When ChatGPT launched in late 2022, it felt like the internet collectively lost its mind. Overnight, AI went from a distant concept to something right there in your browser – drafting emails, summarising reports, even making meetings feel slightly less painful.
Some people overhauled everything, building new workflows around it. Others took a cautious step forward. And plenty of folks still haven’t touched it at all.
At EHE Venture Studio, we back AI-driven startups – so naturally, we’re walking the talk. We take an AI-first approach across the board. Before we open a new doc, schedule a meeting, or launch a project, we ask: Can AI do this better? Can we automate it? Will this save us time or unlock deeper insight that we hadn’t already thought of?
And the answer is often yes.
So what does this actually look like in day-to-day work? Let’s start with one area that has done a complete 180: content marketing.
At EHE Venture Studio, we’ve found a rhythm with AI and content: a 30–70 split. I handle the first 30%, AI supports the rest.
That first 30% is where the creative weight lives. It’s where I come up with the ideas, sketch the shape of a message, and define the tone I want to hit. This part has to be human. Creativity is the heartbeat of content, and while AI is getting smarter, it doesn’t think like a strategist. It doesn’t know the feeling I want to evoke, or the nuance that comes from instinct and experience. That’s my job. That’s the part I never outsource.
But once the core idea’s in place, AI becomes my collaborator. It sharpens drafts, tidies up structure, checks for clarity, and helps make each piece land with more punch. It’s not replacing my voice; it’s amplifying it.
We’ve taken that one step further by building a custom GPT trained on our brand voice. It understands our tone, style, audience, and even the specific way we format our blogs and social posts. That means I don’t need to re-teach it every time. I can just drop in a rough draft, and it already knows how to make it better.
The result? Content that’s on-brand, on-message, and produced in a fraction of the time, often 60–70% faster. And it frees me up to focus on the stuff that truly moves the needle: big-picture strategy, storytelling, and quality control.
Here’s what that looks like across different content types:
So, how does this approach work when it comes to something more targeted, like email marketing?
When we launched our YouTube vodcast, Fast Growth: The Venture Studio Way, we were genuinely excited – but also realistic. Full-scale video production is no small task. If we wanted to release episodes consistently without burning out or bloating our budget, we had to find a smarter, leaner way to do it.
That’s where AI came in.
Traditional podcast and video production is a full-time job on its own: trimming filler words, syncing audio, writing captions, cutting short clips, uploading to every platform... the list goes on. And it all takes time. Time that startups – and lean teams like ours – simply don’t have.
So, we built an AI-powered workflow that handles the heavy lifting. Here’s how it works:
We record each vodcast episode using SquadCast. It allows us to connect with guests remotely while still capturing high-quality video and audio. The platform saves local recordings from each participant, which prevents glitchy audio or laggy video – a big step up from basic video conferencing tools.
Once we finish recording, the files are automatically uploaded and ready for editing.
Descript is the backbone of our editing workflow. It’s an all-in-one tool that lets us edit audio and video by editing a transcript – like working in a Word doc, but your changes reflect in the actual footage.
Here’s how we use it:
Accessibility is non-negotiable for us. Every episode is automatically captioned using Descript’s AI subtitle tool. It syncs the captions to the audio with high accuracy and lets us tweak formatting and pacing easily. This helps us:
We export caption files in multiple formats for use across platforms (e.g. .srt for YouTube).
Once the episode is edited, we use AI to help repurpose the episode into multiple pieces of short-form content:
This means every 30-minute episode can turn into 5–10 additional pieces of content, all without needing to start from scratch.
Finally, we use AI-powered scheduling tools to post clips and full episodes at the right times. These tools analyse engagement data across platforms and suggest the best times and days to post for our audience.
Some tools we use for this include:
The impact
By building this AI-driven workflow in our vod/podcast production, we’ve:
And just like with content marketing, AI hasn’t replaced creativity. We still define the narrative, choose the guests, and shape the big picture. AI simply clears the path, so we can stay focused on the story we want to tell.
So, what does this mindset look like beyond content? Let’s talk about how we’re building smarter workflows across the business.
A few months ago, I had a great conversation with Toby Remond, an automation consultant and founder of OptiBee, an AI-led recruitment business. Toby isn’t just using AI to tweak a few tasks here and there; he’s building entire systems around it. It’s core to his offer. Core to his thinking.
We got into something I hear more and more lately – especially from teams trying to modernise without losing their soul: How do you use AI in a way that drives efficiency, but still keeps the creative and human stuff intact?
Toby’s take was refreshingly simple and surprisingly familiar - something almost every marketer or copywriter hears early in their career: start with the problems. The pain points. The friction.
“Everyone’s asking how to ‘use AI’,” he told me. “But the real question is: where are you wasting time? What’s the repetitive stuff that drags you down? That’s where AI starts.”
Just like a good marketing brief, you MUST first understand where the real tension is. If you skip that step, you’ll end up applying AI in places that don’t actually matter (and yes, not every single part of your business needs AI).
In his own business, Toby’s built custom agents that automate everything from CRM enrichment to personalised candidate outreach: writing emails, generating summaries, even handling follow-ups. Tasks that used to eat up hours now take 15 minutes, end to end.
But it’s not just about cool tools; it’s also about building around real workflows. Toby helps clients design AI systems that plug directly into the way their team already works.
At the end of the day, it’s not necessarily about which tool you use. It’s about whether it fits how your team actually works.
“Off-the-shelf AI is fine,” Toby told me, “but if it’s not built around your data and your processes, you’ll always be doing extra work to make it fit.”
I’m a firm believer that AI won’t magically solve your problems. But if you start with the friction points – the time sinks, the manual steps, the bottlenecks – it can become one of the most powerful systems in your business.
We’ve applied that mindset internally at EHE – building workflows that aren’t just efficient, but deeply aligned with how our team actually runs. Let’s take a quick look behind the scenes.
Operationally, we’ve always been focused on scaling with precision; doing more with less, without compromising on quality or speed. AI has made that possible. Whenever we’re trying to solve a bottleneck – whether it’s a resource gap, capacity issue, or simply something that feels inefficient – we ask a few key questions:
Once we’ve got clarity on that, finding the right AI setup becomes much easier – and far more effective than chasing every shiny new thing on Product Hunt.
One of the best examples of this thinking in action is how we now review pitch decks.
We receive hundreds of founder decks every month. Reviewing them used to be time-consuming and inconsistent, requiring senior team members to spend hours manually scanning for fit, red flags, and opportunities. It wasn’t scalable. And more importantly, it pulled our best people away from where they add the most value: helping our top founders move faster.
So we asked: how can we free up 80% of that time and still trust the results?
Our first move was simple – we trained an AI agent to evaluate each pitch deck against our investment thesis, generate a SWOT analysis, and flag decks that didn’t meet our core criteria. It worked. That alone delivered a 30% time saving, and – critically – built trust in the output.
From there, we went further.
With the help of Toby from Optibee, we built a full automation flow using low-code platforms like Make.com and Relevance.ai. Now, every application is routed through this system. It produces a detailed summary report, checks for alignment with our thesis, and pushes relevant deals straight into our pipeline, complete with a data room, which we then review.
One thing we’ve learned along the way is that it’s not just about building tools that work – it’s about building flexibly. Every solution we design is intentionally modular. We’re not locked into a single platform, and everything we build can be lifted and shifted onto a new system if needed.
Why? Because AI agents are evolving fast. Tomorrow’s tools might look very different – and we want to be ready to adapt, not start from scratch.
That mindset – building around problems, not products – is what’s helping us scale with focus. Not everything needs to be automated. But when it can be, we make sure it’s robust, reusable, and ready to grow with us.
If there’s one thing we’ve learned from using AI across content, operations, and investment workflows, it’s this: the best AI isn’t about replacing people – it’s about freeing them.
We don’t use AI because it’s trendy. We use it because it saves time, reduces friction, and gives our team the breathing room to focus on the work that really matters – the creative thinking, the founder conversations, the bold decisions that shape our studio.
Whether it’s refining a blog post, evaluating a pitch deck, or automating the admin that quietly eats up your week, our mindset stays the same:
Start with the problem. Design around the process. Build for real people.
Because AI isn’t a one-size-fits-all solution. It’s a set of tools, and those tools are only as effective as the thinking behind them. When we take the time to understand how we work, where we’re stuck, and what success actually looks like, AI becomes a strategic advantage. Not a gimmick. Not a shortcut. A genuine unlock.
That’s how we use AI at EHE. And that’s how we think more businesses – creative, operational, and everything in between – will start using it too.
Not to replace the magic of human input. But to make more space for it.