Generative AI in Finance: A Team Member or a Tool?

Sarah Hoffman is Principal AI Evangelist at AlphaSense.

Have you ever said “please” or “thank you” to ChatGPT or Gemini? Recently, OpenAI stated that chatting with AI like a person can result in misplaced trust, and the high quality of the GPT-4o voice may make that effect worse. Even without voice, these systems are highly responsive and seem to “understand” user needs, making some people treat them almost like humans. We are seeing this trend across different industries, applications and even personal relationships, like the possibility of marrying AI, and beyond interpersonal relationships, such as viewing AI as a higher power. But AI isn’t human. It’s built on data, algorithms, and code. While AI can mimic human interactions, it doesn’t possess intuition, emotions, or the ability to make moral decisions.

However, with human guidance, it can be an extremely valuable tool. At AlphaSense, we’ve seen the immense demand for generative AI-driven search. Since launching our first generative AI feature in 2023, customers reported that they are saving 11 to 50 additional hours per month. McKinsey estimates generative AI will add over $200 billion in value for the banking sector, and 43% of financial services companies use generative AI.

Does that mean we should start thinking of AI as a colleague, capable of taking on everyday tasks just like a human?

Keeping AI in Check

Imagine a seasoned financial analyst facing a complex market decision. Beside them, an AI system rapidly sifts through data, spitting out predictions in seconds. Tempting as it is to lean entirely on AI, there’s a nagging feeling that something doesn’t add up—a geopolitical event, perhaps, or an emerging market trend that hasn’t been fully quantified. This scenario underscores one of the biggest risks of anthropomorphizing AI: over-reliance.

Financial teams require vast amounts of data to guide their success, and leveraging generative AI that can not only source, but can extract insights from, that data is critical. That said, AI lacks the contextual understanding and critical thinking that financial professionals bring to the table. How do we leverage AI’s strengths without falling into this trap?

Financial institutions need a structured approach to implementing AI. First, it’s crucial to define AI’s role clearly. Rather than viewing AI as replacements for human workers, teams should see them as powerful tools that enhance human capabilities. Start by identifying specific, well-defined tasks that AI can handle, and implement a regular review process. AI needs to be monitored, and its outputs should be audited regularly to ensure accuracy and reliability. Training your team is equally important. Financial professionals should be educated not just on how to use AI but on when to trust its recommendations and, most importantly, when to rely on their own judgment.

What Generative AI Can Do for Financial Teams

While AI shouldn’t replace human decision-making, it’s incredibly effective in taking on routine tasks that free up time for more strategic and creative work. Also, generative AI can spark innovation and accelerate learning in ways once thought impossible. Some examples:

Streamlining data analysis for investment decisions: Consider the flood of financial data, including broker research, global news events, and earnings calls. Generative AI can process this information at lightning speed, highlighting key trends and insights that might take analysts days to uncover. At AlphaSense, our AI capabilities are layered over premium, pre-vetted content so that users can not only uncover information quickly but can also have the peace of mind that the insights they are seeing are trustworthy and reliable. In a high-stakes industry, neither speed nor accuracy should be sacrificed.

Boosting creativity and innovation in investment strategies: Generative AI can serve as a powerful brainstorming tool for financial professionals, helping to generate new ideas and perspectives. AI can simulate various market scenarios, analyze historical trends, or identify patterns that may be missed by human analysts, sparking new ideas for investment approaches and highlighting potential risks.

Accelerating financial learning: Generative AI can also act as personalized tutors, rapidly synthesizing complex financial information, news, regulatory updates, and market insights to help professionals keep up. For instance, rather than simply analyzing data, AI can break down emerging trends, explain the impact of new regulations, or summarize the key points of lengthy reports. AlphaSense’s first generative AI tool, Smart Summaries, exemplifies this. The tool provides highly accurate summaries pulled from millions of documents across equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own content. This allows finance professionals to quickly grasp new concepts, deepen their expertise in specialized areas, and expand their knowledge in an industry that’s constantly evolving.

A Tool, Not a Teammate

AI is here to stay, and its role in financial institutions will only expand. But as powerful as generative AI is, it is still a tool—not a teammate. By recognizing the technology’s limitations, establishing clear guidelines for its use, and training teams on how to collaborate effectively with AI, institutions can fully take advantage of generative AI’s potential.

As we move further into the age of AI, financial institutions have the opportunity to become more efficient and innovative. Understanding where AI fits—and where it doesn’t—in the team dynamic is an important step in driving long-term growth. The future of AI isn’t about making it human but using it to enhance our own human creativity and strategic thinking.