We would argue that the greatest barriers to AI adoption are not technological. They are the same human traits that we’ve seen before. Let’s take a deeper dive into a few of the most significant:
1.  The “AI champion” may be the wrong person
Many firms put AI adoption in the hands of technology, data science, operations, or innovation teams. These are professional and well-meaning teams but lack the skills necessary to train PMs on how to use AI effectively.
The message is too often, “Here is a tool. Best of luck in using it in your process.” A more effective approach sounds like, “Here is how this improves the quality of our investment judgment.”
2. Â Time scarcity makes experimentation feel expensive
PMs and analysts are overloaded. Earnings seasons, portfolio reviews, client meetings, market events, risk reviews, and internal meetings consume the calendar. LLM adoption is an investment that requires a temporary productivity dip: learning prompts, redesigning workflows, testing use cases, comparing outputs, and building team norms.
The long-term payoff may be substantial, but the short-term cost is real. Busy professionals often reject new tools not because they doubt their long-term efficacy, but because they cannot tolerate the short-term transition cost.
3. Â Lack of psychological safety, including around learning in public
LLM adoption requires people to be beginners and show vulnerability. Senior PMs especially dislike feeling this way. They may avoid workshops, demos, or peer learning because they don’t want to tarnish their image of being the wise leader.
This is an EQ issue, not a technical one. Firms that adopt faster will already have psychologically safe environments, encourage experimentation, expect and celebrate failure, and be comfortable with peer learning.
4.  The rewards system doesn’t recognize process improvement
PMs are usually paid for performance, asset retention, client confidence, and commercial outcomes. Analysts are rewarded for good ideas, differentiated work, responsiveness, and credibility with PMs.
Few firms explicitly reward better research workflows, higher-quality debate, more systematic decision reviews, better information sharing, or improved knowledge capture. So LLM adoption becomes an unpaid extra effort. The firm may say AI matters, but the incentive system says: “Keep doing what already gets you paid.”
5.  “Responsible use of AI” often feels risky rather than enabling
Fear dominates many AI conversations: privacy, hallucination, cyber risk, compliance, regulation, model risk. These are all valid concerns in our heavily regulated industry. If the organizational narrative is primarily defensive, however, PMs will conclude that AI use is dangerous, not encouraged.
This directly maps to the behavioral finance concept of the penalties for sins of commission being higher than sins of omission. This occurs when an organization creates an environment where an actual mistake is penalized but a missed opportunity is not. Good investment teams understand this and take steps to ensure it doesn’t occur. One way of doing so is when PMs ensure they are taking sufficient risk in their portfolios to deliver the promised alpha. This same concept should apply to AI – make sure you are taking some risk and recognize employees who try new ways of using the models, even if they fail.
6.  Probably the greatest hurdle of all – process engineering
Process engineering is the discipline of deliberately designing, analyzing, improving, and controlling an investment process so that a desired outcome is produced more reliably, efficiently, and consistently. I would argue that many investment firms do not have an AI problem. They have a process-engineering problem that AI is revealing. Many investment managers don’t naturally think in terms of process-engineering, making it harder for them to see how AI can truly advance their investment process.
