How AI Is Reshaping Entry-Level Roles and Training in the Investment Sector
Financial institutions are increasingly incorporating training on generative artificial intelligence (GenAI) for new hires to help them leverage the technology effectively. Major banks have rolled out mandatory programs in recent years. For instance, one leading bank required prompt engineering training for all incoming employees starting in 2024, while another introduced broad upskilling in effective prompting techniques around mid-to-late 2025.
Entry-level professionals stand to gain the most from these tools, as GenAI can help bridge the gap in knowledge and capabilities between novices and seasoned experts. However, industry voices emphasize that AI-focused training should complement, not replace foundational education on core investment concepts, financial products, regulatory requirements and market developments.
Professionals note that new hires must learn to craft precise questions for AI systems, evaluate outputs for accuracy (including potential inaccuracies or “hallucinations”) and guide the technology toward useful results. Without a solid understanding of the underlying domain, users risk becoming overwhelmed by excessive or unreliable information, a phenomenon sometimes described as “infoxication.”
Allocating dedicated time to AI training can also reduce the hours available for other essential topics. One technology specialist at a financial services firm acknowledged that introducing internal AI modules required trimming coverage of areas like company culture and project management. To compensate, the firm repeats key lessons throughout the onboarding process to ensure nothing critical is overlooked.
Leaders at large investment firms see AI as a net positive for development. Efficiency improvements from AI applications are expected to create additional capacity for higher-value activities and ongoing skill-building. This freed-up time can be redirected toward enhancing employees’ capabilities, with plans to expand opportunities for personal and professional growth.
A significant portion of this additional training time is likely to focus on human-centric or “soft” skills, which are becoming key differentiators as AI handles more routine technical aspects of investment work. Some organizations have approved multi-year initiatives to increase in-person interactions and deliberate practice of collaboration, communication and other interpersonal abilities.
Experiments with GenAI-supported training methods, such as those conducted by research arms of major consulting groups, indicate that AI-powered approaches can prove more engaging than conventional training formats while delivering comparable learning outcomes.
Risks of Overreliance on AI
Despite these advantages, heavy dependence on GenAI by early-career professionals may not always support long-term development. Some experts worry that excessive use could hinder the accumulation of hands-on experience needed for independent judgment.
Research from MIT’s Media Lab found that participants who used GenAI assistance when writing essays displayed noticeably lower brain engagement compared to those working without it. Cognitive activity tended to decrease in line with greater reliance on the tool, and users also had more difficulty accurately recalling or quoting details from their own AI-assisted work.
Separate studies have observed that after completing tasks with GenAI support, individuals sometimes show reduced motivation when tackling subsequent work without assistance. In a professional development setting, this raises concerns that overreliance could prevent entry-level staff from building the practical experience required to ask insightful questions and critically assess AI-generated outputs.
Without that foundational experience, novices may struggle to formulate effective prompts or interpret results meaningfully, particularly in the complex environment of investment management. There is also a broader question of whether knowledge and skills gained primarily through AI feel as deeply internalized as those developed through direct effort and iteration.
Regulatory bodies and industry observers have similarly highlighted the dangers of overdependence on automated systems, stressing the need for ongoing human oversight to monitor processes, detect errors and intervene when necessary.
Using AI to Address the Gaps
Interestingly, AI itself may offer part of the solution through personalized learning platforms. These systems can evaluate an individual’s current knowledge, identify specific skill gaps and deliver tailored content matched to their learning preferences and career objectives.
Talent development leaders are monitoring advances in HR technology and learning management systems that enable this level of customization. Concepts like “mass customization” in training, long discussed in theory are becoming practical realities thanks to AI-driven recommendations and adaptive pathways.
Overall, while GenAI is transforming how new professionals enter and grow within the investment industry, success will likely depend on striking the right balance: using the technology to accelerate learning and productivity without undermining the development of deep domain expertise and human judgment.