Future Of Work

India’s green economy is growing fast but LinkedIn data suggests green talent is growing even faster. The LinkedIn Hiring Rate (LHR) for green talent — defined as professionals with green skills, green job titles, or both — is now 59.7% higher than for the overall workforce. This means green-skilled professionals are significantly more likely to be hired than their peers, underscoring the growing demand for sustainability-focused roles. “The prioritisation of green talent by Indian companies is being fuelled by an interplay of policy reforms, rising consumer consciousness, and the need for deep business transformation,” says Neelima Burra, Chief Strategy, Transformation, and Marketing Officer at Luminous Power Technologies. “Government initiatives like the PM Suryaghar Yojna, National Solar Mission, and Smart City Mission, combined with the growing mandate for ESG reporting — are also pushing companies to recruit sustainability experts, carbon auditors, and ESG strategists to meet regulatory and investor expectations,” she adds further. Operational efficiency has emerged as the top skill across the top five industries increasingly hiring for green skills, as per LinkedIn data. In contrast, precision agriculture skills lead in farming, ranching, and forestry — highlighting how sector-specific green skills are evolving. “Operational efficiency offers the fastest route to tangible returns. It moves the conversation beyond regulatory compliance to net profitability, ensuring we can do more with less energy and fewer materials,” says Venu Nuguri Managing Director and CEO at Hitachi Energy. This surge in demand aligns with broader economic trends. Green jobs in India have grown over 10 times in the past five years, with Gen Z accounting for 63% of applicants, reports The Economic Times, citing a report by WeNaturalists. The projections are equally ambitious. India’s green economy will generate 7.29 million jobs by FY28 and 35 million by 2047, as the sector scales toward a $1 trillion valuation by 2030 and $15 trillion by 2070, suggests another report by The Economic Times, citing a report by NLB Services. The message is clear: green skills aren’t just good for the planet — they’re becoming essential for employability. As India accelerates its climate and economic goals, the workforce is already adapting. The question now is whether education, training, and policy can keep pace. Read the full report here: https://lnkd.in/g873CzHT #COP30 #GreenerTogether Source: The Economic Times: https://lnkd.in/d-3bShQP  The Economic Times: https://lnkd.in/dSUMFS58 

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

Cloud Native technologies have long been at the heart of scalable applications. But now, with AI and Agentic Systems, the game is changing!   Unlike traditional AI automation, Agentic AI can make decisions, execute workflows, and adapt dynamically to system changes—without constant human oversight. This means self-healing, self-optimizing, and autonomous cloud-native infrastructure!  Here’s how Agentic AI can transform each layer of Cloud Native skills:  1. Linux & AI-Optimized OS   - AI-powered package managers automatically resolve compatibility issues.   - Agentic AI monitors system logs, predicts failures, and patches vulnerabilities autonomously.  2. Networking & AI-Driven Observability   - AI-driven network forensics using self-learning algorithms to detect anomalies.   - Agent-based routing optimizations, ensuring seamless traffic flow even in congestion.  3. Cloud Services & AI-Augmented Workflows   - Agentic AI predicts cloud workload demand and pre-allocates resources in AWS, Azure, and GCP.   - Autonomous cost optimization adjusts instance types, storage, and compute in real time.  4. Security & AI Cyberdefense Agents   - Self-learning AI security agents actively detect and mitigate cyber threats before they happen.   - Generative AI-powered penetration testing agents simulate evolving attack patterns.  5. Containers & Agentic AI Orchestration   - Autonomous Kubernetes controllers scale clusters before demand spikes.   - Agentic AI continuously optimizes pod scheduling, reducing cold starts and resource waste.  6. Infrastructure as Code + AI Copilots   - AI-driven infrastructure agents automatically refactor Terraform, Ansible, and Puppet scripts.   - Self-adaptive IaC, where AI updates configurations based on usage patterns and compliance policies.  7. Observability & AI-Driven Incident Response   - AI-powered anomaly detection in Grafana & Prometheus—flagging issues before failures.   - Agentic AI handles incident response, running diagnostics and executing pre-approved fixes.  8. CI/CD & Autonomous Pipelines   - Agentic AI writes, tests, and deploys code autonomously, reducing developer toil.   - Self-optimizing pipelines that rerun failed tests, debug, and retry deployment automatically.  The Future: Fully Autonomous Cloud Native Systems!  𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 → 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The result? Zero-touch, self-managing environments where AI agents handle failures, optimize costs, and secure systems in real time.  𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆?

A Return To Office mandate is a funny thing. A trade-off of lower workforce productivity, morale, retention, engagement, and trust in exchange for...managers feeling more in control. It's more a sign of insecurity and incompetence than sound decision-making. The fact that 80% of executives who have pushed for RTO mandates have later regretted their decision only makes the point further, and yet every few months more leaders line up to pad this statistic. In case your leaders have forgotten, return to office mandates are associated with: 🔻 16% lower intent to stay among the highest-performing employees (Gartner) 🔻 10% less trust, psychological safety, and relationship quality between workers and their managers (Great Place to Work) 🔻 22% of employees from marginalized groups becoming more likely to search for new jobs (Greenhouse) 🔻 No significant change in financial performance while guaranteeing damage to employee satisfaction (Ding and Ma, 2024) The thing is, we KNOW how to do hybrid work well at this point. 🎯 Allow teams to decide on in-person expectations, and hold people accountable to it—high flexibility; high accountability. 🎯 Make in-person time unique and valuable, with brainstorming, events, and culture-building activities—not video calls all day in the office. 🎯 Value outcomes, not appearances, of productivity—reward those who get their work done regardless of where they do it. 🎯 Train inclusive managers, not micromanagers—build in them the skills and confidence to lead with trust rather than fear and insecurity. Leaders that fly in the face of all this data to insist that workers return to office "OR ELSE" communicate one thing: they are the kinds of leaders that place their own egos and comfort above their shareholders and employees alike. Faced with the very real test of how to design the hybrid workforce of the future, these leaders chose to throw a tantrum in their bid to return to the past, and their organizations will suffer for it. The leaders that will thrive in this time? Those that are willing to do the work. Those that are willing to listen to their workforce, skill up to meet new needs, and claim their rewards in the form of the best talent, higher productivity, and the highest level of worker loyalty and trust. Will that be you?

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

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.

2017: No revenue, no social presence. 2024: $440k+ in revenue, 875K+ social media followers. 💸 How did I achieve this transformation? 🤔 It wasn’t just about random sleepless nights and hard work. Here are the key strategies that made a difference: 1. Expanding My Skill Set 💻 What I Did: → Started as a content writer and then transitioned into marketing and copywriting. Why It Worked: → Diversifying my skills opened up new opportunities and helped me stand out in a competitive market. Tip: Continuously develop new skills and find the ones that align with your goals. 2. Building a Strong Social Presence 📸 What I Did: → Created a personal brand, studied social media algorithms, produced valuable content, and leveraged trends. Why It Worked: → A strong social presence attracted more followers and clients, ensuring steady business growth. Advice: Focus on growing one platform at a time. 3. Creating Value-Added Content What I Did: → Focused on producing content that provides real value to my audience, such as how-tos, tips, and insights relevant to their interests. Why It Worked: → Value-added content builds trust and positions you as an authority in your field. Strategy: Always aim to solve problems or provide insights that your audience can benefit from. 4. Effective Networking 👏🏼 What I Did: → Connected with like-minded professionals, attended industry events, and engaged in meaningful conversations. Why It Worked: → Networking opened doors to unexpected opportunities and provided valuable referrals. Pro Tip: Share book snippets or insightful articles to start meaningful conversations and build strong connections. 5. Mastering Sales 🛒 What I Did: → Improved my sales skills, including pitching, negotiation, and closing deals. Why It Worked: → Good sales skills are essential for converting prospects into clients, and helping people naturally leads to sales. Hope this helps 😁 Question - What are the top 3 skills you think one must have to grow their business?

A few years ago, I was in a high stakes meeting with colleagues from Japan. I presented my points confidently, thinking I was making a great impression. But as I scanned the room, I saw blank expressions. No nods. No engagement. Just silence. I panicked. Had I said something wrong? Was my idea unconvincing? After the meeting, one of my Japanese colleagues pulled me aside and said, “Sumit, we really want to understand you, but you speak too fast.” That was my light bulb moment. For years, I assumed that mastering English and business communication was enough to build strong global relationships. But the real challenge wasn’t just the language - it was the rate of speech! Most of us don’t realize that speaking speed varies drastically across cultures. Here’s an eye-opener: ·      In India, we typically speak at 120–150 words per minute. ·      The global standard for clear communication is around 60–80 words per minute. ·      In Japan, where English is not the first language, this rate drops even further. So, what happens when we, as fast speakers, communicate with someone who is used to a much slower pace? Our words blur together. The listener struggles to process. And instead of making an impact, we create confusion. We often assume that if people don’t understand us, we need to repeat ourselves. But the truth is, we don’t need to repeat - we need to slow down, simplify, and pause. If you work in a multicultural environment, here are three things that can dramatically improve your communication: a.   Control your pace: Consciously slow down when speaking to an international audience. What feels “normal” to you might be too fast for them. b.   Use simple language: Smaller sentences. Easier words (vocabulary). c.    Pause & check for understanding: Don’t assume silence means agreement. Ask, “Does that make sense?” or “Would you like me to clarify anything?” I’ve seen professionals struggle in global roles - not because they lack expertise, but because they fail to adjust their communication style to their audience. I’ve also seen leaders who thrive across cultures, simply because they master the art of respectful, clear, and paced communication. If you want to succeed in a global workplace, rate of speech is not just a skill - it’s a strategy. Have you ever faced challenges due to differences in speaking speed? Let’s discuss. #GlobalCommunication #CrossCulturalLeadership #EffectiveCommunication #SoftSkills #CareerGrowth #WorkplaceSuccess #HR

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.

I wasn’t lazy. I was just distracted. (And I didn’t even realize it.) Tasks that should’ve taken 30 minutes dragged on for hours. Blank screens. Zero motivation. Endless scrolling. The problem wasn’t Time management. It was 𝗙𝗼𝗰𝘂𝘀 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Then one day, I stumbled upon a 𝘔𝘦𝘥𝘪𝘶𝘮 article that listed a few unusual focus hacks. I tried them. Tweaked them to fit my life. Soon, I started showing up better. With clarity, not chaos. Here’s what worked for me - (If focus has been a struggle lately, this might just help.) 1. 𝗚𝗶𝘃𝗲 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗮 𝗳𝗮𝗰𝗲 We often chase vague goals — deadlines, KPIs, praise. But real energy comes when your work feels personal. One day, I was stuck on a complex analysis. No motivation. Then I pictured telling my mom what I did at work today. Her smile. Her pride. That image changed everything. Suddenly, it wasn’t just a task. It was something to be proud of. ➡ Ask yourself: “Who would I be excited to share this with?” Picture their face. Then start the work. 2. 𝗢𝗻𝗲 𝘀𝗼𝗻𝗴. 𝗢𝗻 𝗹𝗼𝗼𝗽. 𝗧𝗵𝗮𝘁’𝘀 𝗶𝘁. It sounds odd, but looping one instrumental track helps me zone in. I use Shri Hanuman Chalisa – Instrumental. No lyrics. Just rhythm. In no time, my brain quiets down. The repetition becomes an anchor: “You’re working now. Stay here.” ➡ Pick a calm, lyric-free track. Hit repeat. Let it ground your attention. 3. 𝗧𝗵𝗲 2-𝗠𝗶𝗻𝘂𝘁𝗲 𝗣𝗿𝗲𝘃𝗶𝗲𝘄 𝗧𝗿𝗶𝗰𝗸 Before starting a task, I set a 2-minute timer. No typing. No scribbling. Just look at the task. It’s like a warm-up for the brain. You’re letting your mind settle into the work, not crash-land into it. ➡ Try this tomorrow. Just 2 min of stillness before starting. You’ll be surprised how much smoother the task feels. 4. 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗙𝗼𝗰𝘂𝘀 𝗚𝗿𝗮𝘃𝗲𝘆𝗮𝗿𝗱 (𝘆𝗲𝘀, 𝗿𝗲𝗮𝗹𝗹𝘆) Every time I get distracted during work hours, I don’t fight it. I note it down in my phone’s Notes app. • An unfinished Udemy course • A half-watched YouTube video on AI agents • The novel I abandoned after Chapter 7 • A call I owe to a childhood friend It’s not about guilt — it’s about awareness. A quiet system that tells me: “This is not urgent. It can wait.” ➡ Create a “Graveyard” note. Every time your mind wanders, log it. Then return to your core task. The Result? I’m still a work in progress. But I’m sharper. Quieter. Less reactive. The Biggest Shift? Not in my schedule, but in how I protect my attention. REMEMBER - You don’t need more hours. You need fewer attention leaks. P.S. Which of these 4 hacks would you try first? 𝘐𝘧 𝘺𝘰𝘶 𝘧𝘰𝘶𝘯𝘥 𝘵𝘩𝘪𝘴 𝘩𝘦𝘭𝘱𝘧𝘶𝘭 → 𝘳𝘦𝘱𝘰𝘴𝘵 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘯𝘦𝘵𝘸𝘰𝘳𝘬. LinkedIn Guide to Creating #big4 #lifestyle #productivity #timemanagement