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

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.

Careers are changing fast. Skills are changing even faster. And oh yeah...AI isn't waiting for anyone. LinkedIn Talent Solutions just dropped a bomb on the old way of building talent. The new LinkedIn Learning Career Hub from is way more than just another tool. It is a living, breathing map of 1 billion careers. It is the only platform that blends your company’s real-time skills data with the world’s largest career database. It is not about guessing what skills matter. It is about knowing, right now, what is trending, what is fading, and what is about to explode. Here is what you get: - A talent architecture that is not static. It is built on live LinkedIn insights, not last year’s org chart. - AI Role Play that lets you test, tweak, and report on real scenarios, not just theory. - HCM integration that pulls in your company’s talent data, with employee profiles coming soon. No more data silos. - Trending Skills insights that show you exactly how your people stack up against the market. No more flying blind. - Career and mobility reporting that breaks down goals, progress, and the roles your people actually want next. - A new experience for employees and admins, built around growth, not just compliance. - Projects and gigs coming soon, because the future is not just jobs, it is work in all shapes. If I was running talent development, I would do three things right now: 1) I would throw out the old static skills frameworks. I would use LinkedIn’s real-time skills data to build a living, breathing map of what matters now to accelerate impact. 2) I would make AI Role Play a core part of every learning path. Real practice, real feedback, real growth. No more “check the box” training. 3) I would use the new reporting to spot hidden talent and mobility. I would not wait for people to ask for a promotion. I would show them the path, the skills, and the next step, before they even ask. The world is not waiting. Your talent strategy can't either. The future of work is dependent upon what you do right now. It is a dashboard, a data feed, and a set of actions you take today. This is not the “traditional” HR and Talent Development playbook. But it is the only way to build a workforce that is ready for what comes next. Check it out for yourself here: https://lnkd.in/g3qbfBYa #alwaysbelearning #talentdevelopment #LinkedInLearningCareerHub

Atlassian has been fully distributed for almost five years. We don’t have all the answers, but we’ve learned a lot about how to keep teams thriving across time zones—and we’re applying those insights every day.  ➡️ Asynchronous work: Async tools are at the core of how we operate. Confluence is our virtual hub where we share stories, celebrate new hires, and collaborate effortlessly. We also use Loom to share videos and give feedback on our own time—avoiding those dreaded “this could have been an email” moments. In fact, we’ve saved nearly half a million meetings using Loom! ➡️ Designing workdays: We’ve learned to structure workdays for focus, collaboration, and meetings (only when absolutely necessary). Teams work across no more than two time zones, ensuring at least four hours of overlap to get things done together. ➡️ Intentional connection: Data shows that real connection happens when teams meet regularly—not sporadically in an office. We provide Intentional Togetherness Gatherings (ITGs), curated experiences, and focused in-person time to collaborate. ➡️ Adapting for different needs: It’s not one-size-fits-all. For example, new hires and grads often benefit from more frequent in-person meetups, so we make sure to offer opportunities for them to connect early on. https://lnkd.in/g2sSbe3v

𝗜𝘀 𝗯𝗲𝗶𝗻𝗴 𝗽𝗿𝗲𝘁𝘁𝘆 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘁𝗵𝗮𝘁 𝗷𝗼𝗯? — 𝘴𝘵𝘶𝘥𝘺 𝘴𝘢𝘺𝘴 𝘮𝘢𝘺𝘣𝘦. We often talk about meritocracy in hiring — but research keeps reminding us how easily optics overshadow objectivity. 📊 A 2024 study by Harvard Business Review found that 𝗼𝘃𝗲𝗿 𝟱𝟬% 𝗼𝗳 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝘀𝘂𝗯𝗰𝗼𝗻𝘀𝗰𝗶𝗼𝘂𝘀𝗹𝘆 𝗳𝗮𝗰𝘁𝗼𝗿 𝗶𝗻 𝗮𝗽𝗽𝗲𝗮𝗿𝗮𝗻𝗰𝗲 𝘄𝗵𝗲𝗻 𝗺𝗮𝗸𝗶𝗻𝗴 𝗵𝗶𝗿𝗶𝗻𝗴 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. Similarly, research in the 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘈𝘱𝘱𝘭𝘪𝘦𝘥 𝘗𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘺 𝘢𝘯𝘥 𝘗𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 confirms that 𝗮𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝗺𝗼𝗿𝗲 𝗹𝗶𝗸𝗲𝗹𝘆 𝘁𝗼 𝗯𝗲 𝗿𝗮𝘁𝗲𝗱 𝗮𝘀 𝗰𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝘁, 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝗳𝗶𝗲𝗱 — even when their résumés are identical to less “polished” counterparts. This isn’t vanity; it’s psychology. It’s called the “𝗵𝗮𝗹𝗼 𝗲𝗳𝗳𝗲𝗰𝘁” — a cognitive bias where one positive trait (like appearance or confidence) spills over to how we judge unrelated qualities (like intelligence or leadership). And it’s costly. Because every time we let surface cues dictate selection, we risk 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗾𝘂𝗶𝗲𝘁 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝗰𝗲 that doesn’t advertise itself well. The solution isn’t to ignore presentation — it’s to balance perception with structure: • 𝗨𝘀𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 and 𝘀𝗸𝗶𝗹𝗹-𝗯𝗮𝘀𝗲𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀. • Involve 𝗱𝗶𝘃𝗲𝗿𝘀𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗮𝗻𝗲𝗹𝘀 to reduce individual bias. • Train leaders to recognize 𝗵𝗮𝗹𝗼 𝗮𝗻𝗱 𝗵𝗼𝗿𝗻 𝗲𝗳𝗳𝗲𝗰𝘁𝘀 — before they unconsciously act on them. 💬 𝘐𝘯 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱, 𝘴𝘦𝘦𝘪𝘯𝘨 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘣𝘦𝘨𝘪𝘯𝘴 𝘸𝘪𝘵𝘩 𝘴𝘦𝘦𝘪𝘯𝘨 𝘰𝘶𝘳 𝘰𝘸𝘯 𝘣𝘭𝘪𝘯𝘥 𝘴𝘱𝘰𝘵𝘴. #HiringBias #OrganizationalPsychology #Leadership #UnconsciousBias #DEI #FutureOfWork

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

The Executive Assistant manages calendars, filters information, handles logistics, and serves as the critical interface between leaders and everyone seeking access to them. Today, #AI can automate perhaps 80% of these tasks with remarkable efficiency. But I believe automation will not eliminate this role, it will elevate it from #assistant to genuine partner. The future EA will spend less time on bookings and more time on judgment calls: deciding what deserves the executive's attention, prioritizing competing demands, analyzing plans before they reach the decision-maker, and anticipating needs before they become urgent. Throughout my career, my executive meetings were scheduled twelve months in advance, requiring thoughtful planning rather than reactive scheduling from assistants who knew what truly mattered. The qualities that make an exceptional EA cannot be automated: making #leadership possible, providing the human touch that keeps intense schedules bearable, and demonstrating the loyalty built through years of consistent judgment. These are delicate, well-compensated roles that are becoming more valuable as AI handles the routine and frees these professionals to focus on what truly differentiates them. What roles in your organization will AI elevate rather than eliminate?

#PeopleAnalytics: Turning #HRMetrics into #Strategic Insights In today’s data-driven organizations, HR is evolving from a support function to a strategic powerhouse. These HR Metrics are more than just numbers; they’re lenses through which we can understand workforce dynamics, organizational health, and business impact. Let’s break it down: 🔹 Absenteeism Rate: A high rate may signal burnout, disengagement, or systemic issues in workplace culture. Tracking it helps identify patterns and intervene early. 🔹 Employee Attrition & Retention: These twin metrics reveal the stability of your workforce. High attrition can be costly and disruptive, while strong retention often reflects good leadership and employee satisfaction. 🔹 Internal Promotion Rate: A key indicator of talent mobility and succession planning. Promoting from within boosts morale and reduces hiring costs. 🔹 Cost Per Hire & Time to Hire: Efficiency metrics that reflect the effectiveness of your recruitment strategy. Long hiring cycles or high costs may point to process inefficiencies or misaligned sourcing channels. 🔹 Offer Acceptance Rate: A direct measure of your employer brand and candidate experience. Low acceptance rates might mean your value proposition isn’t resonating. 🔹 Human Capital ROI: This is the ultimate business case for HR—how much return you’re getting from your investment in people. It’s a powerful metric for aligning HR with financial performance. 🔹 Employee Engagement: Often measured through surveys, this metric captures how emotionally and cognitively invested employees are in their work. High engagement is correlated with productivity, innovation, and employee retention. 💡 Why it matters: These formulas empower HR teams to move from reactive to proactive. They help diagnose problems, forecast trends, and make evidence-based decisions that drive business value. People analytics isn’t just about tracking—it’s about transforming. #PeopleAnalytics #HRStrategy #HumanCapital #WorkforceInsights #EmployeeExperience #DataDrivenHR #Leadership #FutureOfWork #LinkedInHR #HRLeadership

“In an era of data obsession and AI-powered solutions, we’ve sidelined a crucial catalyst for transformation: the artist's mind. While businesses scramble to hire more engineers and data scientists, could they be overlooking their most powerful allies in navigating uncertainty and driving innovation? […] What if your next strategic hire wasn’t an analyst — but an artist? […] To reimagine the future, we must look back. The Renaissance wasn’t just an art movement — it was a systems reboot. Artists worked alongside mathematicians, philosophers, and merchants to reframe society’s operating systems. From the Medicis to the Vatican, power was paired with imagination, not to dazzle but to direct. […] Today, our systems are stagnating not from lack of innovation, but from a collective amnesia around the role of beauty and imagination in driving societal change. […] Historically, artists have not merely reflected society — they’ve provoked it. Their work has been banned, branded “degenerate,” or erased — not because it lacked value, but because it revealed inconvenient truths. Artists often serve as conduits for a society’s collective emotional undercurrents — the invisible data that businesses and institutions desperately need, but struggle to recognise. […] We’ve been taught to relegate creativity to marketing departments or brainstorming sessions, treating it like a mood rather than a muscle. And in the uncertain terrain of today’s economy, survival isn’t about efficiency — it’s about adaptability. Creativity is what allows cultures to bend instead of break, to reconfigure themselves when the old structures begin to rot. In uncertainty, it’s survival tech. In systemic failure, it’s a generator of renewal. […] To welcome artists into boardrooms, then, isn’t just about diversity of thought. It’s about strategic resilience. It’s about embracing ambiguity as a design material, and story as a strategic asset. It’s about recognising that the artist’s way of seeing — sensitive, symbolic, non-linear — might just be our best shot at shaping a future where human and planetary flourishing is not just a hope, but a strategy.” – great piece by Annalise Lewis & Laura Melissa Williams Read the full article here: https://lnkd.in/eG27rgNg Image below by Joel Uili (it just fit perfectly to the article)

AI is transforming the way we hire but only if it’s done right. Too often, companies treat AI like a shortcut, hoping it will automate away the complexity of hiring. But real results come when AI is used to enhance human decision-making, not replace it. The best hiring outcomes still come from a combination of data and intuition. That starts with feeding your AI the right inputs: culture-informed, role-specific, and industry-relevant data. If you feed it generic or biased data, the insights you get will be flawed. Garbage in, garbage out still applies. Then comes what really matters measuring what most companies miss: soft skills, team dynamics, communication styles, and long-term alignment. These aren't visible on a resume, but the right AI tools can help surface them. And when trained ethically, they can also help mitigate bias not reinforce it. Culture fit can’t be scanned. But with the right strategy, it can be understood. The future of hiring isn’t AI or people. It’s AI + emotionally intelligent leaders who know how to use it. #AIRecruiting #FutureOfWork #SmartHiring #HumanFirst #CultureFit #RecruitmentStrategy #RightFitCulture #HiringWithPurpose #TechMeetsTalent #LeadershipDevelopment #PeopleFirst