[Sustainability Wired] Artificial Intelligence in Finance: How Investors Are Unlocking 40% Productivity Gains

Can the use of artificial intelligence in finance solve the industry’s biggest problems? From data overload to regulatory fatigue investors are stretched thin. Sourcing and validating data, conducting due diligence, and meeting growing disclosure demands have all become a drain on time and resources. And yet, competitive pressure is mounting.

According to Oliver Wyman, asset managers and wealth managers that adopt AI could see up to 40% productivity gains by streamlining manual research and administrative tasks. That kind of lift isn’t just about efficiency, it could be the key to uncovering new sources of alpha and gaining an edge in a crowded market.

In this conversation, Neil Brown, Head of Equities at GIB Asset Management, joins Clarity AI’s Chief Sustainability Officer, Lorenzo Saa, to share how AI is already changing the way his team approaches research, compliance, and client reporting. The conversation offers a firsthand look at the tools, risks, and real-world gains behind the AI transformation, and why early adoption may offer a competitive edge.

Meet the Experts

What emerges is a nuanced view on the use of artificial intelligence in finance. Neil emphasizes the importance of using these tools in areas where investors already have deep domain knowledge, allowing them to validate outputs, catch errors, and make faster decisions without sacrificing quality. The discussion also touches on how AI may reshape market dynamics, influence data infrastructure, and create a new standard for what clients expect from sustainable investment teams.

Key Moments

00:00 – 00:48	Introduction
00:49 – 02:30	How AI is powering sustainable investing
02:40 – 5:34	Introduction to Neil Brown
05:35 – 08:19	Can AI add value to investment decision-making?
08:20 – 10:11	How Neil is using AI in investment decisions
10:12 – 11:22	The exponential power of AI
11:23 – 14:29	How companies are leveraging AI for better reporting
14:30 – 18:42	AI and the data paradox: too much data or not enough?
18:43 – 21:02	How AI is solving for data gaps
21:03 – 24:56	Solving reporting and compliance burdens with AI
24:57 – 27:08	Can AI tools improve performance?
27:09 – 29:34	Responsible AI governance in asset management
29:35 -31:24	Rapid fire questions
31:25 – 34:28	The art of sustainability
34:29 – 37:13	Closing commentary

Notable Quotes and Insights

These moments from the episode highlight how AI is already being integrated into investment workflows, from speeding up research to reshaping how firms approach compliance and data analysis. Here’s are five insights from Neil Brown that stood out:

1. Research Workflows Are Being Rebuilt

Traditional investment research often follows a linear process—one task, one data set, one output at a time. But Neil explains how AI enables teams to run multiple layers of analysis in parallel, dramatically accelerating the pace and depth of decision-making.

“We’re moving from linear compute… to parallel compute. So while that GPT is answering principal adverse indicator questions I can be loading in the SASB materiality factors. And I can be having another one look at the peer.”

2. Custom GPTs Are Already Reshaping Research

Rather than relying on generic tools, Neil has built specialized GPTs trained exclusively on company filings and earnings transcripts. These custom models return answers with page-level citations, offering speed without sacrificing traceability or rigor.

“We’ve built GPTs that analyze ten years of reports and earnings calls, then cite specific pages for verification. That’s changed the pace of our research.”

3. Compliance Is Being Automated Too

It’s not just research where AI is making an impact—regulatory and client-facing processes are being transformed as well. Neil describes how his firm uses AI to generate accurate, timely responses for due diligence questionnaires, freeing up valuable time across the business.

“We have an incredible, RFP, due diligence questionnaire engine that can hoover up all of our processes and philosophies and teams and give a swift answer.”

4. AI Makes It Possible to Process Data at Scale, But Still Requires Expertise

Neil makes it clear that while AI can process massive amounts of data quickly, it’s not a plug-and-play solution. Investors still need domain expertise to interpret results, catch errors, and avoid being misled by flawed outputs.

“This is about an easier ability to drink from the firehose. This is about summarizing, aggregating, distilling, pulling out the material things..[but] do this where you are an expert, because when you use…something else to summarize your data, you need to spot those errors.”

5. There Are Still Unknowns About AI in Investing

While the short-term benefits of AI are clear—greater speed, deeper insight, improved efficiency—the long-term implications for the investment industry are still unfolding. Neil poses critical questions about competitive advantage, market behavior, and whether widespread adoption could ultimately flatten advantages rather than deepen them.

“The first thing is, does it give us an edge? The second is how long will it persist? And then the third is what does it do to the underlying market structure?”

Discover What Fast, Accurate Insights Actually Look Like

AI is already transforming how investors approach research, reporting, and client communication. But the tools you use and how you apply them matter more than ever.

To see what’s possible, explore a sample of Clarity AI’s GenAI-powered Company Briefs. You’ll see how our technology instantly distills thousands of data points into clear, actionable insights.