Bobby Gaza

Dashboards don’t create accountability. They create meetings. Charts get shared. Everyone nods. Action items get “noted”. Same problems. Next quarter. Accountability doesn’t come from visibility. It comes from systems — where ownership is clear and decisions can’t be ignored. I wrote a short note on why dashboards feel productive but rarely change behaviour — and what actually does. If your org has great dashboards but the same issues every review… this will sound familiar. Dashboards Don’t Create Accountability — Systems Do: https://lnkd.in/gCGpm-VU

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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Brightcove has released its 2026 product roadmap in a detailed, extensive blog post [https://lnkd.in/gYPZ7mhH]. I've had the chance to see some of the new features and provide feedback, but more importantly, Brightcove has spoken with many customers about how they want to see the platform evolve. The roadmap includes several new features and improvements across Brightcove's two core tracks: AI Innovation and Quality of Experience. They include: - Native real-time captioning, improved accuracy and expanded language support - Accessibility and compliance to meet WCAG, ADA, and European Accessibility Act requirements  - Auto-generated video chapters and AI-assisted live clipping - Full Video Cloud redesign - Web/mobile player and SDK upgrades for iOS and tvOS - Expanded publishing flexibility with native support for TikTok, Instagram, and Facebook Reels - Improved visibility and tracking for user activity logs and advertising metrics - New search functionality within Live Channels In addition, they are enabling new AI workflows to enhance the viewer experience, new user activity logs, advertising-improved Google-Tag-Manager metrics, and initiatives to improve Brightcove Player and SDKs. You can read the blog post here: https://lnkd.in/gYPZ7mhH - #SVOD #AVOD #cloudvideo #streamingmedia #videoworkflow Bending Spoons

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New!! "The Bay Area Has 54% of All SaaS Funding. Everyone Else Isn’t Even Close" Based on Carta’s latest startup funding data (Q2 2024-Q1 2025), here are the Top 5 SaaS Learnings that every founder needs to know: #1: The Bay Area Still Owns B2B and SaaS (And It’s Not Even Close) The Bay Area dominates SaaS funding with 54.2% of all capital — that’s more than 5x the next... Continue Reading

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More proof assets do not equal credibility. When proof is thin, don't make loud claims. Use a repeatable way to prove what you’re saying. The failure mode is simple. Startups makes claims without evidence and buyers can see the guess work As a PMM fix it with stacked proof One layer per core claim and standardizes it- claim → proof → measurement. Level 1: Mechanism proof Share screen flow, workflow diagram, demo steps that customers can relate to Level 2: Workflow proof Show steps removed, steps added, handoffs changed in the customer's process Level 3: Early signal proof Activation, time-to-first-value, usage frequency, retention trend, pilot completion. Level 4: Outcome proof Show potential win rate, conversion, cycle time, costs saved, revenue impact. Only if you can attribute it. Operating rule: Do not publish a claim above your highest proof level. If you don’t have outcomes, you still have options. Run a pilot to validate what is sticking and that can be the proof point Define the pilot user and workflow. Define one success metric tied to the workflow. Define one adoption metric tied to usage. Define the time window. Show what “good” looks like. At Awvent when we had no customer we applied this process with countless potential customer..refined it with every feedback...eventually landing our first customer ( always a sweet feeling) As early stages startup this is one way you stay credible while the product is still earning outcomes.

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Here’s why Claude’s recent progress has unlocked the next leap forward for Canvas Medical. Claude’s tools, combined with our Deep Unified Architecture and SDK, are enabling new use cases for automation while simultaneously making us exponentially faster in shipping customer specific enhancements. It's a double win. Said another way, Canvas is an EMR built to provide context and tools for automation. And now we can enable AI driven development for any user or customer, whether technical or non technical, to build their own custom workflows in Canvas. Just in the past couple of days our team has shipped: For Brigade Health - a customized panel management dashboard for high-risk patients For Vida Health - ability to parse unstructured lab results from a fax into the chart as structured clinical data For Radial - improvements to the provider onboarding experience to accelerate the rapid scaling of their medical group operations For Doctronic - in-workflow evals for clinicians to submit structured feedback on the performance of their AI-driven note generation The best part? All of this work was done without software engineers 🤯 We used the Canvas Plugin Assistant, powered by Claude Code and the Canvas SDK. We’re living in the future. Here’s a graphic from Anthropic’s presentation yesterday with a couple of annotations to explain. Sometimes a picture is worth a few hundred words.

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A useful discussion of the differences between co-pilots, agents and AI workflows. From Jack Mu at Paragon. Clarity leads to better design, and to better pricing. https://lnkd.in/g_Xkf5AT? #AI #agents #copilots #workflows

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