OpenAI's launch of Presence looks like another enterprise AI product announcement, but viewed alongside two other similar launches this year, it begins to look like part of something bigger. Over the past few months, OpenAI, Anthropic and Google have all launched products aimed at helping enterprises deploy AI in production, working beyond the boundaries of their respective models. Customer-facing operations appear to be one of the primary functions in which this shift will be felt and it is already impacting markets.

Presence, launched last week, is a managed platform for deploying voice and chat AI agents across customer support, sales, IT and HR workflows, including policy controls, simulation tools and human escalation. OpenAI says it already uses Presence on its own English-language phone support channel, where the system now resolves 75 percent of inbound issues without human assistance, and describes Presence as helping enterprises “deploy trusted AI agents” that can access company systems and take approved actions.

A customer service agent that can retrieve account information, process a refund and escalate a complaint safely needs far more than a capable language model. It also needs governance, integrations and business context, which is precisely what these systems are being built to supply.

OpenAI is not alone. Anthropic's Managed Agents tackles many of the same operational challenges, while Google's Gemini Enterprise Agent Platform, introduced as the next evolution of its Vertex AI platform, is positioned as a system for building, governing and scaling enterprise AI agents.

Perhaps the clearest sign of this transition is that Google’s platform gives customers first-class access to more than 200 models through its Model Garden, including rivals' models such as Anthropic's Claude family. If success depended only on winning the model race, Google would have little reason to make a competitor's model a first-class citizen on its own system. Instead, the enterprise layer itself appears to be becoming the prized asset.

The products take different approaches, but the aim is the same: helping organisations operationalise AI, not just access more capable models. Increasingly, the competition isn't just about building smarter AI. It's about making AI work reliably inside the enterprise.

Think about what happens when a customer contacts a company to change a delivery, dispute a bill or report a faulty product. Making those interactions work reliably with AI depends on far more than choosing the best language model.

Customer service operations are one of the few enterprise functions where AI agents can already prove their value at scale. They generate huge volumes of customer interactions, rely on extensive business knowledge and already have well-established performance metrics: resolution rates, handling time, cost per contact. That makes it an obvious proving ground for agent systems looking to demonstrate return on investment.

Historically, these capabilities have been delivered by established CX and enterprise software vendors, including Salesforce, Microsoft, NICE, Genesys, Zendesk and HubSpot. Presence, Managed Agents and Gemini Enterprise blur the line between what a frontier AI lab and a specialist CX platform offer. OpenAI's own support channel, and named early Presence customers including BBVA Mexico and SoftBank, stand in territory that contact centre and CRM vendors have long regarded as theirs. Much of the value in this sector depends on AI understanding customer context across the entire journey; the same capability these systems are increasingly built around.

This is the strategic core of what's happening. Presence's simulation and grading tools, Managed Agents' outcome-based evaluation, and Gemini Enterprise's governance and identity controls all address the same challenge of helping organisations trust AI in production. In many organisations, deploying AI safely has become a bigger challenge than choosing a model. Success increasingly depends on enterprise context, workflow integration and governance rather than raw model capability.

Enterprise context has become increasingly important because AI systems are only as useful as the business information they can access. SAP’s CEO Christian Klein made a similar point on the company's latest earnings call, arguing that enterprise AI must be grounded in trusted business data. It's also why OpenAI, Anthropic and Google are each investing heavily in enterprise platforms rather than simply releasing more capable models. As foundation models continue to improve, competitive advantage is shifting towards the infrastructure built around them.

Investors have noticed too. Business Inside reported that following the Presence announcement, several enterprise software stocks, such as HubSpot and Atlassian, fell sharply as analysts reassessed what OpenAI's enterprise ambitions could mean for established software vendors. That reaction isn’t necessarily proof these vendors are about to be displaced, but it shows markets are already treating frontier AI companies as potential competitors outside of the model layer, not just as suppliers to it.

Whether these companies end up as partners or competitors to established CX vendors still remains to be seen. The competitive landscape no longer has a clear dividing line between model providers and enterprise software platforms. The playing field is now wide open.