Professionals using artificial intelligence (AI) in their work have likely already encountered agentic AI — and its rapid growth reflects the ever-evolving nature and impact of AI. Where generative AI reshaped how professionals create work, agentic AI is now reshaping how people do that work.
Ever since the public release of the generative AI (GenAI) tool ChatGPT in late 2022, AI has continued to develop new capabilities and use cases. Legal, tax and accounting, risk and fraud, government, and corporate professionals have moved quickly from experimenting with GenAI to relying on it daily — 74% of professionals now use AI several times a week. But many are still working out what a fully AI-powered workflow means for their profession, and what comes next.
Enter agentic AI — still earlier in its adoption curve than GenAI, but growing quickly, with most professionals now expecting it to be central to their workflow by 2030. AI’s advancements and their benefits aren’t always easy to grasp. Consider this as a primer on the key differences between agentic and GenAI. One isn’t better than the other — each has distinctive capabilities that can optimize professional practices.
Core differences between GenAI and agentic AI
Generative AI excels at producing specific content based on direct, specific, step-by-step prompts. Agentic AI, on the other hand, excels with an initial set of instructions to autonomously manage multistep processes to achieve a larger objective with less human intervention. Agentic AI makes decisions and takes action to keep a process going, while GenAI reacts to input and creates output.
Simply stated, agentic AI specializes in workflow automation and independent problem-solving, while GenAI’s sweet spot is content creation.
| Agentic AI | Generative AI | |
|---|---|---|
| Core function | Executes multistep tasks autonomously to achieve a goal | Generates content based on specific prompts |
| Task complexity |
Handles complex, chained tasks like research, analysis, and reporting |
Best for discrete, single tasks like drafting and summarizing |
| Autonomy | High — can operate independently toward the set objective | Low — requires user direction for each step and output |
| Key benefit | Automates complex processes and tackles multifaceted problems, freeing significant time | Accelerates specific content creation and simple questions and answers |
| Key consideration | Requires clear goal definition, robust oversight, and validation checkpoints for complex actions | Requires careful prompting, and fact-checking is essential due to the risk of hallucination |
What is generative AI?
Generative AI is a type of artificial intelligence designed primarily to create, when prompted, new content based on patterns learned from training data. This type of AI excels at producing text, images, code, and other media in response to specific, step-by-step human input. GenAI works by recognizing patterns and making statistical predictions to generate human-like outputs, making it particularly valuable for content creation, summarization, translation, and information synthesis tasks. GenAI typically functions in a request-response model, such as a chatbot.
For professionals, GenAI is a powerful tool for creating initial drafts and summarizing large volumes of information in clear language.
What is agentic AI?
Agentic AI is an advanced form of artificial intelligence that can plan and execute complex tasks across multiple systems to achieve specific goals. Unlike traditional AI, agentic AI makes decisions, uses various tools and APIs, and performs sequences of actions without continuous human guidance. This new AI technology excels in workflow automation, proactive monitoring, and multistep processes. It is applicable in professional fields like legal, tax, and compliance, where it can maintain ongoing operations while adapting to changing conditions.
Like other forms of AI, the framework of agentic AI uses machine learning and large-language models (LLMs). But unlike traditional AI, agentic AI systems don’t need to constantly follow preset inputs, giving them a great deal of adaptability. Agentic AI can improve its output and adjust to changing conditions and situations through reinforcement learning (RL). This type of machine learning works through trial and error, where an AI agent learns by taking actions in an environment and receiving rewards or penalties for its choices. Over time, the agent discovers which strategies maximize rewards, similar to how someone learns to ride a bike by experiencing what keeps them balanced versus what makes them fall.
Agentic AI also stops when it runs into situations requiring professional expertise and decision-making, whether in legal, tax, audit, accounting, or risk and fraud.
Differences in workflow benefits
Both types of AI have numerous real-world benefits relating to streamlining tasks, improving productivity, and saving time. Each also has its strengths.
Professionals are bringing GenAI into their workflows to save time, increase productivity, automate routine work, improve quality, and reduce costs. More specifically, GenAI can:
- Produce high-quality initial drafts of documents, reports, and communications as quickly as they’re needed for scalability
- Synthesize and summarize large volumes of information into concise, actionable insights
- Transform complex technical information into plain language
- Present multiple approaches to a problem by creating alternative versions of content
The e-book, Agentic AI 101: What your business needs to know, provides a guide for introducing this newer form of AI into professionals’ workflows. It notes that agentic AI’s overarching benefits are improving efficiency, elevating work quality, and accommodating more business. It delivers these benefits by:
- Automating multistep processes across different digital platforms
- Maintaining consistent application of complex rule sets and compliance requirements
- Coordinating multiple tasks, tools, and data sources
- Reducing time-to-completion for complex and routine tasks through autonomous decision-making while deferring to human expertise when encountering exceptional situations
Differences in use cases
Agentic AI and generative AI tools don’t replace human insight, experience, and judgment — they’re built to support it. What they can do is allow professionals to save time and focus on what they do best — and what only they can do. It can amplify their expertise, boosting the services they provide and what they achieve.
Here are examples of core use cases for each type of AI.
- Drafting. Create initial versions of legal contracts, regulatory filings, corporate communications, and other documents following specific goals, templates, and other user inputs.
- Research. Automate and analyze massive amounts of documents and historical data faster and more accurately than human beings. These capabilities also power tasks such as predictive modeling.
- Interpretation. Translate technical language into a style that is accessible to all audiences.
- Documentation. Assemble comprehensive compliance documentation based on policy requirements, implementation evidence, control descriptions, and testing results.
- Communication. Develop customized communications for various stakeholders — like investors, regulators, clients, and employees — while maintaining consistent core messaging.
- Investigation. Conduct comprehensive due diligence on potential customers or purchases by searching across multiple datasets, identifying relevant documents, extracting key information, and compiling findings into structured reports.
- Monitoring. Track regulatory changes in real time across jurisdictions, identify affected client portfolios or business operations, and generate action plans to address compliance gaps.
- Processing. Manage accounts payable workflows by validating invoices against purchase orders, routing approvals, and scheduling payments.
- Onboarding. Evaluate applications for government programs, financial accounts, and customer or vendor relationships. Agentic AI gathers applicant information from multiple sources, verifies documentation, and accesses sanctions lists and adverse media.
- Project management. Submit a GenAI-generated proposal to the company’s customer relationship management platform, then schedule a follow-up meeting with that potential customer and send additional information.
Preparing for the future of AI
The differences between generative AI and agentic AI represent fundamentally different approaches to how AI can serve organizations, and the most powerful applications use them in conjunction with the other tools and initiatives. While many businesses recognize that AI will be central to their future operations, there remains a significant gap between acknowledging its importance and implementing effective AI strategies with clear training programs and return on investment metrics. That gap is measurable: 35% of professionals whose organizations have a named AI strategy say their day-to-day work doesn’t reflect it, and only 18% of organizations currently track any ROI metrics on their AI use at all. This implementation gap creates substantial risk in today’s rapidly evolving technological landscape, particularly for organizations that understand AI’s potential but struggle to operationalize it.
The real advantage comes from treating generative and agentic AI as one connected system rather than two separate tools bought and adopted on their own timelines. Organizations that plan for them together, rather than layering agentic AI on top of GenAI as an afterthought, are the ones positioned to close the gap between having an AI strategy and living it day to day.
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