2026 is coming in hot.

Models, wrappers, agents, “vibe coding”.

While everyone is focused on what’s visible above ground, the real shift is happening beneath the surface.

It requires second-order thinking.

A contrarian stance.

In 1929, Joseph Kennedy famously said:

“When the shoe shine boy is giving stock advice, it’s time to get out”.

This wasn’t an isolated moment. History repeats this pattern:

• 1849 Gold Rush

• Early 1900s Texas Oil Boom

• Dot-com Bubble

• 2008 Housing Crisis

In every rush, people chase the obvious opportunity.

The winners don’t.

They either serve the rush or bet against it.

• Gold Rush → Levi Strauss sold jeans; Sam Brannan sold supplies

• Oil Boom → Halliburton sold services while operators went broke

• 1929 → Kennedy exited early

• 2008 → Michael Burry shorted housing

Same pattern. Different decade.

This is second-order thinking.

It’s part of the AI conversation that almost no one is having.

AI is the Current Rush

Right now everyone is building.

Models. Wrappers. Agents. “Vibe coding.”

If you’re in tech, it almost feels irresponsible not to be shipping something.

Sometimes, the urge turns into this:

Credit: via X (link to post)

Is that productive or just motion disguised as progress?

This is where second-order thinking starts to matter.

Not can you build something.
But should you.

With everyone chasing Claude, the advantage isn’t just in the obvious serving economy ie hardware, data centers, energy.

It’s in something far less flashy.

Being extremely competent in your domain.

Second-order thinking isn’t about rejecting AI.
It’s about understanding what becomes scarce because of it.

When tools become abundant, leverage shifts elsewhere.

That’s exactly what’s happening in GTM right now.

AI isn’t Scarce. Competence Is.

The ZIRP era trained an entire generation of GTM teams around:

• Process-driven selling

• SDRs to source and qualify, AEs to close

• MEDDPICC, qualification frameworks, dashboards

• “Grow at all costs”

Being a “good” seller was enough.

We’re now seeing the whiplash effects.

Credit of , via Substack (link to post)

The Deal Director captures what’s changed:

• Buyers are more informed

• Noise is everywhere

• Executives are detached from real market signal

• Process without understanding collapses

Command of the sales process alone no longer creates leverage.

Competence does.

The Deal Director also notes that in infrastructure, sellers need to be savvy, technical, and deeply knowledgable.

I don’t sell infrastructure. I sell martech insights and analytics.

But I see the same shift every day.

Customers don’t want better process, they want better recommendations.

Being a “good” seller with command of a process doesn’t cut it anymore.

My sense is that the edge is shifting towards the skillset, not the AI.

That doesn’t mean AI isn’t transformational. It is.

But it doesn’t replace competence.

And what’s interesting is this shift isn’t happening only in GTM.

The “Dead” Functions Are Thriving

The functions that were supposedly "dead" because of AI are the ones adopting it the fastest.

Not replacing themselves.
Amplifying themselves.

Matt Harney at SaaS Letter pointed this out in his State of AI Report. Frontier AI users are overwhelmingly concentrated in writing, communication, and coding.

By a landslide.

Credit

These were the very functions most people assumed AI would eliminate.

The market is saying the opposite.

What’s happening instead is more interesting:

AI isn’t removing the need for these roles - it’s raising the bar for them.

Storytelling is in high demand.

Open AI is hiring SDRs:

Credit: of , via X (link to post)

Writers, too:

Credit: , (link to post)

Why?

Because great writing forces clear thinking.
Clear thinking shapes decisions.
And decisions move markets.

AI makes average output cheaper.

It makes real competence more valuable.

The throughline:

Despite AI expanding what’s possible, the principles endure.

• Deep domain competence

• Applied skill, not surface fluency

• Understanding Emotions

• Synthesizing and recommending

• Purposeful use of tools

This second-order shift isn’t just limited to GTM and “dead” functions.

We’re seeing the same pattern show up in how entire industries are investing and where capital is flowing next.

Where the Smart Money is Going

First-order thinking in AI is straightforward:

Buy more Nvidia GPUs. Scale faster. Win.

Second-order thinking asks a harder question:

What happens when compute becomes a geo political bottleneck?

recently highlighted how OpenAI and xAI Are Losing the AI Wars to Anthropic and Google, pointing to recent comments from Anthropic’s CEO at Davos.

The implication isn’t about model quality. It’s about risk.

So the rationale response isn’t “build more” - it’s to insulate.

Build compute sovereignty. Reduce dependency. Anchor value in things that can’t be easily abstracted away.

Second-order thinking applies the same logic to where lucrative opportunities lie in GTM.

Credit: FidelCacheFlow

The takeaway:

• software and cloud based apps are becoming the commodity

• capital is flowing to AI tied to physical infrastructure and vertical constraints

• relationship mapping replaces cold outreach as the scarce skill

• Boring > sexy

Second-order thinking doesn’t chase momentum.
It positions around constraints.

What This Means Going Forward

This edition wasn’t meant to dismiss the opportunity of AI.

AI is an integral part of my day to day.
You should be building, experimenting, and learning.
You should be fluent in code and AI.

But when something becomes widely accessible, the leverage moves.

Towards real skill.
Towards domain depth.
Towards people who can apply tools with intent.

When the hype builds, that’s usually the moment it’s worth questioning.

A Contrarian stance.

I’ve shared a few additional pieces below that have inform my thinking and approach to GTM. I’d recommend them to anyone operating in this space.

As always, thank you for reading.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Further Reading Recommendations:

• The Complete Guide to Selling to Procurement by

• Earnings Call Analysis for Sales Teams by

• Understanding the Balance Sheet by
  

NEW: The Deal Narrative Kit is now available. Get the complete account research system + POV narrative builder (free) → Download here

CFO’s think your ROI deck is bullshit.

They don’t trust vendor ROI calculations. They want validation from internal stakeholders who understand the actual business context.

But here’s the real problem:

Most sellers can’t articulate why it matters now because they don’t understand how the business actually works.

There’s a difference between selling a product and running a business.

The sellers who win today know how to read financials. They can synthesize what they learn into a clear narrative that shows how their product can help.

Over the past few weeks, I've been sharing financial teardowns of different companies. Those teardowns are built off a system I personally use to turn P&Ls into clear narratives for outbound, discovery, and deal strategy.

Financial Teardown #1: Salesforce
Financial Teardown #2: Adobe
Financial Teardown #3: Target
Financial Teardown #4: Coinbase

Today, I’m giving you the 4 core prompts that power every single one of those teardowns.

These are the same prompts that power every financial teardown you’ve seen on this newsletter.

The Business Engine

Understanding how a company organizes itself tells you where decisions live.

🧠 Prompt:

Based on the [insert year] 10-k, how is [prospect business] segmented and organized?

Why this matters:

• Where P&L accountability lives

• Which divisions control budget

• How to plan your multi-threading strategy

• Who actually makes buying decisions

How the Business Makes Money

This helps you see what customers really pay the company for.

🧠 Prompt:

If [prospect company] disappeared tomorrow, what core capability would their customers lose?

Why this matters:

• What the company optimizes for

• How money flows through the business

• Where operational pressure exists within segments

• What customers actually value (vs. what marketing claims)

The Financial Signal

This reveals the money pressure that shapes all downstream decisions.

🧠 Prompt:

Based on the [prospect business] 10-k, what are the financial metrics that matter to the business in the current fiscal year?

• Topline

• Profitability

• Efficiency

• Cash Flow

Why this matters:

• Shape your hypothesis of where your solution impacts the business

• Identify which metrics leadership is graded on

• Understand what “good performance” looks like for this company

The CFO Mandate & The Strategy That Matters to Leadership

This is where financial signals turn into strategy.

🧠 Prompt:

Based on [prospect company] most recent financials, can you help me understand:
1. What are the CFO's top initiatives?
2. What are the top strategic initiatives [prospect company] is prioritizing?
3. What are the top challenges that [prospect company] is facing?

• CFO’s Top Initiatives

• Company Strategic Initiatives

• Top Challenges

Why this matters: This is your “why now.”

• What the CFO is accountable for

• Where the company is investing

• What challenges create urgency

The Framework in Action

Here’s what this looks like as a complete workflow:

1. Business Segments → Tells you who owns what and where budget lives

2. Core Capability → Shows you what the company optimizes for

3. Financial Signals → Reveals the metrics leadership is graded on

4. CFO Mandate → Translates metrics into strategic priorities and buying triggers

With these four prompts, you go from “I think this might matter” to “I know exactly what the CFO cares about and why.”

You walk into discovery calls with context.
You’ll ask better discovery questions.
You’ll position differently and sound like you belong in the room.

The Deal Narrative Kit

Research is one thing.
Building a sales narrative is another.

That’s where the Deal Narrative Kit comes in.

Research and execution.
Insights and positioning.
Strategy and tactics.

Get the complete system with templates, examples, and the POV narrative builder:

Download here

Try it Yourself

Pick one target account.
Run these 4 prompts.
See what you learn.

Then ask yourself:

“Do I know how to turn these insights into a pitch that actually resonates with the CFO?”

If the answer is no, the kit will show you how.

If the answer is yes, you’re ahead of 95% of sellers. Keep doing what you’re doing.

Either way, you’ll know more about your accounts in 15 minutes in a more succinct way than others on your team.

If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers are prioritized and details are kept anonymous.

Any other questions, comments, thoughts? Send them my way.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Today we’re breaking down the Coinbase P&L→ POV then showing I’d position Grafana as if we were selling into the account.

This isn’t a crypto thesis. It’s a business teardown on how a complex, regulated platform behaves financially, and how a CFO would look at it.

If this is your first teardown, I’d recommend referencing earlier discussions on the difference between running a business vs selling in it, and financial messaging framework.

The core question we’re looking to answer:

How would a CFO look at this?

Read the business engine → translate the financial reality → strategic priority → build the narrative → distill to persona level value

Financial Teardown # 4: Coinbase

For consistency, I’m referencing the Coinbase 2025 10-K (fiscal ends on Dec 31).

If this were an active deal, I’d also review the latest earnings calls and Q3 10-Q, and watch for the 2026 10-K expected on February 13th.

Note that all outputs are from ChatAE (affiliate link).

The Business Engine

Coinbase reports as a single reportable segment, but economic accountability flows through customer cohorts and the platform surfaces that serve them.

Takeaways:

• Although reporting is centralized, P&L accountability lives at the customer and product level. This is most likely where budget decisions are made and where sellers should anchor their narrative.

• Coinbase is highly cycle-sensitive:

  • Trading volatility can drive high-margin revenue, but it’s unpredictable.

  • Subscription and services revenue is steadier, but requires upfront investment.

  • Infrastructure bets are long-term ROI plays that pressure near-term margins.

• Diversification helps at the platform level, but leadership is actively shifting the mix from volatile transaction revenue toward more predictable, recurring services.

• Cost discipline matters here more than at most “growth” tech companies.

How Does the Business Make Money?

Coinbase is not just a “crypto exchange” company. It’s a regulated crypto infrastructure and access platform.

Every part of the platform reinforces this capability across customer segments:

Their mission statement focuses on trust and ease of use which carries through their core customers and revenue drivers:

• Consumer Trading “Access crypto instantly”

  Transaction fees and spreads. Primary monetization engine in active markets.

• Institutional Prime & Markets “Trade at scale”

  Lower margin, higher trust, long-term capital markets credibility.

• Base (L2 Blockchain) “Build cheaply at scale”

  Sequencer fees today, infrastructure leverage tomorrow.

The Financial Signal

Growth is driven by market activity and participation, not linear customer expansion.

Topline

• Total net revenue

• Transactional revenue

• Trading volume + MTUs (MTU signals consumer engagement)

Takeaway: Coinbase’s topline accelerates when volatility and engagement rise, not when product breadth increases.

Profitability

• Gross profit margin

• Adjusted EBIDTA

• Net Income

Takeaway: Profitability is cycle-amplified. When volumes spike, operating leverage shows up fast. When markets cool, fixed costs become visible immediately.

Efficiency

• Operating Expenses

• Take Rate

• Revenue Mix Shift

Takeaway: Efficiency is the control knob. Cost discipline and mix shift, not headcount growth, determine whether Coinbase can turn volatility into durable earnings.

Cash Flow

• Cash and liquid assets

• Operating cash flow

• Capital allocation discipline

Takeaway: Growth must be self-funded. Balance sheet strength allows Coinbase to survive downturns and invest through cycles without external dependence.

Synthesis:

Coinbase looks like a crypto trading business. But the way the P&L behaves, it’s really a volatility-management business.

Trading volume drives upside, but cost discipline, revenue mix, and balance sheet strength determine whether that upside turns into real earnings. Subscriptions, custody, and infrastructure aren’t just diversification, they’re shock absorbers.

This is the real P&L story.

What’s most interesting here is how transferable these patterns are. Even if Coinbase isn’t an account you’re actively dissecting, the same financial signals show up across other companies in this category.

You’ll see these trends across public fintech platforms including payments, brokerages, and infrastructure providers - where activity-driven revenue, fixed platform costs, and regulatory overhead force leadership to prioritize operating leverage over pure growth.

Once you see these signals, you start noticing them everywhere:

The Strategy that Matters to Leaders

What the CFO Cares Most About

• Cost discipline and unit economics by tightening pricing and incentives on low-value, high-cost activity

• Revenue diversification by accelerating recurring subscription and services revenue to reduce volatility

• Margin protection by prioritizing higher-margin products and managing transaction costs

• Operational leverage by scaling revenue without linear headcount or expense growth

CFO Mandate:

Make Coinbase structurally more resilient by converting market volatility into operating leverage through cost discipline, margin management, and a more durable revenue mix, not by chasing volume.

What the Company Is Focused On

• “Everything Exchange” expansion via stocks, ETFs, derivatives, prediction markets

• Stablecoins and payments by scaling USDC to challenge traditional rails

• Onchain ecosystem expansion by investing in Base and developer platforms

• Institutional infrastructure by expanding custody, prime brokerage, and issuer services

• Automation and AI by improving product quality and efficiency

Strategic Initiative:

Evolve Coinbase from a cyclical crypto exchange into a diversified, global financial and onchain infrastructure platform.

What’s Getting in Their Way

• Regulatory and execution complexity uncertainty across asset classes

• Fee compression from zero-commission trading and competitive pricing

• Operational and reputational risk under heightened scrutiny

• Customer experience challenges around support and account access

• Market volatility with revenue still highly sensitive to trading cycles

Challenges:

Coinbase must execute a broad platform expansion and efficiency transformation while managing regulatory risk, margin pressure, and cyclical demand - without sacrificing trust or operational control.

Every strategic decision Coinbase makes is a tradeoff between growth, efficiency, and risk.

The Sales Translation

If I were selling Grafana, I’m not pitching “observability”.

I’m helping Coinbase turn platform complexity into operational leverage by giving leadership real-time control over cost, reliability, and risk without adding headcount.

Value Pillars

• make cost drivers visible across infrastructure and activity

• maintain reliability as volatility spikes

• reduce operational risk without scaling teams

• protect margins through system-level insight

• support audit and compliance readiness

Example narrative:

Companies like Coinbase face margin pressure from volatility and scale in an always-on, regulated platform.

As complexity increases, operational risk and cost rise faster than visibility.

Grafana restores operating leverage by giving leadership real-time control over cost, reliability, and risk as activity scales.

What company should I teardown next?

DM me or email andrew@hackingsales.xyz if you have a suggestion. Subscribers are prioritized and all details are kept anonymous.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Happy New Year. Hope everyone had a solid break and is ready to get after it in 2026.

We’re picking the series back up by breaking down Target’s P&L→ POV then showing how to position Samsara as if we were selling into the account.

If this is your first teardown, I’d recommend referencing earlier discussions on the difference between running a business vs selling in it, and financial messaging framework.

The core question we’re looking to answer:

How would a CFO look at this?

Read the business engine → translate the financial reality → strategic priority → build the narrative → distill to persona level value

Financial Teardown # 3: Target

For consistency, I’m referencing Target’s 2024 10-K (fiscal ends on Feb 1).

If this were an active deal, I’d also review the latest earnings calls and Q3 10-Q, and watch for the 2025 10-K expected in early March.

The Business Engine

Target operates as a single reporting segment under a mass-merchandising model. The business is comprised of nearly 2,000 stores, with digital channels ( target.com and mobile app) fully integrated into the core retail operation.

Takeaways:

• P&L accountability is centralized at the enterprise level across all activity - stores, digital, fulfillment

• The business is organized internally by major merchandise categories for planning and execution

• This implies category leaders feel operational pressure but do not own standalone P&Ls or set mandates

• Margin performance depends on execution consistency, not portfolio optimization.

For sellers, this distinction matters:

Category leaders → strong internal champions
Enterprise leadership → economic decision makers

This explains why the financial pressure Target feels isn’t about driving demand. It’s about running the machine more efficiently.

How Does the Business Make Money?

When you think of Target, it’s easy to think “retail store”.

But really, Target is a mass scale discovery and fulfillment platform for affordable, trend-driven consumer goods.

Every part of the business supports that capability at national scale:

• Physical Stores: “Discover and buy now”
  ~2,000 locations for everyday needs and impulse purchases.

• Owned & Exclusive Brands: “Differentiate and protect margin”
  Private labels and collaborations reinforce Target’s style-at-scale platform.

• Digital Channels (App + Website): “Shop anywhere”
  A single interface for browsing, purchasing, loyalty, and fulfillment.

• Same-Day Fulfillment (Drive Up, Pickup, Delivery): “Get it fast”

  Stores double as local fulfillment nodes from a shared inventory pool.

• Loyalty & Personalization (Target Circle): “Come back more often”
  Drive repeat visits without friction or tiering.

Target is an integrated retail ecosystem built for convenience, discovery, and repeat behavior. Business success is driven by habit and ease.

(Target does this well. If you’ve ever gone to Target for “one thing” with your partner and leave with a full cart after an hour - you get it. IFYKYK)

The Financial Signal

Topline

• Net sales performance

• Comparable sales (traffic + execution)

• Digital and same-day sales growth

Takeaway: Growth is driven by traffic and execution, not category expansion

Profitability

• Gross margin rate

• Operating income and operating margin

• Diluted EPS

Takeaway: Margin pressure is constant; profitability depends on cost discipline while maintaining value perception.

Efficiency

• SG&A expense rate

• Labor productivity

• Inventory turnover

Takeaway: Efficiency is the primary lever management uses to protect margin in a low-growth environment. This is the link between margins and AI investments.

Cash Flow

• Operating cash flow

• Free cash flow

• Capital expenditure

Takeaway: Growth investments must be funded internally through disciplined operations and capital efficiency, reinforced by leadership’s focus on ROIC.

Synthesis:

Something I find really interesting is this: Target looks like a retail business. But when you step back and look at how the the P&L actually behaves, it’s really an efficiency business. Labor productivity, margin discipline, and execution consistency do the heavy lifting. That’s the real story in the P&L and where I’d start forming my POV.

The Strategy that Matters to Leaders

What the CFO Cares Most About

• Enterprise cost optimization by simplify operations and reduce complexity

• Margin recovery to control inventory, reduce shrink, and tighten pricing discipline

• Fulfillment economics, lower cost per order across stores and last-mile delivery

• Capital discipline, invest in store expansion, supply chain, and tech while protecting cash flow and ROIC

CFO Mandate: Make Target structurally more efficient by funding reinvestment through execution discipline and capital efficiency, not top-line growth.

This is reinforced by a multi-year $2-3 billion cost saving program alongside $4-5 billion in annual CapEx.

What the Company Is Focused On

• Merchandising authority, strengthening owned and exclusive brands

• Guest experience by improving consistency in stores, in-stock, and service

• Deepen omnichannel and same-day fulfillment across stores and digital

• Apply AI to merchandising, supply chain, and personalization

• Store expansion, new store locations and remodeling to support shopping and fulfillment

Strategic Initiative: Reinforce Target’s differentiated omnichannel position while redesigning the operating model to run leaner and faster at scale.

What’s Getting in Their Way

• Softening traffic and comparable sales declines, less people means less sales

• Persistent margin pressure from promotions, markdowns, shrink, and fulfillment

• Intensifying competition from Walmart, Amazon, Costco, and other value chains

• Organizational complexity and speed and too many layers slow decision-making

• High cost of transformation because of CapEx and constrained growth

• Labor disputes and legal exposure add risk and distraction.

Challenges: Target must execute a large-scale efficiency transformation while protecting the guest experience without the cushion of strong top-line growth.

The Sales Translation

If I were selling Samsara, I’m not pitching “fleet management” or “telematics”.

I’m helping Target build a physical operations execution layer that reduces labor, lowers cost per delivery, and removes variability across its store-fed supply chain—so margin and cash flow improve without adding headcount.

Target may look like a retail business, but the P&L tells a different story. This is an efficiency business, and Samsara helps leadership control the physical systems that make or break execution at scale.

Value Pillars:

• Reduce labor tied to physical operations oversight

• Lower cost per delivery and asset utilization

• Stabilize execution feeding stores

• Turn physical operations into measurable performance

• Protect margin and cash flow without adding headcount

Example narrative:

Companies like Target are under pressure to restore profitable growth while protecting margins and cash flow—forcing leadership to confront inefficiencies in cost control and execution across supply chain and operations.

Samsara makes those drivers measurable and controllable by turning physical operations into enforceable performance inputs, helping reduce costs, stabilize execution, and improve margin and cash flow without adding headcount.

What company should I teardown next?

DM me or email andrew@hackingsales.xyz if you have a suggestion. Subscribers are prioritized and all details are kept anonymous.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Hope that everyone had a relaxing holiday. As we close out 2025, we’re looking back at the themes that mattered most this year and how they’ll shape GTM in 2026.

This is about patterns and trends. Not predictions in isolation but the underlying shifts that explain why certain approaches are breaking and where advantage is moving next.

We’ll cover what actually changed in 2025, how AI reshaped output and access, and why judgement, emotion, and domain expertise will become the GTM edge heading into next year.

What Actually Changed in 2025

Cost reduction, layoffs, and operational efficiency defined much of 2025.

Across public markets and company financials, the signal was clear: pressure to do more with less intensified.

My perspective: AI isn’t eliminating work - it’s compressing the timeline of change.

What once unfolded over centuries, then decades, is now happening in years. Soon, maybe months. Categories are shifting faster. Skills are depreciating quicker. Leverage is beginning to concentrate toward the hands of those who know how to apply AI with judgment, not just deploy it for output.

In a recent conversation with Joe Rogan, Jensen Huang describes it best: the jobs that endure are the ones with purpose.

Purpose is human.

As AI accelerates output and automates execution, this trend keeps resurfacing.
When technology makes production cheap, the value doesn’t disappear - it moves.

Away from tools and toward judgment.
Away from volume and toward meaning.
Away from software and toward the people who decide how it’s used.

Output is abundant. Thinking is not.
Execution scales. Understanding doesn’t.

As we look ahead to 2026, the GTM teams that will succeed won’t be the ones who ship the fastest or automate the most. The winners will be the ones who understand their market deeply, exercises judgment, and use AI to amplify thinking rather than replace it.

Let’s look at how this materialized in 2025.

Output vs Judgement

When information and output become cheap - context, judgement, and insight become expensive.

Credit TJ, Salesxsaas

AI dramatically increases what we can produce but it doesn’t tell us what matters.
That distinction is where the real shift begins.

As production scales, noise overwhelms signal.
Filters tighten. Attention becomes scarce.

Access, not output, becomes the constraint.

The New Constraint is Access

Technology made access easier. As a result, relevance became the filter.

Information isn’t lacking, signal is.
Output exploded, attention fragmented.

This brought a resurgence of familiar GTM behaviors that breaks through the filter:

Credit Anthony Natoli

Introductions through trusted people.
Thoughtful, contextual communication.
Sharing ideas through earned credibility, not volume.

Access and attention look and feel different as technology evolves.
The underlying principles have remained the same.

Gaining access however isn’t just about relevance, it’s about resonance.

Emotion is the Last Non-Commoditized Layer

AI can simulate intelligence.
AI cannot originate resonance.

Emotion is harder still.
Trust. Curiosity. Tension. Belief.
These aren’t outputs, they’re reactions.

This is why, throughout late 2025, we saw a resurgence in storytelling across GTM:

Credit Signull ()

Great GTM doesn’t just transfer information. It creates felt understanding. When buyers feel seen and understood, they gain confidence in decisions that carry risk.

Credit Deal Director ()

This is not a branding exercise or marketing trend.
It’s a survival mechanism in response to a world of abundance.

Resonance without depth however, is fragile if meaningless.
Meaning cannot exist without understanding.

Which is why emotion alone isn’t enough. It needs to be grounded in domain expertise, context, and judgement.

Domain Expertise > AI

AI can accelerate learning.
It cannot replace experience.

Domain expertise - knowing how buyers think, what they fear, and how they decide, compounds slowly. Time has always been the most expensive asset to acquire.

The price of real understanding is repetition, exposure, and failure:

Credit Fidel Cache Flow

You can use AI to learn faster.
You can’t use AI to skip the work.
This hasn’t changed - and it won’t.

Credit Alex Lieberman

As tools converge and workflows standardize, the advantage shifts away from the systems and towards the people operating them.

Everyone has access to AI. Differentiation comes from applying judgement, which is shaped by time spent in a given domain. AI is a powerful technology that accelerates output and learning, but expertise must be earned.

Individuals Over Systems

Systems provide output.
Individuals provide perspective.

How people see the market.
How they frame problems.
How they exercise judgement.

As AI models and tools have become widely accessible, returns don’t converge - they diverge. The advantage shifts to those who know how to make AI effective: by giving it context, shaping workflows, and applying judgement.

That doesn’t live in systems alone. It lives in people:

Credit Aaron Levie

AI doesn’t eliminate jobs. It eliminates the middle.
Those who pair fundamentals with leverage pull ahead.
Those who don’t drift toward irrelevance.

As tools standardize, performance gaps widen. Not because of technology, but because individuals differ in how they think, decide, and adapt.

This is the quiet reshuffling of the market:

Credit Kyle Asay

Predictions in 2026

• Output continues to increase. AI keeps accelerating execution across GTM and creating more will remain easy.

• Access tightens. As noise compounds, attention becomes harder to earn and easier to lose.

• Judgment becomes visible faster. Weak thinking will be exposed while strong judgement will compound.

• Emotional signatures will matter more. Buyers will gravitate towards voices that feel credible, human, and grounded in understanding.

• Generalists without depth will struggle. Breadth without domain mastery becomes increasingly fragile.

None of this is new. The pace is.

How to Stay Ahead

Principles, not tactics.

• Build judgement before leverage

• Learn markets and master your craft, not just tools and your product

• Use AI to pressure test your thinking, not replace it

• Do the unscalable when everyone else automates

History is cyclical.
Technology evolves rapidly.
People evolve far more slowly.

2025 feels like a freeze-frame in a fast-moving film - screens everywhere and instant access to anything we can imagine. But when the camera pulls back, the story beneath it all hasn’t changed nearly as much as it seems.

We’re still humans huddled around the campfire - sharing stories, navigating uncertainty, and deciding who to trust.

That pattern has endured across time, business, and industry. And in GTM, it’s still where the advantage lives.

Despite rapid advances in technology, the human elements haven’t changed at all.

The tension between accelerating technology and enduring human behavior is the throughline heading into 2026.

Purpose.

Below are a few additional pieces that informed this perspective but weren’t directly referenced above.

As always, thank you for reading.

Wishing you all a Happy New Year. See you in 2026.

-Andrew K

Acknowledgments: Further Reading & Influences

• Best Software & AI Content of 2025 - Matt Harney

• The End of Software - Chris Paik

• A Buyer Sharing How They Buy - Kyle Asay

• The Best Salespeople Have One Thing in Common - Chris Orlob

• 2026 AI Predictions - Greg Isenberg
  
  

This week we’re breaking down the news and trends that GTM sellers, leaders, and operators should be paying attention to this December.

Here’s what we’re covering:

• Is AGI here?

• The rise of storytelling

• Making yourself valuable in the Age of AI

• The reality of implementing AI inside companies

Is AGI here?

On November 24th, Anthropic released it’s newest model: Claude Opus 4.5.

Reaction?

Blown. Away.

Followed by the unsettling feeling of … “it’s over”.

Credit Deedy

It doesn’t help that every earnings call sounds the same right now.

“Efficiency”
“Operating leverage”
“Headcount reduction”

We’ve covered similar themes in financial teardowns for Salesforce and Adobe.

Layoffs aren’t slowing down as we head into 2026.

Credit Only CFO

And yet - I don’t think “it’s over”.

I’m bullish on humans.

Credit Haider

This also applies to GTM roles.

In my opinion, this is the right way to be think about AI.

But AI isn’t the most important shift happening right now.

The Rise of Storytelling

With the emergence of any new technology, the market adapts.

Jobs will be eliminated.
New jobs will be created.
New solutions will create new problems.

The internet commoditized access to information.
AI is commoditizing output.

The edge is hardcoded into human nature.

Enter storytelling.

Credit Natalie Sportelli

This speaks to brand, editorial, and corporate - but don’t be mistaken. It directly applies to GTM teams.

Deals don’t close on logic.
They close on emotion.

The POV.
The narrative.
The story.

This is the ethos behind Hacking SaaS Sales.

It’s why frameworks like MEDDPICC, while still useful, no longer create separation on their own.

Process manages deals. Stories win them.

Credit Deal Director of Infraplay

Couldn’t have said this better myself.

If you’re in GTM you need to be thinking about what separates you from every single other seller.

Storytelling isn’t the only edge.

Making Yourself Valuable in the Age of AI

If the internet commoditized access to information and AI is commoditizing output, then where does value live?

When information and output become cheap - context, judgement, and insight become expensive.

Credit Sam Marelich of Offer Accepted

The takeaway?

Having such deep insight on an account, category, or domain does one thing for you:

Builds leverage.

Imagine someone walks into an interview.

One candidate shares the chronological order of their career written on their resume, like 99% of other applicants.

OR

The other candidate does what Sam’s guy did.

Who stands out?

That’s not interview advice.

The point:

You can AI a resume.
You can’t AI your value story.

The Reality of Companies Betting on AI

In theory, AI should usher in a golden age of efficiency and shareholder returns.

In practice, it’s harder than it looks.

Credit Nikunj Kothari of Balancing Act

Transformation isn’t easy.

Matt Harney touched on this reality in his earlier post this month, Is AI an Abject Disaster?

What stood out from Nikunj’s article was what companies need in order to be successful:

Conviction.
Talent.
Leaders having firsthand experience with tools.

The theme?

People.

Closing Thoughts

AI is going to continue to get faster, cheaper, and more accessible.

With it comes uncertainty.

But the GTM leaders who win won’t need to be the best at using AI. They will be the ones who understand them deeply enough to apply judgment.

As information and output converge into a commodity, a few things remain scarce:

Judgement.
Taste.
Context.
Nuance.

This is now where the leverage lives.

Before we dive in, quick shout-outs (no pitches):

• Deal Director (Infra Play): I’ve followed his work for ~3 years across X and his newsletter. His depth on AI + infrastructure for GTM leaders has been invaluable, even if you’re not in the infra space.

• BowTiedCocoon: a tech sales interview savant who’s helped hundreds break into $100k+ tech sales roles. His most recent post directly connects to today’s topic.

What We’ll Cover Today

Two Practical Workflows:

• Automating post call notes

• Context Driven AI for Deal Decisions

  • The setup

  • The problem

  • The solution

Workflow #1: Automate Post Call Notes

In BowTiedCocoon’s recent post, the 5-Click Sales Workflow (featuring TechsSalesGuy), he outlines a simply system to automate post-call work:

TL;DR:

1. Have the call

2. AI processes the recording

3. Summary and transcript created

4. Follow-up drafted automatically

5. Notes synced straight into your CRM

Stack: Use Gong (or call recording)> Zapier> CRM

Workflow #2: Context Driven AI for Deal Decisions

Here’s where this is relevant and extends further:

You can ingest call transcripts into GPT along with:

• Account research (ChatAE, internal docs)

• CRM notes

• Gong history

• Persona context

This lets me build a single thread per account where GPT acts as a thinking partner.

I’m not using AI to think for me.

I’m using it to pressure-test how I’m thinking.

Example (not a “prompt,” just a question):

I’m stalled with [Account]. Based on [recent context] and [challenge XYZ] from the discovery transcript, my plan is to [tactic]. Is this the right path to reach [next goal], or should I refine?

The output helps guide:

• Account strategy

• Outbound messaging

• Deal progression

The Set Up:

I use a GPT Project Folder that includes:

• Product context

• Account scoring system

• Core persona files

For each account, I upload relevant research and work inside a dedicated thread within the project folder.

The Problem: Memory & Thread Decay

With my current set up, I run into challenges with memory. Over time, account specific threads can get long over time. When they do, responses can become:

• Jumbled

• Inaccurate

• Blended with other account context

I’m not the only one: enter BowTiedFerret

The Solution: credit BowTiedFerret

For Prospecting:

I’m continuing to use threads within my overarching global project folder. This helps me keep:

• Broad prospecting context

• Universal persona files

• Lightweight commands like:

“Use [Persona File] stored in the project folder and [insert task]”

When a Thread Gets Too Long

Inside the account thread, I ask:

Create a concise, structured account snapshot suitable for future messaging and POV work.

Then:

• Copy that snapshot

• Paste it into a fresh thread (within the project folder)

• Keep moving with clean context

Deals in Cycle:

I’m using the BowTiedFerret set up, this way I have all account specific folders so that I have exact context I need and mitigate any context overlap.

That’s it.

Simple workflows. Real leverage.

If you have questions or want to compare setups, feel free to reach out.

If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers are prioritized and details are kept anonymous.

Any other questions, comments, thoughts? Send them my way.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

How would a CFO look at this?

This week we’re covering Adobe and breaking down how you’d sell into their world if you were in the shoes of a seller at Pigment.

The structure:

Read the business engine → translate the financial reality → strategic priority → build the narrative

Let’s dive in.

Financial Teardown # 2: Adobe

For consistency, I’m referencing Adobe’s 2024 10-K (their fiscal ends on the Friday closest to November 30th).

If I were prospecting today, I’d pull their Q3 10-Q and earnings calls to get the most up to date signals. If Adobe was an active deal, I’d be on the lookout for their 2025 10-K due to release December 10th .

What we’ll cover today

• The business engine

• How the business makes money

• The financial signal

• The strategy that matters to leaders

• The sales translation

NOTE: If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers are prioritized and details are kept anonymous.

The Business Engine

Adobe is broken out into 3 segments:

NOTE: This is a mental model for how I’d go about account planning. These are hypotheses I’m drawing off public data. You need to validate with prospects.

Do your due diligence with internal data. Reference your CRM, Gong, etc. to further refine and understand how your prospects are talking about projects, orgs, initiatives, etc.

Takeaways:

• P&L is clearly distributed across each segment. Unlike Salesforce, segments do not ladder up into a unified mission.

• Salesforce clouds are “mini businesses” that serve one buyer journey.

• Adobe’s segments serve different customers, different workflows, and different different economics.

• Their P&L’s aren’t separate versions of one business - they’re separate businesses.

Adobe is predominantly an “ARR” machine, accompanied by smaller revenue streams (ie, licensing, advertising, and services). They exist not to maximize margin but to support workflows, increase stickiness, and extend customer lifetime value “LTV”.

How Does the Business Make Money?

Adobe isn’t a “creative suite”. Adobe provides customers with an end to end content pipeline. They own the full lifecycle: creation → activation → management → measurement.

Each segment monetizes a different part of the content engine:

• Digital Media

  • Creative Cloud: “Create more.” Tools for producing assets, videos, and designs.

  • Document Cloud: “Work smarter.” PDF, e-sign, and document automation.

• Digital Experience

  • Experience Cloud: “Activate and measure.” Marketing, analytics, personalization, and commerce.

• Publishing & Advertising

  • Legacy Products: “Support and extend.” OEM, PDF licensing, and ad-tech.

Adobe’s ecosystem isn’t horizontal like Salesforce - it’s vertical. Unified around a single workflow: the content pipeline.

Salesforce sells to one department, many workflows.

Adobe sells to multiple departments, one workflow.

AI is deeply embedded across all segments, which matters heavily in their financials and leadership focus. More on that below.

The Financial Signal

Topline

• Digital Media subscription revenue expansion

• Experience Cloud contract growth

• RPO expansion

Takeaway: Subscription growth is predictable, but leadership needs AI monetization to accelerate ARR without inflating CAC (customer acquisition cost).

Profitability

• High gross margins in Digital Media

• Balance mixed margins in Digital Experience (enterprise services + infra costs)

• Operating income expansion

Takeaway: AI investments must convert into margin-accretive revenue growth and protect Digital Media, it’s highest margin business.

NOTE: EPS (earnings per share) expansion is a key investor focus, but as sellers, focus on the metrics that drive EPS, not EPS itself.

Efficiency

• OPEX discipline

• GTM Productivity

• R&D Leverage

Takeaway: operational leverage is essential - this is how Adobe funds the AI roadmap. Each incremental ARR dollar is fuel for AI.

Cash Flow

• Robust free cash flow

• Strong deferred revenue

• Predictable cash generation

Takeaway: cash needs to be measured and produce disproportionately higher ARR. Strong cashflow means aggressive AI investment without sacrificing financial stability.

NOTE: Patterns emerge across companies and categories. Like Salesforce, Adobe isn’t increasing spend to fund AI - they’re increasing efficiency to fund it.

The Strategy that Matters to Leaders

What the CFO Cares Most About

• Make AI a durable revenue engine, not hype

• Prove AI is accretive to ARR and margins.

• De-risk AI: compliance, IP, customer trust

Takeaway: AI must be measured like an economic engine - every dollar tied to ARR, margin, and cash flow impact.

What the Company Is Focused On

• Three-Cloud AI integration

  • Firefly → creative workflows

  • Acrobat AI → document workflows

  • Agentic AI → enterprise workflows

• AI-influenced ARR as KPI (>$5B, explicit goal to double)

• Agentic AI workflows for automating cross-cloud processes

• Enterprise expansion and deeper account wallet share

Strategic Initiative: Turn AI from a feature into a measurable financial engine.

What’s Getting in Their Way

• Competitive pressure in AI (Canva, Figma, AI startups)

• Investor skepticism on long term pricing power

• Perception risk around training data and creative IP

• Portfolio complexity across clouds and SKUs

Strategic Initiative: Move fast in AI and prove the economics. Defend pricing power. Simplify the value story.

NOTE: Adobe’s value story isn’t an easy task. If the CFO struggles to articulate, imagine selling.

I included investor-related signals in this breakdown: they correlate and sometimes directly shape the pressure prospects feel inside accounts. And oh yea, remember the Figma acquisition deal?

The Sales Translation

Pigment:

If I were selling Pigment, I’m not pitching “FP&A” software.

I’m helping Adobe built a financial intelligence layer that connects AI adoption, subscription economics, and segment performance into unified model leadership can use to steer the business.

Value Pillars:

• unify financial + operational signals (ARR, usage, AI credits, retention, infra cost)

• run real-time “what-if” scenarios on AI adoption, pricing, and product mix

• shorten planning cycles so AI performance flows into guidance sooner

• give CFO + BU leaders a shared, trusted source of truth for AI economics

• make AI’s impact on ARR, margins, cash flow, and EPS clearly measurable
  

Example narrative:

Finance teams at SaaS companies are under pressure to prove that AI investments translate into measurable financial returns - ARR growth, margin expansion, and stronger free cash flow.

Pigment gives FP&A teams the clarity and real-time modeling to tell that story: showing exactly how AI impacts the P&L, improving forecast accuracy, and enabling decisions that strengthen long-term performance.

Hope this was helpful.

If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers are prioritized and details are kept anonymous.

Any other questions, comments, thoughts? Send them my way, contact info above.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

How would a CFO look at this?

Each teardown will focus on one enterprise company and then show how to position 1–3 different companies as if you were selling for them.

The structure is simple:

Read the business engine → translate the financial reality → strategic priority → build the narrative → distill to persona level value

NOTE: If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers will be prioritized. Requests featured will maintain confidentiality unless otherwise approved.

Financial Teardown # 1: Salesforce

What we’ll cover today

• The business engine

• How the business makes money

• The financial signal

• The strategy that matters to leaders

• The sales translation

The Business Engine

Salesforce is broken out into five key sectors:

Takeaways:

• Segmentation across these sectors imply that P&L accountability is distributed across each cloud. So each unit will most likely have it’s own GM, financial targets, and product specific priorities.

• Despite having separate semi independent businesses inside a shared ecosystem, there are most likely more line leaders based on geography, products in a suite, etc. You have to talk to prospects to validate and understand the org structure to get this information.

• Salesforce’s entire model is built on annual recurring revenue (ARR), but professional services is not. If money is lost here, that’s probably okay. It’s purpose serves to de-risk investment or accelerate activation for a customer. This feeds initial lands, up sells, cross sells, which increase the life time value (LTV) of a their customer base.

How Does the Business Make Money?

When you think of Salesforce, you think CRM. But really, Salesforce provides their customers with revenue infrastructure for predictable growth.

Every segment aligns to revenue outcomes:

• Sales Cloud: “Sell more”. Pipeline, lead management, CRM.

• Service Cloud: “Serve better”. Support, ticketing, automation.

• Platform: “Connect everything”. Workflows, custom apps, data integration.

• Marketing & Commerce Cloud: “Find and Convert”. Campaigns, customer journeys, personalization.

• Professional Services: “Get it Set Up Fast”. Implementation, training, and support.

It’s a platform ecosystem designed for expansion and “stickiness”. Once a customer lands, the next offering is always within reach. The deeper a customer is embedded into the ecosystem, the harder it is for them to churn.

The Financial Signal

Topline

• Subscription revenue expansion

• Multi-cloud adoption

• RPO (future contracted revenue)

Takeaway: growth is steady, but leadership is optimizing for quality over speed.

Profitability

• Margin expansion

• Net income improvement

• EPS growth

Takeaway: Salesforce must prove operating leverage. Every function is expected to produce more value per unit out of every dollar that goes in.

Efficiency

This is the link between margins and AI investments

• OPEX control

• GTM productivity

• R&D leverage

Takeaway: operational excellence is not optional; it’s how Salesforce funds the AI roadmap.

Cash Flow

• Robust free cash flow

• Strong deferred revenue

• Predictable cash generation

Takeaway: cash needs to be measured and tied to outcomes; ensuring Salesforce can continue to invest aggressively in AI amidst a tight macro environment.

The Strategy that Matters to Leaders

What the CFO Cares Most About

• Cut costs without slowing innovation - especially around AI and automation.

• Use data to justify every dollar spent - procurement, vendors, internal operations.

• Automate finance workflows - better forecasting, fewer manual processes.

• Create long-term value - reinvest efficiencies back into high-growth areas.

CFO Mandate: make Salesforce more efficient today while still funding the AI future.

(Efficiency mandates aren’t isolated to tech. Across industries, volatile macro conditions are pushing CFOs toward the same playbook: tighten spend, increase productivity per dollar, and de-risk future investments. It’s one of the most common cross-category patterns I see.)

What the Company Is Focused On

• AI everywhere - embedding AI into every Cloud and workflow.

• Customer-centric growth - deeper industry solutions, better customer experience.

• Global + product expansion - more markets, vertical solutions, translates to a bigger ecosystem.

• Operational excellence - automate more, streamline more, ship faster.

• Responsible tech - data privacy, security, sustainability.

Strategic Initiative: Salesforce is doubling down on being the AI leader for enterprise software AND the most operationally efficient version of itself.

What’s Getting in Their Way

Even with strong execution, Salesforce still faces meaningful headwinds:

• Heavy competition (Microsoft, Oracle, HubSpot) pushing pricing pressure.

• Balancing cost cuts with innovation needs - AI investment is expensive (duh).

• Macroeconomic uncertainty reducing enterprise IT spend.

• Talent tightening after workforce reductions and restructuring.

• Data privacy + compliance as AI and cloud scale globally.

Challenges: Salesforce needs to evolve fast enough in AI while managing costs AND competition.

The Sales Translation

This week, we’ll break down how to position the overarching narrative for the following companies:

1. LaunchDarkly
2. Gong
3. Perplexity

1. LaunchDarkly

If I were selling a developer platform like LaunchDarkly, I’m not pitching “feature flags.”

I’m helping Salesforce build a software efficiency platform - converting engineering cycles into quantifiable, ROI-backed outcomes that support the CFO’s demand for innovation with discipline.

Value pillars:

• ship faster without added risk

• isolate cost + performance impact

• reduce engineering waste

• convert developer work into measurable ROI

• accelerate safe AI experimentation

Example narrative:

Companies like Salesforce are under pressure to prove operating leverage — to show that every new dollar invested in engineering produce positive returns in product performance, customer retention, or ARR.

LaunchDarkly gives product and engineering leaders the control, data, and speed to do exactly that: ship faster, waste less, and measure impact in real time.

2. Gong

If I were selling a revenue intelligence platform like Gong, I’m not pitching “call recording.”

I’m helping Salesforce operationalize a revenue efficiency system. Helping transform customer conversations into predictable, measurable signals that support the CFO’s push for margin expansion, GTM discipline, and customer centricity.

Value pillars:

• improve GTM productivity without increasing headcount

• strengthen forecast accuracy with real buyer data

• identify deal risk early to reduce pipeline waste

• reduce onboarding and coaching cost

• unify GTM insights for AI-powered execution

Example narrative:

Salesforce is under pressure from competition and needs to prove operating leverage across its GTM motion , demonstrating that every dollar spent on sales produces more predictable revenue.

Gong gives revenue leaders real buyer intelligence, improving forecast accuracy, accelerating rep ramp, and reducing GTM waste.

Every customer touchpoint converts into performance data the business can act on, enabling Salesforce’s GTM teams to be more resilient amidst competition, prioritize for cash conversion, and better understand the customer to increase LTV.

3. Perplexity

If I were selling an AI research platform like Perplexity, I’m not pitching “AI search.”

I’m helping Salesforce deploy an intelligence acceleration platform. Compressing hours of research, analysis, and synthesis into minutes so teams can execute the company’s AI strategy without expanding OPEX.

Value pillars:

• reduce research time for GTM, finance, and product teams

• accelerate competitive and market analysis

• support forecasting and strategic planning

• improve cross-functional alignment through shared insights

• increase decision velocity across clouds and markets

Example narrative:

Salesforce needs to innovate in AI while keeping operational costs tight. Perplexity gives every team, from finance to product to GTM - a faster, AI-native way to research markets, buyers, and competitive moves.

It compresses hours of manual analysis into minutes, helping Salesforce move faster with fewer resources. Perplexity amplifies the company’s operational intelligence without increasing headcount or resources.”

Closing Thoughts

When I’m prospecting, the narrative is my anchor for the entire sales cycle. It guides which accounts and personas I prioritize, and how I distill the core financial themes into messaging that actually land with each line of business. I’m also intentional about reading what tools like ChatAE (or any LLM) surface, instead of blindly copying it, so I can validate the inputs, stand behind my POV, and ensure I truly understand.

That same narrative becomes the backbone of the collaborative roadmap I build with prospects: where our solution fits, how it creates leverage, and how it ties back to ROI. It shows where we help, how we help, and makes the investment defensible.

Does this take time? Yeah. But the upfront work makes the rest of the sales cycle MUCH easier. Taking time to synthesize is the separator. It’s what pulls you out of the noise of hundreds of generic outreach emails.

The narrative you build upfront becomes the through-line for every conversation, email, and deal review. It’s the anchor of the entire motion - don’t half-ass it.

Hope this was helpful.

If you have an account you’d like me to teardown, DM or email andrew@hackingsales.xyz. Subscribers will be prioritized. Requests featured will maintain confidentiality unless otherwise approved.

Any other questions, comments, thoughts? Send them my way, contact info above.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

The shift hinges on one question:

Before jumping into live financial teardowns in future posts, this week is all about the overarching approach. It’s the thinking model you can reference every time you analyze a company. The goal is a resource you can use across every account you’re prospecting.

I’ll also show you how to pair this workflow with ChatAE, a platform that is integral to my daily workflow (transparency: I’m affiliated). However, you can replicate everything using GPT, Perplexity, Claude, etc.

I’ll be tearing down real company financials to show:

• how to derive a narrative

• how that narrative becomes a business hypothesis

• how that hypothesis turns into clean, relevant messaging for line leaders and ICs inside your ICP

This framework is a running theme - think of this as the “master doc” we’ll keep referencing.

What We’ll Cover Today:

• The business engine

• The financial signal

• The strategy that matters to leaders

• The sales translation

If you want me to do a breakdown on your target account, message me - this stuff has been fun and extremely useful for me, and it’ll turn into a reference library you can build on.

The Business Engine

The first step in any financial teardown is understanding how the business is organized and how it actually operates.

Start with the basic segmentation inside their 10-K, annual report, or investor materials.

This tells you where budget likely sits:

• Is budget centralized or distributed?

• Is spend per segment, per BU, or per functional owner?

• Who owns strategic vs operational outcomes?

During discovery or outreach, you’ll have to validate and pivot off of what you learn. As I’ve shared before, getting to the right answer is an iterative process. And the quality of your questions determines the quality of your answers.

How the Business Makes Money

Once you understand the structure, zoom out and look at how the company actually earns revenue. This is the economic truth behind every business - the “real job” the customer is paying for.

“If this company disappeared tomorrow, what core capability would their customers lose?”

Let’s use Salesforce as an example.

At a high level, Salesforce is a SaaS company, which provides a CRM. But really, the core capability they provide their customers is revenue infrastructure for predictable growth.

Other examples:

• Snowflake → not “cloud data,” but data liquidity (turning data into an exchangeable asset).

• Adobe → not “creative tools,” but monetizing creativity (turn ideas into output).

• HubSpot → not “marketing automation,” but growth orchestration (align acquisition motions).

When you reframe companies this way, you start seeing patterns, and patterns become prospecting shortcuts. Especially helpful when a company is private and doesn’t have public financials you can access.

The Financial Signal

Now that you know what the business is, you can understand what metrics matter. Every company optimizes around four universal levers:

• Topline: How fast is revenue growing? Where is growth concentrated (net new, existing expansion, or price)?

• Profitability: For each dollar in, how much comes out? What are the margins?

• Efficiency: How well-oiled is the machine? Are they growing without expenses scaling linearly?

• Cash Flow: How fast do they collect? How quickly can profits be converted to usable cash?

Simple prompt: What are the key financial metrics that (company) is focused on based off of the most recent financial reports?

The P&L isn’t just a report card, it’s a story.

A story that tells you:

• where the business is strong or weak

• what leadership is trying to optimize

• what the company is betting on for future growth

• where cost pressure or strategic prioritization actually show up

This is REALLY important.

When you’re selling a product, you’ll start noticing trends and patterns based on a category. You’ll be able to articulate to prospects what you’re seeing in the market. This establishes domain expertise and trust.

You’re not a seller anymore, you’re a trusted operator.

For one of my ICP categories, companies will almost always will fall into one of these buckets:

• Cost discipline / OPEX control → efficiency & faster payback.

• Volume-led growth → speed to learn, launch, iterate.

• Margin expansion → mix improvement and waste reduction.

• Cash flow focus → lower time-to-value and less working capital tied up.

These patterns help me:

• know where to focus prospecting

• craft business hypotheses quickly

• message in the language of executives, line leaders, and IC’s

• evaluate private companies even without public filings

When you can do this, you’re being proactive. You’re seeing where the company is headed, giving you the ability to shape narratives and strongly position yourself against competitors.

Timing is everything in sales.

The Strategy that Matters to Leaders

Financials only give you the what, but you still need the why.

This is where you combine:

• CFO commentary

• earnings calls

• press interviews

• industry trends

• trigger events

• headcount changes

• cost structure shifts

• leadership mandates

• And more …

You can do this in ChatAE >New Research Job

Upload CSV or manually enter account(s)

Use predefined or custom triggers

Scroll to “deep dive”

Prompt:

Based on the most recent financials, can you help me understand:

1. What are the CFO top initiatives?
2. What are the top strategic initiatives is (company) prioritizing?
3. What are the top challenges that (company) is facing?

This helps develop your prospecting and messaging strategy. And most importantly, helps you map where your solution fits in the org.

The Sales Translation

Here’s where things start to click. What stands out at a quick glance:

• cost optimization

• data-driven spending

• operational excellence

The CFO, and the org, are looking to reduce OPEX and measure investments that demonstrably tie back to ROI. This is the narrative.

***Note: Day to day, this is the step that takes the most time - as it should. Remember, you use AI to collect and summarize information quickly. The real value comes from your ability to synthesize it, form a perspective, and translate that into a narrative that resonates with the right personas.

That’s the human skill. It’s what separates meaningful outreach from the AI-slop your prospects are getting.

The upfront work is hard, but it pays dividends. More importantly, it’s where you make your money.

If I were selling, say, a developer platform like LaunchDarkly, I’d weave that narrative into my messaging. We’re not pitching “feature flags”.

We’re helping provide Salesforce with a software efficiency platform - converting developer work into quantifiable, ROI backed outcomes.

• ship faster without added risk

• isolate cost + performance impact

• reduce engineering waste

• convert developer work into measurable ROI

Example:

“Companies like Salesforce are under pressure to prove operating leverage and show that every new dollar invested in engineering returns two in product performance, customer retention, or ARR.

LaunchDarkly gives product and engineering leaders the control, data, and speed to do exactly that: ship faster, waste less, and measure impact in real time.”

Now you’re selling capital efficiency, not software.

It’s a solution to the hypothesized problem.

Which is the whole point of the framework.

Translate financial reality → strategic priority → distill to persona-level value.

Now, this framework isn’t a magic bullet. You have to talk to people and validate.

Next week, we’ll apply this framework to a live financial teardown of a real account.

Hope this is helpful. If you have questions, shoot me an email or DM. To all my sales FRENS in the US - enjoy Turkey Day and Happy Thanksgiving.

***If you want me to break down your target account, shoot me an email or DM me. I’ll include it in future editions.

If you have any questions on this topic or thoughts, shoot me a DM or email andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Credit: FidelCacheFlow (also see: Fidel’s SDR contracting Cohort)

This post hit me.

Fidel hits on something sellers forget when we’re deep in pipeline, advancing deals, or chasing signatures. What might look like a great opportunity to us often looks completely different to a CXO or GM. Once you start looking through their lens, everything changes.

Selling in a business and running a business are two VERY different perspectives.

We’ve covered how to read a 10k, connecting buyer motivation to compensation, and understanding how a business makes money. However, were mostyl tactical and piecemeal.

Over the past few months, my approach to prospecting and building narratives has leaned heavily into financial analysis. It’s taken me down a bit of a rabbit hole and I’ve been geeking out onit. It’s helped me understand the actual mechanics of a prospect’s P&L, forcing me to think like someone running a business, not someone selling into it.

It’s probably why I gravitate to writers like Matt Harney or . It’s helped me think in terms of patterns, trends, and financial priorities - even though numbers aren’t my strong suit.

The mindset shift has been helping me filter out the signal noise and obtain:

• Higher order values in the past 6 months

• Senior Executive Intros

• Cleaner TAM

• Accelerated Expansion

Common Patterns & Applications

It’s been helping me connect the dots and see trends across accounts:

• Cost discipline / OPEX control → efficiency & faster payback.

• Volume-led growth → speed to learn, launch, iterate.

• Margin expansion → mix improvement and waste reduction.

• Cash flow focus → lower time-to-value and less working capital tied up.

There’s applications outside of sales too, helping evaluate:

• Opportunities for career growth

• Investment opportunities

• The value of a product/company in basic terms

The system I’m operationalizing

Getting more comfortable with P&L’s has helped me spot trends, develop stronger POV’s, and easily translate that into narratives across all lines of the business:

1. Read the business engine. Revenue streams, gross margin, OPEX, cash conversion.

2. Spot the current emphasis. Efficiency? Mix? Volume-led growth? Margin expansion?

3. Map initiatives. What projects unlock those priorities this quarter?

4. Translate to value. “Here’s how this improves margin/throughput/cash.”

5. Layer the narrative:

   • C-suite: outcomes & capital efficiency

   • Midline operators: systems, workflows, SLAs

   • BTL users: jobs-to-be-done, speed, error reduction

AI helps, but synthesis is the edge

Tools like ChatGPT, Perplexity, and ChatAE make it easy to gather the information. Use them to speed collection. But invest time in synthesis—deciding what matters and how it ties to your buyer’s metrics.

If you don’t understand, you can’t explain.
If you can’t explain, you can’t communicate.
If you can’t communicate, you can’t sell.

That’s the craft.

Next week I’ll break down the financials of an org, showing you how to use AI to analyze them, and turn that into a POV.

Credit: Chris Orlob

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

• More slacks and less in person discussions

• More emails and less in person team meetings

• More information to evaluate and less sales people to talk to

Nate Nasralla put it best:

This is why I’m so bullish on writing being one of the most important skillsets to develop as a seller. Every successful seller needs to be able to effectively communicate a business narrative that justifies a purchase.

In today’s selling environment, there’s a lot of buying conversations happening behind closed doors. And we won’t always be able to get in a room with a budget holder or decision maker to explain the value of your solution. Instead, you have to work with influencers and stakeholders, and communicate value through writing it.

Sometimes though, you can effectively influence by not providing a business narrative at all.

You can simply help stakeholders connect dots.

A few weeks ago we discussed mapping out executive career history to identify signals of familiarity. This is why smart sales organizations are restructuring around AI and relationships.

Today we’re going to talk about how to leverage familiarity and help stakeholders connect the dots - so that you don’t even have to be in the room at all. We’ll cover:

• Silent Influencers

• Familiarity beats personalization

• Connect the dots not cal to action

Silent Influencers

I’m seeing more and more inbounds from prospects who are familiar with us - but we’re not familiar with them.

How?

These prospects aren’t in our CRM.

Sometimes, they weren’t even a buyer of our solution.

But they’ve seen out outputs and results, indirectly, to make decisions that impacted the business (in a positive way).

To give you a sense, I’ve had organizations ranging from F500 to emerging brands, where a CFO or President has recommended their teams (our ICP), speak to our team.

C level executives that have heard of us or had some exposure to us. Even though they aren’t our traditional users or buyers, they have influence.

And although they’re not in our universe, they provide perspective and familiarity, silently influencing decisions about our product.

Familiarity Beats Personalization

Familiarity not only makes it easier to start a conversation, it helps streamline sales cycles:

• Out of the gate, you have trust

• Prospect’s don’t need to vet (or not vet as much)

• Deal cycles accelerate '

The trends I’m seeing and examples I shared above, tell me that familiarity doesn’t necessarily have to be a direct correlation.

You win more often when buyers are familiar with you — even if you’re not in their direct lane.

IMO, silent influencers can be leveraged to your benefit. Be it for initial outreach to begin a dialogue or even bring awareness to other stakeholders of an executive, or other persona, who has had some exposure to our product in the past.

***Worth noting, personalization shouldn’t be disregarded. I’m cognizant that there are instances where you might not be able to leverage familiarity or brand equity. Like with any tactic in sales, they’re tools in a toolbox. Use them when you need them or can.

Connect the Dots, Don’t Call for Action

I’ve been using this tactic in multiple scenarios, for multi-threading or initial outreach. The key here is to ask for nothing, simply bring awareness, and let info speak for itself. Ideally, this sparks internal dialogue.

The psychology here is simple:

• It provides a line into power or someone with a sphere of influence

• It establishes social proof, and with it, trust

• It’s low friction: there’s no lift and it’s not “going around” anyone

Here are some examples:

F500 org: In current RFP cycle, I noticed a recently hired VP of marketing once led insights at one of our ~$500k value customers.

At the current org, it’s a different BU, different use case - but now I have high visibility.

The note I sent wasn’t a pitch, nor was there a CTA.

Congrats on the move to (company), (name). Saw you previously ran (department) at (customer), appreciate all your support there.

Reaching out as I imagine our name might ring a bell. We’re currently working on similar use cases for an RFP part of the (initiative name) at (current org).

Want to be respectful of the process but thought it’d be helpful to connect dots.

Good luck on settling in the new role.

F500 org: In current sales cycle, completed phase 1 of pilot. C level executives recommended their team reach out to us.

I’ve been sending progress updates to those executives intermittently, when I noticed that an executive on the finance team was hired and had reached out to our founder on their own accord at their prior company.

Once again, no pitch or CTA:

Hi (Name),

Reaching out as I noticed (executive) recently joined the team. They’ve previously connected with our founder, (name), during their time at (old company) - a customer of ours.

Given ongoing discussions between our teams, thought it’d be helpful to have someone internal with exposure to (my company).

[blurb/update on specifics of the initiative]

This has netted me responses from stakeholders to begin a dialogue and sometimes even helped give teams a “push” in the right direction. I’ve been adding this to my playbook upfront since if you’re in cycle, or have progressed a deal - you need to use discretion with this tactic.

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

P.P.S. Want to see how I’m using AI to actually drive pipeline (not just prompts)? Check out Prompts to Revenue

Credit: Dustin Beaudoin

The takeaway?

Too many sellers use AI in a way that isn’t operationalized. It’s not built into structured workflows you can tweak and improve over time.

It’s quite literally “prompt and pray.”

Sure, there are tools that handle parts of a workflow. But they don’t account for the nuance of your industry, segment, or role. This is where critical thinking with AI comes into play.

Something I recently spoke about with Fidel and his Sales Desperado Cohort:

This builds on last week’s post—not just prompting with AI, but thinking with AI. When you combine that with operationalizing the use of AI, you create real leverage.

For example, there are timeless principles of great sellers that haven’t changed and probably never will.

• Are you curious?

• Are you a clear communicator?

• Are you resilient?

These traits made great sellers 50 years ago, and I’d bet they’ll still matter 50 years from now. The how might look different today, but the why hasn’t changed.

Therein lies the opportunity: You don’t need to reinvent the wheel.

The most underrated skill in sales today is knowing how to embed AI into your day-to-day in a way that reflects how you sell—the nuances of your industry, your segment, your style.

The tech is there to accelerate the science of sales, so you can double down on the art.

Focusing on the human aspects of selling: connecting dots within accounts, building relationships, and developing your own voice.

As Dustin put it:

“AE>AI”

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

P.P.S. Want to see how I’m using AI to actually drive pipeline (not just prompts)? Check out Prompts to Revenue

Credit: Future of Prospecting

Aside from using AI to be more efficient, sellers should be using AI to critically think. An example of what Future of Prospecting lays out above isn’t just useful for prospecting - it’s also useful for prospects who are already in your pipeline and in deal cycles.

Today, we’re covering just that. It’s a process I use with current deals so that I can refine or strengthen my narrative, multi thread, and provide more insight on future forward goals so that the prospect can better understand how our solution can help.

So today we’ll cover:

• Account Research

• How to Leverage Info from Initial Discovery Calls

• Questions to Progress (and Close) Deals

Account Research

First things first, you need to be financially fluent and understand how a business makes money. Whether you’re working a small patch of large enterprise accounts or you’re in mid market or SMB - being financially fluent is critical.

From there, refine how you prospect based on signals and develop persona messaging.

Distill signals and initiatives into actual business points. Which is what Future of Prospect lays out nicely:

Credit: Future of Prospecting

How to Leverage Info from Initial Discovery Calls

I’m a big believer that AI gives sellers the advantage of being able to leverage the “human” side of sales more effectively.

AI provides insights into understanding human incentives, providing social proof, and speaking the same language as the customer for relevant messaging and narratives.

Talking to prospects, via cold calls or discovery calls, amplifies this.

Once you’ve had an initial discovery call - copy the transcript (or just prompt in Gong).

1. Set up GPT folder with account name
2. Prompt the below

Edit or use this however you’d like.

Questions to Progress (and Close) Deals

This is where things get fun.

Now that I have an outline of a hypothesis, pain points, next steps, and multi threading opportunities - I can also get actionable ideas to progress deals or continue momentum (especially if you have longer sales cycles).

Using GPT folders helps me stay organized so I can have fluent dialogues about specific accounts. When something about an account triggers my attention, or I’m following up, I simply use that info and plug it into my GPT chat and ask away:

• A new job posting has come out that is in the department I’m selling to - what should I infer based on the job description and how can my solution help?

• A news release has just been published and discussing a recently released product - what tools might be relevant to existing workflows based on the category [prospect company] is in and what we learned in my discovery call?

• I’m working with a smaller account where a CFO has had exposure to our product - based on prior transcripts, my business hypothesis, financial documents and earnings transcripts, what type of messaging might resonate?

• A prospect mentioned [use case] - how might I lay out other opportunities to help provide future value based on past customers we’ve worked with?

• I’ve lost momentum in a deal - based on account research and initial discovery call info, what are ways to engage to continue momentum?

The list goes on. If I ask a question frequently enough, that’s when I turn it into a prompt. The point here is that the dialogue drives more curiosity in you as a seller. So you ask more questions, refine your thinking, provide more value to customer - all while progressing and closing deals.

This is exactly how I use GPT in my day to day. Hope this gives you ideas on how you can use AI to think through, progress, and close your deals.

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

P.P.S. Want to see how I’m using AI to actually drive pipeline (not just prompts)? Check out Prompts to Revenue

Get Access to Prompts to Revenue

A few quick shares that are relevant to using AI and today’s post. Both TechSalesGuy and BowTiedSystems are accounts I’ve been following for a while - highly recommend giving them a follow if you’re not already.

TechSalesGuy: 2025 GTM Trends

Recommend reading the full article. The key items that stood out to me:

“smart companies are restructuring their teams around AI and relationships instead of volume and automation” - TechSalesGuy

Other key items that TSG points our with regards to trends in today’s GTM environment. Organizations that are leveraging partnership channels and relationships are winning over investment into automation, volume, and SEO. In addition, organizations removing the friction of irrelevant outreach, redundant meetings and bad timing

I’m going to expand more on this in today’s article. It ties into the my thesis on AI and sales overall: AI should be leveraged to drive deeper human relationships.

BowTiedSystems: How to Create a Custom Sales Coach

Like the article above, this one’s worth a full read. I’ve spoken about the use of custom GPT’s in a previous article discussing how to use custom gpts for event prep. BowTiedSystems does an excellent job of sharing another great using GPT to customize your own AI sales coach.

You can take this further with applications like building custom GPT’s act as product or SME experts (which I’ve done and will share in future articles). I’ve also explored use cases like event prep, tailored messaging to a specific persona or account, and even learning a new product. Lately, I’ve even been experimenting with using Perplexity Labs to build apps for things like custom org mapping.

In today’s article we’ll build on these two themes - tailoring GPT’s and using AI to map human relationships for daily prospecting.

It comes as no surprise that in order to succeed as a rep - you need to be able to self generate pipeline. I’ve been realizing more and more in my career, that although the rush of getting a deal across the line is exhilarating - prospecting might be more fun that actually closing the deal.

Prospecting is like a puzzle, and once you crack it, and find repeatable patterns, it can snowball quickly. In Prompts to Revenue, I break down the weighted scoring system I used to build $680K in pipeline across 5 high-tier accounts — all within my first 3 months of ramping.

And as I’ve stated before, the winners in today’s GTM environment are going to leverage AI to amplify the human aspect when selling - connecting dots, familiarity, and narratives that resonate.

In a recent meeting, one of the reps on my team share how they researched executives career history to uncover gems through cold outbound.

So today, let’s dive into how to bring this to life:

• Leveraging Executive Career History for prospecting

• Using leadership background and segments to find champions

• Using GPT to accelerate and enhance the process

Leveraging Executive Career History for Prospecting

Just like in sports, when a new GM takes over, they often clean house and bring in their own people. Business is no different—especially during times of change. We’ve talked about how to understand the incentives of senior executives at an org.

But what about the teams they are building and where they’re recruiting from?

Change agent executives are often hire people they’ve had success with in the past. For example, a VP of Sales moving to a new company will likely bring in reps they trust to succeed in that org.

That’s why it’s worth looking beyond past customer and digging into where executives came from. You’ll often spot patterns in the previous companies of the personas you’re prospecting. If those patterns align, there’s a good chance those individuals have some familiarity with your product.

Using Leadership Background and Segments to find Potential Champions

Start off by finding executives from your target department or buyer persona. You can filter by past customer, which is the lowest hanging fruit.

Take it a step further by inputting your customers into the “past companies” field to start filtering for personas, adjacent or target personas, that might have had exposure or familiarity to you product.

Here’s a screenshot:

Using GPT to Accelerate and Enhance the Process

There are probably more aspects to think through besides just where an executive might recruit his team from. You can jump to hypothesis like where you can comfortable assume that an executive might recruit from orgs that are similar in size, segment, or ICP.

To do this, I’d recommend getting a list of your customers and input into GPT. Then prompt the following:

Prompt:

You can take this a step further and even add things like ICP uses cases, upsells, etc. Or even better - upload the finalized CSV into a GPT project folder and reference for future ways to cross reference your accounts.

And this works - I recently closed a deal that took less than <60 days. Because C level executives were navigating transformation. With that they hired my champion, hired and both had exposure my offering. No POC, just straight to a longer term commitment.

Familiarity builds trust and with it, there’s less friction in the buying process. This is using AI to deepen the human aspect of sales.

Hope this helps. You can find more content like this in my course: Prompts to Revenue

Get Access to Prompts to Revenue

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

P.P.S. Want to see how I’m using AI to actually drive pipeline (not just prompts)? Check out Prompts to Revenue

No filler, no fluff — just the workflows and strategies I use every day to bring relevance and edge to my sales process using AI.

• Why “AI for sales” is mostly noise — and how to build your own quiet edge

• How to use prompts to think like a strategist and elite sales tactician

• AI workflows to build smarter territory plans and prioritize accounts

• Reverse-engineer buyer incentives using AI

• An AI framework to prep smarter for discovery calls - fast

• Bonus: Prompt library + templates included

Early access pricing: $50 until July 6th, 2025 ( $99 full price will kick in after that)

→ Grab your copy here

For the early crew who joined the waitlist, I have a special bonus offer coming your way as a thank you for signing up 🙏

If you have any questions on the course or feedback - shoot a note over to andrew@hackingsales.xyz.

Appreciate all of your support - see you all after the July 4th break.

-Andrew K

To join, sign up here:

Get Access to Prompts to Revenue

I recently returned from Cannes, a week long, MASSIVE creative event. I was fortunate to meet a lot of interesting people and prospects (because of work, duh). Needless to say, there’s A LOT of sessions and events, happening day and night. To help me approach this efficiently, I asked myself:

Who can I connect with in target accounts?
What prospects from existing sales cycles are currently there?
Which sessions would be relevant to attend to get in front of relevant personas?

So today, I wanted to share a how I set up a custom GPT to efficiently sort through everything and answer the questions I had.

In some cases, say a smaller one or two day, niche event - a custom GPT might be overkill. But nevertheless, I wanted to share since the application might be helpful.

Here’s what we’ll cover:

• Custom GPT vs GPT workspace
• Prompting for Boolean searches —» Feed into Custom GPT
• Creating the Custom GPT

Custom GPT vs GPT workspace?

As I was writing this I realized that this may be a question that pops up. Quick breakdown:

• Custom GPT’s are great for specific tasks (ie help me find people at an event or help me write outbound emails templates).

• GPT workspaces are great for free flowing dialogues or ad hoc brainstorming (ie unique messaging across personas at various accounts)

I dive into more into how my workflows are set up in my course.

Prompting for Boolean searches —» Feed into Custom GPT

With all the talk around AI, it’s easy to over-engineer. Sometimes, it’s better to just do the regular human stuff like search on LinkedIn the good ol’ fashioned way: manually.

I’m not great at boolean searches but that’s what AI is for. In most cases you can ask how to boolean search with GPT. Here I just used the “event'“ keyword and filtered by the following:

1. Use outlined filters below
2. Filter by account and/or persona list
3. Input “event” keyword
4. optional: you can filter more with who’s following your company or recent job changes and/or use Clay to further enrich.

From there, upload those contacts into a CSV, along with event URL(s)/CSV(s) from the event and any other info you’ve received from your event/marketing team.

Creating the Custom GPT

After getting the CSV, you can then start building your customer GPT. Compile all the info above ie: list of accounts, relevant csv’s, and links/files to any sessions, agendas, dates, so that the custom gpt has all the info it needs to help.

Then prompt:

If this prompt doesn’t suit your needs - just ask to clarify or refine what matches best for you.

We’ll cover this in depth when Prompts to Revenue: An Actionable Guide for B2B Sellers to Sell with AI is released on June 26th.

Join the Prompts to Revenue Waitlist

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

To join, sign up here:

Get Access to Prompts to Revenue

Last week we talked about why AI for sales is mostly noise and what actually matters is building deeper human connections. Because if you’re asking about what prompts to use, you’re NGMI.

Credit: Signull

Prompts are a means to an end. As Signull puts it - it’s a dialogue. It’s having a resource that not only makes you more efficient, but a lever to help you sharpen your thinking in a more effective way or ask deeper questions on highly complex topics.

So today we’re going to be covering ways to use prompts to drive a dialogue that produces an effective sales strategy, along with tactics to execute against it:

• Creating Strategy and Workflows

• Clear, Concise Communication

• Synthesize Information

Modifying daily workflow or sales approach

• “What’s the path of least resistance in order to achieve quota attainment and how should I approach my target account list in order to hit/exceed my number?”

• “How would you add to, or modify, existing signals that I’m using in order to tier and prioritize my prospects in order to streamline sales cycle velocity?”

These few questions evolved into a “dialogue” that drove valuable insights and a plan of attack for prospecting and quota attainment. With it, I can continue to provide inputs, evolve it, and be flexible amidst changes and real world feedback - all rolling up to answering how to hit quota.

You can even take historical outbound activity and map it back towards your quota to get a benchmark of what daily activity might look like. Creating daily, weekly, and monthly systems that you can implement and modify based on outcomes.

Clear, Concise Communication

Being able to write well is a super power. And great writing is an outcome of clear thinking. Clear communication doesn’t need to be reinvented - it can be repurposed.

From a strategy lens, you can take the learnings from the exercise above and apply it to your overall messaging strategy. Tactically, you can use outbound messaging frameworks that you have, your company has, or leverage other frameworks:

Credit: Tech Sales Guy

There’s more folks in this space who have great content on message market fit and outbound frameworks, like Kellen Casebeer, Anthony Natoli, Brian LaManna, or Kyle Asay.

With AI, you can have dialogues that incorporate your approach, their approaches, or a combination. Adding in your prospecting strategy and business POVs, you’ll be able to create clear, concise messaging.

You can apply this to high impact communications as well. For example, maybe you need to clarify a question about a proposal, or multi thread to other C level personas in your sales cycle - you can edit and refine what you’re trying to get across and tune it to brevity and tone.

Synthesize Information

The highest lever AI provides is being able to quickly consume, understand, and synthesize information. These are tools that not only strengthen your skillset but also help you think critically about next steps.

• Aggregating multiple data points (ie Gong, CRM, LI, financial and recent event news) to prepare better for discovery calls.

• Uploading gong transcripts to get feedback on how to further solidify a business case or next steps. Maybe even get feedback on questions you’ve missed.

• Condensing learning curves for ramp or interview prep: this is a dated post but here’s how I used ChatGPT to help me better understand my company’s product and use cases.

• Leveraging company documentation and subject matter expert knowledge: Making it easier for you to relay info to a customer or take the heavy lifting out of an RFP:

Credit: Pipeline Guy (and highly recommend his cohort which I endorse)

These are all ways of thinking so that you can prompt and have valuable dialogues to truly leverage AI as a seller.

We’ll cover this in depth when Prompts to Revenue: An Actionable Guide for B2B Sellers to Sell with AI is released on June 26th.

Join the Prompts to Revenue Waitlist

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

You can sign up here:

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“Despite claims that "people want to talk to people," the data shows otherwise”
-Jason Lemkin, SaaStr

Joking … sort of.

SaaStr shared data across 100,000+ conversations, along with other perspectives in 10 ways that AI will change sales forever. It certainly hit some chords as to what the future of sales will look like with AI.

A lot of what was mentioned in the article is happening already:

• AI joining sales calls

• AI BDR’s

• AI handling full sales cycles < $10k

• AI merging sales, support, and customer success

So today, we’ll cover two takeaways so that you can stay a step ahead:

• Takeaway #1: The human aspect of sales, industry expertise vs sales expertise

• Takeaway #2: Using AI to deepen the human connection, Mech AEs vs Obsolescence

Industry Expertise vs Sales Expertise

Betts released their 2025 compensation guide earlier this year, which is a great resource for overall trends in tech and compensation data. (Thank you Matt Harney for the share.)

A few trends taking shape in the tech industry from this resource:

• Decline of traditional entry level roles in tech

• Expansion the enterprise landscape to operationalize tiered high end transactions

• The rise of strategic customer success roles

All of this ties into how AI is beginning to transform the tech enterprise sales landscape:

• It’s harder to open up entry level roles when AI can do most of the heavy lifting at a fraction of the cost.

• Allocation of expertise and resources towards deals that net $250k/year vs $2 million/yr. or across relative enterprise revenue spectrums.

• With macro economic events over the past couple of years placing uncertainty around predictable new business growth, focus is shifting towards specialized resources which can help drive retention and “stickiness”.

The most interesting trend, however, is that organizations are increasingly shifting towards hiring based on the persona you sell to over industry expertise.

It signals that more value is being placed on how well you understand selling to an individual vs how much domain expertise you have.

AI is accelerating the commoditization of information. It’s why EQ holds more weight than ever.

Credit: Brian LaManna

• Truly understanding the challenges faced by your buyers and the “people” involved in a sales cycle

• How to get attention the “human” way, ie, referrals, intros, learning from prospects

• Telling compelling stories that resonate and connect, not repeating use cases or case studies.

Mech AEs vs Obsolescence

Which segues nicely into the original quote to start off today’s post.

Data showed that people are more satisfied with AI conversations. Moreover, the data also showed that people prefer great AI over mediocre humans.

Meaning that, if you can execute well on Takeaway #1 - you’re probably not mediocre.

Aside from the observations laid out in the SaaStr article and recapped in today’s post, the one that really caught my eye:

The best reps will become “mech-AEs” - augmented by AI to handle product knowledge, follow-up, and administrative tasks.

This, in my opinion, is already happening today.

I personally have been using AI in my f/t role to help me build territory plans, do account research, fill out RFP’s, and prep for discovery calls.

Efficiency outputs, although extremely helpful, isn’t what I’m really after.

I’m using AI to better understand the humans involved, like how executives might be incentivized, building narratives and positioning based off interviews from key stakeholders, or even getting people to open up easier on initial discovery calls.

I’ve been passionate about how to incorporate AI into my day to day for a while now. My thesis from about a year and half ago has for the most part, held true:

This is the reason I’m launching Prompts to Revenue.

Because the “Mech AE” won’t just be augmenting their daily tasks with AI. They’ll be leveraging their time to do what every sales person did before there was MEDDPICC or Challenger. Before there was data enrichment or email campaign blasts.

Using AI to not just to augment their day to day, but free up time to build deeper human relationships.

Join the Prompts to Revenue Waitlist

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

PS - if you liked this article, feel free to give a “like”, “comment”, or “share” with your network

Credit: FidelCacheFlow (if you’re looking to break into tech sales or stack SDR contracts - you can learn more about the Desperado Sales Group here).

Fidel brings up a good point and quite frankly, he’s right. The vast majority of people talk about using AI like a “feature set”. An input that will lead to an output where you the work is magically done for you.

This leads to questions like:

• “What prompts should I use?”

• “How do I get started on prompting?”

• “Should I use ChatGPT or Claude?”

This is like running a discovery call that structures questions largely focused on features. As sellers, we know that features won’t win deals. Business outcomes do. You can take the same thinking and apply it to how to use AI in your day to day.

Here’s a better way to think about using AI

Credit: Signull (highly recommend the newsletter, signull vs noise)

This is probably the best description I’ve read on how to be using AI. It also describes how I personally use AI in my day to day. This mental model doesn’t apply to only sales either - you can apply this to any pillar of your life (ie health, finances, career, etc.).

The prompts I share with you each week allow me to synthesize how I want to drive towards an outcome. Some prompts are tactical, yes. Understanding how to prompt is also extremely useful.

But, more often than not, prompts are a starting point which is tied to a much larger question/objective:

• “What’s the path of least resistance in order to achieve quota attainment and how should I approach my target account list in order to hit/exceed my number?”

• “Who will care about this business problem in a given account and how does that align towards their objectives and the objectives of the organization?”

• “How do I quickly learn subject matter material in order to be knowledgable ‘enough’ and effective on an initial discovery call?”

The best prompt in the world won’t get me the answer to these questions. In fact, I’m going to let you in on a little secret:

It was never about the prompts.

The prompts provide you with a directionally accurate starting point. Finding the answer in the real world is hard work, derived from an iterative process.

The iterative process, where you synthesize information, is the best way to leverage AI in your day to day.

This is also why I’m going to be releasing a course, Prompts to Revenue, to help sellers like you leverage prompts and AI - the right way. You can join the waitlist here.

Hope this perspective on AI and prompting helps.

If you have any questions on this topic or thoughts, shoot me a DM or email to andrew@hackingsales.xyz.

As always, thanks for reading and see you all next week.

-Andrew K

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