Launch Edition → How to Escape “Replaceable Analyst” Status Before AI Does It for You

A handful of curated links

Let’s be blunt: if an LLM can spit out a tidy window function faster than you can type SELECT, your technical edge is shrinking by the minute. That’s not a reason to panic—it’s a reason to pivot. McKinsey’s latest workplace survey found that almost every company plans to pour more money into AI over the next three years, yet only one in a hundred feels even close to “AI-mature.”

Read that again: the tech is racing ahead, but the people who can shepherd the tech are still in short supply. That’s your opening.

What the bots still can’t do

MIT Sloan researchers just catalogued five human abilities machines stumble over—empathy, presence, judgment, creativity, and something they call hope. When those skills show up in the real world, they look a lot like leadership. And leadership, according to an HBR study of 600 employees, is exactly where staff don’t want a robot in charge. They’ll trust an algorithm to crunch numbers; they still want a flesh-and-blood boss who can read a room.

Promotion in an AI era isn’t about shipping more Python — it’s about becoming the calm, credible voice your CFO trusts when the model goes off-script.

Your three-step “Ladder” playbook

Step 1 Turn insights into business stories

Every time you finish an analysis, cap it with a three-sentence memo:

1. Decision at risk (“On-time delivery is slipping 2 pp”).

2. Insight (“Ninety percent of misses come from one carrier on lane 27”).

3. Action & payoff (“Re-bid the lane and save $1.2 m next quarter”).
   Send that memo to the decision-maker, not just your team chat. You’ll stop being “the dashboard person” and start being “the revenue fixer.”

Step 2 Shadow senior conversations

Ask to sit in on one exec meetings —finance forecast, product roadmap, whatever. Don’t say a word; take notes like an anthropologist. What metrics make them lean in? What jargon do they use? Those are the levers you’ll reference when you pitch an idea. Suddenly your recommendations feel eerily more on-target—because they are.

Step 3 Run a self-imposed 30-60-90 sprint

Forget new tooling; level-up human skills:

• Days 1-30 — Map relationships. List the five stakeholders who secretly decide your project’s fate. Book virtual coffees; learn their success metrics.

• Days 31-60 — Practice executive translation. Rewrite one technical update a week as a story a CFO could read on a phone. Time-box to 200 words.

• Days 61-90 — Mentor & delegate. Pick a junior analyst, coach them through a project end-to-end, and present their work (crediting them) in the next steering committee. Nothing screams “ready to lead” like building others up.

Do this once and you’ll have proof of influence, communication range, and team-lifting chops—the very signals promotion committees hunt for.

Five years ago, Sami Rahman was a business psychologist who thought “people like me can’t work in data.” A single conference talk on using machine-learning to decode human behaviour flipped that script. Sami taught himself Python, built a Kaggle portfolio in his spare time, and landed an entry-level analytics role at WPP’s Essence agency.

Today he runs the Data Engineering & Data Platform team at Penguin Random House. His climb didn’t hinge on exotic algorithms; it hinged on human leverage:

• Active listening as a super-skill. “A data scientist must be surgical and precise… Active listening is key.”

• Business fluency > model fluency. Early on he forced himself to pair every technical project with a crisp answer to “How does this move the bottom line?”

• Stakeholder empathy. Rahman advises “never stop listening to the business - the stakeholders are your biggest allies” - spend effort to make time with executives to catch the real success metrics hidden between agenda items.

Take-away for Ladder readers: your non-traditional background isn’t a blocker—it’s an empathy moat. Double-down on listening, translate tech to value, and mentor someone junior this quarter. That combination carried Sami from “could I ever?” to Head of Data Engineering and Data Platforms in half a decade.

^{Tarran, Brian. 2023. “‘I always thought someone like me couldn’t work in data, let alone data science.’” Real World Data Science, April 24, 2023. URL}

Your move

I’m throwing down a friendly gauntlet:

Before next Thursday, get two yeses.

• Yes #1: a senior leader agrees to let you shadow a meeting.

• Yes #2: a junior teammate agrees to co-present your next findings.

Bag those approvals and you’re already operating above your pay grade. Shoot me a reply when you’ve done it—I’ll cheer you on in the next issue.

Start now: block 15 minutes today to fire off both asks. Most analysts will spend that time tweaking dashboards; you’ll spend it proving you’re ready to lead the room.

Best,

Tom.