OpenAI Pivots to Enterprise with Focus on Practical AI Adoption

OpenAI is making a significant strategic shift in 2026, moving away from showcasing advanced AI capabilities toward making artificial intelligence a practical tool for everyday work. Chief Financial Officer Sarah Friar outlined the new direction in a recent blog post, signaling that the company sees its biggest growth opportunity in helping businesses, hospitals and research institutions actually use AI to solve real problems.

The move represents a major change in how the company positions itself. What started as ChatGPT, a research project released to test new technology, has now become a tool millions of people use daily. OpenAI's leadership realized early adoption far exceeded their expectations, forcing them to rethink how advanced AI should be built and rolled out to the world.

Background

OpenAI has grown at an extraordinary pace. The company generated $2 billion in annual revenue in 2023, jumped to $6 billion in 2024, and surpassed $20 billion in 2025. That growth matched the expansion of its computing power almost exactly. The company's computing capacity tripled from 0.2 gigawatts in 2023 to 0.6 gigawatts in 2024, reaching 1.9 gigawatts by 2025.

This parallel growth between computing power and revenue tells an important story. OpenAI believes that having more computing capacity directly enables faster adoption of its products and better monetization. The company has announced roughly $1.4 trillion in infrastructure deals over the past year, betting heavily that demand for AI will justify these massive investments.

The company's early focus was on innovation and experimentation. It released ChatGPT as a research preview, then watched as millions of people began using it for everything from writing to coding to analysis. That success proved AI could move beyond academic circles into mainstream use, but it also revealed a gap. While people were experimenting with AI, most businesses and institutions were still figuring out how to actually integrate these tools into their operations.

Key Details

The Shift to Practical Use

OpenAI is now positioning itself as both a research organization and a deployment organization. The company wants to narrow the gap between what AI can do and what people, companies and countries actually do with it every day.

Friar identified three sectors where applied AI can deliver measurable results: health, scientific research and enterprise operations. In these fields, improvements in how AI reasons through problems and analyzes information directly translate into better decisions and more efficient work.

"Our focus for 2026: practical adoption. The priority is closing the gap between what AI now makes possible and how people, companies, and countries are using it day to day. The opportunity is large and immediate, especially in health, science, and enterprise, where better intelligence translates directly into better outcomes."

This represents a fundamental shift in how OpenAI thinks about its role. Rather than optimizing for isolated performance milestones, the company is now emphasizing usefulness in real contexts. That means focusing on tools that work within actual business processes and professional environments, not just impressive demonstrations.

Computing Power as the Limiting Factor

Friar identified computing capacity as the most limited resource in the AI ecosystem. This constraint shapes everything OpenAI plans to do through 2026. The company intends to continue expanding and diversifying its computing infrastructure, treating capacity as a flexible portfolio rather than a fixed asset.

The company's theory is straightforward: more computing power allows AI systems to be delivered at lower costs, making them viable for routine business use rather than restricting them to specialized applications. OpenAI plans to manage this expansion through partnerships, phased capital commitments and close alignment between infrastructure growth and actual customer demand.

New Business Models on the Horizon

OpenAI is also preparing to change how it makes money from AI. The company started with subscriptions, then moved to a multi-tier system that includes consumer subscriptions, team subscriptions, a free ad-supported tier and usage-based APIs tied to production work.

Looking ahead, OpenAI expects new business models to emerge as AI moves into scientific research, drug discovery, energy systems and financial modeling. These could include licensing agreements, intellectual property arrangements and outcome-based pricing where OpenAI shares in the value its AI creates for customers.

AI Agents and Workflow Automation

A major focus for 2026 is developing AI agents and workflow automation systems. These are designed to maintain context over time, operate continuously and perform actions across multiple tools. For individual users, this means assistance with ongoing projects. For organizations, it could function as a persistent operational layer for knowledge work.

What This Means

OpenAI's new strategy signals confidence that AI has moved past the experimental phase. The company is betting that the real money lies not in selling individual AI capabilities, but in becoming embedded within how organizations actually work.

For enterprises, this shift could mean AI becomes less like a specialized tool and more like fundamental infrastructure. If OpenAI succeeds, AI systems would handle routine analysis, decision-making and coordination across departments and projects.

The timing matters. OpenAI faces real competition, particularly from Anthropic, which many view as the more practical choice for enterprise customers. By explicitly targeting health, science and business sectors with tools designed for actual use rather than demonstration, OpenAI is trying to reclaim ground in the enterprise market.

The company's massive infrastructure investments also suggest confidence that demand will justify the spending. If OpenAI is right about the connection between computing power and adoption, the next phase of growth depends on successfully converting that capacity into products people and organizations actually want to use.

What remains uncertain is whether OpenAI can execute this transition while managing enormous capital commitments and whether new business models like outcome-based pricing will actually work with enterprise customers who typically prefer fixed-cost arrangements.