Evan Rodriguez

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Anrok generated $300K in pipeline in 3 months by unifying marketing and sales outbound in Unify. Here's the playbook growth marketing lead Kathleen Kong, and founding SDR Rydian Searles used to make it happen:   Anrok was growing fast. But before Unify, their outbound was fragmented. Kathleen was running marketing campaigns in HubSpot, but every new campaign took forever to build, and segmentation was painfully slow. Rydian's SDR team was worse off, switching between 3 different tools to execute every sequence.   "We had the growth and the signals," Rydian said. "What we needed was a system that helped us act on them."   There was only one answer.... Unify. Kathleen started by building automated plays targeting new hires, website visitors, and accounts showing sales tax exposure via AI signals. Once marketing proved the model worked, they expanded Unify to the SDR team.   Suddenly, both teams were operating on the same page. Campaigns could be built 20% faster, SDRs could work 4X faster, and cold calls converted better with out-of-the-box AI insights.   This made it easy for Kathleen & Rydian to support Anrok's growth and expansion into new markets; easily spinning up new sequences without rebuilding a single workflow.   Kathleen summed up their experience best: "Unify gave us the flexibility to run everything in one ecosystem. We could move fast, measure impact, and stay aligned."   Ready to follow Anrok's lead and unify your teams? Use the link below to learn more.

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Every org chart tells you where the next systems of record will emerge. Look for the roles that exist because software failed. RevOps exists because someone has to reconcile sales, finance, marketing, and customer success. DevOps exists because someone has to bridge development, IT, and support. Security Ops sits between IT, engineering, and compliance. These "glue" functions are a tell. They emerge because no single system owns the cross-functional workflow. The org chart creates a role to carry the context that software doesn't capture. An agent that automates that role doesn't just run steps faster. It can persist the decisions, exceptions, and precedents the role was created to produce. Palantir Technologies built what’s now a $400B+ company on prescribed ontologies: define the schema, then map messy enterprise data into it. The next $400B+ company will be built on learned ontologies. This is what Jaya Gupta and I mean by “context graphs”: not graph databases or structured memory, but living records of how decisions get made, learned bottom-up from how the work actually happens. cc Animesh Koratana

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Most companies talk about "culture." We’d rather show you. Last week, the Unit21 team touched down in San Francisco and Sonoma. 4 countries, 18 states, and one massive mission. While the video shows the karaoke, the 5K runs, and the wine tasting, here’s what was happening between the frames: 🚀 Real-world impact: We’re monitoring 5B+ transactions a month and filing ~5% of all U.S. SARs. ☕️ Deep-dive sessions: Fireside chats with partners like Float and companies like Casetext, Part of Thomson Reuters and CodeRabbit to bridge the gap between code and customer. ❤️ Zero ego: Engineers, Product, and GTM in one room, obsessing over how to stop bad actors more effectively. The bar is high. The energy is higher. 🚀 #Fintech #ai #amlcompliance #crypto

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In early January, we quietly added native support for Tracing with OpenTelemetry to Sematext. 🚀 Let me skip the usual blah blah about OTel and no vendor lock-in and such and jump straight to what you get. Links at the bottom. 💰50–80% lower cost than Datadog, New Relic, Dynatrace or Grafana Cloud This is self-explanatory. :) ... and may be enough for some folks to give it a go. ✅ Really dead-simple setup If you’re already using OpenTelemetry, you’re already set! Sematext Agent acts as the Collector. It exposes HTTP and gRPC endpoints. Just point your instrumentation SDK at the agent. ✅ Immediate out of the box insights For each service see RED metrics (rate, errors, duration), see slow traces, alerts, overall latency distribution, top slow endpoints, health of each service… ✅ Intuitive, SRE-friendly UI Fast, clean and modern UI. Quickly drill from service → endpoint → trace → span. ✅ Auto-instrumentation out of the box For languages/runtimes and frameworks that have auto-instrumentation just use that. For others, instrument manually. All instructions are copy/paste in the UI. Some links: - docs: https://lnkd.in/e5mh_nzW - some screenshots: https://lnkd.in/eZZs5757  - agent & OTel: https://lnkd.in/euE2nDdP - product info: https://lnkd.in/erhEK6SJ #OpenTelemetry #Tracing #Observability #DevOps #SRE #SoftwareEngineering #Microservices #Monitoring #Sematext

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When Lightspeed DMS tripled their software engineering team, they hit scaling challenges fast: unclear performance, slower collaboration, and too many blind spots. That’s when they turned to Appfire Flow. Flow gave Lightspeed DMS the visibility and metrics to pinpoint bottlenecks, run more productive 1:1s, and drive change across the SDLC, not just at the team level, but across the entire business, achieving: 93% faster time to merge 54% increase in commits per day 43% reduction in cycle time https://lnkd.in/d925Mbpt

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ARR was built for products. But what happens when your product starts acting like a person? In our latest [Un]Churned episode, Brett Queener dropped a spicy take: | “It's the end of ARR — and I feel fine.” If software behaves like a hire — an agent that actually does the work — then the old 12-month license model doesn’t make sense anymore. You wouldn’t sign a new employee to a two-year, prepaid, non-cancelable contract. You’d hire them at will and expect them to perform. That’s where AI agents take us. Software you don’t just buy — you employ. You pay for the work it performs and the capacity it creates, not the number of seats or licenses you own. For founders and operators, that means re-thinking: ✔️ Pricing → value delivered > seats sold ✔️ Renewals → prove impact every day, not once a year ✔️ Success metrics → how fast it delivers value > how long it retains revenue AI is pushing SaaS toward product purity — either it does the job, or it doesn’t. Would you feel fine if ARR ended tomorrow? How would you rebuild your model around outcomes, not contracts? Full episode with Brett Queener (Bonfire Ventures) and Chuck Ganapathi (Gainsight): https://lnkd.in/eaTaRbKH

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Stop sprawling DX. The 2025 report confirms what operators feel. Leaders fund outcomes, not slogans. Three goals: productivity up, legacy out, CX up. Big blockers: silos, surprise costs, missing skills. So, we shipped one route in Sonoma, not twelve. Stack: Cursor + Lovable + n8n + Manus AI + Supabase with Twilio. Route: webhook → validate → enrich → schedule. Act: send SMS, create job, assign owner. Why this fits the report: reduce debt, center data, measure. Agents that can’t write back aren’t agents. Upskilling matters: we ran a six-week AI + automation sprint. Silos fall when every update lands in one table. Costs drop when glue code stays tiny. Governance: retries, limits, audit logs by default. Goal stays simple: 95% pass before we scale. Pick one process → Measure → Optimize. No fluff, just ROI.

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CPTO Revolution: Why SaaS Companies Are Merging Product and Tech Leadership I’ve lived the pain of siloed leadership and the inefficiencies it creates many times. And I’ve also seen how tightly integrated product and tech can transform not just delivery, but outcomes. Traditional org charts are no longer keeping up. The most competitive SaaS companies aren’t hiring separate CTOs and CPOs anymore - they’re bringing both roles under one roof with CPTOs. This isn’t about headcount optimization. It’s about reducing friction and moving faster in a world where complexity is climbing and customer expectations evolve by the week. Here’s the core issue: when product and engineering leadership are separate, you’re always playing a game of telephone. Product defines what’s needed. Engineering estimates and negotiates. Product pushes. Engineering pushes back. Handoffs everywhere. Every step is a delay. A CPTO owns the entire journey - from insight to shipped product. One person accountable. One vision. One continuous flow. And it works. Companies running this model are seeing up to 40% faster go-to-market. Less tech debt. Clearer priorities. Higher team velocity. What Actually Changes 1. Aligned Metrics You stop optimizing for isolated outcomes. Product isn’t chasing adoption while engineering shields uptime. Both roll into shared business goals. 2. AI Gets Strategic With unified leadership, AI isn’t a bolt-on. It becomes core to how you design, build, and validate products - through data, feedback loops, and automation. 3. Developer Experience Matters Platform engineering gets the attention it deserves. Internal tools align with product delivery, and suddenly teams aren’t waiting - they’re shipping. UX maturity, platform architecture, technical scalability - all of it becomes a single continuum. You’re not just improving “process”; you’re aligning every decision to what users need and what’s feasible to build. The CPTO Skillset This isn’t a hybrid role - it’s an integrated one. You need the technical depth to guide architecture and the product judgment to say no to overengineering. You talk to engineers about latency and reliability, and to the board about market traction and differentiation. As AI accelerates software development, what matters is orchestration—of systems, teams, and strategy. You need to see the whole board. Cloud infra spend is growing ~10% yearly, mostly due to AI. That spend needs direction. CPTOs are the ones who can match innovation speed with platform efficiency. Companies clinging to the old CTO/CPO split are playing a slower game. Integration is no longer optional - it’s a competitive advantage. So—what’s keeping your org from making the leap?

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AI is creating 10x engineers, but if unchecked, can also create 10x tech debt. Last week I joined an executive dinner for engineering leaders in SF, hosted by incident.io in partnership with ELC. Three takeaways that stuck with me: 1. Adoption is uneven: Power users are flying. In some larger orgs the approach is still let it emerge bottoms up, which creates wide skill gaps inside the same team. 2. Guardrails beat raw speed: Leaders are putting controls in place so new joiners learn the codebase before shipping with AI assist. Think stricter reviews, pairing, tests, and release gates. As one leader put it, AI can make you 10x better or 10x worse without discipline. 3. Hiring is adapting: Teams now assume AI was used on take-home work. Interviews focus on walking through the code, defending choices, and live iteration to test understanding. My read: the orgs that win pair ambitious AI use with clear standards, coaching, and measurement. The goal is more value, less complexity, and fewer incidents, not just more commits. Curious how your team is balancing speed and reliability right now. What is working, and what backfired?

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Shipping software is easier than ever. Shipping beautifully crafted software that truly solves customer problems? Harder than ever. That’s the opportunity. The best designers today operate more like “design engineers” - close to the metal, blending craft with code. At Clarify, our principal designer Matt Bond spends half his time sweating the details in code. Years ago that was rare. Now, it’s becoming more common. When I managed a design team at Atlassian, I learned quickly: not all designers are the same. Just like engineering, design has many functions: → User research → Prototyping → Interaction design → Motion design → Visual design → Product strategy But underneath it all, do you understand the medium you’re working with? If you’re designing a chair but don’t know how leather stretches or plywood bends, you’re designing blind. Software is no different. The closer you are to the development lifecycle, the better the outcomes. You don’t need to be an engineer, but you need to understand the constraints and possibilities. And why does it matter? The data backs it up. The Design Value Index shows design-led companies consistently outperforming the S&P 500—because when you put the right product in front of customers with care, they pay more and stick longer. Look at Airbnb (Brian Chesky), Figma (Dylan Field), Dovetail (Benjamin Humphrey), Linear (Karri Saarinen). All design-founder companies that defined categories. At Clarify, without Matt in the room, we’d be in a very different place. Because without design expertise embedded, it’s almost impossible to build with true craftsmanship.

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RevOps has always been a data-driven function. From funnel mapping and CVR optimization to forecasting, territory design, renewal planning, quotas, and commissions; the list of data use cases is long. But here’s the irony: While RevOps spends enormous effort wrangling data from every source imaginable, the most foundational dataset —customer contracts— is often a complete mess. In recent conversations with RevOps leaders, I’ve seen a shift. A growing awareness of the operational debt caused by poor post-signature workflows. When contract data doesn’t flow properly into CRM and ERP systems, the downstream impact is brutal: Invoices go out with incorrect terms Renewals are missed due to wrong dates or terms Upsell opportunities are lost due to incomplete product data Quotas are set using inaccurate baselines Commission plans break Forecasts fall apart And that’s just the start. Historically, scaling the contract review process to fix this was operationally unmanageable; manual, error-prone, and too expensive. 💡 The good news? This is fixable. AI now makes it possible to automate contract data ingestion, validation, and system updates at scale. Smart RevOps leaders are leaning in, turning contract chaos into clean data, and reclaiming their role as the stewards of the single source truth. #RevOps #RevenueOperations #AI #OrderToCash #SalesOps #CRM #ContractIntelligence #ARR #DataQuality

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AI is changing how we execute - less so what the foundations of great RevOps look like. On a recent Domestique webinar with Alex Biale, we unpacked why the best AI-native teams treat GTM like a product: clear strategy, defined processes, the right tech, ongoing enablement, and clean data - all instrumented in real time. My core takeaways: - Data is the operating system. If inputs are messy, AI just compounds the chaos. Start with a shared data dictionary (MQL/SQL/ICP, stage criteria, ownership, etc). - Orchestration is earned. You don’t get AI co-pilots and shadow agents until the foundation is trustworthy. Focus on your data-layer first to earn your right to orchestration. - Build weekly GTM pulse checks (pipeline movement, inter-stage conversions, leading signals) so there’s still time to act - not just diagnose at the end of the quarter. - Enablement isn’t a QBR slide anymore - it’s real-time feedback from call transcripts into Slack, fueling better discovery, handoffs, forecasts, and coaching. We also touched on a couple other key points like: - The goal posts for greatness are moving and the benchmarks are going up. - Why GTM still wins on the basics done right - now executed with AI. Check out the link in the comments for the full conversation.

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Big news! 🚀 Mission Andromeda has landed, kicking off Gong’s new product launch cadence. This launch brings powerful new capabilities to help revenue teams move faster and execute with confidence, including: ✨ Gong Enable — AI role-plays to help reps practice real moments before they happen live ✨ Gong Assistant — always-on conversational guidance for everyday workflows ✨ Account Boards — unified view of all accounts to align AE + CS ✨ Account Console – deep dive view of a single account ✨ Gong MCP Support — integrated AI across your tech stack Proud to be part of a team building the #1 revenue AI OS, and excited to see how customers put these updates to work. Learn more: https://lnkd.in/dPZiM_PA

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Check out this video featuring Rick Houlihan, Field CTO of Enterprise at MongoDB. He discusses why traditional databases aren't suited for AI workloads, resulting in increasing complexity, duplicated data, and slower performance. He also explains how MongoDB Atlas offers an alternative approach with a unified solution. Worth watching if you're reconsidering your AI stack. 👇

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No, a CS platform won’t fix your broken process. It’ll just expose it faster. Too many teams think buying a shiny new tool will save their CS org. It won’t. If your workflows are chaotic, your handoffs are sloppy, and your goals are fuzzy, your new platform will just amplify the mess. Tools don’t fix strategy. They scale it. If your CS engine is misfiring, fix the process. Then buy the tech.

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