After serving up dozens and dozens of AI recipes, Chef David Maldow and I finally invited someone else into our AI kitchen. Our first guest on The AI Chefs Live was Sam Kennedy, who leads go-to-market strategy for NetSpeek: a company that isn't simply adding AI to an existing product, but building a business around what AI makes possible. Sam opened with a somewhat provocative statement: "You don't want a single dashboard. You want everything to always be working." Having spent decades in IT and unified communications championing the single pane of glass, I wasn't sure I agreed. But Sam is right; a dashboard is a means to an end, not the end itself. If AI can identify a problem, find the root cause, fix it, and verify that everything works again, why should I spend my time staring at red and green lights? I still want reporting, visibility, and control over what AI is permitted to do. But perhaps the ultimate measure of a great monitoring solution is how infrequently you need to look at it. Meet Lena, Your New AV Expert NetSpeek's AI platform, Lena (Language Enabled Network Administrator), is designed to proactively identify, diagnose, and resolve problems across multi-vendor AV, unified communications, and digital signage environments. In one demo, Sam deliberately misconfigured a meeting room display, then told Lena the Barco ClickShare wasn't working. Rather than accept his diagnosis, Lena checked the ClickShare, found it healthy, identified the real issue with the display, and fixed it. Lena solved the problem Sam actually had, not the one he described. Anyone who has worked an IT help desk will appreciate that! What's Still Cooking in the Full Article In the full article on LinkedIn, I dig into why NetSpeek's approach matters well beyond meeting rooms, including: Why less AI can be more: why depth, accuracy, and guardrails beat breadth for enterprise use, and why "right 95% of the time" stops sounding impressive once you do the math across thousands of rooms. Predictable AI pricing: how NetSpeek offers it, and the roughly $100 AI "meal" I recently paid for that wasn't worth the bill. Helping IT and AV play nicely together: a virtual AV expert for every support person, and the metrics that actually matter. Five ingredients for a great enterprise AI recipe that apply whether you work in meeting rooms, finance, HR, customer service or IT. And which food Sam thinks Lena would be. Hint: pizza was only his first answer. The full article also includes the video of our complete discussion with Sam. ➡️One thing that really caught my attention was the enthusiasm that Sam displays when talking about Lena. He takes great pride in demonstrating what Lena can do, as he believes it can assist many organizations in managing the complexities of AV systems at scale. Join the Conversation The comments on LinkedIn are already simmering. One reader sees the same vision for agentic AI on manufacturing shop floors, where years of sensor data need to become contextual, actionable exceptions. Another raised a nuanced point: "working" isn't always measured, or dashboarded, usefully. That sparked a discussion about whether we still want a quick glance at an "all green" dashboard, even when agents are supposed to find and fix every issue. So, a question for you: if you could apply AI to eliminate the need to regularly look at one of your enterprise dashboards, which one would you choose? Share your thoughts, opinions, or questions in the comments on the LinkedIn article. Learn more about Lena at netspeek.ai and explore more AI recipes at aichefsshow.com.

David Maldow and I have been cooking up The AI Chefs for more than 90 episodes. Each one serves a practical, bite-sized way to get more real work out of AI. We’ve now organized the best of them into one menu: The AI Chefs Bistro at aichefsshow.com. The goal is simple: find what you’re hungry for, watch a short episode and use the technique the same day. No hype; just recipes that work across the tools people actually use, including Microsoft 365 Copilot, ChatGPT, Google Gemini, NotebookLM, Claude and Zoom AI Companion. The menu is organized by effort and appetite: Appetizers: five-minute tips. Examples are “Short Order Grill” for cutting AI chatter down to bullet points, and “Twice Baked Potatoes” for asking AI to check its own work. Soups & Salads: foundational concepts, such as why AI gives dramatically better answers when it can see your email, calendar and documents. Entrées: full workflows. These include a Teams Channel Agent that writes weekly status reports, and “Obstacle Omelette”: a pre-mortem prompt that tells you how your project failed before it starts. Chef’s Specials: signature techniques. “Doggy Bag” packs a messy conversation into a reusable system prompt, and “Deconstructed Dish” captures your writing voice. Sides: platform-specific tips, from the Microsoft 365 Admin Agent to keeping Copilot Researcher from citing your own drafts back to you. Desserts: fun experiments, because AI should be enjoyable too. Each dish is also tagged by business outcome, so you can filter by what you need. The tags are Prompt Craft, Writing & Content, Research & Analysis, Workflow Automation, Meetings & Comms, Data & Documents, Learning & Upskilling and IT & Admin. If your organization is rolling out AI and wondering why adoption is stalling, start here. Tools don’t drive adoption; habits do. These recipes are designed to build them. Pull up a chair: The AI Chefs Bistro.

2026 may be remembered as the year AI agents moved from impressive demos to real business interactions. The technology is advancing quickly; the business models, operating practices, and expectations around it are struggling to keep up. In a recent BCStrategies podcast, five of our analysts and consultants, Tom Brannen, Blair Pleasant, Kevin Kieller, Chuck Vondra, and Steve Leaden, compared notes on what stood out most this year across customer experience, unified communications, enterprise strategy, and hands-on consulting. The common thread: AI's potential is enormous, but realizing it takes far more than deploying another agent. Tom Brannen: When AI Agents Become the Customers Personal AI agents are starting to research, book, buy, and request support on behalf of consumers. This is no longer theoretical: a major hotel brand is already fielding contact center calls initiated by AI agents. That raises questions traditional CX models were never built to answer. How do you authenticate an agent acting for a customer? Does it get the same service priority as a human caller? How do upselling, loyalty, and sentiment measurement work when the immediate participant is software? The takeaway: businesses will need to design experiences for both human customers and the AI agents representing them. Blair Pleasant: CX Is About Outcomes, Not Interactions The focus is shifting from handling and routing interactions to the complete customer journey and relationship, and from answering questions to completing workflows and resolving problems. Blair pointed to Zoom's "conversation to completion" message and Cisco's emphasis on relationship management as examples. If AI agents are digital workers, they need to be supervised, governed, and measured like workers. And with personal agents on one side and business agents on the other, agent-to-agent interaction looks set to be one of the defining CX challenges of 2027. The takeaway: the differentiator is moving from efficiently managing interactions to delivering meaningful outcomes across the whole customer relationship. Kevin Kieller: AI Has Thrown Enterprise Pricing Into Disarray Subscription licensing was predictable; consumption pricing, AI credits, and usage-based models are not, and customers often have no reliable way to forecast usage before they deploy. Kevin described the market moving from "token maxing" to "token axing" as organizations shift from encouraging experimentation to controlling costs. If an agent is positioned as a digital employee, what happens when its monthly allowance runs out mid-month? Which department pays when one agent serves several? Outcome-based pricing sounds appealing, but defining a successful outcome (was the issue really resolved if the customer calls back?) is far from simple. The takeaway: AI pricing must become more predictable, transparent, and outcome-aligned before organizations can build critical workflows on it. Chuck Vondra: The Problem Isn't Hype; It's Unrealistic Expectations Many organizations are enthusiastic about AI but haven't defined what they want it to accomplish. Chuck described a credit union that bought an intelligent virtual assistant and expected it designed and live within two months, overlooking the substantial requirements, integration, and testing work involved. As Kevin put it during the discussion: "No one wants AI. They want the magic that they think AI brings." AI isn't necessarily overhyped; it is often misapplied. The takeaway: successful adoption starts with the business problem and measurable success criteria, not with selecting the technology. Steve Leaden: Test AI to Fail, Not Just to Succeed In a healthcare deployment covering prescription refills and appointment confirmations, planned across 120 groups, pilot testing exposed long silences before transfers and repeated "do you want to be transferred?" prompts. Minor in a demo; damaging in a real patient interaction. The team recommended more testing rather than launching, including scenarios outside the scripted workflows, such as a patient describing a medication informally. The economics can still be compelling: one model compared roughly $1.2 million in annual staffing cost against about $200,000 in annual AI usage, though real results depend on resolution rates, implementation costs, and workforce decisions. The takeaway: try to break your AI before your customers do. ROI depends on disciplined implementation and testing against real-world scenarios. Five Lessons Heading Into 2027 Prepare for agent-to-agent interactions. Engagement models, security, authentication, and service policies will need to evolve. Measure business outcomes, not AI activity. Completed workflows and resolved problems matter more than interaction counts. Understand the economics before scaling. Consumption pricing introduces real budgeting risk. Start with clearly defined, achievable use cases. Executive enthusiasm cannot substitute for requirements, realistic timelines, and readiness. Test aggressively before deployment. A successful demo is not proof of production readiness, especially in high-stakes environments. Looking ahead, the panel expects context and orchestration to get much more attention as agents multiply across applications and departments. The winners won't be the organizations deploying the most agents; they will be the ones that understand where AI creates value, manage its costs, and implement it in ways that reliably deliver better outcomes. This post summarizes the original BCStrategies article. Read the full piece and join the discussion in the comments on LinkedIn.

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Experts BC Strategies Expert analysis, insights, and opinions We are a collection of experienced, independent analysts, consultants, and thought leaders focused on unified communications, customer experience, artificial intelligence and other technologies associated with effective business communications and collaboration. Together we participate as presenters and panelists in major communication and collaboration industry events, create hundred of articles, blog posts, and videos every year, have deep industry relationships with every significant vendor, have a combined social media reach of 250,000+ LinkedIn followers and 400,000+ X (Twitter) followers. Jim Burton Dave Michels Blair Pleasant Jon Arnold Stephen Leaden Kevin Kieller Joseph Williams David Danto Melissa Swartz Evan Kirstel David Smith Nicolas de Kouchkovsky Chuck Lear Robert Harris Elizabeth English Tom Brannen Chuck Vondra Ted Colton Martha Buyer David Maldow Our Experts

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