At Unily’s Unite conference this week I watched someone turn a set of source documents into a podcast, an infographic and a video in the space of a few minutes.

This was Content Studio, Unily’s new AI-powered tool for taking trusted company information and turning it into different formats for different audiences: podcasts, videos, infographics and more, grounded in the original sources and with humans still in control of what goes out.

It was impressive. Genuinely impressive.

And I could immediately see the use case. Take a health and safety update written centrally, turn it into a visual explainer for factory workers, translate it into the languages spoken at that site, make an audio version for people who spend their working day away from a screen. Do in minutes what would previously have required designers, translators, video editors and several increasingly desperate emails to an agency.

There was, though, a rather lovely irony to the timing.

At the very same conference, I was running a workshop called Signal Over Noise: Winning Attention in an Age of Infinite Content. We’d expected about 40 people. More than 100 turned up.

Either internal communicators have suddenly developed an unexpected passion for information theory, or having too much stuff and nobody paying attention to it has struck a nerve.

So there we were. In one room, demonstrating how astonishingly easy it has become to produce more content. In another, communicators were crammed around tables trying to work out how to stop drowning people in the stuff.

Welcome to internal communications in 2026.

Because the interesting thing about Unily’s new Content Studio isn’t whether it’s good. It is. The interesting thing is what happens when tools like it collide with a communications system that already produces far more than people can reasonably consume.

Unily’s own framing gets to the heart of it: content is abundant, impact isn’t. And now, having identified the abundance problem, we have invented a machine for making abundance virtually free.

This is what we’ve been asking for

Credit where it’s due, because there is a lot to like here. The central argument of Digital Communications at Work is that communication needs to be designed around how people actually work. Not around the org chart, and certainly not around what’s convenient for the communications team.

A frontline employee on a twelve-hour shift does not have the same information needs, available time or working context as someone sitting at a laptop in head office. Yet for years we’ve routinely given them the same 800-word intranet article and called the job done because technically everyone could access it.

Congratulations. It’s responsive.

The problem with doing this properly has always been partly practical. Adapting communication for different roles, languages, locations, channels and contexts takes time and money. Most internal comms teams simply don’t have the capacity to make twelve versions of everything.

AI changes that equation. One source can become an infographic for a factory screen, an audio summary for someone on the move, a short article for the intranet and a translated version for a local team. The underlying information remains grounded in approved source material while the presentation changes to fit the person receiving it.

That’s not gimmicky personalisation. That’s good communication design.

For years we’ve said we should meet employees where they are. Now we actually can.

Which makes the next question rather important: How often should we?

We are already drowning

The communications problem inside most organisations isn’t that employees need more information.

It’s that they are surrounded by it.

Emails. Teams messages. Intranet news. Town halls. Newsletters. Notifications. Communities. Videos. Podcasts. Viva Engage posts. PDFs attached to emails summarising PDFs attached to other emails, with Copilot in the same window offering to summarise the summary.

Then we look at falling engagement figures and ask how we can persuade people to read more. Wrong question.

Information overload isn’t a personal failing by employees who need to manage their inbox better. It’s a design outcome.

Organisations have built systems in which hundreds, sometimes thousands, of people are independently authorised to demand everybody else’s attention. Every function has something important to say. Every project needs visibility. Every leader has an update. Every initiative has a launch. Every programme has a newsletter.

Individually, almost all of these decisions are reasonable. Collectively, they amount to being trapped in a lift with the entire organisation barking its priorities at you.

And then along comes AI and removes one of the few remaining constraints: making the stuff.

Attention is a budget.

Every communication spends some of it. Every notification asks someone to stop doing one thing and pay attention to another. Every beautifully personalised two-minute video is still two minutes in which they aren’t doing the work they’re ostensibly employed to do.

Personalisation can make each individual item more relevant.

It does nothing, by itself, to reduce the number of items.

And when the cost of producing those items approaches zero, I have a nasty feeling we’re about to discover that the natural volume of organisational communication, when freed from all physical constraints, is infinite.

The problem with removing friction

The first risk is obvious: volume.

Production cost has always acted as a crude form of governance.

Making a video used to be sufficiently expensive and annoying that someone, somewhere, had to believe the subject warranted a video. You needed a camera, an editor, perhaps an agency, a budget, and the particular form of psychological collapse that comes from hearing the same thirty seconds of Intro by The XX for the twenty-fourth time while someone adjusts a crossfade.

That’s not an especially sophisticated editorial process, but it was a brake.

Remove the friction and suddenly almost everything clears the production bar. Why wouldn’t we make a podcast version? It takes three minutes. Why not an infographic too? Why not a video? Why not six localised versions, translated into eight languages and personalised for four employee groups?

Soon the organisation hasn’t solved information overload. It has achieved information overload in twelve beautifully targeted formats, each carefully optimised for the precise demographic characteristics of the person ignoring it.

And I don’t think employees will become cynical about AI-generated communication simply because a machine made it. Most people have better things to worry about.

They’ll become cynical if there’s more of it, especially if all of it starts to sound eerily similar.

The signal that somebody bothered

The second risk is trust.

Part of the signal carried by communication is that somebody bothered. The medium is the message and all that.

Someone wrote the note. Someone chose the words. Someone stood in front of a camera and said the uncomfortable thing. Someone decided this mattered enough to spend some time on it.

Effort itself communicates importance.

If the CEO records a five-minute message after a difficult restructuring, that act carries information beyond the words themselves. They showed up. They put their face to it. They had to look into a camera and say “I know today’s news will be hard for many of you” out loud, while arranging their face into the internationally recognised expression for I am not, in fact, a sociopath.

If software turns their announcement into a jaunty three-minute podcast, the information may be perfectly accurate.

But something has changed.

Communication isn’t simply information transfer. If it were, internal comms would have been solved by SharePoint in about 2003 and we’d all be doing something else now.

Who said something, why they said it, what they chose to emphasise and how much effort they were prepared to expend saying it are all part of the message.

The cheaper communication becomes to manufacture, the more valuable those signals of human intent may become.

There’s an odd possibility here that the more polished content we can generate, the more we’ll value the slightly awkward video of an actual person saying actual words into a camera without three keywords sliding smoothly into view beside their face while ambient electronica burbles underneath.

Human imperfection may yet become a premium feature.

Month six is the bit that worries me

The third risk is complacency.

I’m actually less worried about hallucination here than I am about habituation.

In month one, everyone will scrutinise the generated storyboard. They’ll check every sentence against the source. They’ll debate the emphasis. They’ll inspect the images. Someone will zoom in to 400% and discover the reassuringly diverse group of colleagues in the background includes a woman with seven fingers and a security pass apparently growing directly from her sternum.

By month six?

Yeah, you know how this works.

You’re late for another meeting. Teams is flashing. Somebody needs this published by four. You’ve generated versions of this sort of thing dozens of times before and they’ve always been fine.

You glance.

Looks alright, dunnit?

Publish.

Grounding a system in approved sources is important because it reduces the risk of invention. But a communication can contain no invented facts whatsoever and still be wrong.

It can emphasise the wrong thing. Bury the caveat. Miss the thing employees actually care about. Turn a complicated and politically sensitive decision into something technically accurate yet completely tone-deaf.

Those are judgement problems, not generation problems, and no amount of source grounding removes the need for judgement.

Governance needs to happen before the produce button

This is why I’m not convinced the most important governance question is who approves AI-generated content.

By that point, we’ve already made it.

The more useful question comes earlier: Should this communication exist at all?

In my Signal Over Noise workshop at Unite, I used what I call the Fletcher Test: Means, Opportunity, and Motive. I stole the method from the iconic Jessica Fletcher, because I did a sufficiently wishy-washy arts degree to spend three publicly funded years of university watching an enormous amount of trashy daytime television. Murder, She Wrote taught me that every murder needs three things: the means to do it, the opportunity to do it, and a motive.

(It also taught me that Cabot Cove had a murder rate that really ought to have attracted federal attention, but this is not the time, nor is it the place).

The same test turns out to be surprisingly useful for internal communications, with fewer suspiciously well-dressed corpses.

Do we have the means to reach this audience effectively?

Do they have the opportunity to pay attention to it?

And, most importantly, do they have a motive to care?

AI makes the Means bit astonishingly easy.

That’s precisely why Motive and Opportunity become more important.

If I can make you a personalised video in your preferred language, beautifully formatted for the device you’re holding, I have solved the delivery problem. But I have not magically made you care.

I’d go further. Communications teams should start thinking in terms of attention budgets for audiences. Not simply how much content a team produces, but how much attention it’s reasonable to demand from the people receiving it.

Approval shouldn’t just ask whether something is accurate, on-brand and compliant. It should ask whether this thing is worth interrupting people for.

What if we’ve got the direction wrong?

There’s another possibility I find much more interesting. Most enterprise personalisation still starts with the organisation.

We have a message.

We have twelve audiences.

We will create twelve versions and push the appropriate one towards each person.

It’s cleverer broadcasting, really. But generative AI opens up another model entirely. Imagine a housekeeper sees that the CEO has published a long update and asks:

What does this mean for me?

The system knows enough about their role and context to answer usefully. It draws from the original, approved communication. It explains what’s relevant and leaves the rest alone. If they want more detail, they ask another question. The original remains there, intact, as the source.

That’s pull rather than push, and I think that’s potentially much more transformative.

Instead of communications teams trying to anticipate every conceivable information need and manufacturing twelve versions just in case, employees interrogate trusted information when they actually need it.

That doesn’t eliminate broadcasting. Some things genuinely need to be pushed. Emergencies exist. Mandatory information exists. Leaders sometimes need to speak to everyone. But most organisational information isn’t an emergency, however enthusiastically its owner filled in the comms request form.

Pull changes the economics of personalisation because we don’t need to manufacture every possible version in advance.

And if we’re going to personalise proactively, our understanding of audiences needs to get much better too. Personas shouldn’t simply be lovingly hand-crafted descriptions of what we imagine “frontline Fiona” wants, replete with a stock photo of a suspiciously cheerful woman in a hi-vis jacket.

We have years of behavioural and engagement data telling us what different groups actually use, ignore, search for and respond to. Use it.

The scarce skill is restraint

AI is going to have us hammering the produce button with the glassy-eyed enthusiasm of a Vegas gambler who’s three hours past bedtime and halfway through the children’s inheritance.

That’s inevitable. And, mostly, it’s useful.

But the interesting consequence is that production itself becomes less valuable. If everyone can turn a policy document into a podcast, infographic, video, article and six translations before lunch, doing so is no longer much of a skill.

Knowing whether to do it is.

Which brings me back to the bigger question I came away from Unite thinking about: what exactly is the internal communications job becoming?

A significant chunk of communications work has historically been production. We write the article. Build the newsletter. Edit the video. Create the campaign. Polish the CEO post. Turn the strategy into an infographic. Then, because we’ve made the thing, we need people to consume the thing, so we spend another chunk of time trying to persuade them to click on it.

It’s an entire professional ecosystem built partly around the fact that making things used to be difficult.

Now it isn’t. Or increasingly, it won’t be.

And I don’t think the answer is to reassure ourselves that AI simply frees everyone up to do more valuable, strategic work, as if there’s a vast strategic nature reserve somewhere waiting to receive several thousand newly liberated newsletter editors.

There may well be fewer traditional communications jobs. The production layer is getting thinner. Pretending otherwise won’t make it thicker.

But the work that remains becomes more judgement-heavy, not less.

What deserves attention?

What doesn’t?

When does personalisation make something genuinely more useful, and when does it simply manufacture more noise?

When does a leader need to show up themselves?

When should an organisation speak, and when should it make good information available and trust people to find it when they need it?

And perhaps most uncomfortably: if we no longer need quite so many people to make the content, are we prepared to define the value of communications by something other than the amount of content we make?

For a long time, communications teams were constrained by what they could produce. This week I watched that constraint disappearing in real time.

Then I walked into a room designed for 40 people and found more than 100 communicators waiting to talk about how to produce less. There’s probably something in that.

The produce button is the easy bit. The difficult bit is resisting the obvious next step: pressing it over and over until the entire organisation disappears beneath a perfectly personalised slurry of podcasts, infographics and three-minute videos explaining why nobody has time to get any work done.