Nine in ten companies are increasing artificial intelligence (AI) spending this year. Understanding when to use generative AI, agentic AI, or both is critical to value realization.
Sydney Scott
Editorial Strategist, AI
Workday
Over the last few years, generative and agentic AI have shifted business AI focus from automation to a collaborative, intelligent workflow. And with more than 90% of organizations planning to increase their AI spending in the coming year, knowing where and how to deploy both effectively is crucial.
Getting the most from AI investments in corporate settings now requires knowing the difference between generative AI and agentic AI. It’s important to understand not just their baseline functional differences, but also how to use and govern them effectively in real-world scenarios.
More than 90% of organizations plan to increase AI spending in the coming year.
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Generative AI systems create new content—text, code, summaries, images, or data analysis—in response to human prompts. In business settings, they're used to accelerate knowledge work (e.g. marketing copy or financial reports) by helping to generate content quickly and at scale.
Generative AI is largely responsible for the massive increase in general-use AI adoption. Since ChatGPT’s launch in 2022, enterprise use of generative AI has surged from roughly one-third of organizations to more than 70%. Large language models (LLMs) like ChatGPT have impacted every aspect of business, from marketing to operations.
Here are five areas where generative AI excels:
It's important to note that while generative AI dramatically increases productivity, it does not make autonomous decisions or even understand the broader implications of its outputs. Designed to operate with humans in the loop, it provides insight and high-quality content, but it's up to people to apply it in practice.
Since 2022, enterprise use of generative AI has surged from roughly one-third of organizations to more than 70%.
Agentic AI systems are outcome-focused, making decisions and taking action toward specific goals. While generative AI responds to prompts, agentic AI operates semi-autonomously within company workflows, using predefined rules, training data, and context to determine the next best step.
In this way, AI agents can move beyond decision or productivity support and actually problem solve and execute tasks with minimal human oversight. More than 80% of leaders report they're already expanding their use of AI agents in some capacity, with adoption still on the rise.
Here are the key characteristics of agentic AI for businesses:
Agentic AI builds on generative capabilities. In modern enterprise implementations, agentic AI treats generative AI as a core capability. This way, GenAI provides the real-time analysis, while the agentic system serves as the decision-maker, governing whether and how to execute the next task.
More than 80% of enterprise leaders report they’re increasing use of AI agents.
Even with an understanding of their core features and use cases, many companies group agentic and generative AI together because they rely on similar models.
In practice, however, they impact work differently: Generative AI tools support creation and analysis based on patterns learned, while agentic AI systems use that information (and other sources) to act autonomously. The 5 key differences between agentic AI and generative AI are:
Generative AI can be deployed incrementally, often alongside existing tools and workflows, without requiring deep structural change. Teams can adopt it quickly and expand usage over time.
Generative AI usage typically aligns with existing role-based ownership. Responsibility for outputs remains embedded within teams and functions.
Agentic AI requires explicit ownership at the process or platform level. Because systems can initiate and complete actions independently, organizations must clearly assign accountability for outcomes, exceptions, and failures before scaling usage.
Governance for generative AI technology is primarily usage-oriented, focusing on acceptable use, data inputs, and output validation.
Agentic AI requires governance to be designed into the system itself. Policies must define decision boundaries, escalation paths, and human intervention thresholds in advance because actions occur automatically once the system is live.
Generative AI introduces primarily informational risk, such as inaccuracies, bias, or misuse of generated content. These risks are usually addressed via review and controls at the point of use.
Agentic AI introduces operational risk. Errors can propagate across systems or repeat at scale, making monitoring, rollback mechanisms, and auditability critical components of deployment.
Generative AI investments often deliver fast, highly-visible returns through productivity gains and efficiency improvements at the individual or team level.
Agentic AI investments tend to require more upfront design and integration effort, but deliver value through sustained operational improvements, reduced manual coordination, and increased system-level throughput over time.
Generative and agentic AI are now table-stakes for any company’s tech stack. The next step for leaders is understanding the specific AI capabilities and value each provides. Success depends on knowing how they work separately and how they power each other.
Generative AI builds the foundation you need to introduce agentic models responsibly. Over time, the two work together in a simple loop. GenAI informs the decisions, and the agentic system determines how to turn those decisions into action.
Getting this sequencing right while still maintaining human oversight allows organizations to scale AI-powered tools responsibly and achieve meaningful ROI without sacrificing control or trust.
A remarkable 82% of organizations are already using AI agents. But is your team ready? Read our latest report to learn how businesses are maximizing human potential with AI, featuring insights from nearly 3,000 global leaders.
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