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This is a long one because this is very much a “I WILL DIE ON THIS HILL” topic for me, but I think it’ll be worth it. There are frameworks, worked examples, and prompts you can use this week, wherever you are on the journey. 

So strap in, grab a coffee, and let’s help you make AI work for you. 

AI tools, AI tools, come and get your AI tools

AI is here, and it's here to stay, so if you’re not using it in your commercial strategy, you will fall behind, and you will miss out on pipeline. But adding AI into your sales and marketing process without first having intelligent foundations is unlikely to do you any good.

Things I am getting sick of seeing, and the main reason why I’m writing this newsletter:

A team decides this is the year they get serious about AI and they buy a bunch of tools. A writer, a research agent, an enrichment tool, a scheduler, maybe an AI SDR that supposedly never sleeps and is all up in people’s LI dm’s pissing them off by saying things no credible person would ever say. They wire it all together and sit back to wait for the pipeline to roll in.

SHOCKER, it doesn’t. Plenty of activity, very little to show for it. The dashboard is busier than it has ever been, and the calendar is emptier than they would like.

The instinct is to blame the tools, usually the tools are fine. The disconnect is they’re not built for life sciences sales, they need the data and most importantly, the human experience, strategy, and creativity injected into them, and working in tandem with them. 

You can only scale something successfully if you already have something what works

AI is a photocopier. Feed it a masterpiece and it will hand you ten thousand copies of a masterpiece. Feed it a mess and it will hand you ten thousand copies of the mess. If your commercial strategy is broken from the get-go, AI will not fix this, it will multiply it.

The other half is the machine itself, your process. If the process was never any good, automating it will not make it good. It just makes it wrong at scale, in more places, more quickly. A broken sales motion run by hand costs you a few missed meetings. The same motion automated costs you your sender reputation, your brand, and a founder asking why the numbers went the wrong way.

So before we reach for artificial intelligence, it is worth trying ordinary intelligence first. The human insight and judgment, the data, and the hard-won sense of what actually works in your market. Get that right, and AI becomes real leverage. Skip it, and you have bought a very expensive way to fail at volume.

Here’s how to build the intelligence in the first place, decide what AI should be allowed near, and hold onto the parts of selling that stay human no matter how good the tools get.

Two starting points

If you are just starting out, or you are doing outbound and it is not working, you do not have a proven motion yet. Your job is to find one. Design the smartest system you can, test it, and hunt down the thing that actually earns replies and meetings.

If you already know you have something that works, and the data says doing more of it to more of the right people would mean more leads and more sales, your job is different. It is to take that proven motion and use AI to make it more efficient, more automated, and much bigger, without letting the quality slip.

Two jobs: Find or Scale.

And it is a journey, not a pair of boxes. Nobody stays in one forever. You start in Find, you prove something, you move it into Scale. Then you open a new Find lane for the next segment or channel while the proven one runs. But you have to know which job you are in for any given thing, because the two need different amounts of AI, and different amounts of you.

Let's take them one at a time.

Job one: find what works

The job in Find is to use your own expertise to design smart systems you believe will work, test several of them properly, and let the results tell you which to keep. AI is your thinking partner and your analyst here, not your autopilot.

Design from your expertise, with AI to sharpen it. You know more than you think. You’ve got the science background, so you have instincts about who buys, what they care about, and why you are different. Get them out of your head and stress test them. These prompts are a good start. Treat every answer as a first draft to test, and then inject your nuanced know-how to help sharpen it.

"I sell [your product] to [rough audience] in life sciences. Help me map the segments who might buy this, the roles inside each who would care, and the situations that would make it urgent for them. Ask me questions wherever you need more detail before answering."

"Here are my notes from twenty customer conversations. Cluster the recurring problems, and for each one, write it in the language the customer used, not in our product language."

"Here are my notes from twenty customer conversations. Cluster the recurring problems, and for each one, write it in the language the customer used, not in our product language."

"Here is how I describe what makes us different. Play a sceptical buyer who already has three other vendors on the shortlist. Where does this fall apart, and what would make you stop listening?"

"Given this buyer and this sales cycle, help me design a sales process from first touch to closed deal. Flag every step where a human absolutely needs to be involved versus where automation could take the load."

For the deeper versions of this thinking, Get out of your head and into your buyers and How to Build a Complete GTM Motion are good past newsletters to go back to, and Pressure Is Lazy. Pain Is Effective on getting the pain right.

Then go and get the real signal. Book the conversations, show up to the events, and listen. You are trying to find the truth of your market, and a lot of that truth is not written down anywhere for AI to find. Use your personality to build relationships, trust and get the human insights you need from the human across the table. Come back, brain dump everything you heard into AI, messy and unstructured, and ask it to help you find the pattern.

"Here are twenty things I heard and noticed talking to buyers this month. What themes emerge, what might this tell me about their real priorities, and what should I test as a result?"

Test several things at once, properly. This is the part that is tempting to rush. Early on, you do not know what works, so do not bet everything on one message, one segment, one channel (push back on that founder who thinks they know exactly what will work, and fight to test as much as you can early, it’s your commission on the table, so it’s worth them taking the ego hit). 

Run a handful of well designed tests in parallel. Different angles, different audiences, different hooks. The goal is to learn fast which direction has legs.

Give each idea a real run, with the targeting, the messaging, and the deliverability actually in place, before you rule it in or out. Half the "that doesn't work" verdicts are really "we tried it once, badly, and stopped before it had a chance." We went deep on this in Is Biology a Scam and does Cold email work.

Then keep the winners and scale them. When you get something working and you understand why it worked, it stops being a Find problem and becomes a Scale problem. 

Which is the second job.

Job two: scale what works

This is for the teams who already have a motion that works, and know from the data that the path to more leads and more sales is simply doing more of it, to more of the right people, without dropping the quality that made it work in the first place.

If that is you, your job with AI is leverage. You have the masterpiece, now run the photocopier well. That means three things.

Efficiency. All the manual work that eats your week, the list building, the enrichment, the research before a call, the formatting and the follow ups, can move to AI so your people spend their hours where it counts. The same output for a fraction of the time.

Effectiveness. Scaling is not just doing more, it is doing more without going generic. The reason your motion works is the human insight inside it. The job is to teach that insight to AI so it holds across ten times the volume, instead of flattening into mail merge the moment you scale it.

Automation and reach. Once the quality holds, you widen the aperture. More of the right companies, more of the right people, more channels, running consistently without a human hand on every single step. This is where AI genuinely changes the economics, letting a small team run credible outreach across a market that used to need an army.

The risk in Scale is different from the risk in Find. In Find, the danger is never landing on something that works. In Scale, the danger is scaling something good badly, so it loses the exact thing that made it work. 

Whichever job you are in, the mechanics of handing work to AI are the same.

Here is the model:

Keep, Coach, Copy

How do you decide, task by task, what AI should touch?

The rule that sorts anything into a tier is simple. Can you write down exactly what makes it good? If you can put the recipe into words, AI can help you cook it at scale. If doing it well depends on judgment you cannot put into words, it stays with you.

Keep, the irreducible human core. The parts no dataset can hand you. Getting the signal that is not written down anywhere. Deciding what good even means. Judging the genuinely new. Being the human someone actually trusts. This is small, permanent, and where your real value lives. 

Coach, you lead, AI assists. You do the thinking, AI does the reps. You create the standard by hand, then teach it. This separates teams who get real leverage from teams who just get more volume.

Copy, AI leads, you check. The reproducible volume List building at scale, first pass research briefs before a call, pulling campaign performance into one place, turning one strong long form piece into a month of posts and follow-ups. You set the standard once, spot check the output, and move on.

Careful though, the tiers are not a fixed list

It is tempting to draw up a permanent list of human jobs and AI jobs and call it done. Targeting is human, enrichment is AI, and so on. 

Take "who fits, and why." Starting out, that is pure human expertise. You have no history to learn from, so you lean on judgment, market knowledge, and the scars from your last role to decide who is worth chasing. But a mature team is sitting on years of won and lost deals and thousands of replies. At that point, asking a human to eyeball who fits is leaving money on the table. Feed that history to AI and it will find patterns in your best customers you would never catch by hand: the overlooked segment that always closes fast, the title that always kills deals, the company size where your win rate falls off a cliff.

So "who fits" is not a human job or an AI job. It is a job that starts human and moves to AI as your data grows. The better your foundation and the more outcomes you record, the more of your work slides from Keep into Coach and Copy over time. 

Which raises the real question, if tasks keep sliding to AI as you feed it data, what never slides? What stays human no matter how much data you have?

What stays human, no matter how much data you have

AI looks backwards, human creativity looks forwards. AI is a brilliant, tireless student of your past. Everything it does well, it does by finding patterns in things that already happened. So parts of your sales process should always stay human, because they are the parts that a study of the past cannot give you.

1. Getting the signal that is not in any dataset. Reading the tone and facial expressions to know when a deal is stalling or moving forward before the emails or call notes pick up on it, using personal anecdotes to build rapport, sensing when the right time to ask the difficult questions is and knowing how to phrase them. You are the sensor, and it is where your sharpest edge comes from. And it can come from places AI does not live, the corridor conversation, the conference coffee chat, the networking drinks overshare. No machine goes and gets this for you. What it can do, brilliantly, is help you make sense of it once you bring it back, which is exactly the brain dump move from the Find job.

2. Deciding what good even means. This is about taste and credibility. Do your emails sound like how a scientist would want to receive them? Are they natural, engaging and technically sound to your specific niche? Does that piece of content look sexy enough for someone to want to click on it (AI is incapable of the same feelings we have when we see shiny pretty things)?  Your background, training and human eye can help you train AI on the foundations of what something good looks like, help you review each batch of AI outputs, and then guide it to be better as it scales production.

3. Building a brand outside of your product differentiation. When you’re up against competition, one thing that can make you stand out is how people perceive your brand. What kind of tone and vibe do you want to portray to your customers? Are you the no-nonsense, simple but effective brand that people come to when they want to get the job done? Or do you want to lean into being more fun and whimsical, building trust through being unapologetically human and making people want to work with you as people and a brand, not just because your offering is good? This is a uniquely human decision, and requires real personality to push AI in the type of brand direction you want.

4. Being the human someone actually trusts. People buy from people, and never more so than when a scientist is staking their programme, their budget, and their credibility on your product. Someone has to be in the room, put their own name on the outcome, and be accountable when it matters. A machine can prepare you for that conversation beautifully. It cannot have it for you.

So as AI takes more of the volume, your job is not to disappear. You should be spending your time sensing what the data cannot see, deciding what good means, judging the genuinely new, bringing the personality, and owning the relationships that close. 

The same discipline for content and design

Marketing breaks the same way, just more visibly, and it is fixed with the same moves.

Find your pillars. Pick the three or four themes you actually want to be known for, the ones where you have a real point of view and the right to speak. Everything you publish ladders up to one of them.

Define what good looks like, with evidence. Find your best-performing posts, content pieces, webinars, and work out what they had in common. A clear point of view, a specific claim, proof, a human voice, glowy dark mode branding with well-placed engaging figures. Write that down as your standard. 

Coach the machine with the real thing. Give it your pillars, your best examples, your tone, and your brand guidelines. Show it the design you are proud of and design you would never ship. Now it drafts from your voice instead of the sector's average voice.

Then let it Copy the production. Turning one strong idea into a week of posts. Reformatting the long piece into the short ones. Resizing the graphic across every channel. SCHEDULING. None of that should be eating your afternoons.

Good content is a point of view backed by proof and made specific to a real audience. Good design is clarity and consistency. Once you have decided what they mean for you, a tool can help you produce them relentlessly.

Point AI at the scoreboard, not just the pitch

Most people aim AI at the output. Write the email, make the thing. Fair enough, that is the obvious use.

The higher leverage move is to aim it at the feedback loop. Use it to see what is actually happening, honestly and quickly. These are the things worth watching:

• Reply rate broken down by segment, by message, and by channel, so you see where it is really working rather than the blended average that hides everything.

• Positive replies versus polite brush offs, because they are not the same result and a raw reply rate treats them as if they are.

• Which step in the sequence is doing the work, so you know whether your follow-ups earn their place or just wear people down.

• On content, which pillar drives sign-ups and demo requests, versus the one you enjoy more than your audience ever will.

For the Find job, this is how you discover what works. It is the scoreboard that tells you which of your parallel tests has legs. For the Scale job, it is how you keep a proven motion improving instead of slowly decaying, and how you catch the moment a new segment is worth a fresh test. Every outcome you record is another data point that lets AI take a little more of the fit, the messaging, and the timing off your hands.

A human can run three versions and squint at the results, but a well-pointed system can run a dozen, hold them against real outcomes, tell you which two are worth keeping, and show you why. 

Build one small ritual around it. Once a week, look at what the data says, decide one thing to double down on and one thing to cut, and move. The learning only compounds if it changes what you do next week.

Start here, wherever you are

Start with the job you are actually in.

If you are still finding what works:

1. Design before you send. Spend an afternoon with the strategy prompts above to draft your market, your buyer's pains, and your differentiator. Treat all of it as a first draft to go and test.

2. Go and get the real signal. Book the conversations, show up to one event. Then brain dump everything you learned into AI and ask it to find the pattern.

3. Test several things properly, at once. Run a handful of well-designed tests in parallel, and give each a real run before you judge it. You are hunting for the one that works, not proving you were right first time.

4. The moment something works, switch jobs. Understand why it worked, then move it into Scale.

If you already have something that works and want to scale it:

1. Audit for one hour. List everything you and your team do across sales and marketing in a typical week and mark each item Keep, Coach, or Copy. You will spot the miscategorised work immediately, especially the things you are still doing by hand that your data could now do better.

2. Fix one inversion. Take back one thing you are Copying that belongs in the human functions, and hand over one thing you are still doing by hand that your outcome data is ready to run.

3. Coach one thing properly. Map out what good looks like, find the examples and feed it to AI whilst explaining what you like and what you like it. 

4. Set up one measurement. Pick the single number that tells you most, break it down properly, and put fifteen weekly minutes in the diary to look at it and decide on one change.

Build the human version first

So before you buy another tool, get the manual version right.

Know what a good customer actually looks like and why. Have a message you have proven can earn a reply. Know what good content and good design look like for your brand, specifically. Make sure the plumbing underneath works, your deliverability, your targeting, your follow-up, so that when something misses you know it was the idea and not the pipes. And give every channel a proper run before you write it off.

Get those right, and AI stops being an expensive gamble and becomes the best hire you ever made. It will take everything your judgment produces, and everything you sense out in the real world, and give you far more of it. It will not sense the room for you, decide what you should want, judge the genuinely new, or be the person your buyer trusts. Those stay yours. Everything else, in time, it can carry.

Intelligence first, then artificial intelligence. In that order, it is a genuine advantage.

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