The New Selling Economy

B2B selling is going through its biggest structural change in three decades — a mandate for selling teams to do things differently.

If B2B buying has changed, why hasn't what sales teams do? Despite a wave of AI tools, a rep's week, month, and quarter are still buried in everything around the moment — the deal, the escalation, the competitor, the forecast call, the pipeline.

AnyTeam is reinventing how B2B selling works for the AI era — and how organizations win in the New Selling Economy.

01 · The shift

Today's sales orgs were built for an era that's ending

A decade ago, the seller held the information — pricing, capabilities, customer stories. Buyers came to a meeting to learn, and the job was to inform. Now buyers arrive already knowing what we sell.

Buyer · evolved
Arrives already knowing what we sell.

• Reads every review before the call
• Watches every competitor demo on their own
• Talks to peers in private channels
• Asks AI for the comparison matrix

Seller · unchanged
Running the same motion, buried in the moment.

• Still pitches what the buyer already knows
• Still opens with the same discovery
• Still scrambles for context mid-call
• Still loses the week to busywork

The buyer changed. The seller hasn't.

02 · The problem

B2B sales still run on people

For B2B organizations, reps are the engine that makes the quarter. Hitting the plan means hiring, ramping, and replacing reps year over year. Does AI fix this — or just compound it?

$20M
of new ARR to find next year
→
34
AEs hired, ramped, and kept in seat
→
30%
of ARR spent to get there

Illustrative model for a typical scaling B2B sales org.

03 · The hidden cost

The cost is everything under AI

Bolting on AI creates custom work to make everything fit. The real cost is the engineering to build it, the security to govern it, and the inference you pay to run it everywhere.

• AICRM
• AIEmail
• AICalendar
• AIDocs
• AICPQ
• AISlack

Build Custom work to integrate every AI feature into the stack

Security Guardrails, redaction, and audit across every model call

Inference The bill for every AI call, every day, forever

AI bolted across the stack, with build, security, and inference stacking up underneath.

Bolting on AI isn't free — it's metered, and it compounds.

Anthropic · Claude

$400K → $1.4M overnight.

One team's Claude bill jumped 3.5× the moment they crossed 150 seats — usage never changed, the pricing tier did. Salesforce now budgets roughly $300M a year in Claude tokens.

Miro AI

Metered by the credit

AI is bolted on as credits, not seats — a diagram costs 5, an image 3, and none roll over. Run out and you buy an add-on. The AI bill sits on top of what you already pay per seat.

A small tip of visible outcomes — the real mass hidden below the waterline.

AnyTeam owns everything below the line.

04 · The sales tax

More reps. More tools. Less selling.

If AI were really working for B2B selling, why is the sales tax still so high when CFOs report to Wall Street?

Sales & marketing, as a share of revenue

• Asana: 62%
• Monday.com: 53%
• GitLab: 50%
• HubSpot: 48%
• Snowflake: 44%
• Salesforce: 34%
• ZoomInfo: 33%
• SaaS industry average ≈ 47% of revenue (dashed line).

Figures are approximate, from recent public filings — source line to be finalized.

~70%
of the week spent on non-selling work
5.7 mo average time for a new AE to ramp
25–34% of revenue spent on sales & marketing

05 · Winners in the New Selling Economy

Reinventing how B2B selling gets done

The gap between winners and losers compounds every quarter. Winners become AI-first companies that reinvent how selling works. Losers do much of the same — and carry the tech debt for years.

Winners — move first

• Build the company brain once — memory compounds
• One AI-native system across the stack
• Grow output per rep
• Selling time climbs every quarter

Losers — stand still

• Knowledge walks out the door when a rep leaves
• Bolt-ons stitched across 10–12 tools
• Add headcount to hit the number
• Busywork stays; tech debt grows

06 · The new metrics

New Selling Economy metrics

The old scoreboard measured activity and headcount — calls made, reps hired, pipeline added. The new one measures whether the system makes each rep, and the whole org, structurally more productive.

New sales metrics

• Time to ramp: months → weeks
• Quota attainment: 42% → 78%
• Shared memory across the team

System efficiencies

• ~70% of busywork converted to selling time
• 10–12 tools → 1 governed system
• +18 hrs/week back per rep

Financial metrics

• S&M down as a share of revenue
• Higher output per rep
• Net new revenue per AE

Built on a new AI-native system — one company brain across the org.

07 · The new mandate

What it takes to win

Four requirements separate the orgs built for the new selling economy from the ones still adding tools and headcount.

1. Convert non-selling hours into selling time Move the ~70% of busywork off the rep and into pipeline and customer conversations.

2. Compound memory, forever Knowledge accrues to the org — not to whoever holds the seat this quarter.

3. One system across the tools reps already use It works inside the calendar, email, CRM, and Slack — not as another silo to feed.

4. Drive S&M down as a share of revenue Grow output per rep, so go-to-market gets structurally more efficient — not just bigger.

Join the New Selling Economy

Transform how you sell today — give every rep the AI workforce built for revenue teams.