AI in GTM - PLAYBOOKS Series
Seven playbooks for B2B leaders who are done with generic advice.
Most GTM content tells you what AI can do. These playbooks tell you what to actually do with it, in your pipeline, your positioning, your sales process, your content, your revenue operations, and your leadership decisions.
Written by a practitioner with 20 years of building GTM engines across global markets. No frameworks dressed as strategy, no tool listicles, just clear, opinionated thinking on where AI moves the needle and where it creates noise.
Playbook - 00 AI for GTM Foundation
Most B2B companies are not losing because their product is weak. They are losing in the places they never looked such as an ICP built on assumption, messaging that reflects the product rather than the buyer, market intelligence that was current when gathered and quietly wrong by the time it informs a decision. This playbook covers the four foundation layers every other AI investment depends on market intelligence, ICP definition, messaging architecture, and pipeline readiness. It is the playbook the other six are built on top of.
Four foundation layers · Decision flowchart · Tool map · Case study · Before/After
Playbook - 01 AI for Pipeline Generation
A Series B company sends 40,000 outbound touchpoints in a quarter and books fewer meetings than the year before with half the team and no AI tooling. The list is wrong, the signal layer does not exist, and the message reflects what the company wants to say rather than what the buyer is experiencing. Sending the right thing to the wrong people faster is not a pipeline strategy, it is an unsubscribe machine. This playbook shows how AI changes account prioritisation, signal identification, and outreach when deployed in the right sequence.
Three-layer signal framework · Five steps with outputs · Tool map · Case study · Before/After
Playbook - 02 AI for Positioning Research
Playbook - 03 AI in Sales Enablement
Playbook - 04 AI for Content and Messaging
Playbook - 05 AI in Revenue Operations
Playbook - 06 AI Strategy for GTM Leaders
A B2B SaaS company spends eight weeks rebuilding their website, three rounds of leadership review, two external copywriters. Sixty days after launch, one enterprise rep has stopped sending the homepage link and sends a case study instead because it explains the value better. The problem is not the execution, it is that nobody outside the building saw the positioning before it scaled. This playbook shows how AI compresses the gap between internal thinking and market reality from months to days.
Four research lenses · Buyer language mining · External validation · Tool map · Case study · Before/After
Before a discovery call, a senior enterprise rep checks LinkedIn, reads her last call notes, and searches Slack for something a colleague mentioned about this prospect's competitor. She does not open the enablement library. She has not looked at the battlecards in eight months. This is not a discipline problem, the talk tracks were written by someone who last ran a discovery call four years ago. This playbook shows how to build enablement from what your best reps are actually doing on calls, not from what someone in a room thought should work.
Four enablement layers · Call data extraction · Tool map · Case study (23%→31% win rate, 12%→67% adoption) · Before/After
The content team publishes 12 pieces a month and the VP - Sales cannot name a single piece a rep has sent to a prospect this quarter. When they introduced AI to speed up production, they produced more of the same thing faster and more content nobody sends, more content the sales team ignores. AI is not the cause of bad B2B content. It is an efficient accelerant of whatever content strategy you already have and this playbook is about fixing the strategy before scaling the production.
Four content layers · Brief-led production system · Tool map · Case study (2%→11% pipeline influence) · Before/After
It is Tuesday morning. The CRO pulls up the forecast: £2.4 million in best case and the Head of RevOps has already flagged that 34% of those deals have had no activity in 21 days, 18% have no next step recorded. The number is not a forecast , it is a collection of what reps believe about their deals, entered into a system they update when they have to. This playbook shows how AI changes what RevOps can see, how often, and how fast it can act on it.
Four RevOps layers · Daily hygiene scan · Variance forecast model · Tool map · Case study (±34%→±11% forecast variance) · Before/After
A director asks the CRO how AI is being used across the go-to-market function. The CRO gives a confident answer about tools deployed. and when the director asks whether those tools are producing measurable improvements in close rates or pipeline quality, the CRO pauses some are, some are not, and the team has no clear way to tell which is which. This is the most common state of AI adoption in B2B right now - tools deployed, budget committed, impact unclear and this playbook is about fixing that.
Four strategic layers · 30-day pilot framework · Measurement system · Tool map · Case study (9 tools→2 pilots, £14k→£6.2k/month) · Before/After