Nowhere is this more evident than in the cloud software market where software providers have had to respond to the so-called SaaSpocalypse – the term used to describe the fear that AI coding will replace some existing software-as-a-service tools, which has caused investors to sell off associated stocks and led to volatile share prices in recent months.
But it’s not just software businesses that face a threat from AI – all companies seeking to raise capital have a job to do to persuade investors that their business is not only set up to adapt to the impact of AI as the technology evolves but ready to embrace associated opportunities. From the investor perspective, the challenge is to build knowledge of AI capabilities and understand their likely effects on the business models and financial projections of prospective targets.
The SaaSpocalypse: death of the moat?
In the software market, amidst fast-moving developments with AI – such as increasing sophistication of generative AI and emergence of agentic AI – investors have been increasingly focussed on whether AI can perform functions that previously required dedicated software, whether products could become obsolete more quickly, and whether companies will need to rethink their licensing, pricing or go-to-market strategies as well as their workflows and associated staffing needs.
Traditional software businesses have long benefited from high switching costs, proprietary technology, embedded workflows, network effects, valuable datasets, strong customer relationships and predictable, stable revenue streams – a ‘moat’ protecting their business models. AI has challenged that model in a major way. Earlier this year, matters came to a head when around $300 billion was wiped from some software company valuations. Subsequently, SaaS providers have been fighting to address concerns about their longevity in the AI age.
Analysts have moved to assess this so-called SaaSpocalypse.
In March, UBS warned that “LLM providers, AI-native entrants, and DIY initiatives are dismantling some moats” and said SaaS providers face a challenge to “remain relevant”. Some applications are particularly prone to disruption, it said. These include “purely workflow-oriented SaaS tools” since “their value is concentrated in the user interface and process coordination layers, both of which AI agents can readily replicate or abstract”.
However, UBS also highlighted how some SaaS applications are difficult for AI tools to replicate. It said: “Our checks were of the view that apps software with embedded governance and regulatory complexity and high levels of customisation are generally tougher for AI to disrupt, given their role in business-critical processes that demand trust, auditability and compliance, and where switching costs are high due to deep integration and change management barriers.”
In a June paper, HSBC analysts suggested that the productivity boost that can be obtained from AI tools had been overestimated and said it had led the market “to overestimate AI risk to software”. It cited “hard evidence”, such as revenue growth and forecasts, as reasons for believing the concerns of investors are “unwarranted” and highlighted how some of the biggest SaaS providers had engaged in major share buyback initiatives in a bid to install confidence in investors.
“If a software apocalypse is in the works, we do not see signs of concerns coming from the largest enterprise software companies on the planet,” the HSBC analysts said. They reflected on “strong” double-digit growth figures reported by the likes of Adobe and Salesforce and predicted “revenue acceleration” for others like SAP and Oracle.
Actions for all businesses seeking to raise capital
Today, there remains a great deal of uncertainty among the investment community about how AI affects the risks of obtaining returns from prospective targets across a wide range of sectors.
For businesses seeking capital to grow, the nervousness of investors places the onus on them to have a good ‘AI story’ to tell. Taking a relatively early example, Japanese medtech business Olympus offered some insight into how to adapt to changes in technology to enhance the products it offered.
With its strategic acquisition of cloud-AI endoscopy startup Odin Vision in 2022 – a deal that Pinsent Masons advised on – Olympus married its existing camera and medical technologies with AI-enabled data aggregation and analysis that helps clinicians better detect conditions such as colon cancer. The move has helped Olympus strengthen its position as the global market leader in gastrointestinal (GI) endoscopy and formed part of a broader pivoting of the company’s focus in response to changes in technology and customer habits, with Olympus having sold off the part of its business that was focused on manufacturing cameras and audio recorders in 2021.
As they try to understand and get comfortable with anticipated AI-related impacts, investors will examine whether businesses can continue to differentiate their products or services, who they are competing with in an AI-enabled market, and whether large AI platforms could erode their customer base.
Businesses should expect to have to present a credible AI strategy in response, as investors want evidence that management understands where AI can reduce costs, improve productivity, create new products, open up new markets or strengthen the company’s competitive advantage. Every single business plan now must address these points.
How investors can seek out opportunities
Even in markets like software where investment has commonly flowed more easily, investors are becoming more reticent about basing their investment decisions on traditional assumptions about growth, revenue and market position.
Amidst these prevailing concerns, tech investors are exploring alternatives opportunities. A new report by Mergermarket suggests some are turning their focus to information services and data companies – a recognition, perhaps, that AI models depend on large volumes of high-quality data to deliver the benefits the technology can offer. To take the Olympus example again, its technology is trained on a large, enriched dataset, enabling deeper comparative analysis and more accurate flagging of anomalies for clinician review and assessment.
As investors explore new opportunities, they will want to understand how exposed a business is to AI-driven disruption and what effect AI could have on its future performance. The product of that is an increasingly complex and time-consuming due diligence process. In the work we do with private equity funds that aim to establish a ‘buy and build’ platform, it means every single prospective acquisition – from the platform investment to the bolt-on acquisitions that follow – is now subject to significant checks on whether the investment is AI-proof.
These checks can be expensive and delay deals, and often the different teams of advisers that investors select to support them operate in silos, reducing the effectiveness of their work. One way for investors to address this is to select a team of legal advisers, corporate finance advisers and technologists that can work together, to inform AI-focused due diligence and decision-making in a coordinated way. This should help them manage costs, price deals more effectively, and get greater certainty over whether the opportunities they are pursuing are robust in the AI age.