This isn’t about “using ChatGPT.” It’s about reprogramming how you think about research, planning, and selling.

AI isn’t a shortcut. There’s no magic prompt, no silver bullet.

If you’re chasing a hack or quick fix, this guide isn’t for you.

But if you’re serious about changing how you think and work, this will help.

Introduction

So, my LinkedIn post caught some attention last week. As of writing, the post has over 1,200 likes, 115 comments, 36 reposts and more than 350,000 impressions...

Of course, you never expect your LinkedIn post will get so much traction and engagement but it’s forced me to think about the Why behind it.

Not Why the LinkedIn algorithm picked up on it, but rather, Why senior Sales professionals from large companies, specifically those with Account Executive titles from large organisations felt compelled to engage.

Looking at the analytics:

• Nearly 18% of people engaging came from companies with 10,000+ employees.

• Over 42% of the audience were in senior-level sales roles.

• The leading industries engaging were Software Development (21%) and IT Services & Consulting (20%).

• Account Executives alone accounted for nearly 11% of the total engagement.

And I think the answer is pretty simple…

The pace of AI in Sales has accelerated so fast over the past few years that Sales professionals are struggling to keep up and stay ahead. If you make it to the end of this, you’ll see exactly how fast this space is constantly changing and updating (Gong + Clay).

The pressure isn’t just hitting pipeline numbers or closing deals—it's also the overwhelming sense of needing to stay ahead of every new AI tool, every new LLM, every new workflow promising to 10x productivity or automate 80% of manual tasks.

I know this pressure intimately because I lived it firsthand. I was an Account Executive at Salesforce for nearly five years and have spent over a decade in Sales & Business Development.

Let's dive deeper into what's changed, what you should be doing today, and how AI can (and should) practically and pragmatically reshape your approach to Sales.

The Modern Enterprise Selling Guide – Based on Salesforce ECS

If you're an AE still running generic discovery, manual outreach, and following the same playbook from five years ago, you’re cooked. It’s game over.

This isn't an exaggeration—it's already happening. AI has completely reset what's possible for individual contributors. You either wake up, level up, and learn fast or you're gone.

At Salesforce, Enterprise Corporate Sales (ECS) meant $250k/month new pipeline monthly. €50k+ minimum closed every month and €150k to hit your actual target.

✅ Miss target once, questions asked.

❌ Miss it twice, welcome to pressure cooker mode.

Everything was manual and high pressure. Here’s how I’d approach it today with AI, systems, and real workflows.

Everything I did then was manual:

• Hours pulling data from earnings calls, websites, reports.

• Guesswork account planning meetings.

• Generic email templates.

• Days spent building target lists and following up on LinkedIn.

• Preparing slide decks, ROI calculations and the rest.

The Big Shift

Right now, AI isn't just incremental efficiency, it's a complete reset of what's possible. If you don’t see this yet or believe it, someone working your territory does. AI isn’t replacing you—but the AE who learns to work with it already has.

Quickly pulling critical insights (like challenges, goals, risks, priorities) from dense resources like earnings calls, annual reports, podcasts, or webinars.

• Manually skim annual reports and earnings calls, hoping you spot key points.

• Take notes, spending 30-60 mins per document—time-consuming and inefficient.

Step-by-step approach using Gemini Deep Research (with screen recording walk through):

Step 1:
Open Google Gemini (or Chat GPT)

I find Google Gemini deep research better and outputs it directly into a google word document.

Step 2:
Create a new "Deep Research" project in Gemini.

Step 3:
Write a detailed, precise prompt instructing Gemini exactly what you need researched.

Example Prompt:

Step 4:
Run the Deep Research. Once complete, open and review the auto-generated Google Doc, ensuring the insights make sense and align with your prompt.

(Here is the output using the example prompt above https://docs.google.com/document/d/1VXa8pa0TCBQ3Ir4XVAWFzfNrW0fYz30fMt9Qq_7z4E0/edit?usp=sharing)

Step 5:
Refine and personalise the document as needed. You now have clear, immediate strategic talking points directly from the source—without any manual copy-paste effort.

Once you're comfortable manually doing this, move onto automation in Clay:

Step 1: Use Claygent or Enrichment Tools like Oakie.ai

• Claygent can run live AI searches and summarise data from earnings calls, investor pages, or public documents with structured prompts.

• Oakie.ai or similar enrichment tools integrated in Clay can extract company insights directly (e.g., 10-Ks, funding info, product updates, etc.)

Step 2: Prompt AI directly inside Clay

Use Clay's AI column to run structured prompts like:

You don’t need external scraping. Clay can pass in company name or domain and dynamically generate responses.

Step 3: Automatically Tag or Prioritise Accounts

If your prompt detects key phrases like “expanding into Europe” or “hiring CX leader,” you can:

• Score the account higher

• Move them into a hotlist

• Trigger email draft generation for outreach

This turns hours of manual research into a repeatable, automated, and highly actionable system.

Tip: Create a project in ChatGPT for each opportunity you’re working on and upload any files, slides, transcripts, annual reports, earning calls into here and any time you’re working on it, you revert back and share everything.

Ensure your discovery calls are insightful, tailored, and impactful. Using AI means you're never going into a call unprepared or relying solely on memory and intuition.

• Manually reviewing previous call notes or CRM entries quickly before your next call.

• Trying to remember what exactly was said previously and hoping you cover relevant points.

• Using generic questions that don't deeply engage the client or uncover real pain points.

Step-by-step approach using Gemini or ChatGPT:

Step 1:
Open Google Gemini or ChatGPT

Step 2:
Provide context clearly within your prompt. For instance, reference previous conversations, call transcripts, or key documents relevant to your upcoming discovery call.

Tip: I use Otter.ai to record all my calls and then I’m easily able to go back and ask questions or extract key points of information. AI call recording is just the norm now so don’t be afraid to use it. I’m sure a lot of people reading this are.

Step 3:
Write a precise prompt instructing AI exactly what preparation is needed.

Example Prompt (it’s long):

Step 4:
Generate and review the suggested questions. Refine for accuracy and specific applicability.

Step 5:
Copy these refined questions into your notes or CRM, clearly defining your call agenda.

Once comfortable manually preparing with AI, automate parts of this:

Step 1 (Automation):
Use Make.com or Zapier to automatically pull call notes or CRM updates after each call.

Step 2 (Integration):
Automatically send this data directly into Gemini or ChatGPT via API, prompting it to create structured discovery call prep documents immediately.

Step 3 (Clay Workflow):
Use Clay to track these AI-generated preparation documents, triggering follow-up actions or reminders automatically when specific signals (e.g., mentions of hiring, growth initiatives, or technical issues) appear.

This ensures your discovery calls are consistently precise, insightful, and ready to move deals forward effectively.

Generic outreach doesn't cut it and we’re now getting to a stage where generic personalisation doesn’t cut it. Effective outreach needs to be hyper-relevant, highly personalised, and timely, responding precisely to your prospect's current business context.

• Sending generic messages like "Congrats on funding" or “Congrats on the recent job” without deeper context.

• Manually scanning LinkedIn, press releases, or company news randomly to find insights.

• Maybe have Google News alerts set up and then spend hours taking action.

• Figuring out what company the person used to work at and checking if they’re a customer.

• Time-consuming and rarely effective.

Step-by-step approach:

Step 1:
Use Clay to automatically monitor and scrape LinkedIn posts, company news pages, and press releases for predefined signals such as funding announcements, strategic hires, or expansion plans.

Step 2:
Set Clay workflows to flag these signals in real-time and automatically generate tasks or notifications prompting timely outreach.

Step 3:
Leverage AI (Gemini or ChatGPT) integrated with Clay to instantly draft personalised outreach messages referencing these triggers.

Step 4:
Review and refine the AI-generated message before sending.

Step 1 (Deep Automation with Clay):
Configure Clay to automatically monitor multiple news sources (Crunchbase, LinkedIn, company blogs, Google News APIs) for deeper, multi-dimensional insights on a daily schedule.

Step 2 (Advanced Integration):
Use Make.com or Zapier integrations to push these signals instantly into your CRM, Slack or a database, adding detailed context and automatically segmenting based on triggers (e.g., funding rounds, new executives, geographical expansion).

Step 3 (Real-Time Personalisation at Scale):
Trigger Clay and AI to proactively generate entire sequences of highly targeted outreach messages—each precisely tailored to the unique context Clay identified.

This transforms outreach from generic and reactive to strategically personalised, automated, and always timely, significantly increasing engagement and conversion.

Account planning shouldn’t live in a static deck or be revisited once a quarter. In 2025, great reps use real-time signals to build dynamic, living account plans.

• Using slide decks or Excel to map out accounts once a quarter.

• Guessing at stakeholder priorities based on outdated info.

• Siloed research that never makes it back into the workflow.

Step-by-step approach:

Step 1:
Use Clay to centralise all key signals—funding, hiring, tech stack changes, executive movements, job posts, intent data, website updates and score accounts in real-time.

Step 2:
Set up filters or scoring logic (e.g., companies hiring for CX roles, using HubSpot, recently raised Series B) that reflect your ICP. Clay lets you stack these together with zero-code logic.

Step 3:
Visualise and categorise accounts into tiers based on readiness, fit, and engagement. Sync this plan into your CRM or outreach tools to drive next steps.

Example Filters in Clay:

• Companies hiring for Customer Experience or RevOps roles.

• Using Salesforce + Gong in tech stack.

• Recently expanded headcount by 20% in EMEA.

• Posted a job with the term “retention” in the description.

Step 4:
Export your dynamic account plans into a Notion dashboard or Google Sheet (auto-updated via Clay) that you can use for weekly pipeline reviews.

Step 1: Build a Real ICP Scoring Model in Clay
Use Clay's native logic builder to create a scoring model using real-time firmographic and intent data.

Example:

• +20 points if company raised funding in last 6 months

• +10 if they use Salesforce + HubSpot

• +15 if hiring for Ops or CX roles

• +5 if CEO posted on LinkedIn about retention

Clay continuously updates this score based on live data refreshes.

Step 2: Automatically Re-Tier Based on Score Thresholds
Set up a simple tiering rule inside Clay:

• Score >75 = Tier 1 (High intent)

• Score 50–75 = Tier 2 (Warm)

• Score <50 = Tier 3 (Low priority)

Clay updates these tiers in real-time. You can add tags or custom columns to reflect tier status across workflows.

Step 3: Trigger Next Steps Automatically (Using Clay's Native Integrations)
Clay has direct integrations with CRMs like HubSpot and Salesforce. Based on changes in score or tier:

• Automatically create or update CRM records

• Push high-scoring accounts into outbound tools like Smartlead or Instantly

• Generate tasks for AEs to follow up

• Trigger webhook-based workflows for more advanced GTM actions

Following up after a sales call is where deals are won or lost. It’s your chance to demonstrate you listened, understood, and can act fast. But most follow-ups today are too slow, too vague, or just plain lazy.

• Manually writing follow-up emails based on rough notes or memory

• Important points get missed, action items are forgotten

• No system or structure, inconsistent quality and speed

Most AEs today use tools like Gong or Chorus to auto-record and transcribe calls. These tools summarise the conversation, flag objections, and sometimes suggest action items or next steps.

• Transcripts are searchable

• Post-call summaries help frame the conversation

• You still have to manually translate insights into strategic follow-up

It’s better, but it’s still reactive. You’re only working from what was said.

This is how fast the space moves…As I was writing out what you could do with call transcripts with Gong or Otter.ai I.E. downloading the transcript, pasting it into ChatGPT, prompting it to summarise pain points, buying signals, objections, and suggested follow-up copy — this email lands in my inbox (no joke):

Clay just launched a direct integration with Gong.

You can now:

• Pull Gong transcripts directly into Clay tables

• Use AI to extract insights, objections, buying signals, or feature requests

• Automatically trigger workflows based on what was said in the conversation

Step-by-step approach:

Step 1: Clay pulls your Gong calls into a live table.

Step 2: It extracts the transcript and runs an AI summarisation block to generate:

• A short summary of key themes

• Buying signals and objections

• Suggested follow-up copy

Step 3: Clay enriches the account with real-time data:

• New hires

• Tech stack changes

• Recent funding

• Website or product updates

Step 4: Based on the content of the call and the enrichment:

• Create a personalised follow-up email

• Push that into your CRM

• Alert a rep in Slack

• Trigger a task if a new stakeholder or competitor is mentioned

Example Use Cases:

• Competitor mentioned → auto-draft battle card follow-up

• Feature requested → alert product team in Slack

• New stakeholder referenced → enrich their LinkedIn + CRM record automatically

This is no longer theory — it’s live.

You can now turn Gong conversations directly into personalised GTM workflows.

That’s how I’d do it today.

Everyone is going to be in a different situation and circumstances.

“But, my company won’t let me…”

“We’re not allowed…”

There is a way around everything if you really want to do something, so do it.

If you’re an AE trying to keep up, steal the workflows. If you lead a sales team and want to implement this across the board, let’s talk. And if your team’s still talking about ‘personalisation’ while sending the same template to 500 leads — send them this.

Stephen Bates

Founder - cabot-insights.com