AI in Knowledge Work Productivity

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.

It was a pleasure to contribute to the latest study by IBM Institute for Business Value on best practices to help organizations today maximize their IT investments. From my recent engagements with Middle East and Africa CIOs and leaders, it's becoming clear how IT complexity isn’t just a technical challenge. It’s a business challenge. The numbers don’t lie: organizations that embrace intelligent automation and generative AI are unlocking measurable ROI, reducing costs, and enhancing cybersecurity. This research reveals that highly automated organizations have been able to address the growing complexity in their IT environments more effectively than peers. When compared with organizations that have less advanced AI capabilities, they spend less on IT without compromising on business outcomes: 6.8% of revenue compared to 8% for less automated organizations. These highly automated companies employ only 90 IT staff per $1 billion in revenue, versus 140 for less automated peers. In short, embedding AI powered automation in IT helps organizations do more with less. Grateful for the opportunity to represent the specific challenges faced by #MiddleEast and #Africa organizations in optimizing their IT investments in this key research piece by #IBV, and I invite all leaders in the business and tech space in the region to read the full report to understand how they can cut the costs, as well as complexity, of today's IT environment with AI.

Most AI tool lists miss the point. The advantage doesn’t come from knowing more tools. It comes from knowing where they fit in your workflow. Right now most people use AI like this: → Try a tool → Generate something → Move on No structure. No repeatability. So the productivity gains stay small. The real leverage appears when you treat AI tools like a stack, not a collection of apps. Almost every modern AI workflow fits into four layers. If you understand these layers, you can build systems that run every week without starting from scratch. 1️⃣ Thinking layer Tools that help you clarify problems and structure ideas. → ChatGPT → Claude Use them to: → research unfamiliar topics → break down complex problems → outline strategies and plans → stress-test ideas before execution Most people jump straight to creation. The real value often starts one step earlier: better thinking. 2️⃣ Creation layer Tools that turn ideas into assets. → writing tools (Jasper, Writesonic) → design tools (Canva AI, Flair) → image tools (Midjourney, DALL-E, Stable Diffusion) → video tools (Runway, HeyGen, Synthesia) This layer turns raw ideas into: → presentations → visuals → videos → marketing assets → documentation Think of it as production infrastructure for knowledge work. 3️⃣ Automation layer Tools that connect steps together. → Zapier → Make → Bardeen Instead of repeating tasks manually, these tools: → move information between systems → trigger actions automatically → remove repetitive work Example: Research → draft → create visuals → publish. Automation turns that into a repeatable pipeline. 4️⃣ Deployment layer Tools that deliver work to customers and teams. → websites (Framer, Durable) → chatbots (Chatbase, SiteGPT) → marketing tools (AdCreative, Simplified) This is where work becomes: → websites → marketing campaigns → customer experiences → digital products Without deployment, great AI output never reaches the real world. If you run a business or lead a team, here’s a simple playbook. Step 1 Pick one tool per layer. You don’t need ten tools doing the same job. Step 2 Design one repeatable workflow. Example: → research with ChatGPT → draft content → create visuals in Canva → automate publishing with Zapier Step 3 Automate the steps that repeat every week. Anything you do more than three times should become a system. Step 4 Improve the workflow over time. Small improvements compound faster than constantly switching tools. The people getting the most value from AI right now are not the ones testing every new tool. They are the ones building simple systems that run every day. Tools will change. Workflows compound. 💾 Save this if you’re building your AI stack. ♻️ Repost to help others move from experimenting with AI to actually using it in their work. ➕ Follow Gabriel Millien for practical insights on AI execution and building real leverage with AI. Image credit: Aditya Goenka

Is AI delivering real productivity gains? What's the ROI so far? Hot takes abound, but data have been scarce. Noam Segal and I took it upon ourselves to find out what’s actually happening on the ground by running one of the largest independent, in-depth surveys on how AI is affecting productivity for tech workers (1,750 respondents). We surveyed product managers, engineers, designers, founders, and others about how they’re using AI at work. tl;dr: AI is overdelivering. 1. 55% of respondents say AI has exceeded their expectations, and almost 70% say it’s improved the quality of their work. 2. More than half of respondents said AI is saving them at least half a day per week on their most important tasks. We’ve never seen a tool deliver a productivity boost like this before. 3. Founders are getting the most out of AI. Half (49%) report that AI saves them over 6 hours per week, dramatically higher than for any other role. Close to half (45%) also feel that the quality of their work is “much better” thanks to AI. 4. Designers are seeing the fewest benefits. Only 45% report a positive ROI (compared with 78% of founders), and 31% report that AI has fallen below expectations, triple the rate among founders. 5. Engineers have accepted AI as a coding partner and now want it to handle the more boring (but necessary) work of building products: documentation, code review, and writing tests. 6. n8n is currently dominating the agent landscape, though actual adoption of agentic platforms in 2025 has been slow. 7. A whopping 92.4% of respondents report at least one significant downsides to using AI tools. There’s definitely room for improvement. Here's the full report: https://lnkd.in/gR5G88yA Inside: - What exactly AI is doing for people, function by function? - Where are the biggest opportunities for AI startups? - Which AI tools have product-market fit? - The downsides of AI productivity - Bonus: The state of agentic AI: promise outpaces practice - What this all means - Appendix: Who took this survey

A new study published in Harvard Business Review confirms what every high-performer already suspects: AI tools don’t reduce work, they intensify it. Researchers Aruna Ranganathan and Xingqi Maggie Ye spent eight months studying a 200-person tech company and found that employees who adopted AI worked faster, took on more tasks, and extended their hours, all without being asked. We did not need a study to confirm that productivity tools increase productivity. Still, this one is worth a quick read. They buried the most interesting finding in the middle of the article. Friction points (waiting for a colleague, staring at a blank page, struggling with an unfamiliar task) create natural rest periods for knowledge workers. When AI eliminates them, the boundary between working and not working becomes trivially easy to cross. The pause disappears, and the work expands to fill every available minute. The researchers mapped an obvious escalation cycle. AI made tasks faster, which raised expectations for speed, which increased dependence on AI, which expanded the scope of what workers attempted, which increased the total volume of work. One engineer put it plainly: “You had thought that maybe because you could be more productive with AI, you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more.” Every productivity tool in history has punished the people willing to sprint. Spreadsheets punished the fastest accountants. Email punished the most responsive managers. AI just removed the speed limit. Your best people will hit the wall first, because they are the ones running the hardest. If your AI deployment strategy does not include guardrails for pacing, you are optimizing for burnout.

Where are the productivity gains from AI? 🤔 The technology has been advancing rapidly but when do we actually see its impact in the real economy? For the UK, this turning point might be happening right now. 🇬🇧 I was excited to read a new report by Snowflake, based on research conducted by YouGov among 500 senior decision-makers across major UK organisations: ➡️ 23% report that AI is already delivering productivity improvements at scale ➡️ Another 45% see gains emerging in specific use cases ➡️ Just 1% planning to reduce AI spending This is a big deal. We may finally be moving from AI promise to measurable productivity reality 📈 What is slowing AI productivity gains down? The report also highlights something equally important: The main bottlenecks are not technological. Only 19% of organisations cite technology as a key barrier. Instead, the real challenges are poor data quality, organisational silos and, most crucially, a shortage of skilled talent. This strongly aligns with findings from our www.skillscale.org research group at the Oxford Internet Institute, University of Oxford. Firms are desperately looking for AI talent and workers with AI skills 💷 earn ~23% higher wages, 🏡 are 3x more likely to enjoy job perks like remote work, 📩 have higher chances of landing a job in the first place. What should we learn from this? AI is not just a technology story, it’s a skills story. If we want to sustain and scale these productivity gains, the priority is clear: Invest in people, not just in tools. You can find the Snowflake/YouGov report here: https://lnkd.in/e73t_MRd Curious to hear your thoughts—are you already seeing productivity gains from AI in your organisation? 👇

AI is moving fast, but don’t let it move without you. This year, I made a mindset shift: From: “AI is interesting, I should learn more about it.” To: “AI is a teammate, let me actually use it every day.” That simple reframe changed how I work. Here’s how I use AI weekly- not in theory, but in practice: 🧠 Idea generation From podcast topics to product features, I use LLMs to explore angles I’d never think of alone. 📄 Docs & writing Speed isn’t the only gain. AI helps me get past the blank page, structure faster, and punch up drafts. 📊 Data & coding I’ve used AI to debug Python, generate scripts, and even mock up dashboards based on plain text input. 📈 Growth Audience insights, trend spotting, CTA optimization. If it can be tested, AI can help scale it. 🧰 Stack: ChatGPT, Perplexity, Notion AI, GitHub Copilot, Midjourney, Rewind (and custom GPTs I’ve built for specific workflows) None of this happened overnight. But if you want to level up your productivity, creativity, and output, I genuinely think building your personal AI stack is the best investment you can make right now. Start small: → Pick 1 area where you’re stuck → Choose 1 tool to help → Try it for 1 week → Iterate, compound & build from there This isn’t about replacing you — it’s about augmenting what you already do best. AI isn’t the future. It’s the multiplier for the present. What’s your AI stack looking like right now? Drop a tool or use case you’re loving👇 #AItools #Productivity #BuildInPublic #WorkSmarter #CareerGrowth #FutureOfWork

Real AI agents need memory, not just short context windows, but structured, reusable knowledge that evolves over time. Without memory, agents behave like goldfish. They forget past decisions, repeat mistakes, and treat every interaction as brand new. With memory, agents start to feel intelligent. They summarize long conversations, extract insights, branch tasks, learn from experience, retrieve multimodal knowledge, and build long-term representations that improve future actions. This is what Agentic AI Memory enables. At its core, agent memory is made up of multiple layers working together: - Context condensation compresses long histories into usable summaries so agents stay within token limits. - Insight extraction captures key facts, decisions, and learnings from every interaction. - Context branching allows agents to manage parallel task threads without losing state. - Internalizing experiences lets agents learn from outcomes and store operational knowledge. - Multimodal RAG retrieves memory across text, images, and videos for richer understanding. - Knowledge graphs organize memory as entities and relationships, enabling structured reasoning. - Model and knowledge editing updates internal representations when new information arrives. - Key-value generation converts interactions into structured memory for fast retrieval. - KV reuse and compression optimize memory efficiency at scale. - Latent memory generation stores experience as vector embeddings. - Latent repositories provide long-term recall across sessions and workflows. Together, these architectures form the memory backbone of autonomous agents - enabling persistence, adaptation, personalization, and multi-step execution. If you’re building agentic systems, memory design matters as much as model choice. Because without memory, agents only react. With memory, they learn. Save this if you’re working on AI agents. Share it with your engineering or architecture team. This is how agents move from reactive tools to evolving systems. #AI #AgenticAI

Productivity goes up when you add a second AI tool. It goes up again with a third, but slower. After three, it drops. That's from a BCG study of 1,488 workers, published in HBR. As companies roll out more multi-agent systems, people end up toggling between more tools. Contrary to the promise of freeing time for meaningful work, juggling becomes the work itself. The researchers call the cognitive cost "AI brain fry": 33% more decision fatigue, 39% higher intent to leave. I'd been noticing the same dynamic around me, so it's good to see it named and measured. More tools follow the same logic as any scaling decision: returns diminish, then reverse. Go deep on a limited number. We already have an AI force on the technology side, and within the people function, we've done a lot of experimenting and filtering already. A formal AI scouting team feels like a logical next step: people who understand the domain processes and knowledge, connected to our tech team on one side and to the external landscape on the other. Their job: filter what's coming, test what fits, and protect the rest of the function from adopting everything at once. Scale doesn't mean more tools. It means more from fewer. https://lnkd.in/eBuZcWxg #FutureOfWork #AIStrategy #TalentStrategy

I've received a few questions on this, so thought I'd share 5 ways I'm using AI in my day-to-day at work to boost productivity, insight, and strategic clarity: Benchmarking: I use AI daily to quickly validate metrics and performance benchmarks. For instance, when reviewing email open rates, I ask ChatGPT (and other LLMs) for industry benchmarks segmented by email types, industries and content. This provides immediate clarity on performance against the rest, and I can see if we're good, great, or have work to do. This information was hard to find or non-existent before and instantly helps builld context. Thought Partner: LLMs elevate my strategic thinking. Whether analyzing competitors or drafting new strategies, I leverage AI to rapidly identify gaps, assess my thoughts against frameworks like "Seven Powers," and run game theory on them with competitive response and market players. It uplevels my thinking and leads to more comprhensive considerations. Deepening Customer Insights: By processing sales call transcripts and meeting notes through AI, I can surface customer pain points and uncover new insights, which improves my understanding of customer needs, sales blockers and messaging that otherwise would be hard to come by. Writing Partner: I use AI to power my writing process—from refining documents to constructing logical, concise, and compelling arguments. It helps draft outlines, provides examples and proof-points to reinforce my assertions, and streamlines my writing. All-in it makes my writing better and faster. Automating Daily Tasks: I use AI-powered tools daily to track competitors, monitor market trends, and check-in on things I care about. It never stops working and so I always have this information available as needed. Today, AI is integral to about half of my workday. And this is just the beginning—there's even more potential to unlock with automations such as reviewing and drafting replies for my emails, prioritizing which documents to review next, and automated meeting prep. How are you integrating AI into your workflow? I'd love to hear what's worked for you.