When AI Can Replicate Your Software, What Are Customers Still Paying For?

AI is making established software features easier to reproduce, challenging SaaS companies whose competitive advantage rests on reporting tools, interfaces and other functions that once required years of development. For specialist providers, the value of their products may increasingly lie in the data, integrations and business processes behind those features.

Artificial intelligence is reducing the time and technical effort needed to develop many software features, exposing established software-as-a-service (SaaS) companies to competitors that can build comparable products with smaller development teams. Reporting tools, analytical dashboards and routine administrative functions are becoming easier to create, while AI agents are beginning to perform some of the work traditionally carried out inside dedicated applications.

The competitive pressure is particularly acute for specialist software providers that have spent years developing products for narrow business markets. Their customers may still depend on the underlying technology, but features that once justified a substantial investment can become less distinctive when competitors reproduce them more quickly.

Financial technology offers a useful example. A wealth management platform may consolidate assets held across several banks, calculate investment performance and provide reports through a dedicated interface. AI can already assist with analysing financial information and preparing similar reports, provided it has access to reliable data.

The distinction between producing an analysis and maintaining the information behind it is becoming increasingly important. While AI can simplify the former, the latter still requires specialist integrations, accurate records and ongoing oversight. Software companies must decide where those capabilities justify continued investment and how much of the functionality surrounding them customers will still value.

When Software Features Become Easier to Copy

Swiss wealthtech companies have developed platforms that give family offices, wealth managers and private clients a consolidated view of their financial assets. These products typically collect information from multiple custodians, process transactions and present portfolio holdings, performance figures and analytical reports.

The development work can be substantial, particularly when platforms support private investments, different currencies and complex ownership structures. Providers have traditionally differentiated themselves through the range of information available, the quality of their reporting and the convenience of accessing everything in one place.

AI-assisted programming is beginning to lower the effort required to develop some of those visible features. Developers can generate interfaces, automate calculations and produce analytical tools more quickly than through conventional programming alone, although the resulting code still requires testing and maintenance.

AI agents introduce another challenge by changing how customers use the information. A family office with access to consolidated portfolio records could ask an agent to analyse currency exposure, prepare a quarterly investment summary or identify changes in asset allocation.

The user may no longer need to navigate separate reporting modules to complete those tasks. An external AI interface can retrieve the relevant information, perform calculations using connected tools and present the findings in a format suited to the request.

For a software provider that sells sophisticated dashboards and reporting capabilities, this changes the competitive environment. The customer may continue paying for the consolidated financial data while placing less value on the interface used to analyse it.

The underlying data remains difficult to reproduce. Banks and custodians supply records in different formats, valuations must be reconciled across dates and currencies, and transactions need to be classified consistently. Private assets can require additional information that is neither standardised nor readily available through market-data feeds.

A report assembled from incomplete records can give an inaccurate impression of a portfolio, regardless of how convincingly an AI model presents its conclusions. Maintaining a reliable consolidated dataset therefore remains a service for which customers have good reason to pay.

The pressure is greatest where a provider’s commercial proposition depends primarily on displaying and interpreting information rather than collecting, validating and maintaining it.

AI Developers Are Entering Specialist Financial Workflows

The development of financial AI agents is bringing this competitive threat closer to established software markets.

In May 2026, Anthropic introduced ten agent templates for financial services, covering work such as preparing investment presentations, screening know-your-customer files and supporting month-end accounting. The company also expanded connections between its Claude platform and financial information providers, including FactSet, Morningstar, MSCI, PitchBook and LSEG.

These integrations allow authorised users to work with information from several specialist services through one AI interface. Rather than opening each application, retrieving data and transferring the results into another document, financial professionals can increasingly delegate parts of that process to an agent.

The arrangement does not eliminate demand for the underlying data providers. Their information, licensing arrangements and specialist coverage remain necessary for the work to be completed.

It does, however, reduce the importance of some functions that software companies previously developed to make that information accessible.

The implications extend beyond wealth management. Providers of accounting tools, document processing, business intelligence and compliance software may encounter similar competition as general-purpose agents acquire the ability to carry out tasks previously confined to specialist applications.

Smaller competitors can also use established AI models to enter markets where developing sophisticated analytical software once required considerable engineering resources. Their products may not initially match established platforms in reliability or coverage, but they may be sufficient for customers with simpler requirements.

Incumbent providers could consequently face pressure from both directions: competitors reproducing selected features at lower development cost and AI platforms taking over activities previously performed within their applications.

Why SAP Has a Stronger Position

Germany’s SAP is responding to AI by incorporating agents into the enterprise systems that customers already use to manage essential business operations.

At its Sapphire conference in May 2026, the company introduced its Autonomous Enterprise strategy and plans to expand AI across finance, procurement, supply-chain management, human resources and customer experience. Its proposed Autonomous Suite includes more than 200 agents and over 50 assistants, designed to operate within established business processes.

SAP also announced an expanded partnership with Anthropic, bringing Claude’s capabilities into its business AI platform alongside technology from other external providers.

The strategy relies on assets that competing AI applications cannot easily reproduce. SAP’s customers maintain financial records, procurement information and operational data within systems that have often been configured around their particular business requirements.

An AI agent can prepare a financial report or initiate a procurement task, but completing the work correctly requires access to reliable records, established approval procedures and the rules governing individual transactions.

SAP can develop agents around those processes while retaining responsibility for much of the software infrastructure on which customers depend.

For the company, external AI models offer a way to introduce more advanced capabilities without developing every component independently. Its own applications continue to provide the business information and operational context needed to make those models useful.

The approach also illustrates why established enterprise software may prove more resilient than products focused on individual administrative tasks. Replacing a reporting interface is relatively straightforward compared with replacing a system that manages financial transactions, procurement approvals and years of business records.

SAP’s position is not available to every SaaS provider. Smaller companies generally have narrower product ranges and fewer resources, while some operate in markets where customers can change suppliers with less disruption.

They must determine which of their capabilities are essential to customers and which are becoming increasingly interchangeable.

The Limits of Building Everything In-House

The declining effort required to develop software creates a difficult choice for smaller providers. A wealthtech company may be able to build an AI reporting assistant using an existing model rather than training its own system. It still needs to connect the model to financial data, test the results and maintain the service as customer requirements change.

Building comparable functionality internally may offer more control, but it also requires employees to manage updates, monitor performance and address failures. These responsibilities continue long after the initial development has been completed.

AI-assisted coding can accelerate implementation without eliminating those obligations. The same problem arises when a family office considers replacing purchased software with a system developed for its own use.

A small team may now be able to create a portfolio dashboard or reporting application relatively quickly. Maintaining connections to multiple custodians, reconciling transactions and ensuring the accuracy of financial records is considerably more demanding.

The organisation must also assign responsibility for the system. Someone has to investigate discrepancies, maintain integrations, manage access permissions and respond when a software update disrupts an established process.

Buying an established product may therefore remain commercially attractive even when building a basic alternative has become less expensive. For SaaS providers, the calculation works in the opposite direction. Continuing to develop features that external AI models can already perform effectively may consume resources without improving the company’s competitive position.

Some functionality can be obtained through third-party services, leaving developers to concentrate on the specialist infrastructure, data processing and customer requirements that distinguish the product.

Switzerland’s financial sector provides evidence of how businesses are managing the transition. In a survey published in April 2025, the Swiss Financial Market Supervisory Authority, FINMA, found that around 50% of approximately 400 surveyed institutions were using AI or developing initial applications. A further 25% intended to adopt the technology within three years.

FINMA identified data quality, confidentiality, model reliability and dependence on external suppliers among the principal risks associated with adoption.

These concerns remain commercially relevant for specialist software providers. Customers may welcome faster development and more capable AI features, but they also need assurance that the systems handling their information will remain accurate, secure and available.

What Happens When Customers Stop Using the Interface?

AI agents also introduce competition over the relationship between software companies and their customers.

SaaS providers have invested heavily in application design because the interface has traditionally been central to how users experience a product. Employees learn to navigate its features, organisations develop procedures around it, and regular use helps establish familiarity with the brand.

An external AI agent can reduce that direct interaction.

A wealthtech platform might continue supplying consolidated portfolio records and calculations, while another company’s assistant prepares client reports and answers questions about investment performance.

The software provider remains essential to the process, but the customer may spend increasingly little time inside its application.

This could weaken the advantage of products whose appeal depends on ease of use, particularly when agents can retrieve similar information from several competing services.

It also creates opportunities. Software that offers valuable data or specialised functions may reach customers through AI platforms without having to persuade them to adopt another standalone application.

The commercial outcome will depend partly on the provider’s control over its information and how competing agents access it.

A company offering proprietary financial data or specialised calculations may retain considerable negotiating power. A provider whose product mainly organises publicly available information into convenient reports could find its position more vulnerable.

Software companies will need to consider how external agents access their services, which functions remain under their control and how they maintain direct relationships with customers who increasingly work through other platforms.

The Value Is Moving Behind the Interface

AI is unlikely to remove the need for specialist business software, but it is changing which parts of a product are difficult to replace.

In wealth management, the value may increasingly rest on reliable custodian connections, consistent portfolio valuations and the ability to consolidate complex assets. An AI agent can make these capabilities easier to use without reproducing the infrastructure needed to provide them.

Accounting software may retain its advantage through financial records and established business integrations. Compliance platforms depend on accurate regulatory processes and documented controls, while cybersecurity companies may be protected by proprietary threat intelligence and monitoring systems.

Such capabilities require expertise, infrastructure and continuous maintenance. They are not necessarily visible in the interface, but customers depend on them for the underlying work.

Companies whose products consist largely of routine reporting, document preparation or administrative features face greater pressure because AI can increasingly perform those tasks using existing data and software tools.

The challenge is difficult for established SaaS businesses whose previous development investments no longer provide the same competitive protection. Features that once took years to build may now be reproduced more quickly, even if operating them reliably still demands considerable work.

SAP is seeking to retain its position by embedding AI into the business processes and information systems its customers already depend on. Smaller providers may have to follow a more selective approach, using external AI technology while concentrating their own investment on capabilities that remain difficult to replicate.

For software companies assessing their next stage of development, the question is no longer simply how many new features they can introduce. Their commercial position depends increasingly on whether customers need the underlying service once those features become available elsewhere.

 

Exporis Insight: How to Position SaaS Products When Features Become Easier to Copy

AI is changing the basis on which products compete. Marketing that concentrates on reporting features, intuitive dashboards or the number of functions available may become less persuasive when competitors can reproduce similar capabilities more quickly.

Identify the product’s defensible advantage. Companies should establish which capabilities customers would struggle to obtain elsewhere, whether proprietary data, specialist integrations, established business processes or expertise in a regulated industry. These should form the basis of their market positioning.

Move beyond feature-led communications. A wealthtech provider may offer consolidated reporting, but its stronger commercial proposition could be the accuracy and completeness of the financial information behind it. Product content should explain the work involved and the business problems it solves.

Make technical advantages understandable to buyers. Data quality, reconciliation and integration capabilities can be difficult to communicate without excessive technical detail. Customer case studies, process explanations and demonstrations can show why they justify purchasing specialist software.

Address the competitive alternatives. Prospective customers may compare a SaaS product with general-purpose AI tools, internally developed applications or less expensive competitors. Marketing should explain where those alternatives are sufficient and where the specialist product provides additional capabilities.

Maintain a recognisable position in the market. As AI agents become another way of accessing business software, providers need a clear identity beyond their interfaces. Specialist content, targeted communications and documented customer expertise can help establish why clients should continue choosing their services.

Exporis helps financial and technology companies position complex products through specialist content, digital communications and marketing. As AI makes software features easier to reproduce, communicating the expertise and infrastructure behind a product becomes increasingly important to its commercial position.