OpenAI's partnership with ServiceNow isn't about building a competing product; it's about embedding its "agentic" AI directly into established platforms. This strategy focuses on becoming the core intelligence layer for existing enterprise systems, allowing AI to act as an automated teammate within familiar workflows.
Related Insights
The evolution of 'agentic AI' extends beyond content generation to automating the connective tissue of business operations. Its future value is in initiating workflows that span departments, such as kickstarting creative briefs for marketing, creating product backlogs from feedback, and generating service tickets, streamlining operational handoffs.
Instead of merely 'sprinkling' AI into existing systems for marginal gains, the transformative approach is to build an AI co-pilot that anticipates and automates a user's entire workflow. This turns the individual, not the software, into the platform, fundamentally changing their operational capacity.
OpenAI has quietly launched "skills" for its models, following the same open standard as Anthropic's Claude. This suggests a future where AI agent capabilities are reusable and interoperable across different platforms, making them significantly more powerful and easier to develop for.
ElevenLabs' defense against giants isn't just a better text-to-speech model. Their strategy focuses on building deep, workflow-specific platforms for agents and creatives. This includes features like CRM integrations and collaboration tools, creating a sticky application layer that a foundational model alone cannot replicate.
The partnership where OpenAI becomes an equity holder in Thrive Holdings suggests a new go-to-market model. Instead of tech firms pushing general AI 'outside-in,' this 'inside-out' approach embeds AI development within established industry operators to build, test, and improve domain-specific models with real-world feedback loops.
Becoming an "agentic enterprise" requires a foundational shift to an AI-first, conversational way of working. It involves augmenting every employee's workflow with AI assistance for faster decisions, all built upon a foundation of trusted, accessible data that powers the entire system.
The 'agents vs. applications' debate is a false dichotomy. Future applications will be sophisticated, orchestrated systems that embed agentic capabilities. They will feature multiple LLMs, deterministic logic, and robust permission models, representing an evolution of software, not a replacement of it.
Infrastructure built for app-to-app integration, like Salesforce's MuleSoft, is being repurposed to govern, orchestrate, and secure AI agents. This 'agent fabric' provides a foundational control plane for managing complex agentic workflows across the enterprise, extending the value of existing integration investments.
The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.
The current market of specialized AI agents for narrow tasks, like specific sales versus support conversations, will not last. The industry is moving towards singular agents or orchestration layers that manage the entire customer lifecycle, threatening the viability of siloed, single-purpose startups.
