Paul Adams

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Paul Adams

Paul Adams

@Padday

CPO @Intercom. Building + Marketing Fin, the #1 AI Agent for CS. Try it! https://t.co/xDmJItOkK8 Ex Facebook, Google, Dyson. Like building products and teams.

Dublin Katılım Ocak 2007
347 Takip Edilen26.6K Takipçiler
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Paul Adams
Paul Adams@Padday·
In every company, everything eventually changes. Those who fail to embrace this fact stay in the past, clinging to what they knew, forever stuck working in a company that no longer exists. Embrace change, be radically open minded, hard as that may be.
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Paul Adams
Paul Adams@Padday·
How we build software @intercom is unrecognisable vs 12 months ago. We're fully Claude Code pilled and seeing enormous productivity gains. Excellent thread here from Brian on some things we're doing.
Brian Scanlan@brian_scanlan

We've been building an internal Claude Code plugin system at Intercom with 13 plugins, 100+ skills, and hooks that turn Claude into a full-stack engineering platform. Lots done, more to do. Here's a thread of some highlights.

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Paul Adams
Paul Adams@Padday·
@cjpedregal @soleio You've built a great product Chris, I'm a daily user and it's a core part of my life. Building products right now is very hard because knowing what to build and *how* to build it is hard. Everyone is shipping and learning!
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Chris Pedregal
Chris Pedregal@cjpedregal·
There are some tweets out there saying that Granola is trying to lock down access to your data. Tldr; we are actually trying to become more open, not closed. We’re launching a public API next week to complement our MCP. Read on for context. A couple months ago, we noticed that some folks had reversed engineered our local cache so they could access their meeting data. Our cache was not built for this (it can change at any point), so we launched our MCP to serve this need. The MCP gives full access to your notes and transcripts (all time for paid users, time restricted for free users). MCP usage has exploded since launch, so we felt good about it. A week ago, we updated how we store data in our cache and broke the workarounds. This is on us. Stupidly, we thought we had solved these use cases well enough with our MCP. We’ve now learned that while MCPs are great for connecting to tools like Claude or chatGPT, they don’t meet your needs for agents running locally or for data export / pipeline work. So we’re going to fix this for you ASAP. First, we’ll launch a public API next week to make it easier for you to pull your data. Second, we’ll figure out how to make Granola work better for agents running locally. Whether that’s expanding our MCP, launching a CLI, a local API, etc. The industry is moving quickly here, so we’d appreciate your suggestions. We want Granola data to be accessible and useful wherever you need it. Stay tuned.
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Paul Adams
Paul Adams@Padday·
We believe the future of customer communication will be a single Customer Agent that knows the individual customer, knows the business and it's goals, and will seamlessly move between service, sales, success and more to deliver perfect customer experiences. We just raised $250m to accelerate building that future with @Fin_ai, and have something very exciting to announce soon!
Eoghan McCabe@eoghan

x.com/i/article/2031…

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Mike Belsito
Mike Belsito@belsito·
@Padday @eoghan Happy to share — and it’s nothing short of amazing, Paul. And everyone is fortunate to have an example like yours to strive for. And for them, the real work now begins.
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Paul Adams
Paul Adams@Padday·
If you are a Saas company, this is a must read from @eoghan. We just crossed $400m in ARR. That chart is our growth rate, from Saas to AI. At most, a single handful of companies have completed this transformation. We are one of them, this is our guide on how to do it. p.s. *please share it* with everyone you know working in Saas, it will undoubtedly help them chart their course!
Eoghan McCabe@eoghan

x.com/i/article/2028…

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Jeff Weinstein
Jeff Weinstein@jeff_weinstein·
who are the best people to follow for agent-first design[0]? [0] design of humans collaborating with agents and building for agents themselves
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Paul Adams
Paul Adams@Padday·
It is entirely possible to fully transition *any* Saas company to an AI company. We've done it. It is brutally hard, but anyone can do it if they are prepared to take big risks. The Saaspocalyse need not be so. It was really nice to see our story written up in @nytimes by Sarah Kessler. Link in comments.
Paul Adams tweet media
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Paul Adams
Paul Adams@Padday·
My X feed is broken and I bet yours is too. Every time I open X, I get 3 long form articles prefaced with "The correct take on AI disruption and the Saaspocalypse" alongside 3 more with "The best take yet on why Saas is not dead" not to mention "Why you're already out of date with how you use AI" To make matters worse, this is interspersed with "The most attractive female athletes of the Winter Olympics" and Random clickbait post of some airport/airplane/street/bar incident Yeah yeah yeah what I see is a mirror, "it just feeds you more of the things you like." No. It's an overwhelming, incoherent mess. I get a headache just trying to parse the thing, and it's sadly broken for me. I can't cope with the infinite firehose of AI powered content and neither can you. So what happens next? Every time a new technology emerges, disruptive new ways of communicating emerge. Internet > email > messaging > photos > Facebook Mobile > video > Snapchat > TikTok The same will be true of AI. We're going to see the invention of very new, wonderful ways of communicating with each other. Maybe X will invent something new and great. I'd love that. Others certainly will, and if I wasn't building @Fin_ai I'd work on this problem. Huge opportunity, huge TAM, clear problems to solve. I've no doubt something radically different is coming.
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Paul Adams
Paul Adams@Padday·
btw the Performance Dashboard is one of many features in our deep Insights product: fin.ai/insights
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Paul Adams
Paul Adams@Padday·
Sometimes the smallest product changes can have the biggest behaviour change. This is the Fin Performance Dashboard, we shipped it a few days ago. I think the design thinking here is applicable to AI Agents in other categories also. From a code pov it's not the biggest change. But from a mindset pov it is a huge change. What we're showing here is two things that need to be considered together when designing AI Agents in any category: On the left is Automation Rate. This shows you how much work Fin is now doing. How often Fin tries to resolve customer problems. How often it succeeds. In building AI automation, many people stop here: more automation = good. On the right is CX Score. This measures customers' perception of the quality of the experience. Yes @Fin_ai did the work. But did customers think it was good? It is critical to design and orchestrate AI systems to consider both together. You can drive up Automation Rate and hurt CX Score. Things like this are often measured separately. The Fin Performance Dashboard is the first time any AI Agent in our category has shown a full analysis of both whether the work was done, and also what customers' perceptions of it were. I think this matters for all AI Agents. Yes, the work got done. But did the people using it think it was done to a high standard?
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Paul Adams
Paul Adams@Padday·
Technology changes fast. Humans change slowly. If you want to win, study human psychology.
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Paul Adams
Paul Adams@Padday·
People say to keep teams small, starve teams of resources, because these smaller teams will only then work on the most important things. This is only true IF they know what the most important thing to do is. Do your teams know what the most important thing to do is? Do you have a very clear, sharp, small number (1-3) of goals? If I asked them what the most important thing is, would they eloquently describe the goals? Or what would they say?
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Paul Adams
Paul Adams@Padday·
Should be called Storyselling
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Paul Adams
Paul Adams@Padday·
There are many AI products that aren't really products at all. They are AI Services pretending to be Products. We think this is the wrong approach and have always prioritised two things when building @Fin_ai : 1. Build a horizontal AI layer so that when we find improvements through heavy experimentation, all our 7000+ customers can benefit from them. 2. Build a product that is self-managable i.e. anyone on the team can learn the product, use the product, without needing to contact us. Building Fin (and other products) this way is harder. It is much easier to build custom prompts per customer. But as models change, those prompts will stop working as well, and the 'product' won't age well. It is much easier to hand code configuration per customer, but this introduces deep dependencies on the vendor to make changes. Fin is the only AI Agent that prioritises horizontal improvements for all, and a product that gives the customer full control. There are important lessons in here for anyone building or buying AI products. Learn more about our approach from @fergal_reid, our Chief AI Officer. x.com/fergal_reid/st…
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Paul Adams
Paul Adams@Padday·
This is truly excellent no matter your familiarity with all things GTM. Something there for everyone. I've already shared with three different senior leadership groups in Intercom.
Lenny Rachitsky@lennysan

My biggest learnings from Jeanne DeWitt Grosser (ex-Chief Business Officer at @Stripe, now @Vercel COO): 1. What failed seven years ago now works with AI. In 2017, Jeanne tried to build a system at Stripe that would automatically personalize outbound emails based on company data. Despite working with world-class data scientists, it failed due to too many errors. Today, that exact same approach works. This shows how AI has made previously impossible ideas suddenly viable. 2. A single GTM engineer at Vercel reduced a 10-person sales team to 1 (in just 6 weeks). Jeanne’s team at Vercel had an engineer build an AI agent that handles inbound lead qualification, outbound prospecting, and deal loss evaluation. The agent costs $1,000 per year to run versus over $1 million in salaries for the sales team. The nine displaced team members moved to higher-value work rather than being laid off, and the remaining salesperson is 10 times more efficient. 3. Their AI deal-loss bot has become better at understanding what went wrong than humans. When Jeanne analyzed her biggest loss of the quarter, the salesperson blamed pricing. But an AI agent reviewed every email, call transcript, and Slack message and discovered the real reason: they never spoke to the person who controls the budget, and when ROI came up, the customer clearly didn’t believe the value claims. They are now using AI to analyze sales calls in real time and send alerts like “You’re halfway through the sales process and haven’t talked to a budget decision-maker yet.” 4. Wait until $1 million in revenue before hiring your first salesperson. Founders should continue selling themselves until they reach around $1 million in annual revenue with a repeatable process. The key is having a defined ideal customer profile—customers who look alike. 5. Segment customers on what drives their buying decisions, not just company size. OpenAI has roughly 3,000 employees, which would typically put them in the “mid-market” category. But they’re a top-25 website globally by traffic, so Vercel treats them as enterprise customers requiring complex sales. Effective segmentation combines company size with growth rate, web traffic, workload type, and industry—because selling to e-commerce companies requires completely different language than selling to crypto companies. 6. Most customers buy to avoid risk, not to gain opportunity. About 80% of customers purchase to reduce pain or avoid problems, while only 20% buy to increase upside. This means you should focus your sales messaging on what could go wrong without your product—like falling behind competitors or damaging their reputation—rather than just talking about exciting features. This is especially true when selling to larger companies, where individual careers are on the line. 7. Sales teams should be indistinguishable from product managers—for a bit. Jeanne hires salespeople who have such deep product knowledge that if you put one in front of a group of engineers, it should take 10 minutes to realize they’re not a product manager. This credibility allows sales teams to serve as an extension of research and development—a 20-person sales team talks to hundreds of customers weekly and can translate those conversations into product insights at scale. 8. Building your own AI sales tools may beat buying off-the-shelf software. Because AI is so new and every company’s sales process is unique, Jeanne finds that building custom internal agents often delivers more value than buying vendor solutions. A single go-to-market engineer built their deal analysis bot in just two days, perfectly tailored to their specific workflow. These engineers shadow top salespeople to understand their workflows, then build automation that would have taken months or been impossible just a few years ago. 9. Make every sales interaction great, whether customers buy or not. Jeanne replaced boring discovery calls at Stripe with collaborative whiteboarding sessions where customers drew their payment architecture. Many customers had never visualized their own systems before. They left with a useful asset and a feeling of collaboration, regardless of whether they bought. Many returned years later to purchase. Think about your go-to-market process like a product, not just a sales function. 10. Product-led growth has a ceiling—no $100 billion company runs on it alone. While product-led growth (where users can sign up and start using a product without talking to sales) works well for early growth, customers generally won’t spend a million dollars through a self-service flow. Every major technology company eventually builds a sales team for larger deals. The mistake is waiting too long, since building a predictable sales process takes time.

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