Novakid

What should ops & business teams be vibe coding? No AI 'chiefs of staff'. A few dashboards. But no dashboard has ever moved a metric. We must measure, but briefly, then focus our energy on how we move that measure. In this new industrial revolution, we could learn from the lessons of the previous ones. Factory owners didn't buy steam engines to power automated schedule summarisers or mill output trackers; they bought them to power the looms that produced the cotton. The machine was pointed at the bottleneck. The output was measured: more cotton got spun. Give people AI tools, yes. But don’t just ask what they can build. Ask what metric their token spend is moving. Then measure it - over a month or a quarter - and adjust. Just like we always have. Vibe coding is exciting, but it's not changing the fundamentals of how a business grows. More on my most recent blog post: https://lnkd.in/eDptEZPA

Don't give Claude direct access to the data warehouse. Seems obvious? But let me spell it out: it'll run big exploratory expensive scans, burn tokens trying to work out what everything means, and has no defined metrics to work from. And setting up access for your whole team means fiddling with API keys or service accounts. Luckily, in Rig you can now grant access to internal data for your Claude/Cursor/Codex users via our context & access layer. You see who can see what, and who has seen what. You can check and edit PII and sensitive data masking across your data. You can define custom roles, custom areas, column masks, and row filtering. It's boring but important! It makes Claude work at work, by giving AIs a full understanding of what data you have, but with robust controls for what they're allowed to actually see 👀. Pass-through your warehouse roles, or define RBAC from the schema, right down to columns and rows, full audit logs, and a preview mode to check who can see what before you let them in.

How are top ops & GTM teams becoming AI builders? 4 layers to data --> AI nirvana (...and tag your CTO, CIO or Head of Data so they can sort you out): 1. AI tools ⚒️: Claude, Cursor, etc. you need to allow your teams to try, buy, and use. Stuck in Copilot? You're using a less good tool, but it's improving! 2. Connection 🔌: connecting all your data into the AI is a challenge, but worth the effort 3. Context 📜: this is even harder; building it, maintaining it as schemas drift, semantic layers vs context graphs, and so on... 4. Sources 🪣: if you're not capturing the structured data somewhere, nothing on top of it will work. In the AI era, your data warehouse, lake, or other systems of record are FINALLY going to be really useful! But they need to be in place If you've got all 4 layers working, you can _transform_ the business by finally putting that complex internal data to work. Do you have all 4 sorted? Share your stack - or your blockers - in a comment, and I'll help!

CEOs right now are quietly allowing natural churn to winnow their organisation. Any large org that wants to become truly AI-native will also need to cut deeper. This sounds harsh, but I think it can serve all sides. On the business side, in order to pivot into an AI-native organisation, a CEO and leadership team need to change their DNA. It means a team of majority AI-first builders, whether new hires or re-skilled existing team. It means a small group of leaders who are living, every single day, inside the latest breed of AI tools, and have the space - clear of HR overhead and calendar chaos - to use them. It means a much flatter org, with no middle managers whose jobs are hosting meetings about meetings. It means opening up to flexible AI tool use, letting people buy from more vendors, and try many different types of AI tool in parallel. The signal to noise ratio of the org, and its attitude to risk and expenditure on tools has to change very fast to create the space for change. On the talent side, anyone working in an organisation that is not making this dramatic shift is in a terrible and losing position: stalemate. Stalemate is much worse than losing, because you stay there forever. You are in an org that does not give you the tools or time to rebuild your own skillset and mental priors from the ground up. And by being in the org, you are part of the people-shaped bottleneck to rapid change. Being made redundant is horrible and I do not think every organisation should cut headcount nor needs to. But being ambitious, and stuck in a role where you cannot reinvent your skillset for the fundamentally different technology age that we now live in, is dragging you behind your peers for the next role at terrifying speed. If you are not learning to master AI tools right now, the next role will get harder to land on a log scale, not a linear one, because other people already are. More on my substack this week: "We don't hire juniors anymore". Link in comments.

A new customer onboarded their GTM, Finance & CS teams on Rig yesterday. We finished the workshop at 5pm. By 7pm, a finance person shared an automation they'd built to catch gaps between CRM deal value and invoiced amounts. It used to be a manual month-end review, line by line. A month ago, they'd never used a terminal. This whole automation was built with Rig & Claude Code - from source data, to automation, to report generation. There is a new breed of AI-first builders emerging inside companies. I'm talking about non-technical, high-agency people who are figuring out Claude Code, safely connecting up to internal data via tools like Rig , and building internal apps and automations to tackle their gruntwork. You might be one. Are you being given the right tools? There's a massive underbelly of admin, CS ops & revOps work that can receive this treatment. In every organisation, there are a few people who have the appetite to build, and are now able to do it - if they're given the tools to do the job. What weird and wonderful gruntwork have your team automated? Would love to hear what else is out there.