Best practices in fintech AI:Five key takeaways from QED's portfolio company CEOs

We recently had our 17^th Annual QED CEO Summit in Washington, D.C. and had an amazing turnout from what we (admittedly, somewhat biasedly) think are the most exciting entrepreneurs and builders in the world of global fintech.

Given the time we live in, it should not come as a surprise that we had lots of great discussions around AI and how startups and scaleups are using it across several functions. In the spirit of continuing this very interesting (and certainly fast evolving) conversation, I wanted to share some of the key takeaways from these sessions.

1. AI is being used by all, but to varying degrees

One not so surprising insight was that when we asked CEOs to rate how much they have integrated AI to their organization on a scale of one to ten, nobody thought they were even close to a ten. More interestingly however, as CEOs listened to the practices of other peers, some changed their own rating from high to low single digits.

The twofold takeaway here is that one the variance across organizations is indeed wide, and two that cross fertilization and sharing notes with others brings much improved awareness. So keep sharing, and keep learning – fast!

2. Common frameworks are emerging and quickly evolving as a new AI culture emerges

Frameworks like “Role-Context-Task-Format” or “Role-Input-Output” are quicky evolving and some standardization within organizations are emerging while there are still significant differences across organizations as per point one above.

This then leads to a key skill set around writing prompts, or in other words “managing agents” developing rapidly and becoming very valuable.

In this fast evolving world, it is also quite common to use one agent to write a prompt for another agent, for example using ChatGPT to write a prompt for Claude.

By ways of a specific case study, one interesting example that was shared involved an AI agent joining a customer product discovery call, and then based on the notes from that call writing prompts for another AI agent to create new wireframes for the new product that was being discussed. According to this CEO, just using this simple process cut down on developer needs by a factor of five.

3. HR impact is emerging as not everybody can keep pace

What is however clear is that while common frameworks on how to generate the most effective prompts most efficiently are quickly emerging, not everybody in the organization is able to keep up. As a result, there is certainly an issue around what to do with those in an organization that are not able to adapt to this new world at equal speed.

The not so surprising upshot of this is that organizations are parting ways with what we may refer to as AI luddites (not an ideal outcome for anyone to say the least), but also a lot of thought is going into how to upskill the workforce to adapt to this new world, and how to incentivize everybody to learn and adapt their mindset.

4. Caution around new types of tech debt

One interesting point was also made around AI generated product prototypes creating a new sort of tech debt or friction in the product discovery and design process.

The issue here was that a simple product prototype created by an AI (as in the example above) may not necessarily be easy to productionize due to a host of technical issues. The best practice shared in this case was to treat the AI created demo as a “throwaway prototype” or a PoC and then start from scratch (or at least give the team latitude to make the needed layout changes) later in the development process.

5. Managing agents, humans and their interactions

What also became clear is that while managing agents was a new and emerging skill set, managing teams of agents and humans, as well as their interactions was another.

The process of hiring in this case is replaced by the process of creating and designing custom agents, and indeed almost all organizations already have different agents doing different tasks, and being managed in different ways. On the point around managing a team of humans and agents, a lot the emerging themes were around upskilling, creating a culture around how AI is used, driving adaption, and finding ways to share best practices quickly.

There was also an interesting theme around B2B focused businesses finding ways to educate their business customers on how to use AI in implementation.

While this was a sample of some of the broad topics discussed, if you found this interesting also make sure to refer to the additional content put out by the QED team around this topic for much more. And most importantly, as you continue building your fintech business, do reach out to QED – we’d love to hear your thoughts and incorporate your perspectives, especially around how you are using AI to compete – and win.