Insights on Professional Impact from AI Interactions

There is perhaps no industry more fundamentally disrupted by AI than professional services. Here are some of the top insights in the excellent new ThomsonReuters Future of Professionals Report, drawing on a survey of over 2,000 professionals globally. The industry is based on professionals, so individual capability development - as shown in the image - is fundamental. However it is also about organizational transformation, with most far behind where they need to be. The report shows: 📊 Strategy-first adopters dominate ROI. Having a visible AI roadmap makes all the difference: firms with a clear strategy are 3.5 × more likely to enjoy at least one concrete benefit from AI, and almost twice as likely to see revenue growth compared with ad-hoc adopters. ⏱️ AI is freeing up 240 hours a year. Professionals expect generative AI to claw back about five hours a week—240 hours annually—worth roughly US $19 k per head and a US-wide impact of US $32 billion for legal and tax-accounting alone. 🚦 Expectations outrun execution. While 80 % of respondents foresee AI having a high or transformational impact within five years, only 38 % think their own organisation will hit that level this year, and three in ten say their firm is moving too slowly. 🧠 Skill depth multiplies payoff. Employees with good or expert AI knowledge are 2.8 × more likely to report organisational gains, regular users are 2.4 × more likely, and those with explicit AI adoption goals are 1.8 × more likely to see benefits. 🏅 Leaders who walk the talk win. When leaders model new tech adoption, their people are 1.7 × likelier to harvest AI benefits; active tech investors double their odds, and firms that added transformation roles see a 1.5 × uplift. 🎯 Accuracy anxieties set a sky-high bar. A hefty 91 % believe computers must outperform humans for accuracy, and 41 % insist on 100 % correctness before trusting AI without review—making reliability the top blocker to further investment. 🌱 Millennials are sprinting ahead. Millennials are adopting AI at nearly twice the rate of Baby Boomers, underscoring a generational divide that could widen capability gaps if left unaddressed. 🛠️ Tech-skill shortages stall teams. Almost half (46 %) of teams report skill gaps, with 31 % pointing to deficits in technology and data know-how—outpacing gaps in traditional domain expertise or soft skills. 🔄 Service models are already shifting. Twenty-six percent of firms launched new advisory offerings in the past year, yet only 13 % have rolled out AI-powered services; meanwhile, a third are moving away from hourly billing and a quarter of in-house clients reward flexible fee structures. 🔗 Goals and strategy are often misaligned. Two-thirds (65 %) of professionals who set personal AI goals don’t know of any corporate AI strategy, while 38 % of organisations with a strategy give staff no personal targets—fuel for inconsistent, inefficient adoption

A new study released today by OpenAI and Harvard economists draws on anonymized data from over 700 million weekly ChatGPT users worldwide. It offers the first large-scale, privacy-preserving look at how people actually rely on generative AI for sophisticated reasoning and decision support. Five findings leap out at me: ⭐ Decision support is exploding. Almost half of all messages, and now more than half, are people asking for guidance, advice, or analysis. The real economic value lies here: AI as a thinking partner. ⭐ Workplace reasoning is front and center. Among work-related messages, 56% involve “doing” tasks, and nearly three-quarters of those are writing tasks where the model is helping to solve problems or craft strategy, not just generate boilerplate. ⭐ These tasks match the core of knowledge work. Over 45% of all messages map to O*NET work activities such as “Getting Information,” “Interpreting Information,” and “Making Decisions & Solving Problems.” ⭐ Quality rises with complexity. Interactions in which people ask the model to reason or advise consistently rank highest in user satisfaction. ⭐ AI is becoming a teacher. Roughly 10% of all messages are tutoring or teaching requests, a striking signal that people already trust AI to explain and guide. And for those driving enterprise transformation, the same research adds a powerful call to action: ⚡ ChatGPT adoption has reached 10% of the world’s adult population, with users sending 2.5 billion messages daily, one of the fastest technology diffusions in history. ⚡ Even as personal use grows, absolute work-related usage has more than tripled in a year, proving that employees already incorporate AI into their daily jobs, often before formal corporate programs. ⚡ The highest-value interactions, decision support, strategic writing, and problem-solving are precisely the activities that define knowledge-intensive industries. For enterprises and their advisors, this is more than a trend; it’s an urgent signal. The next competitive edge isn’t just automating routine tasks. It’s embedding AI as a true co-pilot for human judgment, from strategic planning and R&D to regulated decision environments. If you’re shaping an AI strategy today, these data points make the case clear: your teams and your customers are already treating AI as a reasoning partner. The question isn’t whether they will... It’s whether your enterprise is ready to design for it and become truly AI-first. Read the paper here: http://bit.ly/4na2eeA

Most people assume the best way to use AI at work is to outsource tasks. But the biggest impact comes when we use AI to challenge us. We default to using AI for efficiency, like by delegating routine tasks. While this has its place, the real power of AI emerges when we use it as a thought partner, forcing us to think more critically, ask better questions, and elevate our decision-making. For example, I might need to have a difficult conversation with a vendor. I could tell ChatGPT what I really want to say, have it clean up my low-EQ draft, and simply send it. Alternatively, I could tell AI what I’m thinking and have it refine and guide me—offering suggestions for things to consider, such as starting the conversation by acknowledging aspects of the vendor’s work I appreciate, posing questions instead of making demands, and asking for the vendor’s help rather than assuming bad intent. When I use AI in this second mode, I might not save a few seconds right now, but I level up my game in the long run. Before AI, we had to do all the work ourselves, so we focused primarily on execution and meeting deadlines. Now, as we share the work with AI, we must take on new roles—question-asker, director/producer, critical thinker, and emotional actor—making us more curious, creative, and insightful about how things really work, both in the external world and in our own minds. AI doesn’t just make things easier; it makes us smarter. AI can introduce complexity and then explain it, expose us to new concepts and data, highlight where we may be wrong, push us to practice critical thinking and curiosity, and help us explore our own beliefs, behaviors, and theories of mind. As AI reshapes knowledge work, the real competitive edge will belong to those who embrace it as a partner in thinking—not just a shortcut for execution.

AI’s impact on the workforce is no longer theoretical. New data from Anthropic provides one of the clearest pictures yet of how AI is actually being used in professional roles today. By analysing millions of real-world interactions with Claude AI, the study moves beyond speculation and reveals where AI is embedded in work, where its adoption remains low, and whether it is augmenting or automating professional tasks. Some key findings: 🔹 AI is now performing 25% or more of the tasks in 36% of occupations. 🔹 57% of AI use is augmentation, meaning workers use AI as a collaborator, refining and improving their work. 🔹 43% of AI use is automation, where AI completes tasks with little human involvement—raising questions about long-term shifts in work. 🔹 AI’s adoption is highest in mid-to-high-wage professions, particularly in software engineering, content creation, and data analysis. 🔹 Industries requiring physical labour or complex interpersonal skills see much lower AI usage—for now. This data brings important implications for education and workforce development. Rather than broad assumptions about AI’s role in work, institutions now have a clearer sense of where AI is being used, where it isn’t, and how qualifications may need to adapt. So, what does this mean for workforce preparation? The findings suggest that AI fluency will be essential in some fields, while in others, the focus must remain on human-led expertise—critical thinking, ethical reasoning, and leadership. The full article unpacks these insights further, exploring what this data means for jobs, education, and the future of work. 🔹 #AI 🔹 #FutureOfWork 🔹 #AIinEducation 🔹 #WorkforceDevelopment 🔹 #EdTech

New research from Harvard Business School explores a big question: What if AI isn’t just a tool but a teammate? In a large-scale field experiment with Procter & Gamble, researchers tested how GPT-4 affected performance when used by individuals versus teams of experienced professionals working on real product development challenges. Some key findings: - AI-enabled individuals performed as well as teams without AI - Teams using AI produced the best and most exceptional results overall — not only did they outperform others, but they were significantly more likely to generate top 10% solutions - AI helped bridge expertise gaps and broke down professional silos - Participants using AI had better emotional experiences — more excitement, less frustration The takeaway? AI isn't just about individual productivity — it’s reshaping how we collaborate, think, and solve complex problems. It’s acting more like a cybernetic teammate, not just a more efficient tool. The working paper — “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise” — is worth a read. As someone interested in the future of work, this raises important questions: 1. How do we design teams when AI levels the playing field? 2. What happens to traditional boundaries between roles? 3. How do we rethink collaboration when AI enhances both performance and emotional engagement? Curious what you all think — especially if you’re leading teams or exploring how to integrate AI meaningfully into your org. #FutureOfWork #LinkedInWorkplace #LinkedInLife #WorkplaceResearch

What does the future of professional services look like when every client has AI at their fingertips? This isn't a hypothetical. Our data shows a majority of decision makers at large enterprises are deploying or getting ready to deploy agents within the next 12 months...for each major functional area of their business. Sure, these deployments may be small to start, but AI agents are building the war chests (and revenues) to scale them. $6B+ raised just this past year. Legal AI alone captured $1.25B. Coding agents hit $1.8B. Every dollar flowing into these tools is a direct challenge to the traditional advisory model. The existential questions professional services leaders (and enterprise clients) are asking: Why hire consultants when AI agents can deliver expertise on demand? And why hire consultants to build AI agents when the playbooks are still being written? Here's what our research uncovered: The billable-hour model is dying. Enterprise GenAI pilots are stalling at alarming rates. But the firms that will thrive won't be the ones clinging to yesterday's playbook. The future belongs to firms that evolve from advisors to builders and orchestrators. Drawing from CB Insights data on 100+ AI partnerships, investments, acquisitions and 60+ interviews with senior executives, we identified four moves that separate tomorrow's winners from today's casualties: ↳ Orchestrate the AI agent tech stack:  Don't just recommend tools. Build, integrate, and scale them. ↳ Activate proprietary data: Transform decades of client knowledge into fuel for smarter agents. ↳ Productize your services: Shift from custom engagements to scalable AI-powered platforms. ↳ Lead the human-AI workforce transformation: Redesign how humans and agents collaborate, internally and for clients. The winners aren't just advising on AI. They're building it, orchestrating it, and fundamentally reimagining their business models. The transformation is happening now with McKinsey already deploying 12,000 AI agents internally and Accenture repositioning their entire firm around AI building vs. advising. Our report, "The Future of Professional Services," is live and maps out in detail the 4 strategic pivots firms must make to thrive in the AI agent era. Read the full report here: https://lnkd.in/ezre-qWS

Curious if that new AI tool at work is actually making you more productive? Meet Sonia Jaffe, an economist at Microsoft who turns the hype around AI into real, measurable impact. Sonia’s research doesn’t just ask whether AI works. It shows how, where, and for whom these tools are changing the way we work. Sonia’s research maps where AI can make the biggest difference. Her ‘AI Applicability Score’ highlights which jobs are most susceptible to AI-driven change (https://lnkd.in/geXmaCQD), reflecting frequent and successful use of AI for activities such as information gathering and writing. And we can see that shift playing out in real-world AI usage. When employees first got access to M365 Copilot, she found that frequent users spent significantly less time on email and drafted documents faster (https://lnkd.in/gB6xi3wa). In the context of software development, Sonia’s research across three companies showed that GitHub Copilot users completed about 26% more tasks on average, with the biggest gains being for newer engineers (https://lnkd.in/gG3mMRsc). These aren’t just numbers. They’re evidence that AI can meaningfully accelerate routine work without sacrificing quality. Sonia’s knack for measuring the impact of technology on AI started long before AI. I first got to know her during the pandemic, when we co-authored a study exploring how remote work affected collaboration (https://lnkd.in/gQD7qrcg). We found that without offices, people leaned more on their close teammates and less on casual connections. As offices reopened, Sonia then studied how knowledge workers coordinated their in-office time (https://lnkd.in/gEkNWGMY) and managed their communication load (https://lnkd.in/gSz_uV8C), always grounding her insights in real-world data. But Sonia doesn’t just study the future of work – she helps shape it. As a leader of Microsoft’s New Future of Work initiative (http://aka.ms/nfw), she helps synthesize research findings into annual reports that inform decision makers and curates real world research on generative AI adoption (https://lnkd.in/gjnh5jCq). Her blend of forward-looking research and grounded experience in workplace dynamics makes her insights both practical and visionary. If you’re not yet following Sonia’s research, I highly recommend checking it out! #AIInnovators #AppliedResearch #FutureOfWork #LeadingLikeAScientist

What can we learn from 11,500+ conversations with AI about internal comms? I've been analyzing Anthropic's recent Economic Index data, which examined 1 million Claude.ai conversations. The data stands out as it looks at real-world AI usage (vs. self-reported usage or survey information). It turns out that approximately 11,500 of those chats involved internal communications-related tasks. After examining those, some interesting patterns emerged: 1️⃣ Internal comms tasks show 78% higher AI automation (direct task completion) and 84% higher AI collaboration (human-AI teamwork) compared to the average across all tasks analyzed. 2️⃣ Different content types show distinct AI usage patterns: Newsletter creation has the highest automation potential (32%), while policy documents involve the most human oversight (82% collaborative work). 3️⃣ When compared to external communications tasks, internal comms activities involve nearly twice as much information gathering and 70% more content verification with AI. The data suggests internal communications work might be particularly well-suited for AI collaboration, with human professionals maintaining significant oversight while leveraging AI as a partner rather than just a tool. Read the full analysis: https://lnkd.in/g7G2H5r3 How does this line up with your interactions with AI? Is this consistent with your experience? #InternalCommunications #AI

One of the things I've liked about the broad #AI and #automation topic is it has provided rich opportunities to dig deep in specific areas. I sat down with Elizabeth Lascaze, the lead author on a recent piece by Deloitte about the potential AI impact on #Careers - for both early career workers and more tenured workers. This is a focused dive which ties to our 2025 #HCTrends piece about the "Experience Gap" and "Reimagining the Role of the Manager". Highlights: 1️⃣ 𝐀𝐈 𝐢𝐬 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥-𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧𝐝 𝐜𝐚𝐫𝐞𝐞𝐫 𝐩𝐚𝐭𝐡𝐬 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲 𝐚𝐜𝐫𝐨𝐬𝐬 𝐜𝐚𝐫𝐞𝐞𝐫 𝐬𝐭𝐚𝐠𝐞𝐬. 𝑺𝒕𝒂𝒕: 69% of early-career professionals say AI improves their productivity, but 60% worry they may not be developing critical thinking skills at the same pace. 𝑬𝒙𝒂𝒎𝒑𝒍𝒆: Imagine a junior financial analyst who can now generate market reports in minutes using AI. While that’s great for efficiency, it also means they may not develop the deep analytical skills that come from manually crunching data. Contrast that with a CFO who uses AI-powered forecasting models to enhance decision-making. Their challenge isn’t learning how to analyze data—it’s ensuring AI-driven insights align with business strategy. 2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 1. Redesign Early-Career Learning to Prevent AI Over-Reliance 𝑬𝒙𝒂𝒎𝒑𝒍𝒆: A consulting firm restructured its analyst training to require junior employees to validate AI-generated insights manually before presenting to clients. This ensured they still developed fundamental problem-solving skills. 𝑨𝒄𝒕𝒊𝒐𝒏: Ensure hands-on experience and real-world problem-solving remain central in early-career roles. 2. Encourage AI Fluency at Every Career Stage 𝑬𝒙𝒂𝒎𝒑𝒍𝒆: A retail company embedded AI literacy into leadership development, ensuring executives knew how to apply AI insights strategically. 𝑨𝒄𝒕𝒊𝒐𝒏: Make AI fluency a leadership skill, not just a technical competency. Read the full article here: https://lnkd.in/g-FrKRND Kudos to the full author team, including Roxana Corduneanu, Brad Kreit, Susan Cantrell, Abha Kulkarni, and Dany Rifkin! #Deloitte #HumanCapital #GenAI #Careers #MakingWorkBetterforHumans

AI Insights: 5 Key Observations from Practitioners at Our NVIDIA GDC Week Meetup Last week, I gathered distinguished practitioners to discuss #AI's impact on #UI. As these conversations often do, we quickly expanded into broader territory. 1️⃣ The Changing Developer Landscape: Tools like Cursor are making sr. developers truly 10x. What that means: jr. developers are less needed, teams are flattening, and PM-to-developer ratios in startups are approaching 1:1. This is beyond #vibecoding - production systems are running AI-written code. 2️⃣ Return to Centralized Computing: AI-assisted dev tools literally sending every keystroke to cloud-based LLMs —with significant implications for networking infrastructure and energy consumption. Unless a totally different architecture emerges (SLMs at the edge?), it’s like we’ve gone back to mainframes. 3️⃣ Voice as the Primary Interface: Natural language is the only effective AI interface so far. Voice is emerging as the UI for various applications—even coding (as @Karpathy demonstrates). Optimizing voice interfaces everywhere and for everything however is still work in progress. This is where tech like Kardome comes into the picture. 4️⃣ The Trust Factor: Onboarding non-technical users to AI is an *emotional* challenge that requires establishing trust - not through accuracy, but through perceived vulnerability and approachability. Our AIs are creating connections that leverage our psychological tendencies. One could argue they are manipulating users to trust them - already. 5️⃣ Architecture Crossover: Incredibly, the architecture powering LLMs is finding applications in robotics, with "tokens" representing physical movements and sensor inputs instead of text. This convergence of AI, hardware, and robotics is driving innovation across sectors. The organizational implications are staggering. Software engineers are the canaries in the coal-mine. Eventually every profession stands to be transformed. We're already seeing resistance to AI-assisted coding tools from managers who recognize they'll soon be... managing fewer people. And this is only the beginning. People with PhDs in computer science are asking: "Should we even teach our kids to code?" The conversation quickly shifted to universal basic income and societal stratification... Thanks to the brilliant minds who joined: Dani Cherkassky (@Kardome) - Voice technology visionary Campbell Kennedy - Robocar pioneer now revolutionizing commercial robots Gil Arditi (Morph.ai) - Replacing developers with AI Sasha Vladimirov (Lumiflow AI) - Making sure the result is usable. Sergei Burkov (Colibri.ai) - Capturing conversations, fully Mike Prince - Weaving AI agents Risto Lähdesmäki - Bringing wisdom to AI-human collaboration questions Angel Gambino - Enabling AI innovation Imri Goldberg (Port Power) - Organizing Israel's CTO Underground Where do you believe AI is taking work? How will we use computers to do it? Will any be left? #AIFuture #SoftwareEngineering