Enterprise AI is changing rapidly. Organizations are transitioning from chatbots to agentic architectures that deliver accurate results for enterprise use cases. To navigate this change, data leaders and professionals are recalibrating their agent strategies.
What does it take to put AI agents to work for your business? And what are the leading companies doing differently from those that are stagnating?
State of AI Agents The organization leverages data from over 20,000 global customers to uncover the biggest trends in how organizations are interacting with AI agents. This report shares insights about AI in the enterprise: common use cases, assessment and governance, database transformation, and more.
Here are some highlights:
AI deployment focuses on core tasks
Everyone is talking about enterprise AI. But how is this playing out in the real world? We examined the most common AI use cases across different sectors and industries. Conclusion: Companies are automating important, but routine tasks.
These functions range from market intelligence to customer advocacy to regulatory reporting. 40% of the top 15 use cases focus on customer experience and engagement.
And these applications are tailored to specific areas. For example, analyzing medical literature is a top use case for health care and life sciences companies, while predictive maintenance is more common in the automotive or energy and utility industries.
AI assessment and governance are the building blocks of production
one 2024 global survey A survey conducted by Economist Impact found that 40% of respondents believe their organization’s AI governance program is inadequate. It does not adequately define the data, set appropriate guardrails, or provide the necessary accountability. And without strong checks and balances, enterprises will struggle to scale AI agents into production.
According to our data, it’s perhaps no surprise that the use of AI governance and security products saw the most growth last year. And companies that use AI governance tools get 12 times more AI projects into production.
AI assessments are critical to ensuring the high-quality output needed to deploy AI agents in production, and organizations are increasingly adopting AI tools. Organizations that use assessment tools are nearly 6 times more likely to move AI systems into production.
Rethink AI agent architecture
As vibe coding continues to grow in popularity, an AI-powered approach to application development is rapidly changing how companies manage their databases. As a result, companies must provide the elasticity, programmability, and scale in their architecture needed for AI agents to perform as expected.
Agents make this scale possible, and we’ve seen this change happening in our data. On Neon, a serverless Postgres database acquired by Databricks and the core technology behind it Databricks LakebaseAI agents now make up 80% of all databases and 97% of database branches.
327% increase in multi-agent workflow
The value of AI agents in enterprise environments is their ability to orchestrate complex workflows based on an organization’s own data. To understand how enterprises are leveraging AI agents, we analyzed the use of four types of agents on Databricks Agent Bricks.
Among our customers, the top agent use case is the supervisor agent, with 37% usage. The supervisory agent creates systems of multiple agents – automatically optimized using the organization’s own data – that work together to accomplish tasks in particular domains.
Get started with your AI strategy
Building and deploying AI agents is no longer a barrier for organizations. Now, the challenge is to do it in a safe and controlled way that actually adds value to the business.
To learn more about how the most successful organizations are successfully leveraging AI into operations, read in full 2026 state of AI agents Report.
