Become Irreplaceable With the Same Negotiation Move That Saved Pfizer Billions

WHY THIS ISSUE MATTERS

• You'll learn Pfizer-style influence tactics to ensure executives seek your strategy input instead of ignoring your insights and leaving you in report-pulling roles

• You'll discover how to build irreplaceable strategic influence that advances your career even as AI automates your technical skills

• You'll get scripts to strategically concede data access, transforming your "obstacle" reputation into the trusted advisor status that earns promotions

• You'll master the "concede cheap, gain big" skill to escape mid-career plateaus putting you in a position to secure better roles with salary jumps

Your VP launched a new pricing strategy without looking at your elasticity model. Finance hired McKinsey to build the forecast you already built six months ago. Marketing just made a million-dollar bet based on a dashboard they fundamentally misunderstood.

And nobody asked you.

Not because your analysis was wrong. Not because you lack skills. But because you've been wielding power when you should have been building influence. You control the data warehouse. You're the only one who understands the attribution model. You've made yourself technically indispensable.

You've also made yourself strategically irrelevant.

We watched Pfizer's CEO negotiate with the most powerful office in the world last month. He won by giving things away. By understanding what his counterpart needed politically. By making the other side look good while getting what mattered.

The analytics professionals stuck at IC5 for three years are doing the exact opposite wrong thing.

Lets Go!

The Influence Playbook: From Gatekeeper to Trusted Partner

I was listening to a podcast about high-stakes negotiations between the Trump administration and Pfizer over drug pricing. The detail that stopped me to thinking wasn't about the billions at stake. It was about how Pfizer's CEO, Albert Bourla, approached the negotiation (and how it actually relates directly to YOU).

He didn't lead with Pfizer's market dominance. He didn't threaten to pull drugs from the market. He didn't leverage his company's lobbying power. Instead, he built what negotiators call "influence credits" by conceding on cheaper items first, demonstrating understanding of the administration's political constraints, and reframing the conversation around shared goals.

The analytics professionals who plateau are doing exactly what they shouldnt be doing. They're optimizing for power (technical gatekeeping, information control, being the "only one who knows") when they should be building influence (trust, partnership, strategic positioning).

Have you ever truly thought about your position in your organisation in this way?

The Hidden Cost of Power in Analytics

Power feels productive. You control the data. You're the bottleneck everyone needs. You have ‘job security’ because replacing you would be a bit painful.

But power creates a trap.

When you're the gatekeeper, stakeholders work around you. (Ever heard of shadow IT??). They make decisions before asking for data because they don't want to wait. They bring in consultants who speak their language better. They promote people who don't have your technical skills but understand how to shape business conversations.

The deeper problem is identity. When your value comes from controlling access to information, you're incentivized to keep things complex. You can't teach others because that erodes your power base. You can't simplify processes because that makes you replaceable. You become a single point of failure instead of a force multiplier.

Wanna know whats even worse?? AI is getting better at the technical power you're clinging to. Every month, tools get better at SQL, Python, and dashboard building.

The moat you're defending is shrinking.

The Three Levels of Influence

So here is my contention.

Influence in analytics operates on three levels (and most professionals never even get the the first one!!).

1 | Tactical Influence: Delivering What They Didn't Know They Needed

This is where you stop being a data puller and start being a problem solver. When Sales asks for "a report showing Q3 performance by region," you deliver that plus an analysis of why the Southeast underperformed and three specific actions they could test.

You're not waiting for perfect instructions. You're understanding the business problem beneath the data request. You're conceding where it's cheapest (giving them the exact report they asked for) while building influence credits (showing you understand their actual challenge).

Copy-paste script for your next stakeholder meeting:
"I pulled the numbers you requested, but I also noticed [PATTERN] in the data that might explain [THEIR PROBLEM]. Would it be helpful if I dug into that?"

This positions you as a strategic partner, not a service provider.

2 | Relationship Influence: Making Them Look Good

The analytics professionals who advance understand that stakeholders don't care about your technical brilliance. They care about whether you make their job easier and make them look smart to their boss.

When you frame insights as "Here's what this means for your team's goals" instead of "Here's what the data shows," you're building relationship capital. When you proactively flag risks before they become problems, you're demonstrating you understand their world, not just your dashboards.

Here's what this looks like in practice: Before presenting analysis to the VP of Marketing, you meet with her director to understand what's keeping her up at night. You learn about the pressure she's under to improve customer retention. You reframe your churn analysis around specific retention initiatives she's already considering. When she presents to the VP, your data makes her look strategic and prepared.

You didn't change your analysis. You changed your positioning. That's influence.

3 | Strategic Influence: Shaping the Questions

This is the level where you stop responding to requests and start setting the agenda. You identify business opportunities through data before anyone asks. You frame strategic conversations using your analytical perspective. You become the person leadership calls when they're thinking about the future, not just reporting on the past.

The shift is from "Let me know what data you need" to "Based on what I'm seeing in the data, here are three strategic questions we should be asking." You're not waiting for permission. You're using your analytical advantage to spot opportunities others miss.

This requires understanding business strategy well enough to connect data patterns to strategic implications. When you notice customer acquisition costs rising in specific channels, you don't just report it. You connect it to the company's growth strategy and propose a framework for evaluating channel mix going forward.

The Influence Shift for Data Leaders

If you're leading a data team, your influence challenge is different. You're not just building your own influence; you're creating an environment where your entire team can develop theirs.

The mistake most data leaders make is protecting their team from the chaos of stakeholder demands. They become the buffer, taking all the ad-hoc requests and translating them into tickets. This feels like good management, but it's actually limiting your team's influence development.

Instead, create controlled exposure. Have your ICs join stakeholder meetings. Let them present their own findings (with your coaching beforehand). When someone asks for a dashboard, have your analyst talk directly to them about requirements instead of you playing telephone.

Copy-paste framework for delegating strategically:
"I want [YOUR ANALYST] to work with you directly on this. They're the expert on [DOMAIN], and I think you'll get better results working together. I'll stay looped in, but they'll be your main point of contact."

This builds your team's influence while positioning you as a leader who develops talent, not a micromanager who hoards relationships.

The Bourla Principle: Concede Where It's Cheapest

Back to that Pfizer negotiation. Bourla's strategy was pretty brilliant because he understood the difference between his teams hard constraints and his stakeholder's political needs. He gave ground on visible items that mattered less to Pfizer but scored political points for the administration.

Love it or loathe it - it seems to have worked.

In analytics, this could mean identifying the easy wins that build goodwill without requiring significant effort. Maybe it's automating a weekly report someone keeps requesting manually. Maybe it's creating a simple self-service dashboard for questions that don't require your expertise. Maybe it's teaching a stakeholder basic SQL so they can pull their own simple queries.

These feel like giving away your power. They're actually building your influence.

When you help stakeholders become more self-sufficient on routine tasks, they trust you more on strategic questions. When you make their lives easier, they become advocates for your work when budget discussions happen.

The analytics professionals who advance are the ones who understand this paradox: The more you give away your tactical power, the more strategic influence you gain.

I’m trialling a new section to the newsletter this week. I want to start featuring interesting project ideas for you to try - something that will help build out your project portfolio if you are job hunting, or just something to keep you busy in your off-time that shows your commitment and love for data and analytics. And making an impact.

I’m actually thinking of creating a little community around this idea - maybe a discord server that people can join to join in on monthly ideas, challenges, group projects, who knows where it may lead?? (I probably would want help to build this and help run it - put your hand up if you are keen!)

🌍 PROJECT IDEA: Global Supply Chain Carbon Footprint Tracker

Boardrooms are asking "How green is our supply?" but most businesses rely on annual reports and estimates. Climate transparency regulation is arriving in 90+ jurisdictions. Voluntary guesses will soon be penalties.

Build a live supply-chain carbon dashboard from real transport, energy, and trade APIs within one week.

Why now: CFOs and ESG heads at multinationals need proof of decarbonisation progress, and regulators demand granular emissions accounting by Q2 2026 under emerging directives.

One-liner essence: An automated tracker that pulls real-time freight, energy, and trade data to compute the carbon footprint of inbound and outbound logistics movements every day.

Decision it improves: Which shipping routes, carriers, and transport modes to prioritise to hit 2030 net-zero pledges without sacrificing delivery time.

Value: £400K annual compliance cost avoidance for mid-sized manufacturers by eliminating manual carbon audits, plus early warning on high-emitting legs to reroute shipments before reporting deadlines.

Live data source: Climatiq API (emission factors), Open Shipping APIs (vessel position/fuel consumption), and National Grid live intensity feeds (electricity carbon per region), updating daily or hourly.

Why durable: Climatiq maintains 50,000+ verified emission factors; shipping and grid APIs are published by regulators and ports under open-data mandates.

Tech stack: Python, PostgreSQL, FastAPI, Plotly Dash, GitHub Actions.

What you build in 7 days:

• Pipeline that fetches daily shipment legs, electricity use, and emission factors on schedule (tied to "Which mode emits least per tonne-km?").

• Carbon calculation engine applying emission factors to distance, mode, and cargo weight (key metric: gCO₂e per shipment).

• Live view with two decision-titled charts: "Highest-Carbon Routes This Quarter" and "Mode Shift Impact Forecast."

Stakeholders: Chief Sustainability Officer sponsors, logistics planners and procurement teams use daily.

Proof: Route Carbon Intensity = (Shipment Distance × Mode Factor × Cargo Weight) / Total Shipments, tracked weekly.

Packaging: Public GitHub repo with interactive Dash app deployed on free Render tier; scheduled data refresh via GitHub Actions.

Ethics: Anonymise supplier names in public demo; flag data gaps where emission factors rely on industry averages rather than primary data.

Upgrade path: Week 4 addition—supplier scorecards that rank partners by carbon efficiency and flag alternative low-carbon carriers for contract renegotiation.

Scores: Technical 8/10, Analytic 9/10, Data Visualisation 8/10, Stakeholder 9/10, Value 9/10.

Prove climate accountability that earns you ESG analytics offers and supply-chain decarbonisation roles.

🎭 This scenario is FICTIONAL but crafted with enough REALISM to showcase the why and how behind building the project.

Looking for ideas to add to your data portfolio? Go beyond Titanic dataset on a generic PowerBI dashboard.

TADAA: Technology Automation Decimates Analyst Ambitions

Welcome to this week's special report on Career Annihilating Events. We're tracking the most significant job-killing news from the past seven days where AI is replacing the white-collar workforce. Here are the three most devastating stories that broke between October 12–19, 2025. (Check out my cherry picked quote at the end - it’s a doozy!)

#1 MOST CAREER ANNIHILATING: Goldman Sachs Warns of AI-Driven Layoffs in Memo to Staff (October 14, 2025)

Goldman Sachs sent an internal memo to its 48,300 employees announcing it will "constrain headcount growth through the end of the year" and implement a "limited reduction in roles across the firm" as part of its new "OneGS 3.0" AI transformation strategy. CEO David Solomon, President John Waldron, and CFO Denis Coleman stated in the memo: "While we are still in the early innings in terms of assessing where AI solutions can best be deployed, it's become increasingly clear that our operational efficiency goals need to reflect the gains that will come from these transformational technologies". The bank explicitly cited AI's efficiency gains in client onboarding, lending processes, regulatory reporting, and vendor management as justification for reducing human headcount despite posting record Q3 profits.

Source Credibility: 9/10 (Fortune, NYPost, TimesOfIndia)

#2 MODERATELY CAREER ANNIHILATING: Amazon Plans to Cut 15% of HR Staff in AI Restructuring (October 14, 2025)

Amazon announced plans to eliminate approximately 15% of its human resources division—the People eXperience and Technology (PXT) team with over 10,000 global employees—as the company invests up to $100 billion in AI and cloud infrastructure this year. CEO Andy Jassy had previously warned employees: "We expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company". This follows Amazon's elimination of 27,000 corporate jobs between 2022-2023, and marks a continued push to replace middle management and HR analytics roles with AI systems that handle performance tracking, workforce planning, and internal operations.

Source Credibility: 8/10 (Fortune, NDTV, Economic Times)

#3 SOMEWHAT CAREER ANNIHILATING: JPMorgan CFO Tells Managers to Stop Hiring as AI Takes Over (October 15-16, 2025)

JPMorgan Chase CFO Jeremy Barnum revealed during the bank's Q3 earnings call that managers have been instructed to avoid hiring new employees unless "absolutely necessary," stating "We have a very strong bias against having the reflexive response to any given need be to hire more people". Despite the bank reporting a 12% profit increase to $14.4 billion, headcount grew by only 1% as the company deploys AI across all client interactions and internal operations. JPMorgan executives previously disclosed that operations and support staff are expected to shrink by at least 10% over the next five years due to AI adoption, even as business volumes increase.

Source Credibility: 7/10 (Times of India, American Bazaar)

Interesting times!! (I have specifically pulled out the following quote for you to ponder)

CLOSE-OUT

Here's your challenge for this week: Identify one stakeholder relationship where you've been relying on power instead of building influence.

Maybe you're the gatekeeper for certain data. Maybe you've been positioning yourself as the "only person who can do X."

Schedule 20 minutes with them and ask: "What business challenges are you thinking about that I might be able to help with?"

Not what reports they need.

What problems they're trying to solve.

The analytics professionals who advance are the ones who shift from technical gatekeeping to strategic partnership.

What's your experience with building influence versus wielding power? Hit reply with your thoughts.

Best,

Tom.

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