Brandon Redlinger

I still remember the first time I presented to a board. I spent the entire week convincing myself I wasn't ready. I rewrote the deck every night, practiced every answer I thought they might throw at me, and second-guessed every slide. The morning of the meeting, I sat in my car thinking, "What if they figure out I'm not supposed to be here?" I don't even remember the presentation itself. What I remember is waiting for my confidence to magically appear before I walked into the room. But it never did. The questions came, and some I answered well, but a few caught me off guard. Nobody laughed or called me a fraud or anything like that. The meeting ended, and I had survived. The next board meeting felt a little easier, and so did the next one. Presenting to executives started to feel normal, then leading bigger teams, then speaking on stage. It took me years to realize I'd had been thinking about confidence completely backward. I always thought it was something successful people had before they did hard things. In my experience, it shows up after you've done the hard thing enough times to know you'll survive it. I talk to marketers all the time who tell me they're waiting until they feel ready – ready to lead a team, ask for the promotion, present to the board, become a CMO. I don't think that feeling ever shows up on its own. You earn confidence one hard conversation, one presentation at a time. Even now, I still get nervous before the biggest meetings, but the difference is I've collected enough evidence to know I'll figure it out. That's what confidence is for me now: proof I've done hard things before, and I can do them again.

They say outbound is dead, but those are the people who are not using signal-based selling or using it wrong. For most teams, a signal comes in, then a task in Salesforce gets created or Slack message gets dropped in a channel. This is as far as the automation goes and both get ignored, then teams get mad because they’re getting < 1% response rates. I spent last week building the full automation and outreach system that’s getting 7.7% response rates. Here’s what I built: 𝟏. 𝐒𝐢𝐠𝐧𝐚𝐥𝐬 𝐚𝐧𝐝 𝐫𝐮𝐥𝐞𝐬 𝐟𝐨𝐫 𝐰𝐡𝐢𝐜𝐡 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐞𝐚𝐫𝐧 𝐚𝐜𝐭𝐢𝐨𝐧 A competitor's name disappearing from a target account's subprocessor page is worth a call within the hour. But a whitepaper download from a student is worth nothing. That first one is my favorite signal in B2B right now. Any company selling into the EU has to disclose which vendors touch customer data, and most publish it on a page they're obligated to keep current. When a name comes off that list, the account churned from that vendor and announced it themselves. Almost nobody is watching those pages. Rank your signals by that logic, or your reps stop trusting all of them inside a month. 𝟐. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞 𝐒𝐃𝐑 𝐢𝐬 𝐧𝐨𝐭𝐢𝐟𝐢𝐞𝐝. The moment a signal triggers, the account gets researched. What changed, who to contact, what they're dealing with, and whether sales is already working them. That's 10-20 minutes of prep that used to happen only after someone decided the signal was worth chasing. 𝟑. 𝐓𝐡𝐞 𝐨𝐮𝐭𝐩𝐮𝐭 𝐢𝐬 𝐚 𝐝𝐫𝐚𝐟𝐭. Previously, it was just a notification but now we’re taking it a step further and saving them more time. The signal should produce a written opener, a call script, a reply that's ready to send. For the reps, it’s just light edit and go. 𝟒. 𝐒𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐡𝐚𝐬 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐰𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐞𝐝. This is the eval loop. Every week the results get def back into the system. Which signals turned into conversations, which drafts got replies, which ones didn’t, etc. The next batch gets built on that. Skip this and you have automation that never gets smarter, which is faster mediocrity. For the SDR, now the morning starts with a ranked list of who to contact and why, the research already done, the first message already drafted, and the CRM already updated. The day goes to conversations instead of assembling context. Signal detection got commoditized two years ago. Everyone can see the same signals at the same time now. And now once again speed to lead + relevant (non-AI slop) outreach wins.

I almost talked myself out of hiring one of the best marketers I've ever worked with. On paper, she wasn’t the strongest candidate. Another person had the bigger brands on their résumé, more years of experience, and a title everyone on the panel recognized. The decision felt obvious, but I just couldn't shake one thing from the interviews. One candidate kept talking about what they'd already done. The other kept talking about what they wanted to learn. Every answer came back to curiosity. She asked thoughtful questions, she wanted feedback, and when she didn't know something they just said so instead of bluffing through it. So I took a chance on her. The first few months weren't smooth. She asked a lot of questions, needed coaching, and made mistakes. But every week she got a little better, because she treated every project as a chance to improve. A year later she was leading work I wouldn't have trusted her with in month one. Eventually she became one of the people I relied on most. I've thought about that decision a lot since. Experience matters, and skills matter, but a strong résumé only tells you where someone has been, and that's not the same as where they're going. Some of the best marketers I've ever worked with had exactly that kind of hunger and determination and drive. It’s that kind that keeps you learning long after everyone else decides they've got it figured out. I still look for experience when I hire, but I spend even more time looking for curiosity and drive. You can teach someone a new channel, your messaging, your process, etc. It's a lot harder to teach someone to stay curious and driven after they think they've made it. And I guess I have a lot of empathy for this candidate profile because someone took a chance on me early in my career, and I'm grateful he did. I've tried to pay it back by hiring for potential, even when it doesn't fit neatly on a résumé.

They just admitted that the hardest part of enterprise AI is implementation and adoption. Microsoft is committing $2.5 billion to forward-deployed engineers and embedding 6,000 of them inside its customers. Amazon made a similar investment a few days earlier. These are obviously 2 of the biggest companies on earth, and they’re basically saying that the hardest part of enterprise AI is implementation and adoption, and I think they're right. And they're not the only ones. This conversation keeps coming up with enterprise businesses. Everyone likes to cite MIT's NANDA report, which says that 95% of enterprise AI pilots show no measurable P&L impact. And I used to complain about the methodology and question the number itself, but I think it’s directionally right – everyone is running pilots, while very few have much in production. And for B2B marketers, that changes what we’re selling. A big portion of product marketing budgets I see still go to fancy demos, product storytelling, demo environments, demo data, etc. However, the part of the sales cycle that I see slows enterprise AI deals down is when the buyer asks who is going to make this thing work inside their environment. They don’t double the product because they see the power of AI, but they want to know when it will be live inside their environment and who would make it happen. Harvey figured this out early. Every deployment has a forward-deployed engineer and a lawyer. In other words, every account has a tech expert and a domain expert during implementation. I run fractional marketing at a few enterprise AI companies (and I’ve had a few AI clients in the past), and the enterprise AI companies that are seeing the most success are moving GTM budget into 2 things: 1 – Implementation case studies with real timelines, because if you can get them live 4-6 weeks, it beats any feature page or demo. 2 – The deployment team in the content, with names and faces. Right now they're a line item that probably nobody even sees. If you want to go further, publish a time-to-value number that you can deliver on. So, here are some things to think about if you're the one running marketing at an enterprise AI org: – Review your budget allocation. If demo assets are disproportionately more than deployment proof (at most companies I've looked at, they are), think about moving some of it. – Book time with your implementation team and find the deployments the customer bragged about. Use the story, with the real timeline and the number it moved, in your marketing. – Establish a time-to-value metric if you don’t have one. How are you thinking about this?

Most marketers I talk to are still not using agents because they think agents are for developers, but I'm using agents daily to help me 10x my work. The good news is they're pretty easy to learn and understand. Here are 10 agent features with some notes on how I'm using them. 𝟏. 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐬𝐮𝐛𝐚𝐠𝐞𝐧𝐭𝐬 Spawn smaller Claudes in parallel, each with its own memory. Example: My client onboarding uses 5 subagents at once, and now two weeks of research is done in one hour. 𝟐. 𝐓𝐡𝐞 𝐛𝐮𝐢𝐥𝐭-𝐢𝐧 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬 Explore and Plan ship with the tool, and almost nobody knows they exist. Example: I point an Explore agent at 200 interview transcripts and get the top 5 objections with quotes. A Plan agent maps the approach before anything runs. 𝟑. 𝐂𝐮𝐬𝐭𝐨𝐦 𝐬𝐮𝐛𝐚𝐠𝐞𝐧𝐭𝐬 Define your own specialist agent in one file. Example: I have one that runs a brand-voice check on everything that it writes. 𝟒. 𝐌𝐨𝐝𝐞𝐥 𝐫𝐨𝐮𝐭𝐢𝐧𝐠 Assign each agent its own model, Opus down to Haiku. Example: Use Opus for strategy and narrative, Haiku for pulling first names out of a CSV. 𝟓. 𝐌𝐂𝐏-𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐬 Give each agent its own toolkit and nothing more. Example: I have an agent connected to HubSpot only, another to Slack only. 𝟔. 𝐀𝐠𝐞𝐧𝐭 𝐭𝐞𝐚𝐦𝐬 Multiple agents on one project, handing work to each other. Example: I have a research agent, a copywriter, and an editor in sequence. It's the closest thing to a marketing pod that doesn't need headcount. 𝟕. 𝐌𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐫𝐞𝐯𝐢𝐞𝐰 Several agents review the same asset at once, each from a different angle. Example: 4 agent reviewers analyze a web page together: CRO, SEO, voice, positioning. 𝟖. 𝐖𝐨𝐫𝐤𝐭𝐫𝐞𝐞𝐬 Isolated copies of your project, side by side. Example: I tested 3 homepage rewrites in parallel, kept the winner, and deleted the rest. Nothing touched the main version. 𝟗. 𝐀𝐠𝐞𝐧𝐭 𝐩𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐞 Continue an agent instead of starting cold. Example: I ran one across a full week, sharpening the ICP each morning, and it held the whole thread. 𝟏𝟎. 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐬 Agents on a schedule, no code. Example: A Monday content-calendar audit that flags what's missing for the next two weeks and posts it to Slack before you're awake.

I don't remember the last time I read a candidate's cover letter... Here's what I usually do first: 👉 Scan their resume, then head straight to LinkedIn 👉 Read their last 10 (or more) posts 👉 Read comments they leave 👉 Find common connections 👉 Look at work experience 👉 Read articles I know not all marketers are active on LinkedIn. If they're not, I won't hold it against them. But if they are active, it will help their case. Often a lot!

Most AI SDRs and GTM-engineered workflows for SDRs suck because they send more emails but lack the context needed to sell effectively. Before a single outbound message is written, you need to start by capturing everything about your business in a CompanyOS, like whom you sell to, why customers buy, how you position yourself against competitors, the language that makes prospects respond, etc. Once that foundation is in place, you can scale outbound that accurately reflects what you do, speaks to prospects’ pains, and still sounds like you. This is exactly what Jerry O'Shea is doing, and he showed me how to set this entire system up. 𝐂𝐥𝐚𝐲 for spotting website visitors, then researching and enriching those accounts. 𝐀𝐈 (𝐢𝐧𝐬𝐢𝐝𝐞 𝐂𝐥𝐚𝐲) for turning that research into an account brief with a loose, context-driven prompt. 𝐧8𝐧 for routing the whole thing (webhook in, call the Company OS, push the finished sequence out). A 𝐂𝐨𝐦𝐩𝐚𝐧𝐲𝐎𝐒 for writing the on-brand copy, so every message sounds like him instead of a template. 𝐋𝐞𝐦𝐥𝐢𝐬𝐭 for running email and LinkedIn in one sequence (LinkedIn invite first, email fallback if they don't accept in nine days). And he's been generous enough to share his Company OS Interview skill for free, which is the foundation that makes all of this work. So first off, check out Jerry and the work he's doing at Not Your Average BDR Then check out this week's issue of Stack & Scale 👇👇👇

About three months into a job I loved, my CEO hired a new head of sales, and that one hire is the reason I'm not there anymore. When this CEO first brought me in, we were genuinely on the same page. He'd been a strong marketer himself before he ran the company, so we saw the business the same way. The new sales leader was a perfectly good hire. We got along fine, and nothing felt wrong. But over time, things gradually changed between the CEO and me. The difference came down to where everyone sat. The VP of sales lived near the office and was in there with the CEO four days a week. I was remote, so I got a couple of video calls a week and our executive off-sites, while the two of them had all the lunches and hallway conversations I was never part of. I honestly didn't think anything of it at first, but across those months, the CEO started describing problems the way a sales leader would, and he brought up the marketing side of a decision less and less. I don't think anyone did it on purpose. He just spent every day in the same room as the head of sales, and I wasn't there. Meanwhile the marketing numbers were great, and they kept getting better. Cost per opportunity was dropping, CAC payback was improving, and we were sourcing more pipeline every quarter while headcount and budgets were slowly getting cut back. But none of it mattered. Every budget season he cut marketing anyway, and he still found a way to point the finger at us, because marketing is the easiest team to blame when the CEO only ever hears one side of the story. I tried to fix it the way I knew how. I walked him through the numbers again and again and made the case in every format I could think of, and none of it moved him, because the person he trusted most sat six feet away while I was a face on a screen. By the end we were so far apart on how we saw the business that I was miserable, and I left. I still don't know if I could have done anything differently. Short of relocating to be in that office full-time, I'm not sure there was a fix, and even that might have cost me somewhere else. The face time the VP of sales got with the CEO mattered far more than any number I could put in front of him, and I underrated that completely while it was happening. I probably would have been on better terms with sales too if I'd been there in person. It's why I'm wary of any setup where one person is fully remote while everyone else is together in the office. I've almost never seen it go well for the person who's remote.

Everyone is freezing marketing headcount because of AI, but there’s some interesting new data showing this might not be the best idea. Ramp correlated AI spend with payroll data for 21,000 US companies, and companies with high AI adoption grew headcount by 10% per year over the past two years. On the other hand, low adopters’ headcount stayed flat, and entry-level hiring among adopters grew even faster, at 12%. That I definitely didn't expect. Box found a similar thing in their survey of 1,600 mid-size and large companies. 79% of the most mature AI adopters expect headcount to grow over the next 3 years. Meanwhile, most exec teams I talk to are planning for smaller marketing teams (or have already made cuts), and I think that's largely due to the false narrative that AI is helping most marketing teams do more with fewer resources. The companies in Ramp's data are using AI to expand coverage (into new segments and markets they'd written off as too expensive to serve), and they are hiring people who know how to run it. Aaron Levie's take on Box's own survey is that AI in sales gets you more customers, so you hire more salespeople. That makes a lot of sense to me! If AI lets marketing run 3x the campaigns, that's your shot at the accounts you just couldn’t work before. So before you freeze marketing hiring: – Decide whether you're pitching AI as efficiency or as coverage b/c efficiency gets your budget reduced. – Get your senior marketers' judgment into your systems before they leave for a company that doesn’t have AI delusions. – Hire AI-native employees, especially the ones on the front lines

The CEOs complaining about AI ROI only have themselves to blame. Every failed AI rollout I've seen this year started with the CEO delegated it... then wondered by it didn't get adopted. Research from KPMG show that companies where the CEO personally owns AI are 3x more likely to see ROI from it. When the CEO is accountable, 57% report meaningful business value and 14% have already established measurable returns. When AI gets delegated down, those numbers drop to 21% and 4%. I've seen this play out with multiple clients over the past year, and the pattern is pretty much identical – the CEO declares that AI is a top initiative, then: – AI gets handed to IT, marketing, or RevOps like it's another software rollout – Someone gets an "AI lead" title with no budget and no authority to change anything – Two quarters later the exec team asks why nothing has moved AI changes how you sell, how you market, how you build product, how you hire, and how you serve customers, and nobody below the CEO has the authority to redesign all of that at once. So when you delegate AI, you get what that level of authority can produce: some AI-generated content and a chatbot, plus a lot of Slack demos that never touch pipeline. The 3x difference in that KPMG study comes down to who has the power to change how the business operates. If you're a CEO who wants ROI from AI: – Put it on your own scorecard and tie it to revenue targets, the same way you'd treat a new product line – Get hands-on with the tools yourself, at least enough to know what's possible – Make one exec per function accountable for a number, and review it like you review pipeline Otherwise, enjoy the chatbot and keep asking why "AI isn't working."