At ECI we’ve considered AI and machine learning in our investment theses and value creation plans for years. In the wake of GenAI, they’ve moved to the top of the agenda.
Investors need a deep understanding of the company’s AI defensibility to navigate the risk of AI disruption. Just as importantly, they need the ability to identify the value creation opportunities AI represents.
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
The starting point is always the subsector and business model – and how AI is reshaping both. This is where an investor’s subsector knowledge and experience play in; the implications of AI for a travel business are fundamentally different to those for an insurance platform.
Investors want to see management teams with a sophisticated and forward-looking view on the impacts of AI on their market and business. This may mean considerations around product diversification or monetisation, how their operating model and talent strategy need to change, or potential regulatory constraints.
Moneypenny, the provider of outsourced communications solutions, has leaned into AI as a product accelerator, providing its clients with 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 is typically a distinct diligence topic, AI sits across every part of the investment conversation, including Commercial DD, Tech DD and Operational DD.
As such, the first questions usually aim to understand the leadership team’s approach to AI in the round. Does the Founder and management team have an AI-forward mindset, high awareness of the threats and opportunities AI poses, good judgment and the ability to prioritise the most impactful AI initiatives, and the excitement to experiment?
Often, the biggest risk is not that a company hasn’t yet made all of the AI progress it could have – it’s that the management team are resistant to change. Having the right business model characteristics won’t matter if leadership isn’t excited about AI and how it can improve their products, services and operations, and have taken steps to execute on that opportunity.
After that initial question to leadership, investors will typically look at the business model and its resilience to AI disruption. 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’re evaluating. That includes how easily customers could take a DIY approach, the traction of any AI-native competitors, and how adjacent players could interlope i.e. enter other parts of the value chain using AI.
The flipside of this due diligence of threats is, of course, looking at opportunities – how AI can create value. We have seen material positive impacts of new AI-based products at the likes of Paragin Group, which has built AI into its suite of exams and assessments solutions, and on internal operations and workflows at Croud, with the launch of Agentic Croudies, among many others.
Management teams that can help private equity understand how they are best positioned to capture future growth will stand out – it’s what we look for throughout the diligence process and helps us build the conviction we need to go all-in and win deals.
3. Customer relationships and value-add matter 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 clients’ workflows – evidenced by high retention, inelastic demand, strong NPS, strong upsell and cross-sell…
AI has raised the bar on why those characteristics matter.
Investors like ECI are increasingly focussed on businesses with products or services that are deeply embedded in customer workflows, providing high-stakes or mission-critical functionality, and delivering something proprietary backed by trust, brand and reputation. Businesses with these characteristics can expect stronger valuations, not only as they’re harder to disrupt by competitors or DIY approaches, but also as they have the opportunity to leverage AI to further benefit their clients.
We’ve 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 less isn’t the most relevant question. 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 fewer “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 best-positioned companies are those that can deliver more value through their products or services thanks to AI – not just deliver the same offering for less. Where that is the case, pricing is less sensitive.
Avantia, the digital home insurance platform, provides a compelling example: its AI tool, “Holmes”, improved fraud detection accuracy 3.4x 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, with agents noting that it provided a perspective they wouldn’t have seen without it on 84% of claims. The result is a materially better product for customers, partners and Avantia’s agents, 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 companies 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 in their markets. We have deep resources available to support management teams in benchmarking where they’re at on their AI journey (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, our dedicated Commercial Team, and our Data & AI Growth Specialist).
If you would like to speak with a member of the ECI team on how we’re thinking about AI and the impacts we see in your subsector, we would be delighted to hear from you.
