What AI means for communications leaders and their teams

Artificial intelligence is already changing the way communications work gets done.

Journalists use AI-assisted tools to sort through large collections of documents. Public relations teams monitor conversations and emerging issues across enormous amounts of online content. Marketers generate, test and revise creative work faster than was possible only a few years ago. Communications teams are experimenting with AI for research, audience analysis, brainstorming, transcription, content development and countless other tasks.

For senior business leaders, however, the implications extend well beyond productivity.

AI is changing how communications teams are structured, what skills organizations need and how leaders evaluate the value of experienced communications professionals. It is also raising new questions about oversight, judgment, accuracy, reputation and the appropriate role of automation in high-stakes decision-making.

For leaders, the challenge is increasingly one of talent development: deciding what people need to know, which capabilities need to be strengthened and how organizations can prepare employees for communications work that is evolving quickly.

AI is changing the work and the expectations

One of the easiest ways to misunderstand AI is to think of it simply as a labor-saving device.

AI can certainly make some tasks faster. It can produce a first draft, summarize information, analyze large amounts of text or generate variations of a message in seconds.

But faster tools can also raise expectations.

If a team can generate five campaign concepts in the time it once took to develop one, leaders may expect more options. If audience data can be analyzed faster, they may expect deeper analysis. If content production becomes easier, organizations may increase the volume and frequency of communications across multiple channels.

The result is a shift in where professional value is created.

Producing the first draft may become less important. Deciding what should be said, to whom, why, when and with what consequences becomes more important.

That requires skills AI cannot reliably supply on its own: judgment, strategy, creativity, ethical reasoning, cultural understanding and an ability to recognize when something that looks convincing is actually wrong.

For leaders, that changes the development question. Training employees to operate AI tools is useful, but organizations also need people who can evaluate their outputs, understand their limitations and make decisions when the technology provides an incomplete or misleading answer.

Develop capabilities, not just AI skills

The ability to write a clever prompt is useful. It is not enough.

As generative AI becomes embedded in everyday software, basic prompting will increasingly become part of normal professional fluency. The more durable skills sit above the technology.

Leaders should be asking questions such as:

Can employees recognize a weak argument even when it is beautifully written?

Can they determine whether AI-generated research is reliable?

Can they distinguish meaningful audience insight from a misleading correlation?

Can they identify the ethical and reputational implications of using AI-generated material?

Can they decide which portions of a communications process should be automated and which require human oversight?

Can they explain their recommendations to senior leaders and accept responsibility for the decisions that follow?

These are business questions as much as communications questions.

An editor, strategist, marketer or public relations professional may eventually interact with dozens of AI-powered systems during a typical workweek. Their value will not come from simply having access to those tools.

It will come from knowing how to use them responsibly in service of organizational goals.

Leaders need to redesign work, not simply add tools

AI adoption is often approached as a technology decision: Which platform should we purchase? Which functions should we automate? Which employees should receive access?

Those questions matter, but they come after a more important one: How should the work itself change?

A communications process that was designed five years ago may no longer make sense when research, drafting, analysis and production can happen much faster.

Leaders should examine where AI can eliminate low-value work, where it can improve decision-making and where it may introduce new risks.

They should also be careful about simply filling the time created by automation with additional production.

If AI allows a team to produce twice as much content, doubling content volume is not automatically the best use of the technology. The better opportunity may be to spend more time on audience research, experimentation, evaluation, strategic planning or relationship building.

AI should create room for higher-value work, not merely more work.

Make human oversight visible

As AI-assisted work becomes more common, leaders may also need to reconsider how they evaluate employee performance.

A polished final product tells only part of the story.

Increasingly, the decisions behind the work may be just as important.

When reviewing a campaign, report, strategy or communications product, leaders can ask four questions:

1. What problem were we trying to solve?

2. Where did we use AI, analytics or automation?

3. What did the technology get wrong, overlook or oversimplify?

4. What did the employee change, and why did that intervention improve the result?

That process can reveal more than the final deliverable itself.

It shows whether someone understands the business objective, can evaluate risk and knows how to manage technology instead of simply accepting its output.

Organizations may eventually find that this type of human-in-the-loop thinking becomes an important leadership competency.

Do not let technology outpace judgment

Communications professionals do not need to become data scientists simply because AI has become part of their field.

Some employees may benefit from programming or advanced analytics skills, particularly in highly technical roles. For most communications professionals, however, the more immediate challenge is learning how to collaborate effectively with increasingly sophisticated technology.

That means knowing how to ask good questions, evaluate outputs, interpret data and make responsible decisions.

Senior leaders need many of the same capabilities.

Executives do not necessarily need to understand how every AI system works technically. They do need enough understanding to ask informed questions of employees, vendors and outside partners.

What data is the system using?

How was the result verified?

Where could bias or error enter the process?

What information should never be entered into the system?

Who remains accountable for the final decision?

What happens when the technology is wrong?

Those questions belong in management conversations, not just IT discussions.

Build an upskilling strategy

Organizations will need multiple approaches to developing these capabilities.

Some skills can be developed internally through experimentation, mentoring and new assignments. Tool-specific knowledge may be taught effectively through workshops, certificates or short courses.

Other employees may benefit from deeper professional education.

Graduate education has long provided professionals with a way to deepen their expertise, develop broader strategic capabilities and prepare for increased leadership responsibility. It can also serve as one part of an organization’s workforce-development strategy.

For a senior leader considering a communications master’s program personally, or evaluating one as an upskilling opportunity for employees, several features are worth examining.

Look for programs where AI is integrated into relevant areas such as strategy, research, analytics, journalism, marketing, ethics and content development rather than isolated in a single technology course.

Faculty connections to professional practice matter as well. Students benefit from examining how organizations are actually using emerging technologies, including the limitations and uncertainties that rarely appear in product demonstrations.

Programs should also provide access to contemporary professional tools and require students to connect what they are learning to real organizational problems.

Most importantly, graduate education should move professionals beyond operating technology toward thinking about leadership, strategy, audiences, ethics, risk and organizational decision-making.

A master’s degree is not the only way to build those capabilities, but for employees preparing to manage teams, advise leadership or assume broader organizational responsibility, it can provide a structured way to develop them.

Workforce planning must include existing jobs

AI will create some new positions around automation, audience intelligence, analytics and content operations.

A significant amount of change, however, is happening inside jobs that already exist.

Public relations professionals are becoming responsible for AI-assisted monitoring. Marketers are overseeing increasingly automated content systems. Journalists are using computational tools in reporting. Communications leaders are developing policies governing how their organizations use generative AI.

For senior business leaders, this means workforce planning cannot focus only on creating new AI positions.

Organizations also need to examine how existing jobs are changing, which capabilities are becoming more important and where employees need additional development.

The title on the organizational chart may remain the same while the capabilities required to succeed in the role change substantially.

That creates several choices for leaders. Some capabilities can be built internally. Others may require outside training or formal education. In some cases, organizations may need to recruit new expertise.

Communications leaders should be part of those conversations because AI is changing not only how communications are produced, but also how organizations manage reputation, audiences, information and trust.

Protect the human side of communications

AI will continue getting better at producing things.

It will write more convincing drafts, analyze larger datasets, generate increasingly sophisticated images and video, and automate portions of workflows that once required hours of human effort.

Organizations should take advantage of those capabilities.

They should also be careful not to confuse production capacity with strategic capability.

Communications has always depended on understanding people, organizations and context.

It requires determining what information matters, what audiences need, what an organization should say and what consequences may follow.

Those decisions become more consequential as organizations produce more content, move faster and depend more heavily on automated systems.

For senior leaders, the central challenge is larger than adopting AI.

It is building an organization in which people know when to use the technology, when to question it, when to override it and how to connect it to business goals.

The organizations that develop those capabilities will be better positioned to benefit from AI without allowing speed and automation to replace judgment.

Evan Kropp, Ph.D., is Executive Director of Distance Education at the University of Florida College of Journalism and Communications and a faculty member in the online Master of Arts in Mass Communication program. His work focuses on digital education, media strategy, and the evolving role of journalism in the modern media ecosystem.

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