We’re entering a new phase of enterprise AI adoption — one where companies can choose to harness agentic AI and become truly AI-empowered organizations, or risk being left behind. The real question now is how fast enterprises can grasp the opportunity, scale advanced AI, and rethink the nature of work.

Racing towards secure, agentic AI

AI first movers are already outperforming their peers, a gap that is set to widen as more productivity gains from the technology kick in. According to Microsoft’s 2025 Work Trend Index, there’s a new breed of company: the “frontier firm” that scales rapidly, powered by dynamic AI insights and teams of humans collaborating with AI agents.

“An AI-first company is built around AI from the ground up. It rethinks its entire operational model, replacing traditional workflows with AI-powered decision-making and execution. Where traditional businesses use AI as a tool, AI-first companies treat it as the foundation.” – Jehan Hamedi, Founder and CEO, Vizit

Rather than treating AI as a simple add-on to existing workflows, workers are looking to orchestrate and collaborate with agents to run entire business processes and automations. According to the survey, 81% of leaders expect AI agents to be integrated into their company’s core workflows in the next 12-18 months. Building enterprise AI agents is now the top imperative for businesses.

How to lead the AI charge

From our work with leading companies, we believe there are three levers that matter most for fast enterprise AI adoption: security, efficiency, and customization.

Security: Navigating AI in regulated environments

Strong security isn’t just a nice-to-have for AI deployment — it’s an essential foundation for accelerating adoption. That’s particularly the case in complex, regulated industries, where adoption is being held up by concern over data privacy and compliance issues. Some 39% of bank executives, for instance, say that security and data privacy concerns are dragging on the pace of deployment.

“Anytime you go through an application, you first have to solve for legal and compliance. It’s got to meet our security and risk and controls, and then it's customer experience.” – Darrin Alves, CIO of infrastructure platforms, JPMorgan Chase

Adopting enterprise-grade AI enables companies to insulate themselves from the growing risk of “shadow AI,” where employees share sensitive work information with publicly available models without their employers’ knowledge. Using private deployment, potentially as part of a hybrid solution with cloud-based architecture, can give organizations peace of mind over sensitive data together with the flexibility to scale AI in response to demand and manage costs.

Secure AI takes on added importance in the context of agentic AI. Before integrating these powerful agents into their systems, businesses need clear guardrails, rigorous evaluation, and fallback mechanisms to ensure they behave predictably under pressure.

Efficiency: The enterprise AI superpower

Until recently, better AI was widely seen as synonymous with bigger AI, requiring ever larger amounts of compute power. That narrative is now shifting decisively.

“Everybody wants to use the Ferrari, but if you’re just going down the street, you might just need a bike.” – Monica Caldas, CIO, Liberty Mutual

According to one recent report, the inference cost (the expense of running a trained model) for a system performing at the level of ChatGPT-3.5 dropped 280-fold in the two years following its release in November 2022.

“With these efficiency gains, any business can run AI to increase productivity for employees with agents that can automate work.” – Nick Frosst, Co-Founder, Cohere

Our Command A model, for example, only requires two GPU chips compared to 32 for competing models. This leap in cost-effectiveness will allow enterprises to better leverage private deployments — crucial for highly regulated industries — and to scale agentic systems efficiently.

Customization: Your unique AI value proposition

To maximize the value and competitive advantage of AI, enterprises need it to truly understand their business, including their unique terminology, workflows, and regulatory concerns. The answer is customization.

Recent data shows that 75% of organizations investing in generative AI are prioritizing model customization. High-performing companies are nearly twice as likely to adapt or fine-tune foundation models, unlocking gains in efficiency, accuracy, and risk reduction.

“We have domain know-how — we understand our industries. And we have the data. Together with AI, this is a winning combination.” – Roland Busch, CEO, Siemens AG

Training models on company data unlocks new capabilities like personalized AI assistants, strengthens core functions through agentic workflows, and boosts productivity by streamlining repetitive tasks.

Cohere, for instance, partnered with Fujitsu to custom-build its Takane model, training it on Japanese business language and on complex technical concepts across the specialized, regulated industries that the company serves.

Taking the bold leaps

The window for decisive action is short. Frontier firms’ advantages will compound as they automate more processes, deploy more AI agents, and unlock new productivity from their workforces. Get started with lean, easily governed models, like Command A, customizing them with your proprietary data, and try North, our agentic AI workspace that lets you build and deploy custom AI agents all within your security perimeter. It’s never been easier to launch secure, agentic AI for businesses.

Building enterprise AI agents

For more, check out our comprehensive blueprint for developing scalable AI agents in regulated industries.

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