I’ve just been at Klaviyo's global partner event and frankly, one question kept hitting me in every session.

Are you using AI as theatre, or are you using it to actually drive outcomes?

And look, I see this all the time. Most brands are doing the first one and calling it the second. They have ChatGPT open in one tab, Claude in another, AI features inside Shopify, AI features inside Klaviyo, and someone on the team is testing a new tool they saw on LinkedIn last week.

It feels productive. It looks like the brand is keeping up.

It is not. It is theatre.

What AI theatre looks like

Theatre is when AI is everywhere in your business but nowhere in your strategy. No one on your team can tell you which AI tool sits in which workflow. No one can tell you what the new output benchmark is. No one has rewritten anyone's job description in the last six months.

And the numbers have not moved. That is the giveaway.

Outcomes look different. Outcomes are deliberate. The team knows exactly which tools they are using, where in the workflow they sit, what they replace, and what the new output expectation is. Leadership has reset expectations. The campaign count has gone up. The flow optimisation cadence has gone up. Revenue per recipient has gone up.

That is the gap. And it is widening every week.

The diagnostic: three questions to ask your team this week

Before you spend another dollar on AI tools, sit your team down and ask:

• Which specific deliverables are we producing this month that we could not produce six months ago?
• By how much has our output multiplied per person, and is that reflected in our content calendar and revenue numbers?
• Which AI tool sits in which step of which workflow, and who owns it?

If the answers are vague, you are doing theatre. If the answers are specific with numbers attached, you are running outcomes. At the end of the day, it really is that simple.

What outcomes actually look like in 2026

Here is the bit from the Klaviyo event that should change how every DTC founder thinks about headcount.

Klaviyo said they are seeing a 5x output uplift per employee inside the platform today. And they expect that to push to 12x to 24x within the next 12 months.

That is not a marketing line. That is the new baseline. If your team's output has not moved in the last six months, your competitors are already pulling ahead, and the gap compounds every month you wait.

So what does outcome-driven AI actually look like? I reckon it sits in three places.

1. Flow optimisation cadence

Outcomes-driven brands review every core flow weekly. Welcome series, abandoned cart, browse abandonment, post-purchase, winback. Every week. Adjustments get made. Split tests run continuously.

AI handles the heavy lifting on copy variants, subject line ideation, and segmentation logic. The human time goes into strategic calls, not first-draft writing.

If your team is still reviewing flows quarterly, you are leaving compounding revenue on the table. Full stop.

2. Campaign output benchmarks

If your brand was sending 10 campaigns a month last year, that number should be closer to 30 now.

I know the pushback. "My list will get fatigued." "That is too much email." And look, I get it, that used to be true. But segmentation and personalisation tools have moved on. Sending 30 campaigns is not the same as broadcasting 30 emails to your whole list.

Outcomes-driven brands segment aggressively, personalise at a one-to-one level using AI composers, and ship three to four times the campaign volume with the same headcount. Unsubscribe rates stay flat. Revenue per recipient goes up. That is the trade.

3. LTV is the real game

Most brands at the event were still obsessed with ads and creative. Very few were focused on the actual unlock, which is LTV.

Frankly, this is the bit that frustrates me. Your retention engine is what lets you tolerate higher acquisition costs. If you cannot push LTV up, you cannot bid more aggressively on paid. If you cannot bid more aggressively, you cannot scale. It is that simple.

Klaviyo's AI features are now capable enough that a small retention team can move LTV in ways that previously needed a much bigger headcount. If your team is not pointed at this every week, you are optimising the wrong number.

Crawl, walk, run: how to move from theatre to outcomes

This is not a 12-month transformation. You can start this week.

Crawl: audit your current AI usage

Pull the team into a 60-minute session. Map out every AI tool currently in use, who uses it, what task it sits inside, and what output it produces. You will be surprised how much duplication and how many gaps you find.

Then ask the harder question. If we turned off all this AI usage tomorrow, what would we actually lose? If the honest answer is "not much," you have theatre.

Walk: set boundaries and processes

Once you know what is happening, write it down. One page per function. Email, paid, organic social, customer support.

Each page defines which AI tools are approved, where they sit in the workflow, what the new output expectation is, and what the human still owns.

And this is also where you reset expectations with the team. The old benchmarks are gone. If a copywriter was producing 10 emails a month, the new number is 25 to 30. Make it explicit. People rise to clear expectations and stay flat when expectations stay vague.

Run: switch from deliverable-based to time-based output

This is the biggest mindset shift, and it is how we restructured our own agency work this year.

Old model: 10 emails a month for the client. Defined deliverables. Easy to scope. Easy to underdeliver against what AI now makes possible.

New model: 10 hours a month inside the client's Klaviyo account, with AI tools enabling us to produce three to four times the output the old model allowed. Same retainer. More campaigns, more flow work, more split tests, more analytics.

If you run an in-house team, same logic applies. Stop measuring output in number of emails. Start measuring in hours spent driving revenue inside the platform, with the expectation of what those hours should produce now that AI is in the loop.

The bottom line

AI is no longer a competitive advantage. Every brand has access to the same tools.

The advantage now sits with the leaders who make their team use those tools deliberately, who reset output expectations, and who tie every AI workflow back to a measurable outcome.

The brands that figure this out in the next 12 months will be doing 5x to 10x what their competitors are doing with the same headcount. The ones still doing AI theatre will spend the next year wondering why their team is busy but the numbers are flat.

Pick the side you want to be on. Then audit, set the boundaries, reset the benchmarks. This week, not next quarter.

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