Future Of Work

We’ve all heard about AI’s potential to boost productivity. But what truly matters to me is whether it’s making work better for the people who show up every day. At Cisco, our People Intelligence team, in collaboration with IT, has been exploring this very topic, and the findings are fascinating. Here are five key insights from our research that leaders should take seriously: 1. Leaders are key to adoption. At Cisco, employees are 2x more likely to use AI if their direct leader uses it. 2. Generic AI training doesn’t work. Role-specific, practical training accelerates AI use. 3. Confidence gaps exist among senior leaders. Directors at Cisco often feel less confident with AI than mid-level employees, underscoring the need for tailored support at all levels. 4. Employee autonomy fuels adoption. Hybrid work environments are powerful accelerators for AI adoption, while mandates can hinder it. Employees who voluntarily go to the office are more likely to use AI, while those who are required to work on-site have lower adoption. 5. AI use is linked to employee well-being, but the relationship is complex, with both benefits and trade-offs that require thoughtful navigation. This is just the beginning. Next, we’re looking at how AI is transforming the way teams operate. For now, one thing is clear, employees who use AI aren’t just more productive. They’re also more engaged, better aligned with company strategy, and empowered to focus on meaningful work. #AIAdoption #EmployeeExperience #FutureOfWork

Remote teams don’t work? Here’s the truth: If your team needs constant watching... You’ve hired the wrong people. I've managed a remote team for 3+ years. Here’s what I’ve learned: 1/ The best people don’t need babysitting → They deliver results, not excuses. → Micromanagement kills trust. → Ownership drives real performance. → Accountability beats oversight. 2/ No commute means more growth → Extra hours for learning, not traffic. → Time spent on skills, not sitting still. → Work-life balance fuels productivity. → Efficiency replaces exhaustion. 3/ No office means no politics → Results matter more than appearances. → Ideas win, not egos. → Collaboration over competition. → Culture thrives without drama. Here’s how you can make it work: → Set clear KPIs that actually matter. → Monitor outcomes, not hours. → Document your process with Tango. → Give freedom to work where, when, and how. → Focus on impact—not desk time. Remote success isn’t about location—it’s about results. I started using Tango myself to streamline our workflows, keeping everyone aligned. For our remote team, it’s a game-changer. Why? Less explaining, more doing. ♻️ Repost and follow Justin Bateh, PhD for more.

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

If you want to do creative projects but never have the energy, try this: Nature is more than just a backdrop for relaxation; it actively enhances our creative thinking and problem-solving abilities. Getting out into nature with a clear aim to do creative work as a massively underrated tool. Here’s my protocol for an intentional day of personal projects: → 𝗠𝗼𝗿𝗻𝗶𝗻𝗴 𝗥𝗼𝘂𝘁𝗶𝗻𝗲 Begin your morning by stepping outside. Feel the natural elements (the sun, a breeze, the texture of grass). Just a couple of minutes can really clear the mind, calibrate your senses, and sharpen focus. → 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝘀 Carry a notebook and pen on a short walk in a nearby park or natural setting, away from digital distractions. Write down a maximum of 3 things you’d like to focus on. Put a star next to the one that is your highest priority - the one that, once completed, would make the day a success. → 𝗝𝗼𝘂𝗿𝗻𝗮𝗹 To get the creative juices flowing, handwrite down a short answer to each of these prompts: • Describe your natural surroundings. • What’s 1 trait you want to exhibit today? • What’s 1 thing you’re grateful for? → 𝗗𝗲𝗲𝗽 𝘄𝗼𝗿𝗸 Curate a workspace with natural elements (e.g., plants, natural light, open windows for fresh air). The more minimal and distraction-free, the better. Brew your hot beverage of choice, take a deep breath, and start your day with a 2-hour uninterrupted block focussed on your highest priority task. → 𝗜𝗻𝘁𝗲𝗿𝗺𝗶𝘁𝘁𝗲𝗻𝘁 𝘄𝗮𝗹𝗸𝘀 Introduce short, regular walking breaks in your routine, preferably in natural, green spaces. Experiment with different levels of stimulus: Notebook, no notebook. Music, no music. Use this time for reflection or pondering creative challenges, letting the natural environment stimulate new perspectives. → 𝗪𝗶𝗻𝗱 𝗱𝗼𝘄𝗻 As daylight shifts to dusk, allow your mind to naturally transition to relaxation. Under soft lighting, jot down any lingering thoughts or reflections in a journal. Close the cognitive chapter on productivity, and enjoy an evening of leisurely reading, cooking, and resting. -- This is an excerpt from an initiative I recently took part in called 'The nature of work' - a collaboration between Unyoked and LinkedIn. They invited Lizzie Hedding, Samantha Wong, James Hurman, Cayla Dengate, Jimmy Lyell and I to create a guide on using nature to slow down and focus on the things that really matter. 🏕️ One takeaway for me: Whether you're an athlete, VC, or musician... try to build more exposure to the natural world into your daily, weekly, monthly and yearly rhythms. Hope you enjoy the guide as much as we did making it. Link in the comments👇

Just out in Harvard Business Review, summary of the Hybrid Experiment results and lessons on how to make hybrid succeed. Experiment: randomize 1600 graduate employees in marketing, finance, accounting and engineering at Trip.com into 5-days a week in office, or 3-days a week in office and 2-days a week WFH. Analyzed 2 years of data. Two key results A) Hybrid and fully-in-office showed no differences in productivity, performance review grade, promotion, learning or innovation. B) Hybrid had a higher satisfaction rate, and 35% lower attrition. Quit-rate reductions were largest for female employees. Four managerial lessons 1) Hybrid needs a strong performance management system so managers don’t need to hover over employees at their desks to check their progress. Trip.com had an extensive performance review process every six months. 2) Coordinate in-office days at the team or company level. Schedule clarity prevents the frustration of coming to an empty office only to participate in Zoom calls. Trip.com coordinated WFH on Wednesday and Friday. 3) Having leadership buy-in is critical (as with most management practices). Trip.com’s CEO and C-suite all support the hybrid policy. 4) A/B test new policies (as well as products) if possible. Often new policies turn out to be unexpectedly profitable. Trip.com made millions of dollars more profits from hybrid by cutting expensive turnover.

Your leadership team is underperforming. (And cracking the whip harder won't fix it.) Here's what nobody tells you about accountability: The harder you push, the less they deliver. I've watched CEOs destroy their executive teams this way: 🔥 Public callouts in meetings 🔥 Micromanaging every decision   🔥 Threats disguised as "motivation" 🔥 Fear-based deadline pressure Result: Your best leaders become corporate zombies. They show up. They comply. They stop caring. The expensive truth: Fear creates compliance. Clarity creates commitment. And you need commitment to win. Real story from last month: → CEO constantly berated his team for missing targets → 3 VPs quit in 6 months → Company lost $2M in transition costs alone Different CEO, different approach: → Created radical clarity around expectations → Listened without judgment → Built safety to admit mistakes early → Revenue up 40% in 12 months The difference? One used accountability as a weapon. The other used it as a framework for excellence. The 4 frameworks that create compassionate accountability: 1. RACI Matrix - Ends the "whose job is this?" chaos (Everyone knows their lane AND their value) 2. OKRs - Aligns hearts and minds (Shared goals create shared ownership) 3. EOS Accountability Chart - One person, one seat (Clear ownership without overlapping egos) 4. OGSM - Strategy meets reality (No more "I thought you meant..." conversations) But here's the key: These aren't hammers to hit people with. They're maps to help people win. The paradox of leadership: High standards + High support = High performance High standards + Low support = High turnover Your leadership team doesn't need more pressure. They need more clarity. Because when accountability comes from compassion, not control: → Problems get solved, not hidden → Leaders take ownership, not cover → Teams push forward, not back Stop managing through fear. Start leading through frameworks. Your leadership team is capable of greatness. But only if you create the conditions for it. Save this. Share it with your team. Because the best leaders don't create followers. They create owners. And ownership starts with clarity. P.S. Want a PDF of my Accountability Cheat Sheet? Get it free: https://lnkd.in/dpWsuT4b ♻️ Repost to help a CEO in your network. Follow Eric Partaker for more leadership insights. — 📢 Want to lead like a world-class CEO? Join my FREE TRAINING: "How to Work with Your Board to Accelerate Your Company’s Growth" Thu Jul 10th, 12 noon Eastern / 5pm UK time https://lnkd.in/dCJ-nCxM 📌 The CEO Accelerator starts July 23rd. 20+ Founders & CEOs have already enrolled. Learn more and apply: https://lnkd.in/dgRr89bM

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 is a paradigm shift where AI models 𝗹𝗲𝗮𝗿𝗻, 𝗽𝗹𝗮𝗻, 𝗮𝗻𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝘁𝗮𝘀𝗸𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆, often collaborating as multi-agent systems.  But with so many concepts—LLMs, RAG, Reinforcement Learning, and AI orchestration—how do you structure your learning?  𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗚���𝗶𝗱𝗲 𝗬𝗼𝘂:  𝟭. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 – Understand how AI agents differ from traditional AI models and where they fit in real-world automation.  𝟮. 𝗔𝗜 & 𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 – Build a strong foundation in deep learning, supervised vs. unsupervised learning, and reinforcement learning for smart agents.  𝟯. 𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – Work with 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗮𝗻𝗱 𝗖𝗿𝗲𝘄𝗔𝗜 to design AI agents that interact with APIs and function calls.  𝟰. 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) – Go beyond basic prompting—dive into 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀, 𝗮𝗻𝗱 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 for better reasoning and memory.  𝟱. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 – Explore 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 to enable complex problem-solving.  𝟲. 𝗔𝗜 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 – Learn 𝗥𝗔𝗚 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀, 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝗮𝗻𝗱 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵 to make AI recall and use information effectively.  𝟳. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Implement 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴, 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗴𝗼𝗮𝗹-𝘀𝗲𝘁𝘁𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 with reinforcement feedback.  𝟴. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 – Leverage 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁, 𝘇𝗲𝗿𝗼-𝘀𝗵𝗼𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗰𝗵𝗮𝗶𝗻-𝗼𝗳-𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝘁𝘂𝗻𝗶𝗻𝗴 for better responses.  𝟵. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 – Train AI agents using 𝗵𝘂𝗺𝗮𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗮𝗻𝗱 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 for continuous improvement.  𝟭𝟬. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) – Optimize AI context expansion and hybrid AI search for better responses.  𝟭𝟭. 𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 – Scale AI workflows, optimize latency, and monitor AI behavior in production.  𝟭𝟮. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 – Use AI for 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 across industries.  Agentic AI isn't just theoretical—it’s powering 𝗻𝗲𝘅𝘁-𝗴𝗲𝗻 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀. Understanding how AI agents work will be a defining skill for AI engineers, researchers, and developers in 2025 and beyond.  What’s your take on Agentic AI?

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

Thank you, Google. You just open-sourced a single CLI for all of Google Workspace and it's built for both humans and AI agents. npm install -g @googleworkspace/cli What it does: → One command-line tool for Drive, Gmail, Calendar, Sheets, Docs, and every Workspace API → Zero boilerplate. Structured JSON output. Auto-pagination. → Reads Google's Discovery Service at runtime — when Google adds a new API endpoint, the CLI picks it up automatically → Ships with 100+ Agent Skills so your LLM can manage Workspace without custom tooling → Built-in MCP server for Claude Desktop, Gemini CLI, VS Code, and any MCP-compatible client → Model Armor integration to scan responses for prompt injection before they reach your agent This is a big deal for anyone building AI agents that interact with Google Workspace (everyone?) No more writing custom API wrappers. No more maintaining brittle integrations. One tool. Every service. Structured output ready for agents. The repo is Apache-2.0 licensed and under active development.

My cousin quit his ₹18 lakh job last week. To freelance. My uncle called me, panicking: "Talk some sense into him." Then my cousin showed me his last 3 months: ₹16.2 lakhs earned. More than his annual take-home from the "stable" job. Full-time: ₹12.5L take-home, 1 income stream Freelancing: ₹45L+ annually, 8 clients He's making 3.6x more. "What about security?" I asked. "I have 8 clients. If I lose one, I still have 7. My corporate friends? One layoff from zero." The data backs him: - India: 7.7M gig workers → 23.5M by 2030* - Google: 120K contractors vs 102K employees** - 55% of Indian companies already hire gig workers*** - During downturns: 20-30% "stable" employees laid off Here's the cognitive dissonance: We celebrate ₹50L Google offers. But when someone freelances for ₹80L? "What will people say?" Three months later: My cousin signed a ₹18L/6-month project. His old job: ₹9L for that period. My uncle still doesn't approve. Which carries more risk? 8 income streams or 1 employer? Because by 2030, 1 in 7 non-farm workers will be gig workers. The government knows. They're preparing. But we're stuck in the 1985 "stable job" mindset. The definition of security has changed. Most of India just hasn't realized it yet.