The emerging leadership problem is not AI-generated work. It is unearned authority.
In short: I’m seeing more people use AI to build convincing plans for functions they have never worked in, and I understand the appeal. But an AI polished output is not the same as earned expertise. I want us to welcome AI-assisted ideas from anyone, and I also expect clear ownership, verified evidence, honest disclosure of AI use, and review by the functional experts before anyone acts.
A new pattern is quickly emerging across organizations. Employees are using artificial intelligence (AI) to analyze problems, develop strategies, recommend tactics, and create polished presentations and sometimes for business functions they have never worked in. A person with limited experience in finance, operations, technology, human resources, or sales can now produce convincing plans for that function in an afternoon.
At first glance, this looks like progress. Ideas can travel across departmental boundaries. People can ask better questions, challenge assumptions, and contribute beyond their job descriptions. Those are real benefits, but there is a difference between using AI to become a more informed collaborator and using AI to impersonate an experienced expert.
The concern is not that employees are turning to AI, in fact we encourage that. The concern begins when an AI-generated answer is treated as an expert supposition, and a polished deck is presented as if it reflects the author's own expertise, analysis, and accountability. After all, technology can support the process, but it is people who provide the context, discernment, and judgment that give the final result credibility.
Generative AI is exceptionally good at producing the outward signs of professional competence: structured frameworks, confident language, comprehensive lists, and visually consistent slides. What it cannot automatically supply is the lived understanding of a particular organization: its constraints, trade-offs, history, political realities, customer commitments, operational dependencies, technical complexities and learned lessons from previous failed attempts.
That gap is often visible in the work product. A presentation may devote five slides to generic best practices but only one item is actually relevant to the environment. It may offer a detailed implementation roadmap without identifying the budget owner, regulatory constraints, the people who would have to execute it or many other constraints that AI does not have visibility into.
Of course, visual uniformity or an awkward level of detail is not proof that AI was used. Human consultants and internal teams can produce formulaic decks too. The better test is whether the presenter can explain the assumptions, defend the recommendations, and respond intelligently when the facts change.
The first failure is the absence of meaningful human judgment. The user accepts the AI output without testing it against domain knowledge, company-specific evidence, and practical consequences. AI becomes a substitute for understanding rather than an instrument for developing it.
The second failure is a failure of attribution and representation. When someone presents machine-generated analysis as if it were independently researched and personally developed, the audience may assign it a level of credibility it has not earned. Whether that is formally considered plagiarism depends on the organization's policies and the circumstances; the broader problem is misrepresenting the origin and rigor of the work.
These failures reinforce each other. The less the author understands the subject, the harder it is for them to spot errors or omissions submitted by AI. This is also very noticeable by the experts in that field, further eroding trust and confidence in the colleagues who authored an AI generated recommendation as their own.
Poorly vetted AI recommendations consume leadership attention and precious time. Functional experts are asked to rebut generic proposals, correct false assumptions, and explain why an apparently simple solution is not simple at all. Cross-functional collaboration can turn into an endless cycle of unsolicited decks and defensive meetings. Not to mention that an AI generated deck will have a lot of unrealistic and scattered data that takes the reader down the rabbit hole.
More seriously, decision-making can become distorted. A recommendation that sounds persuasive may crowd out better-grounded alternatives submitted by the actual experts. The organization may reward speed and confidence over evidence and ownership. Over time, experts can become skeptical of outside contributions and not because they oppose fresh ideas, but because they are repeatedly asked to validate work that has not been vetted by the experts.
The answer is not to discourage employees from exploring unfamiliar problems. Organizations need curiosity, constructive challenge, and broader participation in strategy. They also need standards that distinguish a hypothesis from a recommendation and a draft from a decision-ready plan. Anyone bringing AI-assisted work to another department should let the team know that the product was developed by AI and be prepared to answer these questions:
Leaders can reinforce this behavior by inviting proposals while insisting on clear ownership, evidence, and appropriate disclosure of AI assistance. A short, candid note that explains 'AI helped structure the initial analysis; I verified these figures and would like your input on these assumptions' builds more trust than a confident claim of authorship that the presenter cannot substantiate.
AI can democratize access to frameworks and accelerate the first draft of thinking. It cannot democratize experience overnight. Expertise is built through real-world experience, decisions, consequences, context, and the discipline of learning.
The challenge for individual contributors is to be the most valuable employee in an AI-enabled organization. Not by producinghe most content, but byusing AI to ask sharper questions, integrate evidence with judgment, respect and seek domain expertise, be transparent and take responsibility for what you recommend.
The challenge for leaders is to make that distinction explicit. We should welcome AI-assisted ideas from every corner of the organization and not confuse the ability to generate an answer with the authority to prescribe one. That belongs to the experts in the field.
AI Disclosure: AI helped polish an initial draft; IT strategy and communication experts reviewed and commented for assistance with finalizing this article.