Automation In Project Management

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 👇

Most AI agents live in a browser tab nobody opens. I spent an evening putting one in Slack instead, using CopilotKit’s new Channels SDK. Four things stood out: - One codebase. Native Block Kit in Slack, Adaptive Cards in Teams. - Approvals in the thread. Agent pauses, someone clicks, run continues. - Context carries across platforms. Ask in Slack, follow up in Teams. - It’s agent-agnostic over AG-UI. LangChain, OpenAI Agents SDK, Google ADK and more, or your own HTTP agent. The part I’d normally lose a day to (provider connection, delivery, retries, channel state) runs through CopilotKit Intelligence. Free to start, and there's a direct path if you'd rather own the Slack app yourself. Slack and Teams are live now. Discord, WhatsApp, iMessage, Telegram are next. GitHub Repo and setup guide 🔗 https://lnkd.in/ge4RyPkp 

How we saved 10+ hours weekly by giving finance a simple interface. Our finance team was processing invoices the same way for years: 1. Email attachments → 2. Manual download → 3. Print → 4. Physical signature → 5. Scan → 6. Manual data entry The entire cycle took 3-5 days. The request to "build a proper approval system" kept getting deprioritized—it felt like a multi-month project. We reframed the problem: We didn't need a complex system. We just needed to connect two things: the data from our accounting software's API and a simple list where the right people could click "Approve" or "Reject." What actually got built: • A single-page app that pulls unpaid invoices automatically • Logic that routes invoices over $5k to directors, others to managers • A comment field for rejections • A basic audit log showing who approved what and when What changed: ✅ Approvals now happen in under 24 hours ✅ The finance team stopped chasing paper trails ✅ Vendors get paid faster ✅ Every decision is logged automatically The takeaway: Sometimes "digital transformation" isn't about big platforms. It's about giving a team one less PDF to manage by building a simple, focused tool that sits on top of the data they already use. What's the most stubborn, repetitive task in your team's workflow? Often the highest-impact tools are the smallest ones that remove a single point of friction. https://uibakery.io/ #ProcessAutomation #FinanceTech #OperationalEfficiency #DigitalTransformation

650. That’s the staggering number of companies offering conversational AI solutions for sales and service. The flood isn’t slowing: each week brings new entrants or announcements. A year ago, the market was already crowded; today, the latest wave of AI technologies has further lowered barriers to entry, fueling an unsustainable proliferation. Beyond the three hyperscalers, only a handful of providers have surpassed $100M in ARR. I spent the summer making sense of the mayhem. The result: nine categories mapped to the core jobs-to-be-done. Customer service and support solutions fall into four categories: • Virtual Agents. IVAs and their AI evolution operate across digital channels, handling transactional interactions and escalating to humans when necessary. • AI Answer Engines. These retrieve and format answers from knowledge bases. Generative AI has dramatically improved precision for informational inquiries. • Conversational IVR and Voice Agents. Voice remains complex; these agents primarily handle transactional interactions. • Conversational Engagement and Outreach Agents. These manage outbound communications across voice, SMS, and messaging channels, complying with regulations. Historically transactional, they increasingly enable dynamic engagement. Sales solutions are grouped into three categories: • Conversational Commerce & Concierge Agents. Mature agents replacing traditional chat with conversational experiences across pre- and post-sales. "Concierge" reflects their versatility in guiding customers seamlessly. • Autonomous SDRs (Sales Development Reps). Focused on complex B2B scenarios, they enrich and qualify leads, route them to sellers, and schedule appointments. Among the most mature AI applications for B2B sales. • Autonomous BDRs (Business Development Reps). These drive outbound sales motions where relevance is critical. Complex to implement and scale, they work best in highly targeted scenarios where personalization is flawless. Some providers span the full spectrum of service use cases and Conversational Commerce & Concierge Agents. Rather than duplicating them across categories, I group them under Conversational AI Platforms, relying on robust capabilities to design, deploy, and continuously improve applications and agents. Customer Support Automation is an emerging platform category, tailored for handling support requests and a natural fit for GenAI. These platforms deliver full resolutions when possible, automate workflows, and assist agents with context and guidance. It’s a mature use case for Agentic AI, with many providers publicly demonstrating transformative results. The visual landscape below captures this segmentation. A few vendors will emerge as true platforms, while others will focus on niches or become embedded in broader applications. The market remains in motion, and I welcome perspectives on what I may have overlooked. #conversationalai #agenticai #cx #salestech

AI field note: my word of the year is 𝔼𝕍𝔸𝕃: celebrating the art and science of rigorous measurement of AI performance, progress and purpose. (1 of 3) This year delivered a wealth of new AI models, architectures, and use cases - all united by one thread: evaluation. Model benchmarking, evaluation, or just "eval" has evolved from a simple, singular measure to a more complex blend of stats, metrics, and measurement techniques. Today's evals help discerning practitioners make pragmatic, informed technology decisions and measures improvements as AI systems are tuned. With AI innovation accelerating, staying up to date on evals ensures informed trade-offs when building intelligent systems, agents, and applications. Let's start by looking at measuring "performance"; the best way we know how to compare model behaviors, and find the right fit-for-purpose. Defining 'good performance' now involves a sophisticated suite of metrics across diverse dimensions. ⚙️ Task eval - beyond raw performance numbers. Today's evals measure how models perform across diverse scenarios - from basic comprehension to complex reasoning, reliability, consistency, and nuanced evaluation of reasoning paths, output quality, and edge case handling. 👛 Token economics - balancing cost, efficiency, and operation. Understanding token costs - both input and output - was essential last year, but evals have evolved beyond raw price per token, to understanding efficiency patterns, batching strategies, and the total cost of operation. ⏲️ Time-to-first-token. Speed is a feature, as they say, and while streaming responses have improved user experiences, this metric has become particularly crucial as models are deployed in production environments where user experience directly impacts adoption. 🔥 Inference compute: The amount of compute used for prediction shapes what problems a model can solve. More compute enables greater complexity but increases costs and latency - making it a pivotal benchmark for 2024. For some light holiday reading to explore this further: Service cards (OpenAI, Amazon), Meta's Llama 3 paper, and Anthropic's evaluation sampling research (links below).

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

The April 2026 release of Power Automate Desktop (version 2.67) has been out in some early release / US and CA environments for over a week now. I was waiting for the release notes to come out for a while, because I couldn't see anything new in this new release. I bet there are quite a few fixes and improvements that are not as obvious as huge new features, so, I figured I'd wait. But then I accidentally switched to a Canada-based environment during one of my classes and noticed two new action groups available there. And these are quite major. 📌 Microsoft Copilot Studio We can now interact with agents built in Microsoft Copilot Studio via desktop flows in Power Automate. We can execute agents, wait for their response, as well as get various details and run evaluations. This is pretty cool, if you ask me. RPA used in combination with agents is the way to go. And it is absolutely relevant both ways - both being able to invoke a desktop flow as a tool in an agent, but also being able to call in an agent to help out at runtime of a desktop flow. So, this is a great first step in the right direction. 📌 Standard approvals Approvals were long overdue in Power Automate Desktop. There are plenty of use cases where user input is required during desktop flows. And while forms and message boxes in PAD covered this need in attended scenarios, but there was nothing for unattended use cases. So, it's actually great we get to do approvals now in desktop flows. While I obviously always recommend leaving whatever can be done in a cloud flow to Power Automate cloud flows, instead of RPA, there are definitely lots of scenarios where an approval may be needed midway through a desktop flow. And it just isn't feasible to pause or stop the flow and invoke a cloud flow to handle approvals. Of course, waiting for an approval in a desktop flow may also not be ideal, considering that it could easily get stuck if people ignored / missed the notification. And that would lead to a machine being busy, where parallel executions are not allowed. So, we should still be careful when using it. But it is still a great addition. And the best part? Neither of these requires Power Automate Premium. Of course, it would require an M365 license to use approvals, and we would need some MCS capacity and licensing to interact with agents. But it's actually pretty cool that for scenarios where it isn't absolutely necessary, we would in fact be able to use this without a Premium license. Great job by the Power Automate team as always! P.S. These are currently only available in US and Canada. But they're not flagged as preview features. I'm guessing they are being gradually rolled out globally. We'll need to wait for the release notes to come out to know for sure.

Most project managers think Claude Cowork is a tool for developers. It is not. It is your AI teammate for project management. No code. No technical background required. Just a smarter way to manage projects, stakeholders, and delivery. AI Fluency is fast becoming part of job requirement and expectation. Here is how to get started and what it can do for you every week. → 1. Set up your CLAUDE.md file first Tell your teammate who you are, your role, your projects, and how you communicate. It takes 5 minutes. From that point, it stops being generic and starts working the way you work. → 2. Use Plan Mode before any complex task Press Shift and Tab before you give it a brief. Your teammate proposes a plan and waits for your approval before doing anything. You stay in control. Nothing happens without your sign-off. → 3. Let it remember Your teammate saves what it learns about your projects automatically. You do not need to re-explain context every time you open a new session. The longer you use it, the better it knows your work. → 4. Connect your tools once Gmail, Slack, Notion and Jira link to your account once. Your teammate uses them in every session without any setup. Zero configuration. They just follow you. → 5. Set how hard it thinks For simple tasks like status updates, keep it light. For complex tasks like risk planning, ask it to think deeper. Match the effort to the task and it becomes significantly more useful. Here is how you can use it every week. Status reports and executive updates. Give Claude your project data and it drafts the narrative. You refine and send. What used to take an hour takes ten minutes. Risk identification. Describe your project and ask for a pre-mortem. It surfaces blind spots before they become escalations. Meeting preparation. Ask it to brief you before every key session. Agenda, history, open actions. You walk in prepared every time. Lessons learned. Paste your retrospective notes and ask for themes. A whole workshop distilled into a structured output in minutes. None of this is theoretical. This is Tuesday afternoon project management. Where to start this week. Monday — Download the Claude desktop app and set up your CLAUDE.md. 10 minutes. Wednesday — Use Plan Mode on your next complex task. See how it proposes before it acts. Friday — Ask it to draft your weekly status report from your project notes. See what comes back. If you want to go deeper than this and build real AI capability across your full project lifecycle, the AI Capability Cohort for Project Managers starts on 4th May. Small group. Hands on. Built around doing and not watching. DM me APM and I will share more details with you.

Here are 5 machine learning algorithms used for FP&A and #finance time series analysis: ✅ ARIMA/SARIMA: Forecast future revenues and expenses by identifying trends and seasonality. ✅ LSTM: Analyze complex patterns in cash flow or sales data to improve financial planning. ✅ Prophet: Handle unpredictable markets and still make reliable forecasts. ✅ GARCH: Assess and predict market volatility to make more informed investment or budgeting decisions. More detail below ↓ 1. ARIMA (Auto-Regressive Integrated Moving Average) ARIMA helps predict future values by analyzing past data to identify patterns like trends or seasonality. For example, you can use ARIMA to forecast next year’s monthly revenue by recognizing historical trends and seasonal variations, such as higher sales during holiday seasons. 2. LSTM (Long Short-Term Memory) Networks LSTM is an artificial intelligence technique that learns from past data and remembers long-term patterns. It can be used in FP&A to forecast cash flow by identifying recurring inflows and outflows over time, like specific project payments or seasonal cash patterns. 3. SARIMA (Seasonal ARIMA) SARIMA extends ARIMA by incorporating seasonality, making it ideal for forecasting data with regular patterns. For example, you can predict quarterly expenses more accurately if certain quarters have consistently higher costs due to contracts or seasonal demand. 4. Prophet Prophet, developed by Facebook, handles missing data and outliers well, making it useful for complex datasets. To get the code and example for implement it, go here: https://lnkd.in/eJKcHzqU You could use Prophet to forecast annual sales even when your data is incomplete or affected by irregular events like economic shifts. 5. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models volatility and is great for predicting how much financial data varies over time. You can apply it in FP&A to assess and predict the volatility of stock prices in your investment portfolio, helping in risk management and budgeting.

Machine learning models aren’t a “build once and done” solution—they require ongoing management and quality improvements to thrive within a larger system. In this tech blog, Uber's engineering team shares how they developed a framework to address the challenges of maintaining and improving machine learning systems. The business need centers on the fact that Uber has numerous machine learning use cases. While teams typically focus on performance metrics like AUC or RMSE, other crucial factors—such as the timeliness of training data, model reproducibility, and automated retraining—are often overlooked. To address these challenges at scale, developing a comprehensive platform approach is essential. Uber's solution involves the development of the Model Excellence Scores framework, designed to measure, monitor, and enforce quality at every stage of the ML lifecycle. This framework is built around three core concepts derived from Service Level Objectives (SLOs): indicators, objectives, and agreements. Indicators are quantitative measures that reflect specific aspects of an ML system’s quality. Objectives define target ranges for these indicators, while Agreements consolidate the indicators at the ML use-case level, determining the overall PASS/FAIL status based on indicator results. The framework integrates with other ML systems at Uber to provide insights, enable actions, and ensure accountability for the success of machine learning models. It’s one thing to achieve a one-time success with machine learning; sustaining that success, however, is a far greater challenge. This tech blog provides an excellent reference for anyone building scalable and reliable ML platforms. Enjoy the read! #machinelearning #datascience #monitoring #health #quality #SLO #SnacksWeeklyonDataScience – – –  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/g6DJm9pb