How Operator and Computer Use Will Disrupt Legacy ERP Moats

By: Chia Jeng Yang (WhyHow.AI) & Amol Shah (OpenAI)

This week’s launch of Operator by OpenAI, following the release of a similar tool called Computer Use from Anthropic, marks the beginnings of a shift in how legacy ERP moats are maintained, potentially signaling the end of traditional strategies like restrictive API/data access and selective integrations as a means to create a durable competitive advantage in software.

In OpenAI’s Operator release, the demo showcased mainly consumer use cases (shopping via Instacart, reserving a restaurant via OpenTable, or booking a trip on Booking). One critical reaction has been that a lot of what is possible with the Operator tool in the browser, could be made simpler with using APIs that provide the same experience in a more reliable way.

However, the consumer-facing nature of the demo (Instacart has a highly navigable consumer-facing website, plus it makes its APIs freely available) under-emphasizes the larger enterprise opportunity around pulling data and automating data extraction and transformation from more complex systems, which the Operator tool ultimately will enable. In fact, many observers expect these consumer-facing use cases are useful to gain initial adoption and help the technology mature, for enterprise use-cases where extraction is more complex or APIs do not exist, which can be noted from the progression of early benchmark data along the spectrum of UX complexity.

Operator-like Tools for Interfacing with Legacy System of Records

Legacy ERP systems have served as a system of record and a critical backbone for businesses, integrating various processes and centralizing data. Think Salesforce for CRM, Workday for HR, NetSuite for Accounting. However they often lack modern APIs or standardized integration capabilities, making it difficult for their own customers or emerging solution providers seeking to build on top of or extend the functionalities of these systems. The net effect of these “data walls” created by ERP providers essentially entrenches their position within enterprises.

Many of these systems are still operational due to the high costs of migration and the critical functions they perform. However, Operator-like tools make it possible to unlock data trapped within these systems. Important capabilities include:

• Simulating User Interactions: Operator-like tools simulate human interactions with ERP interfaces, automating tasks such as navigating menus, exporting reports, and retrieving specific data. These systems can be programmed in natural language and operate far faster and more consistently than manual processes, significantly enhancing efficiency.
• Scaling Legacy System Interactions: Automation allows businesses to perform high-volume data extraction from legacy systems, scaling operations that were once constrained by human effort. For instance, Operator-like tools in LLMs can extract thousands of transaction records overnight, a task that would take days for human operators, increasing friction for adopting and proving third party vendors.
• Breaking Exclusivity Barriers: By enabling access to legacy ERP data without requiring proprietary API access, enterprises reliant on traditional ERP systems can increase the value and useability of their own data. This challenges the lock-in and pricing power of their vendors, and lets businesses adopt the latest in AI technology without being locked in by slow and traditional vendors.

Consider an organization using a decades-old ERP to manage payroll that does not provide APIs for easy data export. Instead of relying on costly integrations or manual data exports, Operator-like tools could simulate user actions to export payroll records. These records are then automatically formatted and used for modern LLM systems that require the data for context, bridging the gap between old and new technologies. It does not matter whether your ERP system has built APIs for one component of software to pull data for another program, because Operator-like tools can pull that data as a person would.

Another example of this use-case that is perhaps more relatable is LinkedIn data exporting. This process requires a number of steps including going into your LinkedIn account, pressing a few export buttons, waiting for an email, clicking on a link in the email inbox and downloading a ZIP file to your desktop. With Operator-like tools, all of these steps could be automated or running in the background on behalf of a networking tool a user wants to give their data to, creating new use cases across both business and personal domains.

The implications of these developments are that it may become harder for legacy companies to preserve a data moat if they are purely a system of record, and must deliver value-enhancing services on top of the data they hold.

Investment / Building Opportunities

Eroding System of Record Moats & New Openings for AI-Native Software Solutions

One of the great debates for the evolution of software in the age of AI is whether legacy SaaS players have an advantage vs newer AI-native software companies. Legacy players have two main advantages: distribution (ie: an established user base, and relationships with stakeholders at a company) and data moats, as mentioned before.

The elimination of data moats as a default advantage that incumbent ERP players possess over newer entrants has significant implications:

1. Differentiation at the point of the user: Being the system of record no longer creates an automatic advantage for software. Expect increasingly that end user preferences — based on the ability of software to automate user workflows, or offering greater flexibility of features, will increase in importance
2. Easier trial of new software providers: While still needing to overcome a distribution disadvantage (ie: no pre-existing relationship with a company), with lower switching costs it will be easier for companies to experiment with new software solutions outside of the traditional “stack” they were limited to before
3. Downward pressure on ERP margins: Price increases aren’t automatic by default, and ability to switch providers is real

This has implications for investors evaluating the startup landscape, and nascent AI-native players who are helping companies automate workflows, opening up the design space for solution providers and creating new opportunities to bypass legacy ERP providers.

‘Cloudflare/ Captcha for Operator-like tools for traditional ERPs’

We may see a very messy range of solutions pop up by enterprises to combat Operator-like tools in their ERP systems. The notion of Cloudflare for ERP SaaS may emerge. Just as bots were typically targeted against websites, Operator-like tools may be regarded as similarly undesirable botting actions against computer-based ERP systems, and we may see upcoming companies specialize in developing new techniques against Operator-like tools, to help ERP providers preserve their data moat for as long as possible.

Metadata generation and not data records as the new moats

In a traditional ERP system, data that is easily exportable can include things like customer contact details, sales pipelines, and other information that a human user would typically observe in the UI. Metadata could include things like ‘How much time a customer has spent in the Prospecting stage’. Metadata, and metadata generation, may be the more important data moat that ERP players would need to double down and focus on. Many ERP providers (and there are quite a few in niche industries who do nothing additional to the data they store) have not had good systems for metadata generation and this may change. Such ERP providers who are not providing additional metadata value addition may also represent good opportunities to disrupt.

Custom export automation and computer-driven interaction with legacy ERP systems are reshaping how businesses can access and use their data. By mimicking human workflows to overcome traditional API limitations, companies can unlock the full potential and flexibility of their system of records. As tools like Operator and Computer Use continue to improve, API moats for legacy systems of records may continue to erode.