From fear to fundamentals: How AI will drive SaaS leaders over the long term

Widespread fears that artificial intelligence will disrupt the software landscape have driven a meaningful drawdown across enterprise software and information technology equities over the December quarter, and have continued through the February 2026 reporting season.

This fear has been applied largely indiscriminately, with little consideration given to whether software platforms are embedded in core workflows with control of data and distribution, or whether they function as more peripheral point solutions.

A broader risk off market environment and sector rotation have further exacerbated selling pressure. The result has been a growing disconnect between market pricing and intrinsic value, with many of the companies best positioned to thrive in an AI driven world sold off alongside more vulnerable businesses.

This broad based drawdown has repriced some of the most structurally advantaged platforms more on short term fear than long term value, presenting an appealing opportunity for patient investors.

Against this backdrop, our focus is not on predicting the next move in rates or near term sentiment, but on identifying which companies are structurally positioned to benefit from AI rather than be harmed by it.

AI advantage accruing for software companies

Our investment thesis is that AI advantage accrues to platforms that control core workflows and benefit from embedded distribution. As AI shifts from augmentation to automation, economic value increasingly flows to software embedded in systems of record, with access to proprietary data, deeply trusted by customers, and distributed at scale through existing networks.

These platforms are not displaced by AI, rather by virtue of being embedded in core workflows, they become the conduits for AI driven automation and the associated value creation.

Recent developments across the technology sector reinforce this view. Google was not the first mover in frontier AI models and initially mis-executed with Bard, yet its distribution across Search, Chrome, Android, YouTube, and Workspace has allowed it to deploy Gemini at scale directly into existing user workflows.

That distribution and embedding have enabled Google to rapidly narrow the gap with early leaders, despite stumbling in its first iteration. The lesson is clear - in AI, distribution and integration often matter more than who had the most impressive demonstration in year one. Not all software platforms share these characteristics, and the distinction is increasingly important as AI moves from experimentation to production.

There will be some companies that struggle, particularly those that function largely as point utilities solving narrow problems at the edge of workflows without control of meaningful first party data, such as Zendesk. 

While Zendesk delivers value in managing discrete customer support tasks, it sits downstream of core systems of record and does not control the primary workflow or data layer. (ServiceNow NYSE: NOW), in contrast, is embedded at the centre of enterprise operations, where AI driven automation compounds rather than commoditises its role. More broadly, platforms embedded at the centre of mission critical workflows are structurally advantaged by AI adoption.

The value opportunity

The heart of the value opportunity in this sell off is how effectively companies can monetise AI driven automation in a way that materially expands revenue and margins over time. We look for evidence that AI capabilities can be translated into monetisation rather than remaining purely technical features. 

As workflows become more automated and outcomes more measurable, leading platforms are increasingly able to move beyond flat per seat pricing toward usage, transaction or outcome based models. Businesses that control systems of record, proprietary data and workflow orchestration are best placed to participate directly in the economic value their software unlocks.

These characteristics are not merely attractive in isolation, together they create powerful feedback effects. Proprietary data improves the performance of AI driven workflows, which in turn increases customer reliance and raises switching costs. Deep workflow integration and trusted system of record status make platforms costly and risky to replace. 

Scaled distribution allows new AI capabilities to be deployed rapidly across large installed bases, enabling greater usage and richer data over time, while pricing tied to usage or outcomes enables platforms to capture a greater share of the value they create. In combination, these dynamics strengthen competitive moats and support long duration compounding.

Key characteristics we look for

1. High quality proprietary data

Proprietary first party data generated through daily customer activity is a durable source of competitive advantage. It improves automation, accuracy, and workflow efficiency, increasing value delivered to customers while materially raising barriers to entry for competitors.

In an AI context, access to rich, contextual, domain specific data is often more important than model sophistication itself, as models are increasingly commoditised but the structured data required to make them useful is not. 

We favour platforms where everyday usage naturally generates high value data that compounds over time and is difficult to replicate, for example, operational logistics data at WiseTech (ASX: WTC), enterprise workflow data at ServiceNow, medical imaging at Pro Medicus (ASX: PME) and Intuitive Surgical (NASDAQ:ISRG), and transaction data at Xero (ASX: XRO) and Intuit (NASDAQ: INTU).

2. AI embedded within systems of record

AI capabilities trained on proprietary data and embedded directly within core systems of record provide materially greater differentiation than bolt on solutions. When AI is integrated where work is executed , rather than as an external assistant , it can move from suggestion to action, increasing automation, efficiency, and dependency over time.

Deep workflow integration allows AI driven automation to become mission critical, reinforcing platform moats. Systems of record also function as systems of trust, providing the auditability, governance, and permissions needed for autonomous AI operation. Examples include AI within ServiceNow’s enterprise workflow platform and WiseTech’s CargoWise logistics engine.

3. Trust, security, and compliance as structural barriers

As AI agents move from assistive tools to autonomous actors, trust becomes a significant barrier to entry. Enterprises will only permit agents to execute actions, move data, or trigger workflows on platforms that meet strict security, compliance, and governance requirements.

Platforms operating in regulated or high risk domains benefit from this friction. For instance, Pro Medicus in medical imaging, WiseTech in global logistics and customs compliance, and Intuitive Surgical in robotic surgery all rely on stringent regulatory adherence. Requirements around audit trails, explainability, model validation, and vendor accountability materially increase switching costs and protect incumbents.

4. Embedded distribution and customer relationships

Distribution is a recurring but underappreciated source of AI advantage. Platforms embedded at the centre of customer operations benefit from entrenched distribution, high retention, and recurring usage. This extensive distribution network allows AI driven capabilities to be rolled out at scale across existing customers, accelerating adoption, reinforcing workflow integration, and lowering customer acquisition costs.

Google’s experience with Gemini reinforces this point. Despite an early misstep, its distribution across core consumer and enterprise products enabled rapid iteration and scaled deployment. In enterprise software, ServiceNow’s large global customer base and partner ecosystem provide a similar advantage. 

WiseTech’s deep penetration across global freight forwarders allows new AI functionality to be rolled out efficiently across a highly sticky customer base. Distribution determines not just adoption speed, but monetisation potential.

5. Pricing power and scalable monetisation models

AI changes not only how software is built, but how value is captured. As workflows become more automated and outcomes measurable, leading platforms are increasingly able to move beyond flat per seat pricing toward transaction, execution, or outcome based models.

Businesses that control systems of record, proprietary data, and workflow orchestration are best positioned to capture the economic value their software unlocks. Evidence of AI embedded in commercial models includes WiseTech’s CargoWise Value Packs, ServiceNow’s premium AI tiers combined with consumption based pricing, and looking forward Pro Medicus’ opportunities to monetise AI assisted imaging outcomes.

6. Structural barriers that support long duration compounding

The combination of proprietary data, trusted systems of record, embedded workflows, distribution, and pricing power creates enduring barriers to entry that are difficult to displace. Together, these characteristics support predictable cash flow generation, reinforce competitive positioning, and underpin long duration compounding.

Platforms such as ServiceNow, WiseTech, ProMedicus, and Intuitive Surgical exemplify how AI reinforces these barriers , improving product performance, reducing customer costs, enabling richer monetisation, and increasing switching costs. These are the businesses most likely to compound value through multiple technology cycles, including the current AI wave.

Applying the framework: Portfolio examples

We apply this framework systematically across our entire SaaS exposure. Several names exhibit these characteristics and each displays meaningful strengths across multiple dimensions of the framework. Two platforms have experience significant drawdowns, yet stand out as examples of AI advantaged positioning: WiseTech Global and ServiceNow.

WiseTech Global

WiseTech’s CargoWise platform is deeply embedded in global logistics workflows and operates in a highly regulated domain. It controls critical operational data, functions as a system of record for trade and customs, and has built an agentic AI workflow engine directly into that backbone. Its AI features are designed to automate end to end processes and exception handling using pre-trained AI personas aligned to specific logistics roles.

WiseTech’s governance posture, long term customer contracts, and role in cross border compliance create high switching costs and significant regulatory friction, which become more protective as AI automates more of the process. The company is also transitioning customers to value linked commercial models, such as CargoWise Value Packs that bundle AI features and usage based economics, giving it a clear path to monetising AI at scale.

In our assessment, WiseTech scores highly on all dimensions of the framework: proprietary data, system of record depth, trust and compliance, entrenched distribution, and evolving pricing power. AI acts as an accelerant to an already durable business model, not a threat.

ServiceNow

ServiceNow functions as both a system of record and a system of action for enterprise workflows across IT, operations, HR, and customer service, placing it at the centre of how large organisations run day‑to‑day processes.

Its platform captures rich proprietary workflow data, which enhances AI accuracy, drives automation, and strengthens switching costs. AI capabilities including AI powered CRM, Now Assist, AI Experience, and AI Control Tower are embedded across the platform, enabling automation directly within core workflows. 

Strong governance, compliance, and security features reinforce trust and make the platform costly to replace. ServiceNow’s large and growing enterprise customer base, high renewal rates, and partner ecosystem provide entrenched distribution, allowing new AI capabilities to be deployed rapidly.

This disconnect is evident in ServiceNow’s own trajectory, where robust revenue growth has coincided with a structurally lower free cash flow multiple over time. AI is increasingly reflected in its commercial model, combining premium tiers with consumption‑based pricing, enabling monetisation of the value it creates.

Together, these factors strengthen the platform’s moat, support higher ARPU, extend the growth runway, and preserve strong free cash flow generation. ServiceNow is a clear example of a company where AI deepens competitive advantage rather than commoditises it.

Other platforms in the portfolio, including Pro Medicus, Intuitive Surgical, Xero, and Intuit, also exhibit many of the same AI advantaged characteristics in their respective domains. Pro Medicus and Intuitive Surgical operate in highly regulated environments where proprietary data and workflow integration are critical. 

Xero and Intuit benefit from embedded financial transaction data and broad distribution in small to medium business ecosystems. Across these holdings, AI is being layered on top of durable competitive advantages, strengthening moats and monetisation pathways rather than threatening them.

How we are positioned and what this means for investors

AI will not treat all software companies equally. 

It is likely to compress margins and growth for tools with shallow moats, while reinforcing a smaller group of platforms that sit at the centre of critical workflows, own valuable proprietary data and command distribution and pricing power.

Recent drawdowns across software markets largely reflect heightened uncertainty about how AI will reshape business models, rather than clear evidence of structural deterioration in the prospects of the most advantaged platforms.

In several cases, businesses such as WiseTech and ServiceNow, where AI is already integrated into products and commercial models, have been sold down alongside less resilient peers, creating more attractive expected return opportunities. Other companies that investors often group with software, including Life360, have also been caught in the sector-wide drawdown, despite continuing to build strategically valuable platforms, which in our view presents an appealing long term entry point. In Life360’s case, we believe the market is underestimating the earnings potential from emerging revenue streams that are still in the early stages of monetisation.

Within the fund, we are using the broad sector drawdown to concentrate capital in companies that meet our AI advantaged framework. These are platforms whose structural advantages create high barriers to entry and scalable monetisation, where the potential for AI driven growth is both underestimated and undervalued by the market.

For investors, this environment rewards patience and discipline. Maintaining exposure to long duration compounders and adding selectively where prices have fallen more than intrinsic value should position portfolios to benefit as AI driven earnings growth materialises. 

While short term volatility can be uncomfortable, we believe this phase is seeding the foundation for stronger future returns, supported by our portfolio’s structural alignment with enduring AI winners