Artificial intelligence is reshaping what employers actually want from entry-level professionals, and it's not faster work,it's better judgment. While AI can brainstorm, summarize research, draft communications and generate content in seconds, staying relevant requires something the technology cannot provide: the ability to review outputs critically, identify what's missing, and make responsible recommendations when the right answer isn't obvious.

What Skills Will Actually Protect Your Career as AI Advances?

The shift is already visible in how young professionals approach their work. Students and early-career communicators now face real ethical decisions that were once theoretical. Can you trust an AI-generated claim without verification? Should audiences know when content was created with AI assistance? How should you respond when a system reproduces bias ?

Consider a practical scenario: you're preparing a client pitch with an AI-generated competitor analysis. The summary includes a statistic that supports your recommendation, but you haven't checked the source. Once you place that statistic in the presentation, the ethical responsibility becomes yours. The question isn't whether AI found it,it's whether you'll verify it before putting it in front of the client.

This accountability extends beyond individual tasks. As AI moves beyond generating emails and meeting summaries into decisions affecting employment, credit, healthcare, education and government services, the stakes grow significantly. Areas where inequities already exist become even more critical to monitor.

How to Build Ethical Decision-Making Habits Now

• Verify Before Sharing: Check AI-generated information and claims against reliable sources before sharing them with clients, employers, or colleagues, even when the output seems credible.
• Protect Confidential Data: Never input proprietary or confidential information into AI systems, and be aware of what data you're exposing when using these tools.
• Identify Bias and Missing Perspectives: Actively look for whose voices might be absent from AI outputs and consider who could be harmed or excluded by the recommendations.
• Disclose AI Use Appropriately: Tell clients, employers, professors or platforms when you've used AI, especially when failing to disclose could mislead someone about how the work was produced.
• Explain Your Reasoning: Don't stop at what the tool suggested,be ready to articulate why you accepted, changed or rejected its recommendation.

These habits matter because accountability becomes much harder when people don't know an algorithm was involved, can't understand what information influenced it, or have no meaningful way to challenge a bad outcome.

Why Companies Are Struggling With AI Oversight

The challenge isn't theoretical. Stanford University's 2026 AI Index report documented 362 AI incidents in 2025, up from 233 in 2024. At the same time, responsible AI testing isn't keeping pace with rapid development and deployment of increasingly capable systems.

Transparency is a major problem. The average Foundation Model Transparency Index score among evaluated AI developers fell from 58 in 2024 to 40 in 2025, meaning the AI economy may be accelerating precisely when understanding how powerful systems are trained, tested and monitored remains difficult.

A 2025 Pew Research Center study found that 55% of both U.S. adults and AI experts surveyed were highly concerned about bias in AI decision-making. Experts were also considerably less likely to believe Black and Hispanic perspectives were well represented in AI design than White perspectives.

The potential impact is significant. McKinsey research on generative AI and Black communities found that about 24% of Black workers were employed in occupations with more than 75% automation potential, compared with 20% of White workers.

What Does Treating AI Like an Intern Actually Mean?

Dr. Rachel Gillum, Vice President of Ethical and Humane Use of Technology at Salesforce, offers a practical framework for navigating this reality. Her advice: treat AI like your intern.

"It is so critical that we don't delegate our whole brains and judgment to these tools," Gillum explained. "You are the manager, you are the person responsible. You really need to lean in and make decisions about what you delegate and then what you deliver as the final product as the manager in charge."

Dr. Rachel Gillum, Vice President of Ethical and Humane Use of Technology at Salesforce

An intern might bring energy, technical skills and new ideas, but may lack the context, judgment and common sense necessary to make the final call. The same applies to AI systems. You remain responsible for what gets delivered.

Gillum emphasized that responsible AI cannot be something organizations bolt onto a product after development. "One way we approach it, first of all, is implementing these guardrails and work by design from the beginning, so it's not tacked on at the end," she stated.

Gillum

How Should Organizations Approach Responsible AI?

The National Institute of Standards and Technology (NIST) developed an AI Risk Management Framework to help organizations manage AI risks affecting individuals, businesses and society. NIST identifies trustworthy AI characteristics that should be considered throughout an AI system's entire lifecycle, from initial design and development through deployment, use, testing and evaluation.

These characteristics include systems being valid and reliable, safe, secure and resilient, accountable and transparent, explainable, privacy enhanced and fair with harmful bias managed.

Regulatory momentum is building. Colorado, for example, has enacted protections governing automated decision-making technology used in consequential decisions. Under provisions scheduled to take effect in January 2027, consumers will have rights including requesting and correcting inaccurate personal data used by automated decision-making systems.

Gillum argues that communities historically left outside technology's decision-making rooms need more than protection from AI,they need actual influence over it. "They need to have a central role because these tools are being built for everyone in society. Everyone should be taking a seat at the table," she noted.

Gillum

What Framework Should Guide Your Ethical Choices?

The PRSA Code of Ethics provides a practical framework for addressing AI-related ethical challenges. These principles remain relevant even as technology changes:

• Advocacy: Represent clients responsibly while still serving the public interest, ensuring AI-generated recommendations don't compromise broader ethical obligations.
• Honesty: Require accuracy and appropriate transparency in all outputs, including disclosure of AI involvement when relevant.
• Expertise: Understand the capabilities and limits of the tools you use, including where AI systems are likely to fail or introduce bias.
• Loyalty: Protect confidential and proprietary information, being careful not to expose sensitive data to AI systems.
• Fairness: Identify bias and consider who may be harmed or excluded by AI recommendations.
• Independence: Provide objective counsel and accept responsibility for your actions, recognizing that AI may help identify patterns but you decide which recommendation you're willing to stand behind.

Early in your career, AI may help you sound polished in emails and messages. But you'll still need to answer questions and respond when conversations move in unexpected directions. That's where preparation matters. Use AI before a meeting to anticipate questions, identify weaknesses in your argument, generate questions different stakeholders might raise and rehearse difficult conversations.

The communicators who earn greater responsibility will be those who use AI tools well, explain their decisions and can be trusted when the situation is uncertain. The habits you build now are part of becoming that kind of professional.