And is this really a good idea for your branded podcast?
AI tools have brought us to the threshold of easy content creation. But audience trust is at an all-time low. So which AI content creation tools does it make sense to embrace, and when does it make sense to up your human quotient?
Can AI generate podcasts automatically?
Sure. AI can now generate a podcast from start to finish: script, voice, edit, and even distribution.
But “automatic” doesn’t mean “effective.” Today’s AI podcasting tools can take a block of text, turn it into a script, generate voices, assemble a conversation, and publish it as a branded podcast. Platforms like Riverside, NotebookLM, Descript, and ElevenLabs are making this faster and more accessible than ever, and many companies have jumped on the bandwagon.
But like my mother used to say, “just because you can do something doesn’t mean you should.” In other words, just because AI can generate a podcast doesn’t mean anyone will want to listen to it. Audio and video podcasts certainly have their equivalent of “AI slop” – and few brands would argue that this is a good road for them to go down.
In this article, we’ll break down:
- What “automated podcasting” actually means
- Which tools are worth knowing
- Where AI works well
- Where it quietly erodes trust
- And how to strike the right balance for branded content
What are the most popular AI podcast tools right now?
This is by no means an exhaustive list, but here’s a quick overview of some leading tools:
1. AI-generated “conversation” tools
- NotebookLM → turns documents into podcast-style dialogue discussions
- Wondercraft → script-to-voice storytelling
- Play.ht → synthetic narration
2. Text-to-speech and voice cloning
- ElevenLabs → high-quality voice generation
- Resemble AI → custom voice models
3. AI editing and production tools
- Descript → edit audio via text
- Adobe Podcast → cleanup and leveling
- Riverside → Remote recording and editing uses AI transcription and clips
4. Repurposing and scaling tools
What AI podcasting does well
AI, it must be admitted, is genuinely useful in a few key areas:
- Speed and efficiency: Technically, you can go from idea to publishable audio in minutes. This matters in contexts where rapid response and a high volume of output is essential.
- Repurposing content: You can turn reports, webinars, blogs, homework, into an audio monologue or dialogue, quickly and at scale. Tools like Notebook LLM allow the “conversationalization” of dense text, which can theoretically improve retention for some learners.
- Production support: AI legitimately helps with lots of elements of podcast production, from transcription, to drafting and editing show notes, to clip selection, and even basic edits. Tools like this can save hours of manual work, keeping production teams focused on story and structure, rather than mechanics of production.
- Accessibility: AI tools can lower the barrier for internal comms, the creation of educational content on a small budget, and early stage experimentation in many creative processes. For instance, Chat GPT can be a good way to combat “blank page syndrome.”
Where AI-generated podcasts fall short
While AI has definitely earned a place in the podcasting ecosystem, every strength it brings comes with a corresponding weakness. If you ignore that trade-off, you risk creating content that is efficient… yet completely ineffective.
1. Speed and efficiency comes at the cost of depth
The best podcast moments often come from the host or guests sitting with an idea, or an emotion, longer than is comfortable. To a certain degree, when hosts can let a conversation wander, most audiences will enjoy going along for the ride, provided the hosts also know how to “bring it on home.” Real conversations tend to produce unexpected insights in real time; insights that the audience feels “in” on – because they were there when the idea was born. AI skips over that whole process. Audiences know they’re listening to something that has been generated from an amalgam of pre-existing sources. If you rely solely on an LLM, for instance, to derive your content ideas, you tend to end up with clean, structured content, overly familiar phrasing, and conclusions that seem too neat, or a bit “off.” Even when AI-generated content sounds “right” – it rarely surprises you.
2. Repurposing content comes at the cost of originality
AI is excellent at extending the life of existing content, or making dense material more accessible. But it’s critical to remember that repurposing is not the same as storytelling. In storytelling “stakes” and “original point of view” are critical aspects of holding attention. AI tends to summarize what already exists. It will often flatten all nuance to make its point. Typically, prompt engineers have to work hard to reintroduce points of view, a conversational tone, or any sense of conflict or tension. A Notebook LLM Overview dialogue, even when full of pseudo “conversational touches,” simply pales in comparison to a real conversation between equals that teeters on the edge of disagreement, contains an undercurrent that is unspoken but felt, or that reveals some hidden but essential truth. Essentially, if you settle for mere “repurposing,” and ignore real storytelling and human interaction, you end up with content that explains… but doesn’t move anyone.
3. Production support comes at the cost of editorial judgment and learning
AI tools like Descript and Castmagic can do things like transcribe, draft show notes, suggest clips, and clean up audio sound quality. Of course this can reduce manual labour and speed up workflows, but this underestimates the fine art of editing to a ridiculous degree. Editing isn’t simply a mechanical process, it’s an interpretive one. For example, some editors would argue that bad sound, in select places, is more effective than good sound – if it helps to convince the listener of the verisimilitude of the scene. This is the type of call a good editor can make. When to clean, or not clean the sound. When to push the volume to an uncomfortably high level to underscore a point. When to fade a voice up, or down for maximum impact. Great producers and editors know which pauses are significant, and which ones are empty. They understand which conversational tangent matters and which one should be cut. And it goes without saying that the only way to really develop this intuition is to practice the craft of editing. While AI busily optimizes for clarity and efficiency, human editors learn (the hard way) to optimize for meaning and audience connection. So if you lean too hard on these tools, you risk losing the emotional weight, the lessons, or the moments people actually remember.
4. Accessibility comes at the cost of trust
AI lowers the barrier to entry. As a teacher, I like the fact that anyone can now generate a script (tools like ChatGPT are especially useful for overcoming “blank page syndrome”), and with platforms like Spotify for Creators, they can publish their own homemade podcast quickly and easily. This is super helpful in contexts where time or budget may be limited – and where the goal is simply to experiment, or to quickly create an asset as a reference resource. Internal comms teams, educators, emerging writers, and so on can make use of this. But where it breaks down is when accessibility undermines individuality and voice, and thus undermines trust. If listeners sense that a voice isn’t real, or a perspective isn’t truly “owned” by the voice sharing it, they disengage.
This brings up the deeper issue: AI’s lack of lived experience
Across all of these categories, the same limitation shows up. AI has not:
- Been in the room
- Had the disease
- Walked the trail
- Made the decision
- Fought the fight
- Felt the consequence
It can simulate and replicate emotion, insight, and authority, but it can’t – ultimately – ground these things in experience or context, making it feel more elusive, and less trustworthy to audiences.
This is a fairly major red flag for brands.
Because in branded podcasting, the goal is not just to inform. If you wanted to do that, you’d put some bullet points on your website, or drop some flyers from an airplane. The goal is to create content that audiences actually want to consume, and that builds trust and influences decisions.
Why do human-led podcasts still matter?
Trust is built through real conversations and stories rooted in the human experience.
As we say in Creative Writing “the universal can be found in the specific.” Meaning, the more you tell a story that only you can tell, the more people will relate to it. Listeners are highly attuned to the nuances of human speech: hesitation, imperfection. We like stories that contain an odd specificity: this happened, then this happened , then THIS happened and I realized…. We lean in for these beats. That’s because they are are human signals that take us right inside another person’s perspective and experience.
Memorable moments come from unpredictability. Think about it: Do you recall every day from kindergarten? Or just the day you hung upside down on the monkeybars and forgot to wear underwear? (asking for a friend). The best podcast moments are like this: unscripted, awkward, surprising. Humans can’t help but create these moments. It’s who we are. So we instantly recognize and empathize with them when we hear them.
Influence requires perspective. People don’t act because they were informed. We are all drowning in information yet apathy rises daily. People act because something resonated. Something real broke through their defenses. That deeper resonance comes from things like a guest’s lived experience that echoes your own life, or differs from it profoundly. An opinion that challenges one of your deeply held beliefs. Or an understanding that you are being told a story with great consequences at stake; a story whose teller is someone you respect, or at the very least, believe.
What’s the best way to use AI in podcasting?
At JAR, we believe it’s best to think of AI as a support tool, not a substitute for human talent and intuition.
If you take a balanced approach, AI can be beneficial in some aspects such as:
- Research and synthesis (as long as you fact check manually)
- Editing and cleanup support (but be sure to leave some of the mess intact!)
- Transcription and repurposing (check for accuracy)
- Distribution support (check for variantions between publishing platforms)
- Performance analysis (AI reports the data; humans interpret its meaning)
Where to be extra cautious with your use of AI in podcast production:
- Fully synthetic hosts (they suck and they lack experience)
- AI-generated interviews (they bore audiences)
- Replacing real guest perspectives (they lack clout or meaning)
- Replacing real editorial decisions with AI (you miss out on the nuance of interpretation)
- Thought leadership content without human authorship (who cares what AI told you to write?)
A simple rule:
If trust and influence matter to you or your brand, keep human connection at the centre of all you do.
A real-world example from JAR Podcast SolutionsAs video podcasting stormed onto the scene, we found many of our audio-first hosts were having to adapt their interview style to be more camera-aware. This was easier said than done, especially in a remote recording setup. Our hosts needed to look natural and maintain eye contact with both their audience and their guests, while still glancing at notes or a question line. As a backup, AI gaze correction features built into tools like Descript could help keep their eyes forward. But through experimentation we learned that overuse could give hosts an eerily attentive appearance that undermined impact. Like all AI tools, a little goes a long way. |
The real question isn’t “can AI make a podcast?”
It’s: What kind of podcast do you actually want to make?
AI has removed much of the friction from production. And that’s powerful.
But friction is also where:
- Insight develops
- Stories deepen
- Meaning accumulates
- Knowledge is gained
- Influence is earned
The future of podcasting won’t be defined by how efficiently we can generate content.
It will be defined by: who has something real to say, and how well they say it.
