“But AI Can Do It”: Developing Novice Consultants in the Age of AI

Leadership consulting is a dynamic field that requires consultants to continuously adapt, both to the evolving workforce and to the changing needs of their clients. Yet, one source of change that has left many of us both curious and uncertain is the rise of artificial intelligence (AI), defined as “the capability of computer systems or algorithms to imitate intelligent human behavior” (Merriam-Webster, n.d., as cited in Lowman, 2025). AI is beginning to influence every aspect of talent management, including selection and development (Landers & Nakamoto, 2025; Tiwari, 2025). Nearly 70% of major employers plan to hire talent with AI skills to help scale their business (Post, 2025). Although it is too early to gauge the full impact of these shifts, one area where the effects are already emerging is in the training and development of novice consultants.

As leadership assessment and talent strategy professionals, we have noticed AI reverberating through many conversations about our work, from automating report writing to comparing executive candidate profiles. There is a growing notion that generative AI, in particular, can enhance the consulting process by minimizing time spent on tedious tasks (e.g., organizing assessment data, identifying themes), thus freeing consultants to attend to higher order insights. For experienced consultants, this may hold true. However, for those still developing foundational consulting skills, such as interpreting leadership nuances, managing client relationships, and conducting strategic assessments, AI introduces new challenges as well as new opportunities. If technology can “do the work,” how do emerging consultants learn to think critically about the data, build judgment, develop their professional intuition, and expand their thinking?

Drawing on our experiences and insights as an associate consultant, function area leader, and partner, we offer practical suggestions for cultivating competent, thoughtful consultants in the age of AI. This article hopes to spark reflection on how AI is reshaping consultant development.

Eight Tips for Responsible AI Use by Novice Consultants

From our experiences in leadership consulting, we describe eight tips for novice consultants to support practices aiding modern early career training, and ultimately, transformative AI strategy. Across these recommendations, the central principle is that AI should accelerate learning and judgment, not replace the effort required to develop them.

Role of the Emerging Consultant

1. Strengthen your knowledge of AI and the organization’s stance on it: AI understanding and self-efficacy can be developed through increased exposure and hands-on practice (Murtza & Murtza, 2023). Generative AI tools are well versed in scoping the internet and can do so rapidly by synthesizing information across many sources; thus, it may illuminate new information or different perspectives on topics you might not encounter through usual channels. Consistently using or learning how to use AI to stay up-to-date is a simple yet fundamental starting point for gaining comfort and confidence with AI. In parallel, it is important to understand the organization’s stance on AI use, including usage guidelines, compliance polices, and internal training resources that support capability development aligned with organizational needs and expectations.

Takeaway: Keep up with trends and recent news, ask HR or other functions how they are using AI, and sign up for relevant development programs. Use this information to start building your identity as an AI user and remain current in your field.

2. Seek guidance from senior consultants before relying on AI: AI is a great tool for refining ideas and discovering patterns, but it cannot replace contextual judgement and client-specific insight that comes from experience. AI is best when complemented with human expertise, particularly for nuanced decisions and stakeholder-facing recommendations (Murtza & Murtza, 2023).

Takeaway: When working with AI, push yourself to think through the work, even when it is uncomfortable or proves time-consuming. After your best attempts, use those around you as resources by asking for reviews or critiques. Use AI to help iterate such functions as clarifying language, pressure-testing logic, or generating alternatives while keeping human feedback as the primary guide.

3. Create a strategy of self-use cases for AI: AI can expedite the process of executing work, but this is not always synonymous with quality. Consider the optimal ways in which AI can aid you personally, not just how it can increase speed. Think of AI as a “mental map,” where you have a clear, intentional path for a given context (e.g., grammar checking for internal, nonsensitive announcements, drafting of survey items, initial topic ideation for executive coaching sessions). However you decide to use AI, use it in ways that streamline your work, but keep the richness and quality of your insights intact.

Takeaway: Always consider how AI can maximize your own unique insights. Ask yourself how it can add value or depth to your perspectives and how it may increase capabilities the organization currently does or does not offer its clients. Regardless of use, consider how AI can create or support opportunities that do exist without the technology, rather than replacing them.

4. Communicate the process and why of using AI: Important to the work of I-O practitioners are ethical practices for AI use (Landers & Nakamoto, 2025; Lowman, 2025). Every organization is bound to differ in its approach to AI guardrails, but some key safeguards are being transparent about the use case, learnings, limitations, and potential impact of using AI. Such information should be communicated to relevant stakeholders for transparency and alignment.

Takeaway: In practice, this may translate to a novice consultant prompting ChatGPT to provide succinct writing examples for a work product. For example, after implementing some suggestions into your own work, you do two things:

1. Document the use in the deliverable when appropriate, especially if the output is stakeholder-facing or the AI contribution materially shaped the wording. Example note on a deliverable: *Note: this announcement was proofread by AI for enhanced readability.
2. Share how you used AI with your supervisor or project lead, focusing on intent, boundaries, and review (e.g., “I used ChatGPT to provide guiding examples while cleaning up the summary of the themes. No actual client data was used or summarized, but my general engine prompts helped illuminate some perspectives I was not considering, and I reviewed/edited the final language before sharing.”). Communicating this information supports ethical practice by allowing others to evaluate appropriateness, risk, and alignment with organizational values.

5. Use AI as a reflective tool to reinforce learning: One of the most useful applications is using AI as a reflection partner to identify areas for improvement. For example, use it to get explicit feedback on drafts on which you have received “redlines.” Used this way, AI supports learning by helping to understand changes and how to practice those skills in future deliverables (e.g., tone, structure, clarity). It also helps you identify patterns across feedback so you can improve faster over time.

Takeaway: Consider AI as a tool to help assist with and refine your growth, not one that absorbs that growth for you. For example, you received an assessment report draft with substantial mark-ups, but you are uncertain of how to interpret or implement the suggestions. You create a practice report depicting similar insights and exclude the client or candidate’s name and exact context. You upload this report to Copilot to flag further opportunities for improvement and give you a detailed roadmap on how to address this, including additional writing practice.

6. Use AI to challenge what you know: Your organization likely has a framework or guiding approach it takes to its work. You likely have your own unique approach to your work, too. Important to leadership consulting is a strong “why” behind what you do and the ability to support this when questioned. Thus, in addition to using AI to work through feedback (Tip #5), AI can help unearth differing interpretations on a topic or approach or even offer a counter perspective to your process or claims.

Takeaway: Use AI to refine your work strategy by prompting it to play devil’s advocate. Asking a generative AI engine to “give me three arguments someone might make that counter my approach” or “summarize varying interpretations of this framework” can illuminate what you have not considered and challenge what you believe before a client or stakeholder does.

7. Use AI as a mirror for refinement: AI can be used to refine your approach to nuanced consulting moments, such as tough conversations and client presentations, for example. In tools like ChatGPT, you may have speech-to-text, upload, or typing options to share your delivery and ask the algorithm to give feedback in the requested ways. Relatedly, aim to use AI as a tool to help heighten your ability to think critically by pressure testing your logic or unearthing gaps or inconsistencies in your work.

Takeaway: Use feedback from AI tools to help refine your professional presence or audit your work. For instance, you have an upcoming team coaching meeting alongside a senior partner. Your job is to deliver a recap of the last meeting and offer high-level insights to guide the discussion. You use speech-to-text to share your planned summary (excluding any sensitive information) and ask for feedback on how to make it more concise but still confident and warm.

8. Do not get hung up on efficiency: AI will always be the more efficient option, but you do not learn much more beyond how to use AI if speed becomes the main goal. Learning is not We have described ways AI can aid your development, but you cannot take shortcuts to retention. A hallmark of development for many novice consultants is that “aha” moment after trial and error; genuinely lean into that skill-development opportunity rather than bypassing it.

Takeaway: Use AI to streamline your work and training, but do not eliminate the learning moments by relying on an algorithm. Decenter efficiency from your perspectives on the benefits of AI. Efficiency is a baseline for AI-supported processes; insight, stronger judgement, innovation, and new perspectives should be your output aim.

Thus, emerging consultants have several ways they can use AI to refine their development and strengthen their contributions to organizations. However, the organization also plays a key role in developing novice consultants in this age of AI. Keeping up with emerging best practices and policy (internal and external) will be crucial to ensuring AI aids the process rather than creates noise while anchoring talent pipelines to organizational goals. I-O psychologists can guide organizations toward transparent, psychologically safe, and learning-oriented AI use. Yet, no matter what you decide to do, take every opportunity to communicate the “why” behind your AI approach, including the intended value, boundaries, and role of human judgment.

Looking Ahead

Taken together, these practices position AI as a developmental scaffold rather than a substitute for expertise, reinforcing that AI is only as useful as we make it. AI presents quick and easy ways to address some of the work of leadership consultancies, especially that of emerging consultants. Yet, with this comes the possibility that rigor and human expertise will lag if efficiency becomes the primary goal. That presents a dull future for healthy talent pipelines. The tips outlined are not exhaustive, but we hope they spark further dialogue and consideration about the broader implications of internal training and development in leadership consultancies during this age of AI.

References

Landers, R. N., & Nakamoto, S. (2025). Ethical use of artificial intelligence in industrial-organizational psychology research and practice. Practice Innovations. Advance online publication. https://doi.org/10.1037/pri0000310

Lowman, R. L. (2025). Consultants’ and managers’ ethical and legal responsibilities in artificial intelligence applications. Consulting Psychology Journal, 77(2), 104–117. https://doi.org/10.1037/cpb0000281

Murtza, F., & Murtza, A. (2023). Artificial intelligence (AI) transformation leadership consulting framework. International Journal of Management Studies and Social Science Research, 5(5), 20–30. https://doi.org/10.56293/IJMSSSR.2022.4701

Post, C. (2025, July 16). AI is changing talent management—Here’s what to watch for. Forbes. https://www.forbes.com/sites/corinnepost/2025/07/16/ai-is-changing-talent-management-heres-what-to-watch-for/

Tiwari, S. P. (2025). The implications of artificial intelligence in management consulting: A risk and barrier assessment. Multidisciplinary Reviews, 8(8), 2025244. https://doi.org/10.31893/multirev.2025244

About the Authors

Caraline S. Malloy, MSIOP, is a PhD Fellow in Industrial–Organizational Psychology at Baruch College and The Graduate Center of The City University of New York (CUNY). Her work examines how identity (e.g., gender, ability) shapes decision-making, access, and evaluation across workplace and educational contexts.

She has supported executive assessment and leadership analytics initiatives, contributing to assessment design, data analysis, and interpretation for senior-level talent decisions across industries, including defense, financial services, manufacturing, education, and retail. Her experience also includes co-evaluating National Science Foundation–funded STEM programs and examining the validity and fairness of assessment and selection criteria for public safety roles.

Across settings, Caraline’s work focuses on strengthening the rigor, fairness, and practical impact of leadership and talent systems. She is a 2026 recipient of the LERA Susan C. Eaton Scholar-Practitioner Grant and received an honorable mention in the 2023 National Science Foundation Graduate Research Fellowship Program. She previously served as officer of funding for all doctoral programs at The CUNY Graduate Center and actively volunteers with Blacks in I/O Psychology, Inc.

Dr. Melissa Regester is the Research & Data Science lead at Vantage Leadership Consulting, where she focuses on translating complex data into practical insights that organizations can act on. She specializes in integrating research and analytics into leadership assessment, development, and organizational practices, where she helps companies make data-informed decisions that resonate in context. She also leads the graduate internship program at Vantage.

Melissa is passionate about the storytelling side of data, regularly presenting on how organizations can move beyond numbers to understand the why behind the findings. Her work highlights how context shapes meaning, ensuring that leaders and teams can apply insights in ways that drive stronger communication, trust, and collaboration.

She earned her doctorate in Industrial-Organizational Psychology, with a specialization in organizational development and quantitative methods, from DePaul University. There, she led a NASA-funded research project modeling team dynamics for long-duration space exploration missions, such as the Mission to Mars. She was twice awarded the Illinois Space Grant Consortium Graduate Fellowship and was recognized as a 24 Under 24 Leader and Innovator in STEAM and Space.

Eileen Linnabery’s passion for leadership effectiveness brought her to Vantage in 2014. Eileen advises organizations in the areas of leadership assessment, development, coaching, and talent strategy, with a focus on utilizing scientific, evidence-based practices to activate an organization’s leadership talent. Her goal as a coach is to assist clients in developing a better understanding of themselves as leaders, and to help them reframe how they approach their work. She works across a wide range of businesses and industries, including family-owned and Fortune 100 organizations, packaging, manufacturing, financial services, technology, and retail. She serves as the Practice leader for Vantage’s Executive Assessment Practice, where she focuses on maintaining the assessment excellence that has been core to Vantage’s brand for 50 years. As a partner, Eileen focuses on strategy, operations, and building the Vantage brand with the businesses we serve. She has mentored and trained dozens of interns, early career professionals, and students across her career.

Eileen completed her PhD in Industrial-Organizational Psychology from DePaul University, where she focused on the impact leaders have on their employees’ work–life balance. She received her bachelor’s degree in Psychology from Tulane University and then went on to pursue a master’s degree in Industrial-Organizational Psychology from The University of West Florida.