Technology alone is no longer a sufficient moat
AI is putting pressure on technology-based defensibility, particularly where products rely on coding capabilities or workflows that can be replicated more cheaply and quickly. The traditional case for investing in software - recurring revenue, high gross margins and limited capital expenditure - is being reassessed as AI reduces the cost of producing and maintaining technology.
Investment analysis is therefore shifting from whether a company is “in technology” to whether its underlying business model can remain relevant over a five-, 10- or 15-year horizon. Businesses with durable characteristics, such as proprietary data, complex workflows, regulated environments and mission-critical systems, are better positioned to preserve value as AI evolves. In this environment, the focus is moving from sector labels to the structural resilience of a company’s value proposition, customer relationships and operating model.
Defensibility is rooted in embedded advantages
More durable sources of competitive advantage include proprietary data, complex workflows, regulated environments, mission-critical systems, customer relationships and specialised operational expertise. Businesses with these characteristics may be better positioned to use AI to strengthen their offering rather than be displaced by it. AI can amplify value where it builds on clean, proprietary data and is applied within workflows that are deeply integrated into customers’ operations, improving speed, precision and service quality while reinforcing the underlying business model.
In practice, the most resilient businesses are those where AI enhances an existing advantage rather than substituting for it. For example, AI can accelerate the analysis of specialist or regulatory data, automate repetitive elements of complex processes and support more personalised engagement with customers, all while leaving the core value proposition intact. By contrast, businesses whose differentiation rests primarily on easily replicated technology or generic workflows face greater exposure to AI-native competitors that can redesign processes from the ground up. The distinction is therefore not between technology and non-technology businesses, but between business models with embedded, hard-to-replicate advantages and those without them.
AI is an enabler of value creation
AI is increasingly viewed as an enabler of value creation rather than solely a source of disruption. It can improve productivity, accelerate analysis, personalise services and support more efficient hiring and operations across portfolio companies. Its greatest impact may come from augmenting existing talent and workflows, including the transition from software as a service toward software that performs tasks previously completed by employees.
In this context, the focus is on identifying which processes can be meaningfully improved or automated, reducing costs and increasing profitability, while preserving the elements of the business that create differentiation. AI can also expand the scope of what a business can deliver, opening new markets or increasing the value of existing customer relationships. The opportunity is therefore not limited to technology companies; businesses across sectors may benefit where AI is applied to improve operational performance and strengthen the customer proposition.
- Productivity gains
AI can compress tasks that previously required substantial analyst time, such as data preparation, document review and modelling, freeing teams to focus on higher-value analysis and decision-making.
- Talent and customer enablement
AI can improve hiring decisions, support sales and service teams with tailored information, and help employees deliver more personalised, efficient outcomes for customers.
Human judgement remains central
AI can accelerate sourcing, diligence and data analysis, but it does not replace the judgement required to assess management quality, culture, customer relationships or the long-term viability of a business model. Investment decisions continue to depend on combining quantitative analysis with sector expertise and direct engagement with management teams.
The ability to understand how a business is run, how its culture supports execution and whether a management team can adapt to technological change remains difficult to automate. AI is therefore best treated as a tool that widens access to information and increases analytical capacity, while the final assessment of value, risk and partnership quality remains a human responsibility.
Asset selection determines long-term resilience
- Business model durability matters most
AI may accelerate disruption in companies that are already vulnerable, but the central investment discipline remains identifying businesses with models that can remain relevant over five, 10 or 15 years.
- Customer relationships are a key defence
Close, entrenched relationships with customers can support pricing power, retention and the ability to adapt the service offering as technology changes.
- Management capability is decisive
Strong operational talent and leadership with sufficient technology understanding are central to adapting businesses to AI, executing transformation and preserving long-term value.
Speaker Quotes
“We are moving from software as a service to software as a worker… that’s one of the critical shifts.” Anne Laurence Roucher, EDMOND DE ROTHSCHILD ASSET MANAGEMENT
“The disruption by AI is not a bad thing. If you can disrupt and use AI to make your business better, that’s great. Or is this a business that is going to be disintermediated?” Richard Kunzer, INTEGRUM HOLDINGS
“Today, the conversation has completely shifted… it’s really around how do you reimagine that business model and the underlying workflows because you have the ability today to use AI to actually drive a different type of outcome.” Scott Kauffman, MOTIVE PARTNERS
“We are moving from software as a service to software as a worker… that’s one of the critical shifts.” Anne Laurence Roucher, EDMOND DE ROTHSCHILD ASSET MANAGEMENT
