Produced By: Ensombl
Artificial intelligence (AI) has become a defining topic in finance over the last few years. Conferences, industry reports, and company presentations abound with discussions of how AI might transform everything from portfolio construction to client servicing. As adoption accelerates, so too do questions about the ethical and professional implications of relying on AI for key decisions.
Recently, three experienced voices came together to explore these themes. James Whelan, Managing Director of Barclay Pierce Capital’s wealth management team, spoke with two specialists in global equity portfolio management: Trent Masters of Alfinity Investment Management and Bianca Rose of Morningstar. Together, they examined how AI is reshaping the investment world, where the true opportunities might lie, and why a measured, ethically grounded approach remains paramount.
Below is a structured discussion synthesizing their conversation—an exploration of current best practices, emerging use cases, and the delicate balance between leveraging AI’s promise and maintaining the personal, fiduciary, and professional standards that define the financial services industry.
It might seem that AI came out of nowhere—one moment, it was a niche subject. The next, it was being mentioned multiple times on every earnings call. Yet AI’s rise, in fact, has been a slow burn. Early adopters in tech developed tools that hinted at AI’s potential, such as natural language processing or image recognition. These advances started to converge around “generative AI,” epitomized by tools like ChatGPT, which can create original text or code in response to human prompts.
While it can feel like hype, it is critical to distinguish short-term excitement from the long-term fundamentals. As Masters explains, he initially felt skeptical about each “transformative” technology wave:
AI, however, is different. Masters describes it as “exceptionally promising,” pointing out that AI can automate many repetitive, labor-intensive tasks; increase productivity; and support entirely new ways of delivering products and services.
Rose shares this optimism, noting that Morningstar leverages AI in everything from internal analyst collaboration to “chatbot” prototypes that answer advisor queries by pulling from a deep knowledge base. Despite the flashy headlines, both portfolio managers stress that AI’s greatest near-term impact often lies in improving everyday processes—collating research, reviewing transcripts, or drafting marketing materials.
As excitement builds, finance professionals also wrestle with profound ethical implications. While AI may boost efficiency, it must be used responsibly. Advisors, investment managers, and their clients rely on rigorous compliance and professional codes to ensure that technology does not compromise client data, produce misleading analyses, or inadvertently encourage biases.
Whelan underscores that in wealth management, trust is paramount. The massive scale of data in AI models demands particularly careful data governance. From privacy controls to systematic review processes, the industry must cultivate new standards. AI is no longer a “nice to have”; it is a powerful tool that, if harnessed unwisely, could pose real risks.
In financial services, AI’s value stems from two primary sources:
Many investment teams have started by using AI to streamline processes in the back office. Masters, for instance, describes using AI-based tools for tasks like summarizing transcripts or pinpointing crucial financial metrics within corporate filings. Within Morningstar, Rose notes that AI-assisted research enables analysts to gather and organize large swaths of information quickly—though they still verify important points manually.
AI can also serve as a powerful assistant for tasks that previously bogged down professionals. In marketing or product development contexts, employees can use generative AI to draft documents or graphic designs. After AI generates a first draft, experienced professionals step in to refine, fact-check, and finalize. This speeds up the creative loop while maintaining a high standard of quality and accuracy.
However, cost savings can be a double-edged sword. AI’s automation potential may reduce the need for certain roles, leading to job losses. Masters observes that some U.S. companies have moved beyond freezing hiring to actively reducing headcount, expecting AI-based solutions to handle tasks once reserved for human staff. While these gains in efficiency might benefit firms and their stakeholders, they also underscore the ethical need for transparency, fair labor practices, and long-term workforce planning.
Revenue growth from AI remains in its early stages, driven by:
Masters and Rose compare this to the evolution of the internet:
In AI, Masters sees parallels. We are witnessing an intense focus on hardware and infrastructure, but the next wave—likely to arrive in a few years—will bring the most disruptive software and platforms. The big question for investors is which software companies will emerge as winners and whether the largest technology incumbents (Microsoft, Google, Apple) will extend their dominance.
While AI holds promise, it brings practical challenges:
These potential issues reinforce the need for a structured, ethical approach to AI deployment in finance.
At its core, wealth management is about acting in a client’s best interest, from initial strategy to ongoing portfolio reviews. The integration of AI cannot dilute that responsibility. Even if a firm uses AI to supplement research or automate certain functions, advisors must still validate outputs, contextualize data for a client’s personal circumstances, and ensure regulatory compliance.
Masters emphasizes that while AI can swiftly identify stocks that appear on quant-based screens, it remains the manager’s obligation to examine fundamentals, valuation metrics, and business quality. If a stock’s AI-driven signals prove inconsistent with real-world data, the onus is on the manager to uncover the discrepancy. In short, AI can direct attention, but human judgment provides the final decision.
Large language models improve by consuming vast quantities of data, but financial services companies manage particularly sensitive client details. Ethically and professionally, institutions must:
Rose recounts that Morningstar, when building its internal chatbot “Mode,” struck a strict agreement with Microsoft to ensure user questions would not be harvested for further machine training. This is increasingly the norm for major vendors pitching AI solutions—corporate customers want assurances that their data stays private.
Early in AI’s adoption curve, it is tempting to consider every scenario as one AI can solve. Yet overreliance on AI or poorly managed risk controls can produce misleading outcomes. Examples include:
As Masters notes, “Life doesn’t happen in a straight line.” Even if a technology as revolutionary as AI promises great efficiencies and potential returns, prudent advisors must maintain a balanced approach, test assumptions rigorously, and uphold the same ethical frameworks that have always governed the profession.
Many advisors already use AI-driven tools to communicate with clients more efficiently. Whelan describes how he transforms complex market reports into accessible 30-second explainers:
This kind of tailored, clear communication—when checked by a human for accuracy—can address the different preferences of older vs. younger clients. While older clients may prefer in-depth conversations, younger, time-strapped clients might appreciate quick and visually engaging updates.
AI can save countless hours scanning earnings transcripts, market data, and news flow. Instead of searching manually, advisors can use tools that automatically extract salient points:
The output remains merely one piece of the puzzle. Advisors and their research teams should always validate findings. Not every data pattern is meaningful, and no AI is infallible.
Ethical behavior in financial services demands thorough documentation and transparency. AI can simplify compliance tasks—for example, by:
Advisors who harness these tools correctly find they free up more time to focus on client relationships—a crucial advantage in the modern wealth advisory field.
Beyond using AI as an internal tool, many advisors wonder how to position portfolios to benefit from this technological shift. Masters and Rose both highlight that while AI is exciting, a disciplined approach remains necessary.
Similar to a gold rush, some investors focus on the “picks and shovels” of AI—the critical infrastructure providers:
While these firms have benefited from an AI-driven surge in spending, advisors should confirm that the valuations make sense relative to potential earnings. As Masters observes, a company with a skyrocketing share price might still maintain a fair value if its earnings and revenue growth keep pace—but once euphoria sets in, stocks can overshoot fundamental worth.
On the other hand, the end-user applications—like software tools that can disrupt existing business processes—are still emerging. They often represent an intriguing long-term growth story, but not all will succeed. Here, the key is:
When a new technology excites the market, concentrating a portfolio too heavily in any single theme can be risky. Masters points out that Alfinity aims for an overall technology weighting that might be in line with the benchmark or slightly higher if the fundamentals warrant it. The goal is not to “bet the farm” on AI but to include those companies that show tangible earnings or structural growth, balanced by positions in other sectors.
Bianca Rose outlines a practical framework that advisors can use:
Such frameworks help maintain professionalism and objectivity. The narrative around AI can shift rapidly; the underlying numbers offer a clearer view of the sustainability of an investment thesis.
While AI promises to reshape many financial tasks, it remains a tool, not a panacea. The professional responsibility of advisors is to ensure that AI-driven recommendations, research, or portfolio allocations align with each client’s best interest. That may mean adopting AI tools in phases, beginning with pilot programs or internal checks before rolling out automated systems at scale.
Furthermore, advisors must recognize that AI is not static—it evolves. Tools that seem advanced today might be outdated in a year. Clients, especially younger generations, might welcome dynamic online portals or AI-assisted Q&A, but older clients may require more personalized, face-to-face interactions. The advisor who skillfully blends both approaches stands to differentiate themselves in a competitive marketplace.
Financial regulators worldwide are beginning to examine how AI is used by asset managers, advisors, and banks. Firms that demonstrate leadership in areas of data protection, client communication, and transparency can help shape positive outcomes for the industry. In time, we may see more standardized guidelines that set the ethical and professional “guardrails” for AI in finance.
Professionalism in finance requires not only the right initial credentials but ongoing education. As AI’s capabilities expand, advisors should seek relevant training or partner with experts who understand both the technology and the core principles of ethical advising. Upgrading an AI tool without properly training staff to interpret its outputs can lead to missteps. A robust compliance culture—one that integrates AI checks and reviews—helps reduce errors and protects client interests.
AI has moved from a distant concept to a central force in financial services. As James Whelan observed, it has begun to permeate everything from drafting a statement of advice to building entire portfolio models. Trent Masters and Bianca Rose highlighted that while AI unlocks efficiency and growth potential, success requires responsibility, transparency, and a firm grounding in investment fundamentals.
For advisors, this duality underscores the importance of professionalism:
In the years ahead, AI’s role in finance will undoubtedly grow, bringing new forms of client engagement and more efficient operations. The best outcomes, however, will arise where human expertise, judgment, and empathy merge seamlessly with AI’s rapid analytical capabilities. Advisors who cultivate this balance—who harness AI to serve their clients’ best interests while respecting the profession’s ethical bedrock—will be the ones to forge a truly sustainable path into the future.
Disclaimer: The information in this article is general in nature. It does not constitute personal financial advice and does not consider the objectives, financial situation, or needs of any specific person. Before making any investment decision, individuals should seek professional advice tailored to their circumstances.
Accreditation Points Allocation:
0.10 Technical Competence
0.10 Regulatory Compliance and Consumer Protection
0.10 Professionalism and Ethics
0.30 Total CPD Points