---
title: "Software Moats in the AI Era: What Still Creates Defensibility?"
description: Explore how portfolio company value creation depends on durable software moats, customer knowledge, industry expertise, and competitive advantage in the AI era.
image: https://evermethod.com/hubfs/Software%20Moats%20in%20the%20AI%20Era.png
---

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# Software Moats in the AI Era: What Still Creates Defensibility?

![](https://evermethod.com/hubfs/Software%20Moats%20in%20the%20AI%20Era.png)

- October 16, 2026

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Author :  Evermethod, Inc. | October 16, 2026

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For much of the SaaS era, a strong software product could provide a meaningful competitive advantage. Building a sophisticated platform took years of engineering, testing, customer feedback, integrations, and investment. Once customers had adopted the software across their business, replacing it could be expensive, disruptive, and difficult to justify.

Generative AI is changing some of those economics. Development is becoming faster, smaller teams can build increasingly capable applications, and new competitors can enter markets that once required much larger engineering investments. McKinsey estimates that generative AI could increase software vendor switching by 5 to 10 percentage points as data migration, integration development, and user training become less expensive. Its research also suggests that software churn could increase by 1 to 3 percentage points in some categories.

For portfolio company value creation, that creates a more complicated question. A software business can continue to grow while becoming easier to replace if much of its advantage comes from the product itself rather than from the position it has built around the product.

So, what would actually make customers stay if another company could offer similar software at a similar price?

Often, the answer has little to do with the software alone. It can be the customer knowledge accumulated over years, the way the product fits into important business processes, specialised industry expertise, trusted relationships, or the number of systems and workflows connected to it.

 

## **What AI Changes About Software Defensibility**

**![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-07-39-3231-PM.png?width=1600&height=900&name=undefined-Oct-16-2026-01-07-39-3231-PM.png)**

 

The numbers point to a fairly important shift. A strong software product still matters, but the product itself may provide less protection than it once did.

 

## **1. Why Product Functionality Alone Is Becoming Less Defensible**

![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-07-40-0161-PM.png?width=1600&height=900&name=undefined-Oct-16-2026-01-07-40-0161-PM.png)

 

A large software product used to provide considerable protection because competitors had to spend years developing comparable capabilities, testing them with customers, building integrations, and working through implementation challenges.

AI is shortening some of that development cycle. Coding assistants, generative AI models, automated testing, and development agents can help smaller teams build credible software much faster than before.

That does not mean every software product suddenly becomes easy to replace. It does mean that individual product capabilities are becoming less reliable as a long-term source of differentiation.

Take a financial reporting platform. Its dashboards, reports, forecasting tools, and visualisation capabilities may be valuable, but another company could potentially reproduce similar functionality much faster than it could have a few years ago.

The more durable advantage may sit around the product.

The platform might contain years of reporting history, customer-specific rules, integrations, approval processes, and management routines. Employees already know how to use it, and senior teams may depend on its outputs for important decisions. A competitor can recreate a dashboard. Recreating everything that has developed around that dashboard is a much bigger undertaking.

![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-07-40-5283-PM.png?width=1378&height=775&name=undefined-Oct-16-2026-01-07-40-5283-PM.png)

 

For portfolio company value creation, the implication is fairly practical: product investment still matters, but it needs to be supported by advantages that competitors cannot easily reproduce.

 

## **2. Where Durable Software Advantages Still Come From**

Some of the strongest software moats are built gradually through years of customer use. They become part of how a business operates, which makes them much harder to copy than a particular product capability.

**Customer History Creates an Advantage New Entrants Cannot Buy**

A software system can accumulate customer-specific information, historical records, configurations, business rules, and knowledge about how work gets done.

A new competitor may have access to the same AI models and development tools, but it does not automatically have that history. When AI is applied to a deep body of customer knowledge, it can produce more relevant recommendations, automation, and analysis.

This gives established software companies an interesting advantage. The more useful customer knowledge they have captured, the more opportunities they may have to use AI in ways that a new entrant cannot easily match.

**Software Becomes Harder to Replace When It Runs the Business**

Software also becomes harder to replace when employees rely on it for important work.

Consider a system used for financial approvals, regulatory reporting, customer support, or supply-chain management. Replacing it may involve moving information, rebuilding integrations, retraining employees, validating outputs, and changing established procedures.

The software itself may be replaceable. The disruption involved in replacing it can still be significant.

**Industry Expertise Makes Software More Difficult to Replicate**

Specialised industry knowledge can become another source of protection. It can influence how a product is designed, how it is implemented, how automation is applied, and how unusual customer situations are handled.

That knowledge is usually built through years of working with customers. It is difficult for a new competitor to recreate simply by hiring developers or adding an AI model.

**Customer Relationships Extend the Moat Beyond the Product**

Long-standing customer relationships can matter for similar reasons. Implementation expertise, trusted relationships, channel partnerships, and a detailed understanding of customer requirements are all accumulated over time.

Together, these advantages can make the business much harder to displace than the product itself might suggest.

**Integrations and Partnerships Increase the Cost of Switching**

The more closely software connects with other systems, the more complicated a replacement can become.

A customer may depend on integrations with finance, operations, customer data, reporting, or other internal systems. Partners may also be built around the platform.

Those connections create practical switching costs that are easy to overlook when defensibility is judged only by looking at the product.

 

[Build Lasting Moats, Contact Evermethod AI Today!](https://evermethod.com/contact-us)

 

 

## **3. How Customer Adoption Can Strengthen a Software Moat**

![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-09-22-1034-PM.png?width=1378&height=775&name=undefined-Oct-16-2026-01-09-22-1034-PM.png)

 

The strongest software businesses can create a useful cycle in which deeper customer adoption makes the product more valuable, which then gives customers more reason to use it.

Consider a customer-service platform. Over time, it may accumulate information about customer histories, recurring problems, escalation patterns, and resolution processes. AI can use that information to improve recommendations, automate routine work, and help employees resolve issues faster.

As the customer uses the platform for more work, the system gains more useful information. That creates more opportunities to improve the product, which can encourage even greater adoption.

 

**The Cycle That Turns Customer Usage Into Competitive Advantage**

**More customer usage → More customer knowledge → Better AI and automation → Deeper adoption → Higher retention and expansion → More customer knowledge**

This is one of the more interesting ways AI can strengthen an existing software moat rather than simply making software easier to reproduce.

**AI Becomes More Valuable When It Builds on Existing Customer Knowledge**

BCG's 2026 Applied AI Index, based on research involving more than 1,300 senior leaders across more than 20 sectors, found that the top 7.5% of companies by AI maturity achieved 2.4 times the top-line growth of the bottom half. BCG also reported that companies combining strong strategic direction with strong applied AI capabilities generated five times as much value from AI as companies that were weak in both areas.

For software businesses, the lesson is useful. Adding AI to a product is relatively easy to describe, but the economic value can be much greater when AI improves an existing customer relationship, business process, or body of customer knowledge.

 

## **Retention Shows Whether Product Value Is Compounding**

Retention provides another useful way to look at this.

McKinsey found that B2B SaaS companies with net revenue retention of at least 120% had a median EV/revenue multiple of 21x, compared with 9x for companies below 120%.

High retention does not automatically mean a business has a strong moat. It does, however, show why continued customer value and expansion within existing accounts are important indicators of software strength.

 

## **4. How to Test Whether a Software Business Is Truly Defensible**

A defensibility assessment should go beyond asking whether the product is technically strong. A more useful test is to think through what a credible competitor would actually need to reproduce to win the customer.

![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-09-23-7847-PM.png?width=1377&height=775&name=undefined-Oct-16-2026-01-09-23-7847-PM.png)

That final question keeps the assessment grounded in business performance.

PwC Strategy& reported in 2026 that the median EV-to-forward-sales multiple for companies in the Bessemer Rule of 40 software index declined from 9.0x to 5.6x, a roughly 40% decline. Valuations are influenced by many factors, but the change reinforces why it is important to understand which software businesses have durable advantages and which are more exposed to increasing competition.

One of the simplest tests is to imagine that a competitor gets access to the same AI models, coding tools, and development infrastructure tomorrow.

What would still be difficult for that competitor to reproduce?

The answer often tells you more about the real moat than a long list of product capabilities.

 

## **5. Turning Software Defensibility Into Portfolio Company Value Creation**

A defensibility strategy should begin with evidence. Customer interviews, churn, renewal behaviour, win-loss analysis, product usage, implementation costs, and account expansion can show what customers actually value and which parts of the product are becoming easier to replace.

![](https://evermethod.com/hs-fs/hubfs/undefined-Oct-16-2026-01-09-22-9735-PM.png?width=1378&height=775&name=undefined-Oct-16-2026-01-09-22-9735-PM.png)

For a business with valuable customer information, the priority may be using that information to improve customer outcomes through AI. For a company deeply embedded in customer operations, strengthening connections to related processes may create more value than simply adding another AI capability.

The important part is linking AI investment to an economic outcome. That might mean improving retention, increasing revenue from existing accounts, reducing implementation costs, improving pricing, or lowering the cost of serving customers.

The same discipline should apply to product investment. If a particular capability is becoming easier for competitors to reproduce, strengthening the customer knowledge, business process, industry expertise, or relationship around it may create greater long-term value.

The strongest software businesses in the AI era will not necessarily be the ones with the longest list of AI capabilities. They are more likely to be the businesses that use increasingly accessible technology to deepen advantages that competitors still struggle to reproduce.

AI models will become more capable, and software development will continue to get faster. Customer knowledge, important business processes, specialised expertise, strong relationships, and years of accumulated use are much harder to recreate.

For portfolio company value creation, the priority is to understand what makes the business difficult to replace, identify where AI could weaken those advantages, and invest in the areas that can become stronger as AI becomes more widely available.

 

## **Strengthen the Software Moat Through AI-Led Value Creation**

Evermethod AI helps software businesses identify where AI can strengthen competitive advantage, improve operating economics, and create durable value. The focus is on connecting AI opportunities to customer relationships, business processes, data, and operating priorities that can strengthen a company over time.

**Explore how Evermethod AI can help turn AI opportunities into a focused value creation strategy.**

[Accelerate Value Creation, Get In Touch With Us Today !](https://evermethod.com/contact-us)

 

 

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