AI Tools for Identifying Task Bottlenecks

I just watched a VP spend 3 hours explaining why their "simple" app idea would take 6 months to build. The same idea I built during the meeting in 10 minutes. "We need a way to validate our user flows before we build anything." The design team started talking user research, A/B testing frameworks, months of iteration cycles. So while they debated hiring a UX consultant, I opened Emergent. "Build a user flow validator that analyzes website user journeys and identifies UX bottlenecks." 12 minutes later - working User Flow Validator: → Visual user journey mapping → Bottleneck identification → Drop-off point analysis → Conversion optimization recommendations The room went quiet when I screen-shared the live app. "It's called agentic development. AI agents working together like a dev team - all coordinated automatically." Here's what I love about tools like Emergent: They expose how much time we waste on ceremonies instead of solving problems. While traditional teams have their 47th meeting about "technical architecture," you're already gathering user feedback. The VP canceled the 6-month timeline. We deployed that afternoon. The lesson? In 2025, the constraint isn't technology. It's imagination- limited by outdated processes. Most leaders think in "quarters" when they should think in "minutes." 💬 What's the longest you've waited for a "simple" internal tool? Follow Edward Frank Morris for more AI strategy insights.

It is Friday afternoon and your ML service is 𝟭𝟬𝘅 𝘀𝗹𝗼𝘄𝗲𝗿 than usual. Your boss is panicking 😱😱😱 What do you do? ⬇️ 𝗥𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 💁 Imagine you're an ML engineer working on the real-time recommendation system at a large video streaming platform ▶️. The recommendation API is a Rust micro-service that runs  in a Kubernetes cluster, together with hundreds of services. For each incoming request, your recommendation API does 3 things: 1 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, to map raw request data to ML model features. 2 > Raw 𝗺𝗼𝗱𝗲𝗹 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻, to map ML model features to predicted scores. 3 > 𝗣𝗼𝘀𝘁-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, to map raw predictions to actual recommendations, and send them back to the client app. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 For this API to make a real impact on business metrics (aka higher user engagement) you need to make sure that recommendations are generated and served as fast as possible. Example: "95% of requests must complete within 100ms" Otherwise, the end users do not see the recommendations on time, and your system has 𝗭𝗘𝗥𝗢 impact on their engagement in the platform. 𝗡𝗼𝘄 𝗶𝗺𝗮𝗴𝗶𝗻𝗲... 🤔💭 It is Friday afternoon, and you get a call from your boss: “Hey! Over 50% of recommendations are taking over 1 second. What is going on?” 😱😱😱 How do you find the root cause of the problem? And more importantly, how do you fix it? This when 𝗽𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 and a tool like 𝗣𝗲𝗿𝗳𝗼𝗿𝗮𝘁𝗼𝗿 come to the rescue 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗲𝗿𝗳𝗼𝗿𝗮𝘁𝗼𝗿? Perforator is a powerful profiling tool built and open-sourced by Yandex that > Tells you how much time and resources each line of your program code takes > So you can quickly find the bottlenecks in your system and fix them. Perforator supports many programming languages (Python, C, C++, Rust, Go, Java) and can run either For our recommendation API, we can run a scan to realize, for example, that > The latest version of our feature engineering function, that includes new features developed by the data science team, takes over 200 ms, and it is better to drop them. or > The model prediction step is terribly slow, as the underlying model is a deeper XGBoost model than in the previous release. Which means, the incremental improve in test metrics came at an excessively large cost in terms of latency. 𝗜𝗻 𝗮 𝗻𝘂𝘁𝘀𝗵𝗲𝗹𝗹 🥔 Perforator analyzes code in real-time, and > Allows developers to find bottlenecks, optimize code, and understand which functions are in use and which are obsolete > Provides live insights into server and application performance 𝗪𝗮𝗻𝗻𝗮 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗣𝗲𝗿𝗳𝗼𝗿𝗮𝘁𝗼𝗿? Visit the github page of this open-source project and share your love with a star ⭐ 🔗> https://bit.ly/3WFyISF Official Yandex blogpost 🔗> https://lnkd.in/eCQekKpB

Most project managers discover risks after they've become problems. Elite PMs use AI to spot them weeks earlier. This is how top performers are using AI for risk management: 1. Early warning systems: ↳ Machine Learning (ML) algorithms flag anomalies in project data before they escalate 2. Resource optimization: ↳ AI analyzes allocation patterns to prevent bottlenecks 3. Trend prediction: ↳ Natural Language Processing (NLP) scans stakeholder communications for emerging concerns Organizations implementing these approaches see: ↳ 40% reduction in safety incidents ↳ 25% fewer project delays ↳ 20% cost savings through optimized resources The leadership gap is widening between reactive and proactive project managers. PMs mastering AI risk tools today are becoming the strategic leaders organizations need tomorrow.