AI For Time Management

Microsoft's annual Work Index Report is always excellent, a must-read, and the new one released today is no exception. I've highlighted some of key takeaways below. 🚀 Frontier Firms thrive with AI at the core. Frontier Firms—organizations with mature AI strategies and active agent use—report double the success rates: 71% of their workers say the company is thriving versus 37% globally. These employees are also more likely to say they do meaningful work (90% vs. 73%) and can take on more workload (55% vs. 20%). ⏱️ AI bridges the capacity gap. With 53% of leaders saying productivity must rise but 80% of workers feeling maxed out, AI is stepping in to fill the gap. Agents are already being used by 46% of organizations to automate entire workflows, helping companies expand output without overburdening staff. 🧠 Every employee becomes an agent boss. The rise of AI agents means managing digital colleagues is becoming a standard part of many roles. Within five years, 42% of leaders expect to build multi-agent systems, and 36% anticipate managing them directly. Leaders are already ahead: 67% are familiar with agents, compared to just 40% of employees. 📈 AI is transforming team structures and hiring. 47% of leaders prioritize upskilling for AI, and 78% are considering new AI-specific roles like AI trainers, security specialists, and ROI analysts. In fact, leading startups are growing headcount at nearly double the rate of Big Tech, driven by demand for AI capabilities. 🧩 Org charts are giving way to dynamic “Work Charts.” Human-agent teams enable agile, goal-oriented collaboration—akin to how film crews form around projects. This breaks down traditional silos and allows lean, high-impact teams to emerge quickly, guided more by outcomes than departmental lines. ⚙️ AI's unique strengths change why people use it. The top reasons people choose AI over colleagues are its 24/7 availability (42%), faster and higher-quality work (30%), and unlimited idea generation (28%). This shows workers value AI for what humans can't match—speed, scale, and endurance—not because they want to avoid collaboration. 🧪 AI integration redefines job security and career paths. As roles evolve, 83% of leaders believe AI will help employees tackle more strategic work earlier in their careers. Already, some companies are replacing senior roles with junior employees supported by AI. 💡 Thought partnership with AI drives value. 46% of workers now treat AI as a thought partner—using it to brainstorm and enhance creativity—rather than just a command-based tool. This collaborative mindset unlocks more sophisticated and impactful use of AI across the board. 📉 Interruptions and overload highlight AI's value. Employees are interrupted an average of 275 times daily, and 60% of meetings are ad hoc. AI can reduce this chaos, reclaiming focus and freeing up time for higher-value activities by handling routine tasks and managing workflows.

I spend a huge part of my week just managing my calendar — finding free slots, rescheduling meetings, dealing with recurring events, and juggling multiple time zones. It’s tedious and eats into real work. That’s why I decided to build my own solution: a Google Calendar AI agent powered by Google’s Agent Development Kit. This agent can: 👉 Understand plain English commands like “Schedule a 1-hour call with Alex next Tuesday morning”. 👉 Suggest free time slots based on my existing calendar. 👉 Handle recurring events, cancellations, and attendees automatically. 👉 Work across time zones without any manual conversion. While building this, I learned something crucial: AI isn’t just about generating text — it can actually perform actions that solve real problems. Designing this agent taught me how to bridge natural language understanding with real-world API actions. I wrote a detailed step-by-step blog, including code snippets and logic, so anyone can replicate this setup or build their own AI productivity assistant: https://lnkd.in/dsDhtcMr #AIAgents #AgentDevelopmentKit Google Cloud #GoogleAI #GoogleCalendar #CalendarManagement #AgenticAI

🤔 𝐀𝐈 𝐆𝐢𝐯𝐞𝐬 𝐁𝐚𝐜𝐤 𝐓𝐢𝐦𝐞. 𝐁𝐮𝐭 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐈𝐭 𝐢𝐧𝐭𝐨 𝐈𝐦𝐩𝐚𝐜𝐭? 🕰️ The Hidden Truth: "𝐓𝐢𝐦𝐞 𝐢𝐬 𝐀𝐈’𝐬 𝐦𝐨𝐬𝐭 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲." Generative AI reclaims hours, from drafting proposals to automating schedules. Yet without intentional strategies, reclaimed time often drifts into busywork. The result? Wasted potential for innovation, culture, and growth. 📊 The Productivity Gap: Gartner found 66% of teams fail to convert AI-driven time savings into meaningful productivity gains. It’s the classic paradox: 𝐦𝐨𝐫𝐞 𝐭𝐢𝐦𝐞, 𝐛𝐮𝐭 𝐧𝐨 𝐛𝐞𝐭𝐭𝐞𝐫 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. 💥 The Human Risk: Research from Harvard reveals AI can boost performance but lower motivation when people feel their roles are in flux. Uncertainty kills engagement, especially on tasks AI can’t replace. 𝐇𝐮𝐦𝐚𝐧-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐌𝐨𝐯𝐞𝐬 𝐭𝐨 𝐌𝐚𝐤𝐞 𝐀𝐈 𝐓𝐢𝐦𝐞 𝐂𝐨𝐮𝐧𝐭: 🧘 𝐑𝐞𝐝𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐛𝐚𝐥𝐚𝐧𝐜𝐞: Shorter workweeks, no-meeting days, async collaboration. Use time savings to enhance well-being and build trust. 💡 𝐃𝐫𝐢𝐯𝐞 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Dedicate freed time to hackathons, prototypes, or moonshot projects. 📚 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐠𝐫𝐨𝐰𝐭𝐡: Channel extra hours into learning paths that align with strategy and personal career goals. 🤝 𝐁𝐮𝐢𝐥𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: Host team demos, AI forums, and storytelling sessions. Because shared purpose matters more than ever. 🚀 𝐀𝐦𝐩𝐥𝐢𝐟𝐲 𝐢𝐦𝐩𝐚𝐜𝐭: Redefine workflows to direct AI-freed time into activities that create customer and business value, not low-value admin. 🔧 𝐅𝐮𝐭𝐮𝐫𝐞-𝐩𝐫𝐨𝐨𝐟 𝐭𝐚𝐥𝐞𝐧𝐭: Use newfound capacity to upskill and cross-train teams, preparing for evolving skill gaps. Deploying AI isn’t the finish line. What you do with the time AI frees up will decide whether your organization 𝐥𝐞𝐚𝐝𝐬 or 𝐥𝐚𝐠𝐬. Companies that intentionally reinvest saved time into culture, innovation, and skills will set the pace in the AI era. 💬How are you ensuring AI time becomes invested time, not wasted time? 🔗Link to the article from Philipp Morf in the comments. #AI #Productivity #Leadership #Innovation

🔊 Backlog Prioritization Is Broken. Can AI Fix It? "Prioritize with your gut," they said. "Trust your intuition," they advised. But intuition-based backlog prioritization has a steep cost: ❌ Frequent stakeholder disagreements ❌ Repeatedly wasted sprint efforts ❌ Misalignment with real customer needs ❌ Team burnout from unclear priorities What if Product Owners didn’t have to rely on guesswork or endless debates? What if AI-powered predictive analytics could help? As part of an ongoing collaboration with Sumeet Madan, we’ve been exploring how AI can meaningfully support product lifecycle management—and ordering backlog was a perfect starting point. Here’s what we’ve found AI can do for Product Owners: ✅ Data-Driven Value Scoring AI can analyze customer insights, historical sprint data, market trends, and stakeholder feedback to objectively prioritize backlog items by true business value—not politics. ✅ Scenario Modeling It allows you to simulate and compare multiple prioritization strategies instantly, revealing the highest-value path before committing your team's time. ✅ Adaptive Prioritization Your backlog stays alive. As new data emerges, AI continuously recalibrates priorities—eliminating the stale-backlog syndrome. The outcome? 🎯 Predictable sprint goals aligned to business strategy 📈 Maximized ROI through consistently high-value increments 🚀 Improved trust between stakeholders and Agile teams 🔥 Less stress and guesswork for Product Owners Let’s be clear: AI isn’t here to replace your role. Well not yet! Right now, it can enhance your decision-making so you can lead with clarity, not chaos. Sumeet and I are continuing to explore how AI can bring practical, tool-agnostic value to Agile teams. Backlog prioritization is just the first step. Have you considered predictive analytics in your product workflow yet? If yes—what’s worked? If not—what’s in your way? (P.S.: We’ll be opening early access soon to our hands-on training in AI-enhanced Product Ownership. Comment or DM if you’d like first dibs.) #AI #scrum #ReTHINKscrum #ProductOwner #ManagementAndLeadership Agilemania Agilemania Malaysia

MIT and Harvard Medical School researchers just unlocked interactive 3D medical image analysis with language! Medical imaging AI has long been limited to rigid, single-task models that require extensive fine-tuning for each clinical application. 𝗩𝗼𝘅𝗲𝗹𝗣𝗿𝗼𝗺𝗽𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘃𝗶𝘀𝗶𝗼𝗻-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗲𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝟯𝗗 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝘀𝗰𝗮𝗻𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀. 1. Unified multiple radiology tasks (segmentation, volume measurement, lesion characterization) within a single, multimodal AI model. 2. Executed complex imaging commands like “compute tumor growth across visits” or “segment infarcts in MCA territory” without additional training. 3. Matched or exceeded specialized models in anatomical segmentation and visual question answering for neuroimaging tasks. 4. Enabled real-time, interactive workflows, allowing clinicians to refine analysis through language inputs instead of manual annotations. Notably, I like that the design includes native-space convolutions that preserve the original acquisition resolution. This addresses a common limitation in medical imaging where resampling can degrade important details. Excited to see agents being introduced more directly into clinician workflows. Here's the awesome work: https://lnkd.in/ggQ4YGeX Congrats to Andrew Hoopes, Victor Ion Butoi, John Guttag, and Adrian V. Dalca! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

When I found myself with four kids at four different schools, a friend joked that I’d be spending my Friday nights reading school newsletters. And, honestly, between family, a #startup, and everything else life throws in the mix, personal admin is no joke. I am the person who forgets to send their kid with a gold coin for the bake sale, shows up to the football game half an hour late, and asks their mum/brother/friend to help with a pickup or drop-off at the last minute 🙈 So in the weekend, I decided to see if I - a tech founder who can't write a line of code - could use #AI to develop some tools that would streamline my life admin. I managed to create three super useful tools using #ChatGPT and Google #AppsScript in about an hour... ✉️ 1. Next day calendar preview: Every evening my husband, mum and I now get an email summarising the next day’s events, pulled straight from my personal calendar. This avoids chaos like me *thinking* mum is taking Harry to swimming without actually asking her... 🗓️ 2. Automated calendar entries: All the emails from schools,sports clubs, music teachers (the list goes on) now get scanned and events get added directly to my calendar. I'm still refining the script to be more precise with the event names but at least I'm not completely missing things! ✅ 3. Creating time for tiny tasks: Tasks that are hidden in emails get extracted automatically and emailed to me in a daily summary. That then gets auto-forwarded to Motion (which I already use and love), and time is scheduled to get the little jobs done. I'm pretty chuffed I could put these tools together, and hopefully it will lift my working mum game. Happy to share the scripts if anyone else wants to try it, and always keen to hear what tools other people are experimenting with👇

In the rapidly evolving world of workplace dynamics, the integration of AI in predicting employee engagement, sentiment, and productivity is ushering in a new era. This technological leap is not just about enhancing efficiency; it's about creating a more empathetic and responsive work environment - one where employees feel genuinely heard and valued. Historically, companies relied on surveys to gauge employee satisfaction and engagement. Let's face it: surveys feel like corporate chores, seldom sparking enthusiasm. The feedback loop is cumbersome, and by the time the data is processed, the moment for meaningful intervention has often passed. Enter AI, the game-changer in understanding workforce dynamics. AI tools are now adept at analyzing vast arrays of data points, from email tone and frequency to collaboration patterns and even social signals within the workplace. By leveraging natural language processing and machine learning, these systems can detect subtle shifts in employee morale and engagement in real-time. This shift towards AI analytics represents a profound change in how companies understand their employees. It's not just about numbers on a spreadsheet; it's about understanding the heartbeat of the organization. For instance, AI can identify if a team's communication patterns suggest burnout or disengagement, allowing management to step in with targeted support or changes before issues escalate. Moreover, this approach aligns with a growing emphasis on mental health and well-being in the workplace. By detecting early signs of stress or dissatisfaction, AI empowers companies to create a more supportive work environment. This isn't about surveillance but about sensitivity - using technology to tune into employee needs more effectively. The potential benefits extend beyond employee well-being. A happier workforce is invariably more productive and innovative. When employees feel their voices are heard and their well-being is a priority, they are more likely to invest their best selves in their work. AI's predictive capabilities can help create a virtuous cycle where employee satisfaction and company performance reinforce each other. However, as with any technological advancement, there are ethical considerations. Privacy concerns are paramount, and companies must navigate the fine line between insightful analysis and intrusive surveillance. The goal should be to use AI as a tool for empowerment, not control. The rise of AI in predicting and enhancing employee engagement and productivity marks a significant leap forward. This isn't about replacing the human touch but augmenting it with insightful data. It's an approach that promises a future where workforces are not only more efficient but also happier and more fulfilled - a future where employees are heard not through cumbersome surveys, but through the empathetic lens of AI. #askradarai #maxwellai #ai #hrtech

2025 is the Year of ACP, not just MCP. IBM has introduced a new protocol for AI collaboration called Agent Communication Protocol, building upon the foundation laid by Anthropic's Model Context Protocol. ACP takes a leap forward in how AI systems work together, allowing complex multi-agent workflows that were impossible with MCP alone. Here's how ACP works: 1️⃣ Agent Orchestration ACP enables multiple AI agents to communicate seamlessly, allowing specialized agents to combine their capabilities. 2️⃣ Standardized Messaging The protocol uses structured message formats that help agents understand each other across different frameworks and languages. 3️⃣ Task Delegation Complex problems are broken down and assigned to the most capable specialized agents, then results are assembled into cohesive solutions. 4️⃣ Framework Independence ACP works with agents built in any programming language or AI framework, removing technical barriers to collaboration. 5️⃣ Dynamic Discovery Agents can discover and utilize each other's capabilities, creating flexible AI ecosystems that evolve to meet changing needs. Whether you're building complex AI workflows or connecting specialized agents, ACP elevates what's possible, enabling deeper collaboration and more powerful solutions. Here's how ACP is architecturally different from MCP: MCP: - Focuses on connecting a single AI to external data sources and tools - Creates one-to-many relationships between an AI and various resources - Uses JSON-RPC primarily for accessing information and executing actions - Designed to expand what one AI model can access and accomplish ACP: - Centers on connecting multiple AIs to each other in collaborative relationships - Creates many-to-many networks of specialized agent capabilities - Extends JSON-RPC with agent-specific communication patterns - Designed for dividing complex tasks among specialized AI team members Understanding these distinctions matters for building the right AI infrastructure. Some problems need better tools for one AI. Others need multiple AIs working together. ACP isn't just different from MCP; it's complementary: ✅ Solves problems too complex for any single AI agent ✅ Creates AI teams with specialized members handling different aspects of a task ✅ Enables more natural workflows that mirror human team collaboration The combination of MCP and ACP is essential. MCP gives individual AIs access to tools and data. ACP helps those AIs work together as teams. Together, they create AI systems that are more capable, flexible, and effective. Over to you: What complex problems could you solve with a team of specialized AI agents working together?

Tired of managing appointments manually and dealing with missed appointments? Explore how automating appointment scheduling can transform your workflow, enhancing efficiency and patient satisfaction. 1. Set Up Online Booking: Enable patients to book their appointments through a user-friendly online platform. Integration with your Electronic Health Record (EHR) system ensures real-time availability, reducing double bookings and administrative workload. 2. Automate Confirmations: Instantly send detailed confirmations to patients upon booking, including appointment time, location, and any preparation instructions. This helps prevent miscommunication and ensures patients have all necessary information. 3. Send Automated Reminders: Schedule personalized reminders via email or SMS a day or two before the appointment. This reduces the likelihood of no-shows and allows patients to easily confirm or reschedule if needed. 4. Manage Follow-Ups Automatically: After each appointment, send follow-up reminders for future bookings or recurring check-ups. Automated follow-ups keep patients engaged and encourage continuity of care without manual intervention. 5. Simplify Rescheduling & Cancellations: Allow patients to conveniently reschedule or cancel appointments through an online portal. The system updates in real time, freeing up slots for other patients and optimizing scheduling efficiency. 6. Optimize Staff & Resources: Automatically assign rooms, equipment, and staff based on patient needs and availability. This ensures a balanced workload, efficient resource allocation, and smoother day-to-day operations. 7. Collect Patient Feedback: Send automated surveys post-appointment to capture patient feedback on their experience. This valuable data can help identify areas for improvement, enhancing service quality and patient satisfaction. Benefits of Automation: Reduce administrative burdens, minimize no-shows, and improve resource utilization. Automating appointment scheduling elevates patient experience and allows your team to focus on what truly matters—patient care. [Explore More In The Post] Don’t Forget to save this post for later and follow @digitalprocessarchitect for more such information.