I want to acknowledge 👏 Boris Cherny , who has not only highlighted this important idea in modern AI agents but has also contributed to building systems around it: 📂 "file system access as a core capability for agents." 📁 This is one of those ideas that looks technical… but completely changes how useful AI becomes in real work. Because work in any organization is not “chatting with AI”. Work is: Excel files and financial reports CVs, policies, and HR records Logs, scripts, and system outputs In short: real work lives in files. So when AI can actually read and write files, something important happens. It stops being just a conversation tool. It starts behaving like a real digital assistant inside the workflow. That means it can: process documents instead of just describing them update outputs instead of only suggesting them carry work across multiple steps keep track of progress like a system, not a chat This is the shift most people underestimate: AI becomes useful not when it talks better — but when it starts working where the work actually happens. And in most industries, that place is still the file system. That’s where AI stops assisting—and starts operating. That’s the real unlock. #ArtificialIntelligence #AIAgents #MachineLearning #FutureOfWork #DigitalTransformation #BusinessAutomation #FinanceTransformation #HRTech #TechLeadership #WorkplaceInnovation 👍

The gap is widening between workers who lean into AI and those who fight it. Forbes recently highlighted data showing U.S. professionals who use AI daily earn about 40% more than those who don’t, while a PwC study found AI skills can be worth a 56% salary premium. My takeaway from conversations across the energy sector: AI won’t replace people, but people who master AI will increasingly outperform those who don’t.

Most companies think AI Agents are replacing people. They’re not. They’re replacing: 1. waiting 2. follow-ups 3. copy-pasting 4. endless coordination 5. Work that feels like work but creates nothing The smartest companies won’t have fewer humans. They’ll have fewer bottlenecks. That’s the real shift. #AIAgents #FutureOfWork #ArtificialIntelligence #BusinessTransformation

The conversation about AI and jobs is missing the point. The real shift isn't replacement. It's multiplication. One skilled worker with the right AI tools can produce what used to require a team. That changes how you plan headcount, structure contracts, and think about workforce capacity altogether. Workforce models built for the last decade won't hold up. The question isn't how many people you need. It's what output you're actually trying to achieve. Listen to see why that distinction matters more than most organizations realize from TJ Emsley Sara Gordon #WorkforceSolutions #FutureOfWork #AIStrategy #ContingentWorkforce

This is a sharp take from Guidant Global—and an important reframe. AI isn’t just changing headcount models, it’s reshaping the connective tissue of how work gets done. Yes, output is being multiplied. But without the right leadership, strategy, and governance, that multiplication can quickly turn into fragmentation—misaligned priorities, overextended talent, and unsustainable pace. The real shift isn’t just “more output per person.” It’s how organizations redefine capacity, roles, and expectations in a way that integrates human judgment with AI capability. That requires intentional design. Because in this next phase of work, advantage won’t come from the tools alone—it will come from how well we connect them to people, purpose, and outcomes. That’s the leadership moment in front of us.

Everyone's talking about AI in operations right now. And most of the conversations I'm hearing are going the wrong direction. The first question a lot of companies are asking is how many people can we replace with this. I get why that's appealing from a margin standpoint. But I think it's the wrong starting point, and honestly, it's a little shortsighted. Here's how I look at it. Your people already know the operation. They know where the bodies are buried, what the system says versus what's actually happening, which supplier needs a phone call instead of a purchase order. That institutional knowledge took years to build. AI isn't replacing that anytime soon. What AI can do is take the low-value stuff off their plate. The manual data entry. The report pulling. The reconciliation work that eats three hours every Monday morning. Free your people from that, and you don't end up with fewer employees — you end up with better ones. People who are actually spending their time on the work that requires judgment, relationships, and experience. That's where the real value is. Not in cutting headcount. In multiplying the capacity of the people you already have. The companies that figure that out are going to have a serious edge. The ones that just use it to trim the org chart are going to find out pretty quickly that they cut more than they bargained for. #OperationsLeadership #AI #DigitalTransformation #VPOperations #ManufacturingOps #Leadership

AI won’t deliver results on its own. Value comes from redesigning work and governance, not just the tech. Without a clear strategy, work redesign, an AI‐ready workforce and up‐to‐date governance, returns will be limited. Understand, prioritize, and manage people risks for resilience and competitive advantage in 2026. Download our latest report now. https://bit.ly/3R58vgt #people #riskmanagement

"AI delivery is complicated and I agree!" Everyone is building AI agents. But what if your AI agent is failing not because the model is weak… but because it inherited an organization that never had to explain itself to a non-human entity? Right there, is the real bottleneck in enterprise AI deployment. Most companies don’t have “AI-ready” systems. They have business logic hidden inside someone’s head who might resign next quarter. Then leadership wonders why the agent “hallucinates.” Your AI agent is not confused. Bring AI Agents to your workflows force a brutal confrontation: - undocumented processes - contradictory policies - fragmented data ownership - approval flows nobody can fully trace - humans making intuition-based exceptions without realizing it. For decades, organisations have optimized for human ambiguity. Now they’re trying to onboard non-human workers that require explicit reasoning paths, observable state, structured memory, and deterministic operations. The uncomfortable truth is: - AI agents are becoming operational mirrors. - And some organizations won’t like what they see reflected back. - The companies that win in the next 5 years won’t be the ones with the fanciest models. - It’ll be the ones disciplined enough to make their organizations legible to machines. I'm curious though: If an AI agent had to run your company tomorrow with minimal-zero-to human intervention… what part of your organization breaks first? #AI #data #ai-agent

I recently came across an insightful video analyzing how some organizations are approaching AI; specifically those that have reduced their workforce under the assumption that AI can fully replace human contributions. What stood out most is how this approach contrasts sharply with Perficient’s AI First model. At Perficient, AI First doesn’t mean replacing people; it means enabling them. It’s about augmenting human expertise with AI to drive better outcomes, improve quality, and increase productivity. The real value comes from keeping humans in the loop while thoughtfully applying AI where it adds the most impact. Perficient’s continued investment in upskilling our teams on AI reflects a clear belief: the future isn’t AI vs. people; it’s AI with people. If you’re exploring how organizations are navigating AI adoption, checkout Perficient and this independent YouTube video that offers a very interesting perspective and is well worth watching. https://lnkd.in/eJWxFXWQ