Generative AI has become a regular workplace feature, with employees using prompts to do everything from summarizing data to writing press releases, but most people are only scratching the surface of what’s possible.
Increasingly, people are using artificial intelligence to handle complex tasks semi-autonomously, says Harvard Business School Professor Tsedal Neeley. So-called agentic AI are autonomous AI systems that plan, reason, and act to complete tasks. The systems can carry out entire workflows with minimal human oversight.
I can’t see a future without every individual using AI to dramatically improve their work, their relationships, and their collaborations.
Most businesses surveyed by McKinsey recently have applied AI to at least one function, and 39 percent have begun to experiment with AI agents. More organizations are likely to follow, argue Neeley and Ritcha Ranjan, senior vice president of product at Expedia Group and a tech expert in AI and product management. They offer a vision for the potential of agentic AI in the technical note, “Generative and Agentic AI as Strategic Partners for Leaders,” released in November.
“I can’t see a future without every individual using AI to dramatically improve their work, their relationships, and their collaborations,” says Neeley, the Naylor Fitzhugh Professor of Business Administration and chair of the MBA program. “Individuals and companies will be able to do so much more than they did before.”
“People talking about using AI to write an email or draft a document is just the starting point—what we’re doing is taking it to a higher order,” Ranjan says. “It’s the type of thinking that can unlock a 10x improvement in productivity.”
Your personal AI strategic bench
In the technical note, Neeley and Ranjan—who met while serving on the board of Brightcove, a cloud-based video platform—highlight how agentic AI could meet the strategic needs of leaders and organizations. When deployed at its full potential, agentic AI could power a digital support team for leaders that includes:
A competitive intelligence analyst that monitors external signals
While gen AI can distill goals and evaluate metrics, an agentic approach could continuously compare those targets against competitors’ as they evolve, recommending adjustments as peers announce new products or strategic shifts.
Agentic AI can even monitor and respond to external developments, using deep research capabilities to create ongoing summaries of information on collaborators or competitors—acting as a strategic press secretary.
It could really help leaders stay ahead of emerging developments and get the insights to act.
“The instruction for the agent could be to scan the internet every morning and give me everything written about a company or product by the time I start my day,” Neeley says. Far beyond a simple Google News alert, the agent could synthesize and interpret the information, translating it into action items related to your organization’s strategic goals. “It could really help leaders stay ahead of emerging developments and get the insights to act.”
A chief of staff that aligns time with strategic priorities
An AI agent could mine calendars and emails to create an accurate picture of how time is actually spent.
“That’s a hard thing to do mentally,” says Ranjan. “Even if you sat down and started categorizing things in Excel, it would take forever.”
An AI agent could also evaluate meeting transcripts to determine what was actually discussed, rather than what was on the agenda. “Maybe my calendar looks like I was spending time on subject X, but we actually talked about subjects 1, 2, and 3,” says Ranjan.
Beyond categorizing the data, an agent could find patterns and compare them with organizational priorities to recommend how leaders could use their time better. “It effectively becomes a chief of staff, saying ‘here’s where you have been focused, and here’s how that compares with the highest priorities you’ve set,” Ranjan says.
An executive coach that provides feedback and advice
One particularly interesting task the authors discuss in the note involves anticipating feedback from a manager or other senior colleagues ahead of a meeting. For example, an agent could analyze patterns from past interactions, drawing on emails, meetings notes, and other communications, along with publicly available information.
The agent could then create a working profile of that individual, enabling the user to seek feedback ahead of an important presentation, for example. It could also provide tips on how to respond to concerns the person is likely to raise.
“One of the hardest things, especially for a junior employee, is thinking through every angle or question you are going to be asked,” Ranjan notes. AI could surface those likely questions and provide key insights to help them prepare more effectively.
Making the most of your agents
Agentic AI isn’t “set it and forget it.” Wringing its value requires organizations to rethink their processes and set thoughtful guardrails. Ranjan says to think of agentic AI as a three-part loop:
Plan. What data does the underlying model need to perform tasks in a given workflow? To create a person for a particular stakeholder, users might need to provide examples of correspondence, for example.
Execute. Whether ChatGPT, Gemini, or Claude, every major AI program now has an agent mode through which you can set up an ongoing task; a deep research feature that can integrate large amounts of information based on connected data sources; and a scheduler to automate tasks, the authors say.
Learn. As the model begins to operate, it will collect new information and refine its approach.
As with all AI, quality requires user vigilance. People should not only periodically check the agent’s work, but also review its output at crucial junctures, such as before making a major decision.
“As we move towards more agentic workflows, that human-in-the-loop moment is going to be critical,” says Ranjan.
Getting started with agentic AI
Neeley and Ranjan offer the following advice for leaders seeking to bring agentic workflows into their organizations:
Start with boring and redundant tasks
“The ‘no-joy’ work, the repetitive tasks, are ones particularly suitable for these tools,” Neeley says. Not only will that help create efficiencies, but it will also help create trust among employees and get them excited to use AI.
Broaden access to tools
Many firms have benefitted from empowering employees to build their own workflows and prompts, Neeley and Ranjan say. Organizations should encourage employees to keep experimenting. “Make that easy and frictionless to do,” Ranjan says.
Train employees together
Consider creating a shared “boot camp” where employees can learn the basic tools of agentic AI at the same time. Learning in a group builds momentum and confidence, turning experimentation into a shared practice rather than a solitary exercise. “Doing it as a community, employees can support and teach each other as opposed to just having this daunting AI staring at you,” says Neeley, who recently launched an AI training program for HBS faculty and staff. This collective approach helps spread ideas, spark excitement, and accelerate adoption.
Provide top-down support
Employees need resources they can turn to when they want to explore new agentic use cases or ask questions. In addition to tech support, leaders can lift up and celebrate initiatives that work. “Those early adopters are the ones you need to showcase to people to spark their imagination,” Ranjan says.
Ensure legal and ethical use of data
Data sources must be publicly available, or if proprietary, accessed with the explicit permission of the owner. The data the system relies on must remain within the bounds of authorized and accessible sources.
Continue trying new tools
“What was new and innovative yesterday is table stakes today,” says Ranjan. “Leaders need to keep testing, learning, and iterating.”
For Neeley and Ranjan, the real promise of agentic AI lies in its ability to work independently, pulling together information from multiple sources and synthesizing it in ways that go far beyond prompt-based interactions. As these systems mature, they argue, agentic AI could function as a digital support resource for leaders, helping them make sense of complexity, focus their attention, and perform at a higher level.
Illustration created with assets from AdobeStock and edited with Adobe Firefly.
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Generative and Agentic AI as Strategic Partners for Leaders
Neeley, Tsedal, and Ritcha Ranjan. "Generative and Agentic AI as Strategic Partners for Leaders." Harvard Business School Technical Note 426-038, November 2025.
