AI in Action is a Talentedge leadership interview series exploring how artificial intelligence is reshaping business. These are conversations with senior leaders and founders about how they’re adopting, applying or building AI within their organisation.
We hope these perspectives offer a rounded view of how AI is transforming work, strategy and leadership across industries.
I caught up with Anil Noonan , VP of Operations at Colossyan, to talk about the business, the world of AI, and what sets working in an AI-first company apart from traditional tech.
Anil’s journey began in FinTech at Nutmeg Investments (now part of Chase), followed by nearly five years in management consulting with Capgemini Invent and Deloitte. He then returned to the startup scene, first at Paysend, and now at Colossyan, where he transitioned from Chief of Staff to VP Operations.
Colossyan is an AI-powered video generation platform that lets users create training content from documents, prompts, or scratch, featuring hyper-realistic avatars, lip-syncing, and support for nearly 100 languages. It also includes interactive tools like branching scenarios, conversational avatars, and quizzes.
From your perspective, has your role changed moving from a more established Tech business into an early stage venture?
Yes, 100%, for example the role with Paysend was very defined. It was still broad, covering lots of different things from corporate strategy to investor relations but there was an actual function of the business working in this area (OSM).
When I made the move to a business at series A level, purely from a head count perspective you’re looking at c. 100 FTE compared to over 300 so all areas of the business were less defined. Within, I think two or three months of joining Colossians, I was running finance, people and culture, legal and operations. Whilst I’d joined to primarily be a direct support for Dominic (CEO and Founder), I wasn’t necessarily expecting to be accountable for significant areas of business so soon. But that’s the nature of it, right? When working in an early-stage business, you roll up your sleeves.
Compared to previous experience, is this the closest you’ve been working directly with a founder?
As a founder associate, I was very close with the CEO and chairman at Paysend. But this is incomparable, because now I sit on the executive team where there are five of us. My relationship with Dominic is much closer in some ways too. Literally, every single day of the week we speak.
Also, the magnitude is different. Before again it was bigger/more structured teams, more defined roles and so on. Whereas at Colossyan, we’re trying to scale the business together with the rest of the team. So, it’s a lot more, full on and all in; no task is too small, and no one can have an ego.
What have you seen is that the biggest difference in a scaling AI native business, compared to, previous experience in FinTech
The single biggest difference is the pace of execution, adoption of new technologies and the ability to pivot very quickly and do things without constraint. For example, payments businesses are highly regulated entities that deal with license obligations or have to be really considered with the way they manage their capital (segregated funds etc). Whereas, Colossyan is a much simpler business. It’s not regulated by the FCA. If you want to experiment with some pricing, you come up with a structure, get the approvals, roll it out, test it for a week and iterate. If it doesn’t work, that’s fine? Change it, test it again. It’s a lot more fluid, and that just allows you to move so much quickly as a business, without the guardrails and the red tape of being in a regulated industry.
Whilst AI isn’t a brand-new invention, we are seeing a mass rollout in different verticals and usage as part of most people’s day-to-day. Where AI businesses are scaling in this new world, how do you find defining strategy from a business where there isn’t necessarily a playbook compared to some other businesses in more established industries?
The way we think about is in different layers, sort of like an onion. Some aspects of the business are your core, with repeatable, scalable processes; this is where we have established product market fit over the past 4 years or where there are similar and we know that works. For example, Colossyan excels in helping Enterprise L&D teams make more interactive and engaging content through a platform that saves time, reduces costs and provides a scalable solution compared to traditional video creation methods. For this core aspect of our business, we’re scaling this and driving repeatability with our SaaS model.
Then there’s the experimental branches of the business, for example, our API (Application Programming Interface) that allows organisations to embed Avatars, Text-To-Speech, multiple languages and so on into our their on workflows. So, when rolling out the API, no one’s really done it effectively in a scalable way before in the company. So we have built out the API and now we’re getting further traction with clients, finding more unique use cases and helping clients to realise the value.
The key thing with working in AI is that this technology is nascent, so we are still in the experimental phase with validating the commercial application of the innovation. I think about our real-time rendering Avatars that allow people to have a conversation with a hyper-realistic avatar through their webcam. We’re showing this to clients and testing. We don’t have a full fleshed out pricing model for this yet, but we’re assessing customer demand, validating purchasing power and so on. Until we have bit more of an idea of that and proven data and validity, then we don’t know 100% if it’s going to work. We have to try it and pivot accordingly.
When you mentioned there is such speed of innovation and you have to be dynamic with how you react to it. How do you find it from a strategizing & forecasting standpoint, where it’s quite hard to see any sense of truth at the end of it?
I think with any early-stage business, you have the least historical data to reference and pull into forecasts. Our approach is just to be simple, logical and methodical with assumptions on more emerging areas of our product / strategy. We’re conservative until we see traction and try to drive repeatability through process. For the core business, we’re bullish and set ambitious goals, leveraging OKR framework to drive alignment through the company.
As a business, are you, or others across the business, implementing or using any other AI tools to assist with the day-to-day?
As a deep tech company, we probably are differentiated from other AI native companies, in the fact that we have a team of AI researchers. They’re working at the forefront of the academic side of AI research, but also the commercial application of that. AI adoption flows through the company because it’s in our DNA. We try to embed AI across every workflow where feasible and value adding. There is of course more we can do, but we definitely have the mindset of utilising technology across every area of the business, from Sales and Marketing through to Operations, Legal and Finance.
How did you find that transition into the AI space, and what would you say, was the biggest challenge of that, and even how you might counteract that for anyone else making a similar move?
I think I would probably say three things.
The first thing is the actual domain knowledge. It’s a steep learning curve going from reasonably simple FX spread margins, to having diffusion-based transformer models the topic of conversation around the lunch table. For me, it was a challenge getting up to speed with the lexicon and other basics I needed to learn to understand drivers of the P&L
Second thing would be is the scope of the role is much broader. I’ve had to manage various different aspects of the business, which I just didn’t have so much exposure to previously
The third piece is the jump from being at the management team layer to the executive level. I had great exposure at Paysend and will always be grateful to my colleagues for offering me great opportunities, but it’s a different scope being present in every board meeting, directly working on some of the most complex strategic decisions we make as a business and having accountability over key areas of the business.
