Lucian Beebe

I was in a boardroom last week when an executive confidently said, "We need AI agents for our agentic AI strategy." Everyone nodded. Almost no one knew what it meant. Here's what I'm seeing with the most successful AI implementations: Leaders who understand the nuances get better results. Let’s clear something up: • AI agents ≠ Agentic AI • Applied AI ≠ Generative AI These aren’t semantic differences. They shape how you extract value from AI. These distinctions matter because they unlock specific value for your business. If you confuse applied AI with generative AI, you risk overpromising - like promising a smart conversational assistant and delivering a system that can only generate structured reports. When you understand the difference between applied AI and generative AI, you can set the right expectations. You can measure meaningful outcomes. The organizations seeing the biggest wins from AI? They match the right AI approach to the right business challenge. So before your next AI initiative, ask: 1. What specific type of AI will serve our needs best? 2. How will THIS type of AI solve our business challenge? 3. What measurable outcomes can we expect? AI literacy empowers you to make strategic decisions that drive real business impact. What’s one AI use case you’ve implemented that actually moved the needle?

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🚀🚀 AI is rewriting the rules of startup success. In our latest Crunchbase piece, Noah Lin and I dive into how we @ defy.vc have seen AI-first companies scale smarter with value model tactics—leveraging predictive power and network effects to reshape industries. From defying traditional metrics to building data moats, these strategies are a game-changer for founders. 💡 Stay tuned for a deeper follow-up where we’ll share actionable tactics for founders to scale their AI ventures! Full article 👇 #AI #Startups #Innovation #VentureCapital

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The AI skill gap is exploding. It's separating winners from everyone else. If you're leading a team, a department, or an entire company, your AI fluency will either accelerate your impact or hold it back. These skills aren’t just technical. They’re strategic levers for growth, efficiency, and resilience. Here are 11 essential AI skills every leader needs: 1/ Understanding Prompt Engineering ↳ Craft precise AI instructions ↳ Generate consistent outputs 💡 Pro tip: Keep a "prompt library" of your most effective prompts. Document what works and why. 2/ AI Risk Assessment ↳ Spot hallucinations early ↳ Set guardrails for teams 💡 Pro tip: Run monthly "red team" exercises where your team tries to break your AI systems. 3/ Data Quality Management ↳ Validate training inputs ↳ Monitor data drift 💡 Pro tip: Create automated data quality scorecards that flag issues before they impact operations. 4/ AI Tool Selection ↳ Match tools to problems ↳ Scale solutions effectively 💡 Pro tip: Start with a 30-day pilot on non-critical tasks before full implementation. 5/ Ethical AI Governance ↳ Implement fair practices ↳ Protect user privacy 💡 Pro tip: Build an "AI Ethics Checklist" and review it before every new AI deployment. 6/ Integration Strategy ↳ Connect AI systems ↳ Optimize workflows 💡 Pro tip: Map your entire process flow before integration. It's easier to spot optimization opportunities. 7/ ROI Measurement ↳ Track AI investments ↳ Measure productivity gains 💡 Pro tip: Measure both quantitative (time/money) and qualitative (team satisfaction) metrics. 8/ AI Team Leadership ↳ Bridge tech-business gap ↳ Upskill existing teams 💡 Pro tip: Schedule bi-weekly "AI Office Hours" where teams can ask questions and share wins. 9/ Change Management ↳ Navigate AI transitions ↳ Build AI-positive culture 💡 Pro tip: Celebrate small AI wins publicly. Success stories drive adoption better than mandates. 10/ Compliance Navigation ↳ Stay regulation-aware ↳ Maintain audit trails 💡 Pro tip: Build relationships with your legal team early. They should be partners, not roadblocks. 11/ Future-State Planning ↳ Plan capability roadmaps ↳ Build adaptive systems 💡 Pro tip: Reserve 20% of your AI budget for experimenting with emerging technologies. Mastering these AI skills isn’t about keeping up but about staying ahead. The leaders who invest in these capabilities now will be the ones shaping the competitive landscape in 2026 and beyond. Start small, stay consistent, and build momentum. Which skill are you focusing on first? Share below 👇 — ♻️ Repost if your network needs this checklist. Looking to learn how to leverage AI to improve your leadership skills? DM me to discuss how I can help you get started.

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There's plenty of AI ROI ... if you're willing to work for it. That's what I took away from last week's AI:ROI Conference. Leaders like Brice Challamel, Alicia Abella, Ph.D., Michael J Domanic and Eric Porres have found hundreds of use cases that save time, free up capacity, and drive growth. In many cases they're driving thousands - if not millions - in savings. The problem: It's a lot of work! And the work is not that glamorous. It's hosting office hours, conducting training, sending newsletters, building measurement frameworks, nagging business unit leads to report on ROI, sitting on demos, approving tools, etc. etc. All the hard, often shitty work of change management. So it's a lot easier to not do the work, and then say "I guess there's no ROI." One guy in the chat (I won't name names) said: "When are we going to start talking about ROI?" Dude - we are. THIS is the work of ROI. It sucks, and it's worth it. If it was easy, everyone would do it.

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data. Learn more at: https://lnkd.in/gSNHmYbA

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At Glean, we’ve been laser-focused on making agentic AI work reliably inside the enterprise. Today at Glean:LIVE, we’re announcing the next step in our journey to develop enterprise superintelligence: AI that fully understands how your company works -- your people, your knowledge, your workflows -- even better than any individual person. We’re announcing the Glean Enterprise Graph, a living map of how your organization works across knowledge, people, processes, and execution, paired with a personal graph for each employee that reflects their projects, collaborators, and work style. Here’s what that unlocks: - Agents that automate work. Glean Agents are powered by the Enterprise Graph. They understand your systems and processes so they can automate work using native and MCP-enabled actions. - Build on Glean: Today I’m sitting down with Mark Jerome Cruz from LinkedIn to walk through how to build internal AI apps on Glean’s chat API. - Connect Glean’s enterprise context to MCP-enabled IDEs: We’re shipping a remote MCP server that has seen great traction among both Glean developers and our customers, including Canva, who will join us at LIVE as well. We’ve also released an agents toolkit and upgraded ChatAPI so developers can have enterprise context wired in. Join us this morning at Glean:LIVE to hear all about it: https://lnkd.in/g4E2Pift

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The conversation around Amazon's new Alexa+ and its AI capabilities is missing a key perspective: the strategic shift in the AI landscape from a cost-center to a profit-center for businesses. For years, AI was viewed primarily as a technological investment to improve efficiency or provide a better user experience. Now, with generative AI, the focus is changing. Amazon's approach with Alexa+ perfectly illustrates this pivot. Instead of just being a voice assistant, Alexa+ is being designed as a powerful agentic tool that can complete tasks, from summarizing data to making bookings. While critics are quick to point out its current limitations compared to ChatGPT, they're missing the bigger picture. The true long-term value isn't just in the AI's "smarts," but in its ability to directly drive revenue for Amazon's core business—shopping. This is the next phase of the AI evolution. It's not about which AI is "smarter" in a vacuum; it's about which AI can be most effectively integrated into a business's revenue model. For Amazon, Alexa+ is a strategic move to leverage AI to enhance the shopping experience, increase customer engagement, and ultimately, drive sales. The real question isn't whether Alexa+ can catch up to ChatGPT, but whether its integration into Amazon’s vast ecosystem will create a new, more profitable paradigm for AI-driven commerce. #amazon #ai #startups #marketing #alexa #techtrends #aiforbusiness #aistrategy Rachel Kleinman https://lnkd.in/gKukZTte

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Nobody is telling the truth about how disruptive this is going to be. Maybe since I’m ‘unaffiliated’ for now, I can. 👀 AI isn’t just coming for workflows. It’s coming for the foundations of knowledge work — and for the identities built on it. For CMOs and marketing leaders, this hits hard. For years, we’ve found meaning in our playbooks and how we execute. Now, much of that is being automated, flattened, or redefined in real time. And here’s the part we’re not talking about enough: This isn’t just a professional disruption. It’s a personal one. To survive it — let alone lead through it and flourish — we can’t keep pushing at the same velocity, hoping to outrun the change. We need to slow down. Reground. Rebuild. That means investing differently: Mind – Slowing down to build emotional resilience in a time of accelerated everything. Learning to lead from clarity, not reaction. Body – Remembering we’re not brains on a stick. Stewarding our embodied experience through sleep, movement, and nourishment — so we can actually sustain the work. Soul – Recognizing that our sense of identity is often wrapped up in our work — and that AI will inevitably challenge how we define purpose and value. This season demands we examine those foundations, and do so in community, not isolation. This is the real work of leadership in the Intelligence Age. Not just learning to prompt better. But learning to stay human — when everything else is speeding up.

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