171: Kim Hacker: Reframing tool FOMO, making AI face real work and catching up on AI skills

What’s up everyone, today we have the pleasure of sitting down with Kim Hacker, Head of Business Ops at Arrows.

Summary: Tool audits miss the mess. If you’re trying to consolidate without talking to your team, you’re probably breaking workflows that were barely holding together. The best ops folks already know this: they’re in the room early, protecting momentum, not patching broken rollouts. Real adoption spreads through peer trust, not playbooks. And the people thriving right now are the generalists automating small tasks, spotting hidden friction, and connecting dots across sales, CX, and product. If that’s you (or you want it to be) keep reading or hit play.

About Kim

• Kim started her career in various roles like Design intern and Exhibit designer/consultant
• She later became an Account exec at a Marketing Agency
• She then moved over to Sawyer in a Partnerships role and later Customer Onboarding
• Today Kim is Head of Business Operations at Arrows 

Most AI Note Takers Just Parrot Back Junk

Kim didn’t set out to torch 19 AI vendors. She just wanted clarity.

Her team at Arrows was shipping new AI features for their digital sales room, which plugs into HubSpot. Before she went all in on messaging, she decided to sanity check the market. What were other sales teams in the HubSpot ecosystem actually *doing* with AI? Over a dozen calls later, the pattern was obvious: everyone was relying on AI note takers to summarize sales calls and push those summaries into the CRM.

But no one was talking about the quality. Kim realized if every downstream sales insight starts with the meeting notes, then those notes better be reliable. So she ran her own side-by-side teardown of 22 AI note takers. No configuration. No prompt tuning. Just raw, out-of-the-box usage to simulate what real teams would experience.

> “If the notes are garbage, everything you build on top of them is garbage too.”

She was looking for three things: accuracy, actionability, and structure. The kind of summaries that help reps do follow-ups, populate deal intelligence, or even just remember the damn call. Out of 22 tools, only *three* passed that bar. The rest ranged from shallow summaries to complete misinterpretations. Some even skipped entire sections of conversations or hallucinated action items that never came up.

It’s easy to assume an AI-generated summary is “good enough,” especially if it sounds coherent. But sounding clean is not the same as being useful. Most note takers aren't designed for actual sales workflows. They're just scraping audio for keywords and spitting out templated blurbs. That’s fine for keeping up appearances, but not for decision-making or pipeline accuracy.

Key takeaway: Before layering AI on top of your sales stack, audit your core meeting notes. Run a side-by-side test on your current tool, and look for three things: accurate recall, structured formatting, and clear next steps. If your AI notes aren’t helping reps follow up faster or making your CRM smarter, they’re just noise in a different font.

Why Most Teams Will Miss the AI Agent Wave Entirely

The vision is seductive. Sales reps won't write emails. Marketers won’t build workflows. Customer success won’t chase follow-ups. Everyone will just supervise agents that do the work for them. That future sounds polished, automated, and eerily quiet. But most teams are nowhere close. They’re stuck in duplicate records, tool bloat, and a queue of Jira tickets no one’s touching. AI agents might be on the roadmap, but the actual work is still being done by humans fighting chaos with spreadsheets.

Kim sees the disconnect every day. AI fatigue isn’t coming from overuse. It’s coming from bad framing. “A lot of people talking about AI are just showing the most complex or viral workflows,” she explains. “That stuff makes regular folks feel behind.” People see demos built for likes, not for legacy systems, and it creates a false sense that they’re supposed to be automating their entire job by next quarter.

> “You can’t rely on your ops team to AI-ify the company on their own. Everyone needs a baseline.”

Most reps haven’t written a good prompt, let alone tried chaining tools together. You can’t go from zero to agent management without a middle step. That middle step is building a culture of experimentation. Start with small, daily use cases. Help people understand how to prompt, what clean AI output looks like, and how to tell when the tool is lying. Get the entire org to that baseline, then layer on tools like Zapier Agents or Relay App to handle the next tier of automation.

Skipping the basics guarantees failure later. Flashy agents look great in demos, but they don’t compensate for unclear processes or teams that don’t trust automation. If the goal is to future-proof your workflows, the work starts with people, not tools.

Key takeaway: If your team isn't fluent in basic AI usage, agent-powered workflows are a pipe dream. Build a shared baseline across departments by teaching prompt writing, validating outputs, and experimenting with small use cases. That way you can unlock meaningful automation later instead of chasing trends that no one has the capacity to implement.

When AI Systems Meet The Chaos Of Actual Workplace Processes

AI vendors keep shipping tools like everyone has an intern, a technical co-pilot, and five extra hours a week to configure dream workflows. The real buyers? They’re just trying to fix broken Salesforce fields, write one less follow-up email, or get through the day without copy-pasting notes into Notion. Somewhere between those extremes, the user gets lost in translation.

Kim has felt that gap from both sides. She was hesitant to even start with ChatGPT. “I almost gave up on it,” she said. “I felt late and overwhelmed, and I just figured maybe I wasn’t going to be an AI person.” Fast forward to today, and it’s one of her most-used tools. She didn’t get there by wiring up agents. She started small. Simple things. Drafting ideas, summarizing content, clarifying messy thoughts. That built trust. Then momentum.

“There’s a lot that has to happen before your calendar is filled with calls and nothing else. AI can help, but you have to let it earn its spot.”

If you're trying to build that muscle, forget the multi-tool agent orchestration for a second. Focus on everyday wins like:

Turning a messy Slack thread into a clean summary
Writing a follow-up email in your tone
Rewriting a calendar event title so it makes sense to your future self
Cleaning up action items from a sales call without hallucinations
Drafting internal documentation from bullet points

The pace is accelerating. People feel it. You don’t need to watch keynote demos to know that change is coming fast. It’s easy to feel like you’re already behind. Kim doesn’t disagree. She just thinks most teams are solving the wrong problem. Vendors are focused on the sprint. Most people haven’t even laced up. “Everyone wants the big leap,” she said. “But most wins come from small, boring tools that actually do what they say they’ll do.”

That’s the root issue. A lot of AI features today are solving theoretical problems. They assume workflows are tidy, perfectly tagged, and documented in Notion. Real work is messier. It happens in Slack threads, half-filled records, and follow-ups that never got logged. If your tool can’t handle that, then it doesn’t matter how shiny your roadmap is.

Key takeaway: Stop evaluating AI features based on potential. Evaluate them based on current chaos. Ask whether the tool handles your worst-case scenario, not your ideal one. Prioritize small, boring use cases that save time immediately. That way yo...