AI in Knowledge Work Productivity

In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions. The teams using AI were happier as well. Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions. The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences. This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub Subatack about the work here: https://lnkd.in/ehJr8CxM Paper: https://lnkd.in/e-ZGZmW9

Ambient AI is no longer a future concept in healthcare, it’s already reshaping how care is delivered. AI-enabled clinical documentation is changing how physicians experience technology, making it feel supportive rather than burdensome. By reducing the administrative load of documentation, clinicians can spend more time practicing medicine instead of managing systems. At the same time, clinical documentation, which has long been a source of friction, burnout, and risk, has the potential to become a powerful source of real-time clinical insight. At Elevance Health, we’re focused on applying digital technologies, such as ambient and clinical insights - responsibly - not just to document care, but to enable earlier intervention, better coordination, and more effective cost management. Several principles guide our approach: 🚣 Move upstream: Embed payer intelligence, such as risk signals and care gaps, directly into clinical workflows rather than surfacing insights after the fact. 🕵 Focus on moments that matter: Earlier detection of risk allows action before acute events occur. 🩺 Keep humans in the loop: AI should support clinical decision-making, not replace clinical judgment. 🔃 Reduce friction, not add it: Seamless data flow means less manual work for providers and faster, more comprehensive care. By integrating real-time clinical documentation with actionable insights, ambient AI can help surface relevant information at the moment of care, supporting more comprehensive diagnosis, improved coordination, and more affordable outcomes without increasing burden or compliance risk. The opportunity ahead isn’t about adding more AI tools. It’s about turning data into action at the right time, in the right workflow, for the right member. I look forward to continued collaboration across payers, providers, and technology partners as we shape what responsible, AI-enabled healthcare should look like.

Stop building AI agents in random steps, scalable agents need a structured path. A reliable AI agent is not built with prompts alone, it is built with logic, memory, tools, testing, and real-world infrastructure. Here’s a breakdown of the full journey - 1️⃣ Pick an LLM Choose a reasoning-strong model with good tool support so your agent can operate reliably in real environments. 2️⃣ Write System Instructions Define the rules, tone, and boundaries. Clear instructions make the agent consistent across every workflow. 3️⃣ Connect Tools & APIs Link your agent to the outside world - search, databases, email, CRMs, internal systems - to make it actually useful. 4️⃣ Build Multi-Agent Systems Split work across focused agents and let them collaborate. This boosts accuracy, reliability, and speed. 5️⃣ Test, Version & Optimize Version your prompts, A/B test, keep backups, and keep improving - this is how production agents stay stable. 6️⃣ Define Agent Logic Outline how the agent thinks, plans, and decides step-by-step. Good logic prevents unpredictable behavior. 7️⃣ Add Memory (Short + Long Term) Enable your agent to remember past conversations and user preferences so it gets smarter with every interaction. 8️⃣ Assign a Specific Job Give the agent a narrow, outcome-driven task. Clear scope = better results. 9️⃣ Add Monitoring & Feedback Track errors, latency, failures, and real-world performance. User feedback is the fuel of improvement. 🔟 Deploy & Scale Move from prototype to production with proper infra—containers, serverless, microservices. AI agents don’t scale because of prompts, they scale because of architecture. If you get logic, memory, tools, and infra right, your agents become reliable, predictable, and production-ready. #AI

Workflow Agents in #Oracle_Fusion_AI_Agent_Studio are redefining what “#Enterprise_AI_automation” actually means. Most tools can run steps. Some tools can call an LLM. But Workflow Agents do something much bigger---->> they combine deterministic control flow, reasoning, memory, and multi-agent orchestration directly inside the systems that run the business. Here are 4 patterns that give them some real power: 1. Chaining — Step-by-step intelligence Every step interprets context, transforms data, and feeds the next. Perfect for real enterprise flows with dependencies: onboarding, validation, document-to-decision processes, and month-end close. 2. Parallel — Collective decisioning at speed Multiple branches run at once: diagnostics, policy checks, data lookups, history, extraction. Everything merges into a single, high-quality decision. Faster outcomes with better signal coverage. 3. Switch — Context-aware routing without rule bloat Instead of giant rule trees, the workflow adapts to user, policy, intent, and application state on the fly. Same entry point, personalized paths. Automation that’s flexible, not fragile. 4. Iteration — Goal-seeking refinement Great for scheduling, planning, allocation, cost modeling. The agent loops intelligently until constraints are met. Not “first viable answer” — the right answer. This is only one layer of the bigger story. Fusion supports the full spectrum of AI automation: - Workflows for structure. - Workflow Agents for structure with reasoning. - Agent Teams for autonomous digital workers that pursue outcomes. And because all of this lives inside Oracle Fusion Applications, the automation is grounded in real Fusion data, policies, security, and transactions from the start. Enterprise AI that actually does the work — #built_in_not_bolted_on.

If you're using AI agents just to speed things up, you're missing their real value. Working with agents isn’t about shortcuts. It’s about designing collaborative systems that think with you. And this is how it should work: → Start with context Before you ask for outputs, define your goals, your audience, and the “why” behind your initiative. Agents perform best when they understand the bigger picture. → Design the workflow together Map out how agents and humans will interact. Who leads what? What tools are involved? What feedback loops do you need? → Only then, begin prompting This is where most teams start. But if you haven’t aligned on strategy, you’ll get fragmented results. At Mchange, we learned this the hands-on way. We had no background in marketing or content creation. But our AI agent team helped us build a content workflow from the ground up. It looks like this: → We set the mission: who we want to reach and why → We share that with our agents, often including docs, data, and vision → Together, we design the content flow and assign agent roles →Only then do we prompt for drafts, visuals, and distribution plans And the best part, The more we share up front, the more strategic and creative our outputs become. AI doesn’t just support our process, it teaches us how to improve it. Because when agents understand why something matters, they help you figure out how to make it matter more. That’s the real shift. AI inot as a tool, but as a thinking partner in your system. If you want deeper insights into how agent–human collaboration should look like DM me or book a call on our website. And remember, create value, not hype.

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

Most coaches & consultants don’t have a time problem. They have a systems problem. AI doesn’t fix chaos. It scales whatever system you already have. Here are 5 AI tools that actually plug into your daily workflow (with real use-cases): 1. ChatGPT: Use it to think, not just write. Daily integration: Pre-call: Generate 5 sharp questions based on client background Post-call: Convert notes into insights and next steps Sales: Practice objection handling before discovery calls Example: “Here are my client notes → identify blind spots and suggest 3 tough questions for next session.” 2. Notion AI :Your second brain for client delivery. How to use: Create client dashboards with auto summaries Maintain SOPs for your programs Turn session transcripts into insights + next steps Example: Upload session notes → “Summarize key breakthroughs + assign action items” Your client gets clarity instantly. 3. Descript: Content creation without the headache. How to use: Edit podcasts/videos by editing text Remove filler words automatically Repurpose long-form content into shorts Example: Record a 20-min coaching insight → Cut it into 5 LinkedIn videos + 10 reels in under an hour. 4. Otter.ai.: Never miss what your client actually said. Daily integration: Record and transcribe coaching calls Highlight key patterns across sessions Build a repository of client insights over time Example: Spot recurring phrases like “I feel stuck” and use that language in your next session to go deeper. 5. Make: Where everything connects. Daily integration: Auto-send session summaries after calls Connect forms to CRM, email, and task managers Build end-to-end onboarding flows Example: Client fills a form, gets a calendar link, books a call, receives a prep doc, and you get a summary. All automated. Here’s the shift most people miss: Don’t ask, “Which AI tool should I use?” Ask, “Which part of my workflow is still manual?” That’s where AI fits. Because the goal isn’t to use more tools. It’s to free up more thinking time. What’s one task in your workflow you’d love to automate right now?

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

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.

MIT researchers paired 2,310 people into human-human and human-AI teams to create real ads in a collaborative workspace with some fascinating outcomes—tracking 183K messages, 2m copy edits, and over 5m ad impressions. The paper "Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance" examined many facets of the dynamics of human-AI collaboration on what was most effective. Some of the valuable insights: 🤖 AI changes how teams talk and work together. Human-AI teams sent 45% more messages than human-only teams, with a focus on task execution—suggestions, instructions, and planning—while human teams sent more social and emotional messages. Despite this shift, both team types rated teamwork quality similarly, showing that collaboration can remain strong even when social interaction drops. 🧍➕🤖 One person plus AI can match or beat human teams. Individuals in human-AI teams produced 60% to 73% more ads than individuals in human-human teams, closing the productivity gap that usually favors groups. Despite having only one human per team, human-AI groups created just as many ads overall as two-human teams. 🧠 Human-AI success depends on psychological compatibility. When a conscientious person worked with a conscientious AI, message volume increased by 62%, signaling better engagement. But mismatches had negative effects—for example, extraverted humans working with conscientious AIs saw drops in text, image, and click quality across the board. 📊 AI lets people shift from doing to directing. Participants in human-AI teams made 60% fewer direct text edits compared to those in human-only teams. Instead of rewriting content themselves, they communicated what needed to be done—refocusing effort from manual changes to guiding and refining AI-generated output. 🔄 AI redistributes cognitive workload and changes who does what. With AI handling routine and complex text generation, humans shifted attention from editing to strategic input and idea generation. This redesigns roles within teams, suggesting new ways to organize work where humans steer, and AI constructs. Humans + AI is the future. This research provides more valuable foundations for understanding how to do this well.