The Executive Voice

This is the fourth week of a series called ‘Communicate like a …‘, where we look at how professionals outside of tech communicate and what we can learn from their approach.

Communicate like a TRIAL LAWYER

Whether they’re called barristers in Australia and the UK, or trial lawyers in the US, the best courtroom advocates understand one thing: high-stakes communication isn’t a dump of information. It’s a case.

When you’re explaining strategy to a board, defending a difficult decision, repairing trust, or selling a complex solution, a claim isn’t enough.

Trial lawyers prepare by asking: What will the other side attack first? In B2B tech, that ‘other side’ might be a sceptical investor, customer, or employee. Each will test a different part of your argument.

That’s why preparation matters. Trial lawyers research, test evidence, anticipate objections and shape their argument before they step into the room.

Tech leaders need the same discipline. Weak communication often stems from weak preparation: not enough facts, not enough understanding of the audience, and not enough consideration of where resistance will come from.

A trial lawyer doesn’t throw random facts at the jury and hope something sticks. They build a line of argument: what happened, why it matters, what proof supports it, what objections may come, and why the conclusion still holds.

Also, the best advocates don’t hide weak spots. They name them, explain them and show why they don’t defeat the larger case. That’s often where credibility is won.

The point isn’t to be theatrical. It’s to stop presenting strategy like a slide deck and start building a case that can survive scrutiny.

Here is a prompt to help you build a persuasive argument like a trial lawyer. Use it before a board presentation, an investor update, a major customer conversation, or an internal change announcement.

The New Rules of Selling in Tech
Part 4 of a four-part series exploring how AI is changing the B2B sales function and what you need to do to continue succeeding.

A New Commercial Model Based on Outcomes

Last week, we talked about the need for technical storytellers, the type of individual who has both the technical know-how and storytelling skill to clearly explain how our AI solutions safely work in the client’s environment.

Wrapping up the four-part series, this week’s topic is how AI is changing what buyers value.

For years, tech companies sold effort: billable hours, implementation days, user seats, licences, support packages. But when AI reduces manual work, speeds delivery and automates parts of the process, effort becomes a weaker commercial story.

That creates pressure for both tech services firms and SaaS companies. If your value is still framed around time, headcount or access, AI pushes you towards a deflationary conversation: fewer hours, fewer seats, lower cost.

The growth edge is outcomes.

That means shifting the conversation from “we provide software” or “we supply people” to “we expand your capacity to achieve something important.” Faster claims processing. Higher sales conversion. Fewer compliance failures. Better customer resolution. Shorter delivery cycles.

This is where influence changes. Buyers won’t pay a premium for activity they believe AI can compress. They will pay for more discerning judgement, measurable improvement, shared accountability, and the ability to communicate these in a compelling way.

The commercial model has to follow. More tech firms will need to move from time-and-materials thinking to performance-led models in which value is linked to results, not just the work.

That’s harder. Outcomes are messier than hours. They depend on client behaviour, data quality, adoption and market conditions. But that’s also why the opportunity is bigger.

The next GTM playbook won’t be built around selling software or labour. It will be built around architecting results. See the diagram below.

Leadership in 2026 and 2027 will mean holding two ideas at once: being more human than the bot, and more data-driven than the legacy consultant.

⬇️ Scroll down to the last section: ‘Something Extra’, which offers a way to restructure your narrative to better fit this new commercial model.

A Bank Saves 130,000 Hours Using AI

With this week’s issue focussed in large part on how AI is disrupting work comes the Macquarie Bank success story, which has successfully adopted AI across its business. According to its Director of Product for AI Enablement, Shahnwaz Ali, the bank had so far “delivered 130,000 productivity hours back to people” by using Gemini Enterprise to automate mostly manually-repetitive tasks. He added, “They can now focus on what matters the most to us, [which] is delivering exceptional digital experiences to our clients.” I recommend you read the article (no paywall) to get an idea of how they did that in a highly regulated, risk-averse industry.

POV: It’s a great use case (and success story) on how to implement AI the best way across the organisation, and talk about it.

An Internal ‘Venting Machine’

As a leader, you’re often pulled from pillar to post; there are too many demands on your time, and if that’s not enough to deal with, you also have to handle people complaining a lot. That’s just how it is. Why not let a clone take the brunt of it, saving you time and emotional energy? That’s what Klarna’s CMO, David Sandström, did, creating “an AI version of himself to take the heat … describing his digital replica as an internal ‘venting machine’”. The AI version of Sandström is always friendly, asks for forgiveness, and takes the blame. He added, “I just didn’t want to hear the whining in the meetings anymore. So, I said, call this number, get it out of the system. When we meet, we can focus on the future.”

POV: I’m both amused and irritated by the comment about not wanting to listen to the ‘whining’. He could have been more diplomatic by using words like ‘complaints’ or ‘concerns’. I think his clone will do a much better job communicating!

Phrase of the Week

Deepfake Twin: A digital replica of a real person, trained on their existing content, voice, writing, image or public record to imitate how they speak, think, look or respond. Unlike a simple deepfake video or voice clone, a deepfake twin is designed to behave more like a persistent version of the person: recognisable, interactive and seemingly ‘authentic.’

It’s Focussed on Proving Outcomes

If outcomes are the new commercial contract, then the value story has to change, too.

Teams can’t keep presenting solutions as a list of features, services, capabilities and vague benefits. “Improved efficiency” is no longer enough. “Better productivity” is no longer enough. These claims were always weak anyway, but in the AI era, they’re dead.

The stronger value story starts before the pitch. It begins with thinking: what customer problem are we solving, what use case matters most, what outcome can we reasonably predict, and what evidence supports that hypothesis?

Then the narrative needs a structure: a beginning, a middle, and an end.

The beginning defines the pressure. The middle shows the intervention: what changes, why it works, and how risk is managed. The end proves the outcome through measurable results: time saved, cost reduced, revenue lifted, errors avoided, and capacity unlocked. And get specific here: $ amounts, percentages, time blocks, before-and-after, plus examples and scenarios (‘A Day in the Life of’) - all backed by research, reports, pilots, demos, etc.

Then the story loops back. Once results are delivered, the actual customer success story should follow the same structure. See the diagram below.

I used to ask sellers, “What is our promise of value? The Unique Value Proposition (UVP). This doesn’t matter so much anymore. Today and moving forward, the question we have to ask is, “What outcome can we prove?” Defined as the Unique Outcome Proposition (UOP).

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