MMarketing Against The Grain
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08 July 2026

How 1 Human + AI Replaced a 15-Person RevOps Team

9Frameworks
11Insights

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Frameworks in this episode

Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Explainer· 3

Explainer06:30

From CRM Analysis to a Ready-to-Send Executive Dinner Campaign

The dinner-planning workflow can extend beyond audience selection into campaign execution. It enriches contacts, writes outreach copy, creates Apollo sequences, assigns appropriate senders, and adds the relevant prospects, turning a strategic recommendation into an executable campaign.

  • Enrichment tools identify the appropriate audience
  • AI writes personalized campaign copy
  • Sequences can be created directly in Apollo or other connected tools
  • Contacts and senders can be assigned automatically

not only can it pick the audience through enrichment tools, but it can actually make the the copy and insert the sequences or campaigns directly…

Nate · 07:00

I've actually two-line built full Apollo sequences and added uh the relevant contacts to those with the right sender for those people.

Nate · 07:30
#apollo#outreach#event marketing#sales automation
Explainer09:00

How an AI Workflow Graduates From Manual Review to Autonomy

Nate starts with a small task, expands it into an end-to-end workflow, and retains approval checkpoints at consequential stages. Once the workflow repeatedly produces acceptable results, it can run on a schedule and present a completed campaign for a short human review rather than requiring the team to repeat the whole project.

  • Begin with a simple, bounded task
  • Expand the task into an end-to-end recurring workflow
  • Keep approval and review checkpoints where needed
  • Reduce recurring work to a short approval session

I typically have started with something simple, and then it grows into an end-to-end flow where I have my check-ins as to when to approve,…

Nate · 09:30

once this is always on, it turns into a 30-minute check-in of like, oh, let's just approve this each each week versus let's do a…

Nate · 10:00
#human in the loop#autonomous agents#workflow design#trust
Explainer18:00

How Experimental AI Skills Become Organization-Wide Standards

Individuals are encouraged to build skills for their own work and share successful experiments with colleagues. Useful skills such as pipeline hygiene, stale-deal follow-up, testimonial discovery, and personalized gifting are then hardened before being promoted across the organization.

  • Centralized starter skills give teams a useful baseline
  • Employees can freely create and share their own skills
  • Successful experiments are hardened before broad deployment
  • Common examples include pipeline hygiene, lead leakage, and stale-deal re-engagement

My take on this is I do build something to as a starting point, and oftentimes we have improvements every single week.

Nate · 18:30

So everybody on the team is developing cool skills and sharing, and then we usually harden them and share them across the entire org once…

Nate · 19:30
#skills library#sales enablement#governance#knowledge sharing

Story· 2

Story02:00

How One Person Took On the Work of a 15-Person RevOps Team

Nate explains that he currently handles RevOps, enablement, CRM administration, procurement, analysis, and strategy as a team of one. He contrasts this with his previous company, where similar responsibilities were distributed across systems specialists and multiple operations roles.

  • Nate's current RevOps team consists of one person supported by AI agents
  • His remit includes enablement, analysis, CRM administration, procurement, and strategy
  • Comparable work previously required roughly 10 to 15 people
  • AI increased both project throughput and deployment speed

So my team is one right now.

Nate · 02:00

computer has vastly changed the way that we work and just the volume of output and speed that we can deploy new projects and test…

Nate · 02:30
#revops#ai agents#team productivity#automation
Story11:30

A Two-Line Prompt Replaced an Intake Meeting and RevOps Ticket

A finance leader wanted to know whenever the sales team booked a meeting with a sufficiently large finance company. Instead of requesting a report, attending an intake meeting, and waiting for RevOps implementation, the team created the targeted Slack alert with a short prompt.

  • Business users can create narrowly targeted operational alerts
  • The alert combines meeting, industry, and company-size criteria
  • A prompt can replace an intake-and-ticket workflow
  • Finance can join relevant sales calls sooner

This is just a two-line prompt today.

Nate · 12:00

So we just ask computer, send a Slack channel alert when when our sales team books a new meeting with a finance company of a…

Nate · 12:00
#slack#alerts#finance#sales operations

Tool· 4

Tool04:30

Using a Model Council to Choose the Best City for an Executive Dinner

Perplexity Computer can ask several AI models to analyze the same business decision and compare where they agree, disagree, or identify unique findings. In the demonstrated use case, the models combine live CRM opportunity data with multiple analytical perspectives to recommend a city and target audience for an executive dinner.

  • Multiple models independently assess the same decision
  • The output separates agreement, disagreement, and unique findings
  • CRM data grounds recommendations in the company's actual pipeline
  • Different models contribute different analytical lenses

You can prompt it to ask any models for for answers that you'd like and compare and contrast them.

Nate · 05:00

So the checkbox is here and the evidence of why they agree, who they should target.

Nate · 05:30
#model council#events#crm#decision making
Tool12:30

The Control Panel Nate Uses to Manage Recurring AI Workers

Nate manages agent work through pinned flows, scheduled jobs, attention queues, hosted dashboards, and a daily formatted Slack summary. Perplexity Computer serves as the central control panel, while Slack provides a convenient surface for reviewing updates and collaborating with colleagues.

  • Items requiring attention appear at the top
  • Scheduled and pinned jobs remain easy to access
  • Daily Slack summaries surface the information Nate cares about
  • Hosted apps provide dedicated dashboards such as sales pipeline views

So I have my pinned flows in computer.

Nate · 13:00

So I can see at the top here everything that needs attention, and I can collapse these sections if I need to.

Nate · 13:30
#agent management#dashboards#scheduled jobs#slack
Tool19:30

Reconciling Hundreds of Contracts Against the CRM With AI

Perplexity Computer can inspect signed contracts stored in Google Drive or a contract-management platform and reconcile them against CRM opportunities. The workflow extracts contacts, amounts, dates, and line items, produces an audit report, flags records for manual review, and can execute approved uploads at scale.

  • Contracts can be retrieved from Google Drive or a CLM
  • Extracted terms are compared with CRM opportunity records
  • The output identifies generated records and cases requiring manual review
  • Uploads can remain blocked until a human approves them
  • The workflow scales to hundreds of deals

And the goal I put here is to ensure that all signed contracts are represented as opportunities with the relevant contacts, amounts, close dates, opportunities,…

Nate · 20:00

But uh, this with hundreds of deals actually works at scale.

Nate · 21:30
#crm#contracts#data quality#reconciliation
Tool22:30

A Daily Voice-of-Customer Dashboard That Recommends Actions

Nate's preferred RevOps workflow ingests customer transcripts and emails into a dashboard that refreshes daily. It identifies what customers value, surfaces recurring themes, and recommends concrete actions for product, enablement, and marketing teams rather than merely summarizing feedback.

  • The dashboard refreshes every day
  • Transcripts and emails provide the source material
  • It tracks customer sentiment and resonant themes
  • Recommendations are routed to the teams responsible for acting
  • Discovered use cases can become testimonials or marketing stories

I think the main one that I've loved is our voice of customer dashboard, and this refreshes every single day.

Nate · 22:30

but we're suggesting things that I can do and my team can do and our product team can do to get better.

Nate · 23:00
#voice of customer#customer insights#enablement#product marketing

Takeaway· 2

Takeaway03:00

AI Shrinks Compensation-Plan Development From Months to Days

Work such as designing and back-testing a compensation plan once required a lengthy collaborative process. Nate says AI can consolidate expert input, evaluate the plan, and prepare it for approval and deployment within days, while humans remain involved in co-working and decision-making.

  • AI broadens the range of work one operator can cover
  • External expertise can be consolidated quickly
  • Compensation plans can be designed and back-tested faster
  • Human collaboration remains part of the process

Things like that that would have taken months or are taking now days to get by in and deploy.

Nate · 03:30

And there is still uh work with humans, so it's not all agents all day.

Nate · 03:30
#compensation#planning#ai collaboration#revops
Takeaway24:00

Why Customer Obsession Still Matters in an AI-Enabled Career

Nate argues that AI does not replace the durable career advantage of understanding customers. The opportunity is to identify precisely what external buyers or internal stakeholders need, then use AI to execute and deliver that value faster and more accurately.

  • Customer obsession remains a durable career principle
  • Internal teams should be treated as customers too
  • Operators should anticipate what leaders and frontline teams need
  • AI accelerates delivery but does not determine what matters

I think my general point of view is is be obsessed with the customer

Nate · 24:30

now with AI executing on and delivering on what they need it just becomes that much faster and more accurate using tools like Perplexity.

Nate · 24:30
#career#customer obsession#future of work#ai