Automation In The Workplace

The anatomy of a sales call has changed dramatically. Last week, I shadowed some of HubSpot’s top reps and what struck me was how differently the best sellers work today. They’re using AI at every stage: before, during, and after the call. And the results are real. The brain: before the call. AI does the heavy research — scanning 10Ks, news, emails, and past calls to surface the insights that matter most. Tools like Breeze Assistant can prep a full company overview in seconds. According to our State of Sales Report, 74% of sellers say buyers are showing up to calls more informed than ever before. Salespeople need to be just as ready. The heart: during the call. AI notetakers capture everything: next steps, budget mentions, open questions, so reps can focus on listening, not typing or scribbling notes on the side.  Also, AI assistants surface the right case study or testimonial in real time, making every answer sharper and every example more relevant. That means as a sales rep you are more engaged and relevant. The muscle: after the call. AI follows through fast. It drafts personalized follow-up emails in your own voice, outlines next steps, and flags what needs attention. More time with customers and less time writing emails. The result: sellers who prepare better, connect deeper, and close faster. The anatomy of a great sales call used to be manual effort and hustle. Now, it’s human connection powered by intelligence.

306 Practitioners Exposed the Truth About AI Agents in Production UC Berkeley surveyed teams actually deploying agents at scale. The #1 reason they build agents? NOT what the hype says. 73% deploy for one thing: Productivity gains. That's it. Not AGI. Not autonomous systems. Not replacing teams. Just making humans more efficient. Here's what the data reveals about WHY agents get built... The Real Deployment Goals (Ranked) ↳ 73% → Productivity and efficiency gains ↳ 63.6% → Reducing human task-hours ↳ 50% → Automating routine labor ↳ 92.5% → Serving human users (not other agents) Who Actually Uses These Agents? → 52% serve internal employees first → 40% serve external customers → Only 7.5% serve other AI systems The Latency Reality Check Most shocking finding? 66% of teams tolerate response times of MINUTES or longer. Speed isn't the priority. Quality is. The 4 Questions Before Building Any Agent 1→ Does this reduce human task-hours measurably? 2→ Can internal teams validate it before customers see it? 3→ Is quality more important than response time? 4→ Does this augment humans — or try to replace them? If you can't answer YES to all four, you're building the wrong thing. The agents winning in production aren't the most autonomous. They're the most useful. Part 1 of 4 — Drop "PART2" for the architecture that actually works in production. PS: The teams with agents serving internal users first had significantly lower failure consequences. Start inside. Scale outside.

"How to think about agentic engineering autonomy" My latest free deep-dive article: https://lnkd.in/gtUdF-Xx ✏️ The frontier of software development is shifting: the action has changed from prompting to operating. We are stepping into a new era of software factories, background sessions, subagents, and manager agents. But as we scale these capabilities, how do we accurately measure trust and risk? A single-axis ladder is no longer enough to capture multi-agent skill. Most autonomy debates conflate two very different questions: - How far away from ourselves are we letting a single agent go? (Agency) - What is our skill at coordinating many agents? (Orchestration) In my latest piece, I introduce a working model of autonomy for agentic engineering, breaking down this shift into 6 distinct levels: Level 0: Assist - The agent makes suggestions; you always decide. Level 1: Supervised action - The agent edits or runs commands, asking for your approval first. Level 2: Scoped task delegation - You hand off a bounded task with clear constraints and a definition of "done." Level 3: Goal-driven autonomy - The agent cycles through plan ➔ act ➔ test ➔ review until success criteria are met. Level 4: Parallel delegation - work is sliced across many isolated agents running simultaneously. Level 5: Managed-by-exception orchestration - A manager agent runs a continuous factory, dispatching and verifying work - humans only step in when things fail. The forward posture for an engineering team isn't going to be blind delegation but will be calibrated autonomy. We simply cannot build agents we can't answer for. The level of autonomy we grant must follow our verification processes, not the task name. Ultimately, risk and reversibility set the ceiling. Read the full article to explore the four autonomy anti-patterns to avoid, the metrics that make the leap of faith more reliable and how to safely climb the rungs. #ai #programming #softwareengineering

As AI drives massive productivity gains, businesses may consider cutting back on their human workforce to boost efficiency—but I don’t believe that’s the right choice. The real promise of AI isn’t in replacing humans—it’s in amplifying their potential. For centuries, humanity’s greatest leaps forward were not achieved by replacing humans completely with new tools we’ve built but instead by using the tools to accelerate human agency and potential. The automobile amplified human movement. The computer amplified human creation.  And today, AI is amplifying human intelligence. There are certain jobs—especially those involving repetitive, robot-like tasks—that AI will transform. After all, robots will always be better robots than humans. But humans thrive when they’re empowered to be better humans. With AI, people will unlock new skills and deepen natural talents –– achieving what I call “superagency.” Ultimately, the surge in productivity will guide business leaders to a realization: the right move isn’t to do the same work with fewer people but to create even greater value by leveraging more employees with new AI-driven superpowers. Our goal should be clear. Build technology that works with us, not for us—tools that extend what it means to be human, not replace it. Because when we amplify human ingenuity, the possibilities are infinite. 

The latest report from IoT Analytics identifying the top smart manufacturing tech vendors is a must-read, but it also highlights a massive misconception we need to clear up. Smart Manufacturing is not a product category where you pick one "winner." This isn’t a choice between Ford and Chevy; it’s a dynamic landscape. The reality on the plant floor is that manufacturers don't just pick one of these vendors. They might pick five, ten, or twenty. These technologies stack! Your PLM feeds your cloud, which connects to your automation, which relies on your network infrastructure. Here is my take on the state of the "Stack" and where the industry is actually heading: 𝟏. 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐢𝐬 𝐢𝐧 𝐭𝐡𝐞 "𝐒𝐭𝐚𝐜𝐤," 𝐍𝐨𝐭 𝐭𝐡𝐞 𝐒𝐨𝐥𝐨 𝐀𝐜𝐭 The top 10 vendors now control roughly 40% of the market. But market share is a lagging indicator. The real story is that the "un-integrated" vendor is a dead man walking. The leaders of tomorrow are those focused on IT/OT convergence, building the physical and digital "nervous system" that allows these different layers to actually talk to each other. If a vendor isn't building for a multi-vendor, interoperable reality, they aren't a partner; they will end up becoming your biggest bottleneck. 𝟐. 𝐓𝐡𝐞 𝐀𝐈 𝐌𝐢𝐫𝐚𝐠𝐞: 𝐇𝐢𝐠𝐡 𝐇𝐨𝐩𝐞𝐬 𝐯𝐬. 𝐇𝐚𝐫𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 The report shows that AI-related keywords in earnings calls have spiked by 50% year-over-year. We are currently at the "Peak of Inflated Expectations." Installing AI on top of fragmented data architecture is like putting a Ferrari engine in a lawnmower. The real winners are focusing on the unified data layer first. 𝟑. "𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞-𝐃𝐞𝐟𝐢𝐧𝐞𝐝" 𝐢𝐬 𝐭𝐡𝐞 𝐍𝐞𝐰 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 The top vendors are all aggressively shifting toward software-defined architectures. This signals the end of "Black Box" automation. The future belongs to the vendors who provide the infrastructure for Physical AI, where the network isn't just a pipe, but an intelligent part of the solution. Accessibility and data flow are the new ROI. 𝟒. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐬 𝐚𝐧 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 Top vendors are also leaning heavily into energy management. This is finally moving from the Marketing Department to the Operations Department. Why? Because energy is a massive, addressable cost. But you can't optimize what you don't measure. Real-time monitoring at the network edge is the only way to move from "Greenwashing" to actual "Green Performance." The Bottom Line Don't look at this Top 10 list as a shopping list where you pick one logo. Look at it as a component list. Your job isn't to buy a "Smart Factory" from a single provider; your job is to be the Chief Architect who weaves these layers into a resilient, scalable, and connected ecosystem. 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟: https://lnkd.in/e64TrdY9

Don’t Automate Stupid. If the process is broken, AI won’t save it — it’ll just help you fail faster. I know today’s topic is a bit harsh but let me explain 👇 Over the past decade, I’ve sat in too many boardrooms where leaders proudly said: “We’re using AI to automate this workflow!” Only to find out later that the workflow shouldn’t have existed in the first place. The most common AI failure isn’t technical — it’s actually what I call “strategic laziness.” Because we skip the hard part: process deconstruction. We don’t ask, “Does this even need to be done?” And we jump straight to “How can AI do this faster?” And faster isn’t always smarter. If you want AI to truly add value, start here: 1️⃣ Question everything. If no one can articulate why a process exists, pause automation. 2️⃣ Simplify before you scale. AI amplifies everything — including inefficiency. Remember AI doesn’t fix bad decisions. It scales them. Faster failure is still failure!

Every HR transformation conversation eventually arrives at the same question: how does AI actually change the way HR operates, not in theory, but in the specific work of corporate HR leaders, specialists, generalists, and service teams? In this week's article, my colleagues from The RBL Group, Norm Smallwood and Mike Panowyk along with contributors Dave Eberhardt, Rob Dicks, Astik Ranade, and Megan Brody from PwC's Workforce Transformation Team and I tackle that question head on. Together we have been involved in hundreds of HR transformations, and we are seeing AI reshape every element of the HR operating model in ways that go far beyond simple automation. One insight that stands out is how differently AI impacts each part of the operating model. Administrative and service work will see the highest percentage of tasks performed by AI, while the strategic advisory work of HR business partners and corporate HR leaders will be augmented rather than replaced. The real opportunity is not just efficiency but using AI to move HR from reactive reporting to proactive, stakeholder-centered value creation. We also share a practical framework showing how #AI applies specifically to corporate HR, specialists, generalists, and services, with concrete examples in each area. AI is not a panacea for the #HR operating model, but when combined with human ingenuity, it becomes a powerful catalyst for delivering the stakeholder value that HR transformation promises. How is AI changing the way your HR function operates? Where are you seeing the greatest impact, and where does human judgment remain essential? I would love to hear your experiences and start a conversation about what comes next.