How are investors thinking about AI defensibility and opportunity

ECI have considered AI and machine learning in our thinking about investment theses and value creation opportunities for a long time, but clearly in the wake of GenAI this has moved up to the top of the agenda over the past fund cycle. Investors need a depth of understanding of the AI defensibility of the businesses they're backing in order to avoid value depreciation from disruption, and to make sure they're able to identify the value driving opportunities they can capitalise on. How do we assess that balance of defensibility and opportunity? For management teams preparing for investment, these are the questions investors like us are asking:

1. Subsector and business model first

AI is rapidly becoming table stakes and the starting point needs to be the subsector and business model and how AI is reshaping both. To do this you need depth of subsector knowledge and experience - while there are areas of overlap, the implications for a travel business are fundamentally different to that of an insurance platform. Critically, AI adoption on its own isn't enough if there are opportunities to disrupt your sector or business model that you could benefit, or be at risk, from.

Beyond their own perspective, investors want to see and take confidence from management teams that have a sophisticated and forward-looking view on the impact of AI on their market, and business, and potential future effects. For example, this may mean consideration around product diversification or monetisation, how their operating model and talent strategy needs to change, or it may be a considered view on potential regulatory constraints.

Moneypenny, the provider of outsourced communications solutions, operates in a sector where AI is creating opportunities to innovate and so Moneypenny has leaned into AI as a product accelerator, developing an AI Receptionist and Voice Agent that blends automation with human expertise. Critically, Moneypenny has built and filed patent-pending guardrails into its AI communication tools ensuring responses remain accurate, on-brand and compliant, while seamlessly escalating complex conversations to its human team.

2. How does AI play into due diligence

Whereas Tech DD can be distinct, AI is not a niche diligence topic - it is core to every part of the investment conversation. At ECI that includes having its own section in every Investment Committee paper, and that will cross over into diligence workstreams like Commercial, Technology and Operations (and frequently now includes AI DD or stand-alone AI-DD).

Beyond the sectoral impact awareness mentioned above, investors like ECI are looking for evidence of awareness, judgement and an openness to experimentation. The real risk is not a business that has not yet fully deployed AI in all aspects of their business, it is a management team that are resistant to change, or are too consumed by day-to-day execution to recognise and act on the disruption building around them. Having the right business model characteristics won't matter if leadership is not open to or excited about experimenting and adapting to the new world, and are already gathering those learnings.  

A term that is constantly used is 'depth of moat'. At ECI, alongside our diligence providers we often work with our Data & AI Growth Specialist, Orlando Machado, to help us test the barriers to disruption in the businesses we are evaluating. That includes examining what trade-offs are inherent for customers in taking DIY approaches, the presence, traction and potential of any AI-native competitors, and how adjacent players could interlope i.e. enter other parts of the value chain using AI.

The flip side of this is understanding how potential investments can use the technology to create more value for customers or to optimise their own operations - we have seen material positive impacts of new AI based product at the likes of Paragin Group, and on internal operations and workflows at Croud and others.

Having this conviction in the resilience of the business, and its position to capture future growth is what enables us to commit and in turn win deals.

3. Customer relationships and value add matters more than ever

We've always been focussed on the fundamentals of a high-quality business's relationships with its clients - the levels of value add, of advocacy, and embeddedness within its client's ways of working, evidenced by e.g. low customer concentration, inelastic demand, strong NPS etc. AI has raised the bar on why those characteristics matter. Investors are increasingly focussed on businesses where products or services are deeply embedded in customer workflows, where they are exposed to high-stakes or mission-critical processes, and where they deliver something proprietary backed by earned trust and reputation. Businesses operating in this space can expect stronger valuations because not only are they harder to disrupt by competitors or DIY approaches, but they also have the opportunity to leverage the technology to the further benefit of their clients.

We have always viewed technical complexity as a weak moat in isolation, as inevitably technology will catch up with any product, but this is especially true in the wake of AI. Ultimately whether or not your competitors could build your tool or offer your service for cheaper is not the most relevant question - in many ways it's very similar to the conversations people have had historically about outsourcing and offshoring. The most relevant piece of the conversation is about customer impact and the trust they have in your product.

4. Pricing in value

Some of the biggest questions for existing businesses around AI relate to pricing. If a service becomes substantially automated and more efficient to deliver, or it still delivers the same client outcome, but the client has less "seats", can or should it still command the same price? This question is particularly acute where AI has the potential to replace rather than augment human effort. Investors want to understand whether pricing maps clearly to value and how that relationship will be maintained should there be changes to service delivery or consumption. The businesses best positioned are those where AI makes the product or service create more value for clients, not just cheaper to deliver. Where that is the case, pricing is less sensitive.

Avantia, the digital home insurance platform in our portfolio, provides a compelling example of how AI is improving the quality of its offering. Its AI tool Holmes improved fraud detection accuracy sixfold and completed payment calculations with 98% accuracy. Holmes also proved to have a much broader impact, making recommendations on claim coverage, payment amounts and next steps on complex cases. The result is a materially better product for customers and partners, with significant operational and financial benefits for the business.

More broadly, we are seeing pricing models flex to ensure they remain aligned with value creation; be it outcome or hybrid-based approaches or models (like Moneypenny's voice agent), or aligning seat costs with "super users" that are protected in potential future seat compression scenarios.

What does this mean for your business?

AI is a huge opportunity for those able to harness its potential. We work closely with management teams across our portfolio to help them manage and prioritise the questions it raises to ensure they're positioned to outperform their markets. We have a number of resources available to support management teams in benchmarking where they are at (the ECI Data & AI Maturity Model), helping them identify where they would like to go, and supporting them in taking their first steps and beyond (the ECI Data & AI Toolkit, and our Data & AI Growth Specialist). If you would like to speak with a member of the ECI team about how we are thinking about AI opportunities and risks and the impacts we see in your subsector - we would be delighted to hear from you.