AI for teams
AI for marketing and sales teams
From a few people experimenting on their own to a whole team working faster, with clear rules and real results.
Most marketing teams already use AI. Someone drafts posts with it, someone else summarizes calls. But it's scattered: every person has their own prompts, nothing is shared, and nobody can say what it's actually changing.
Used well, AI becomes an accelerator for the whole team. Research, first drafts, account preparation and reporting move faster, and your people spend their time on the work that needs their judgment: talking to customers, making decisions, closing deals.
That takes three things: the right workflows, clear rules, and a team trained to use them. That's what I help you put in place.
Where AI helps, across the whole team
AI isn't just a writing tool. Here's what it can take on in each part of a B2B marketing and sales team.
Area
What AI can take on
Strategy and market intelligence
Competitor tracking, market research, win/loss analysis from call notes and CRM data
Positioning and messaging
Testing messages against buyer personas, keeping every team on the same story
Content and search
Content plans, first drafts in your brand voice, repurposing one piece across channels, tracking how AI search engines describe your brand
Account-based marketing
Account research, stakeholder maps, personalized outreach drafts for target accounts
Sales enablement
Meeting prep, battlecards, call summaries, follow-up drafts
Partner marketing
Partner briefs, co-marketing plans, partner-ready sales materials
Reporting and measurement
Weekly reports pulled together automatically, pipeline analysis, checking that the numbers from different tools agree
Marketing operations
CRM cleanup, lead routing checks, campaign setup
One assistant, one job
The teams that get the most from AI don't rely on one all-purpose chatbot. They set up small, focused assistants (often called AI agents), each with a single job, clear inputs and a defined output.
For example, an account research assistant takes a target company's name and returns a one-page brief: recent news, likely priorities, the people to reach, and a first draft of the outreach. A marketer reviews it, adjusts it, and sends it. What used to take an afternoon takes a few minutes of review.
Because each assistant has one job, it's easy to test, easy to improve, and easy to trust.
It starts with shared context
AI output is only as good as what it knows about your business. Before building anything, I write down the essentials once: your positioning, your ideal customers, your brand voice, your current priorities.
Every assistant works from that same foundation. It's the difference between generic AI copy and output that sounds like your company. It's also where my positioning work pays off twice.
Humans stay in charge
Draft
AI prepares a first version, a person rewrites or approves it.
Review
AI runs a complete analysis or document, and a named person checks it before anyone uses it.
Approve
AI runs on a schedule and proposes actions, but nothing with external or financial impact happens without approval.
Automate
Only for low-risk, reversible tasks, and only after the workflow has proven itself.
A few rules never change. Anything that reaches customers is reviewed by a person. Every fact or number is traced to a real source. Budget decisions stay with people. And anything published under an executive's name is approved by that executive.
Measuring what changes
Before we start, we measure how long the target work takes today. Then we track what changes: hours saved, output per person, quality, and where it matters most, the effect on pipeline.
AI should earn its place with numbers, like any other marketing investment.
Training your team for AI adoption
Tools on their own don't change how a team works. Training does.
Most AI rollouts stall the same way: a few enthusiasts use the tools, everyone else carries on as before, and six months later nobody can say what changed. Adoption is a people project, not a software install. So training is built into every rollout I lead, not added at the end.
How I train teams:
Start with champions.
Three to five people who are curious and respected by their peers. They learn first, help build the workflows, and become the go-to people for everyone else.
Train on real work.
No generic demos. Each session uses the team’s own campaigns, accounts and reports, so people leave with something they’ll use the next morning.
Train by role.
Everyone learns the basics: good prompts, checking output, what never goes into an AI tool. Power users learn to build and improve workflows. Managers learn how to review AI output and measure its impact.
Write it down.
A shared playbook of approved workflows and prompts, so good practice doesn’t live in one person’s head.
Keep a feedback loop.
A dedicated channel for questions and tips, and short regular check-ins to fix what isn’t working.
Keep it current.
AI tools change every few months. Short refreshers keep the team up to date without starting over.
People adopt AI fastest when they see it take away the work they like least. That's where we start.
A 90-day rollout
Days 1 to 30
Diagnose and pilot.
Map where the team’s time goes, pick the first two or three workflows, write the shared context, measure the starting point, and train the champions.
Days 31 to 60
Roll out and train.
Launch the first workflows with the pilot team, run hands-on training sessions, open the feedback channel, and adjust based on what people actually use.
Days 61 to 90
Scale and measure.
Extend to more of the team and to sales, publish the internal playbook, report the results against the starting point, and plan the next round.
Data and security
AI only works in a company if people trust it. Every rollout includes clear rules on what data can go into which tools, the right privacy settings, connections to your systems with the minimum access needed, and awareness of risks like hidden instructions in documents or web pages. For European companies, that includes working within the EU AI Act from day one.
Ways to start
AI diagnostic
A short review of how your team works today and where AI would save the most time, with a prioritized plan.
90-day rollout
Workflows built, team trained, results measured, as described above.
Team training
Hands-on workshops for marketing and sales teams, on their own work, for teams that want to build skills in-house.
As part of a fractional CMO role
AI adoption built into how I run your marketing from the start.
My background
I've used AI in day-to-day marketing for years, for research, targeting, content and campaign work. In 2026 I completed a 140-hour certification in AI and no-code building, covering generative AI, prompt engineering, AI agent design and workflow automation.
I build these systems myself, for my own work and for clients: brand voice guides that AI follows, content and research workflows, and automations that connect the tools a team already uses. So I don't just recommend a workflow. I can build it, and show your team how to run it.
Want to see where AI could save your team time?
Tell me how your team works today, and I'll tell you where I'd start.
Get in touch