "Needs improvement" without specifics leaves employees guessing. 🚫 Avoiding Tough Conversations: Constructive feedback can be uncomfortable, but it’s essential for growth. 🚫 Ignoring Employee Input: This isn’t just your show—make space for their perspective! Tips for Employees: Get Better Feedback 1️⃣ Be Proactive: Ask for feedback regularly—not just during reviews. Questions like, “What’s one thing I could do better?” shows initiative and openness. 2️⃣ Come Prepared: Bring accomplishments, challenges, and goals to the table. Show ownership of your growth. 3️⃣ Clarify Expectations: Ask, “What does success look like in my role / on this project?" This helps align your work with manager expectations. Year-Round Impact ✔️ Schedule Regular Check-Ins: Quarterly or monthly conversations keep feedback fresh and actionable. ✔️ Use Tools to Track Progress: Utilize shared documents or platforms to monitor goals throughout the year. ✔️ Create a Feedback Culture: Encourage real-time recognition and coaching on a weekly basis. A high-quality performance review isn’t just a meeting—it’s a tool for growth, alignment, and stronger relationships. Let’s move away from the “annual checkbox” and toward continuous improvement! What’s your secret to impactful performance reviews? Drop your tips in the comments! #Leadership #Feedback #PerformanceManagement #CareerGrowth

When founders don’t trust their team, they start hovering. Every update is a red flag. Every task feels like a risk. And the worst part? They justify it. "I just want to make sure it’s done right." But micromanagement doesn’t fix problems. It creates new ones. Especially in high-stakes industries like Fintech. Let’s say you’re outsourcing the development of a digital lending app. If there’s no structure. No system for deliverables No timeline No feedback loop Then micromanagement becomes the default. • You follow up • You second-guess • You slow everything down The real solution isn’t tighter control. That's the last thing. It’s clearer processes. Now, you might have also been told to do this: • Define ownership • Use milestone-based contracts • Set communication cadences • Track what matters - not every single step Sure, that helps. But it’s not enough. Because micromanagement is what fills the void when structure is missing. Don’t patch the symptoms. Fix the foundation. So, to make delegation and outsourcing work, here’s what I suggest to my clients: 1 // Milestone-Based Deliverables with Acceptance Criteria • Break the project into clear milestones (UI prototype, backend integration, UAT, go-live) • Define what “done” means for each milestone • Link payments to milestone approvals - not just dates Examples: "UI prototype approved by client within 3 business days of delivery" "Lending workflow passes all test cases as per attached checklist" 2 // Progress Reporting & Demo Cadence • Include weekly or bi-weekly reports (written or demo) • Cover status, blockers, next steps, and demo of completed features • Lack of updates can trigger escalation or pause payments 3 // Feedback & Review Windows • Define time limits for feedback (e.g., 5 business days) • No feedback = auto-approval to keep things moving 4 // Issue Escalation & Dispute Resolution • Add process to resolve rejected deliverables • Example: “Meet within 3 business days to resolve” • Use mediation/arbitration under Indian law for unresolved issues 5 // Ownership, Access & Handover • All code, docs, and credentials handed over at each milestone • Add interim access clauses for termination or delay 6 // Confidentiality & Compliance • NDAs and data protection must comply with Indian fintech laws • Follow DPDP Act, RBI guidelines, and security best practices When these structures are in your contract: • You create accountability without micromanagement • You get transparency and control - without the stress • Your team knows what’s expected, and you know what’s coming next Fix the foundation, and trust (plus results) will follow. --- ✍ Tell me below: What’s one process you added that helped reduce micromanagement in your team?

Before you automate anything, answer this: Can you document your process in 10 steps? If not, automation will just replicate your chaos faster. 🔧 Most GRC teams get this backwards They spend weeks building AI validators, evidence collectors, or risk scorers. Then wonder why outputs are inconsistent, inaccurate, or unusable. The problem isn't the AI. It's the workflow underneath. The workflow audit comes first. The automation comes second. 📧 This week in GRC Engineer: "Engineer Your GRC Process Before You Automate It" The 30-minute audit that shows whether your workflows are ready for automation: ✅ Input Clarity - Do you know what data you actually need? ✅ Process Definition - Can someone else follow your steps and get the same result? ✅ Output Consistency - Does the same request produce the same format every time? ✅ Repeatability - Can anyone execute this without tribal knowledge? Copy-paste checklist included. Score your workflows. Fix one thing this week. Read here: https://lnkd.in/e_-zR2Rv Last week: Fixed your prompts This week: Audited your workflows Next week: Validation frameworks to ensure you can scale automation The GRC professionals who master process engineering + AI scaffolding will define the next decade. #GRCEngineering #ProcessDesign #Automation

Why product roadmaps should be outcome based not feature-driven We do sprints to ship features, and they don’t always work out. Why? Because features alone don’t move the needle -outcomes do. A practice that I usually follow is to ask myself: What problem are we solving, and how will we measure success?” And that’s how we pivot from feature factories to outcome-driven roadmaps with actionable steps to make it stick. 𝗪𝗵𝘆 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Outcome-based roadmaps focus on measurable results (e.g., “Increase free-to-paid conversion by 15%” vs. “Build a pricing calculator”). This shift: - Aligns teams around business goals, not just deliverables. - Empowers creativity (solve the problem, don’t just check a box). - Reduces waste by killing initiatives that don’t drive impact. But how do you actually make this work? Here’s My Practical Playbook 👇🏻 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 “𝗪𝗵𝘆” - Define outcomes tied to business goals: Partner with leadership to align on 1-2 KPIs per quarter (e.g., “Reduce churn by 10%”). - Ask this question: “If we deliver X feature, what outcome does it enable?”. If there’s no clear answer, rethink it. 2️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Outcomes are broad—break them into testable hypotheses. - Example: To “Increase user engagement by 20%,” run:   - A/B test push notification timing.   - Pilot a gamified onboarding flow.   - Measure DAU/WAU ratios weekly. 3️⃣ 𝗔𝗱𝗼𝗽𝘁 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 - OKRs: Link Objectives (outcomes) to Key Results (metrics). - Impact Mapping: Visualize how features connect to goals. - RICE Scoring: Prioritize initiatives by Reach, Impact, Confidence, Effort. 4️⃣ 𝗚𝗲𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗕𝘂𝘆-𝗜𝗻 - Frame outcomes as ROI: Show how “Reduce support tickets by 25%” cuts costs. - Prototype outcomes first: Share a mock roadmap with leadership, highlighting gaps in current feature-centric plans. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Track leading indicators (e.g., user behavior changes) alongside lagging metrics (e.g., revenue). - Celebrate “failures”: Killing a feature that didn’t drive outcomes is a win. 𝟯 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 - - Vague outcomes: “Improve UX” → ❌ | “Reduce checkout abandonment by 20%” → ✅. - Overloading the roadmap: Focus on 1-2 outcomes per quarter. - Ignoring feedback loops: Revisit outcomes bi-weekly—adapt as data comes in. This week, try this: Audit your roadmap. For every feature, ask: “What outcome does this serve?” If it’s unclear, reframe it, or cut it. I believe outcome-based roadmaps is a survival tactic. Let’s build products that matter. 👉 How are you bridging the gap between features and impact? Would love to know your process.

A "sampled success metric" is a performance measure or evaluation criterion calculated from a sample or subset of data rather than the entire population. Its calculation often involves higher costs per sample, such as manual review, leading to a trade-off between sample size and metric accuracy/sensitivity. In this tech blog, written by the data science team from Shopify, the discussion revolves around how the team leverages Monte Carlo simulation to understand metric variability under various scenarios to help the team make the right trade-offs. Initially, the team defines simulation metrics to describe the variability of the sampled success metric. For instance, if the actual success metric is decreasing over time, the metric could indicate how many months of sampled success metric would show a decrease, termed as "1-month decreases observed". Then, the team defines the distribution to run the Monte Carlo simulation. Monte Carlo simulation, a computational technique using random sampling to estimate outcomes of complex systems or processes with uncertain inputs, draws samples from a dedicated distribution that matches business needs. Based on past observations, the team’s application follows a Poisson distribution. Next comes the massive simulation phase, where the team runs multiple simulations for one parameter and then changes various parameters to simulate different scenarios. The goal is to quantify how much the sample mean will differ from the underlying population mean given realistic assumptions. The final result provides a clear statistical distribution of how much extra sample size could lead to metrics variability decrease and increased accuracy. This case study demonstrates that Monte Carlo simulation could be a valuable toolkit to add to your decision-making and data science knowledge. #datascience #analytics #metrics #algorithms #simulation #montecarlo #decisionmaking – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/dKnrZzzV 

50% of employees say performance reviews are useless. Here's how to fix that. I've spoken to hundreds of people over the years. The pattern is painfully consistent. Manager talks. Employee nods. Nothing changes. But the data is even more concerning: more than half of employees feel formal reviews contribute nothing to their growth. No surprise there. The problem exists on both sides of the table: - Employees dump all responsibility for these sessions on their managers - Managers have zero training on how to make these conversations meaningful The result? Monologues that waste everyone's time. But here's the thing about great performance reviews: They're not monologues—they're conversations. Want to transform your review sessions into career accelerators? Here's how: For managers: - Implement structured frameworks like McKinsey & Company's OILS (Observation, Impact, Listening, Solutions/Strategy) - Work together to identify what's actually causing performance challenges (Is it time management? Communication gaps?) - Establish clear priorities with specific targets and timelines for the next period For employees: - Come prepared with defined goals and the specific skills you need to develop in the next 6-12 months - Bring a concise, tactical action plan to ensure alignment and measurable progress Whatever it takes, remember that performance growth is a two-way street. These sessions should empower both sides to grow, not just check administrative boxes. What's your best tip for making reviews actually matter? I would love to hear. __ ♻️ Reshare this post if it can help others! __ ▶️ Want to see more content like this? You should join 2297+ members in the Tidbits WhatsApp Community! 💥 [link in the comments section]