How Asset Managers Are Using AI
- kevincoghlan
- May 25
- 4 min read
Investment management used to be described in fairly simple terms: passive or active. That distinction still matters, but it no longer captures the full picture. Today, a firm’s competitiveness may also depend on how well it uses AI, and whether that AI is being applied to investing, technology, or client service.
A lot of the discussion around AI in finance is still vague. In practice the big firms are using it in three quite different ways and so the same AI label can describe very different business models.
Goldman Sachs
Goldman Sachs is using AI to help its investment teams process huge amounts of public information more efficiently. That can include earnings-call transcripts, company filings, analyst research, news flow, and other market-relevant material.
For investors, the key point is not that AI is replacing analysts. It is that it can help sift through more information, more quickly, and with more consistency than a human team could manage on its own. In a market where data is abundant, the challenge is not access to information but deciding what actually matters.
That gives Goldman a potential edge in research and decision-making, but it is not automatically a lasting one. In investing, strategies tend to weaken once competitors start doing the same thing. So the real question is whether Goldman’s data, models, and process are distinctive enough to stay ahead.
The other point worth noting is that this type of AI use is quite different from consumer tools like ChatGPT. In investing, repeatability matters. The same inputs need to produce the same kind of output, which means the system has to be controlled, reliable, and embedded in a disciplined process.
BlackRock
BlackRock has taken a different route. Its “Aladdin” platform began as an internal risk and portfolio management system, but it has grown into a technology business that is now used by other asset managers, pension funds, insurers, and wealth firms.
That makes BlackRock’s AI story particularly interesting from an investor’s perspective. AI is not just helping the firm manage money more efficiently; it is also helping it sell software and services to other institutions. In other words, BlackRock is turning part of its investment infrastructure into a commercial product.
That is a powerful model because it creates a second source of revenue beyond traditional asset management fees. If more firms rely on Aladdin, BlackRock gains not only through client relationships but also through technology income. That can make the business more resilient and less dependent on market conditions alone.
BlackRock has also been adding AI features that make the platform easier to use in everyday work. Natural-language tools, for example, can help professionals ask questions and complete tasks without navigating multiple specialist systems. That may sound like a small improvement, but in a business built on scale and workflow efficiency, small gains can matter a great deal.
Morgan Stanley
Morgan Stanley has chosen a third model. Its focus is not primarily on investment selection or on selling technology to the industry. Instead, it is using AI to improve the relationship between advisers and clients.
Its partnership with OpenAI brought generative AI into the wealth-management workflow, where the goal is to make the firm’s internal knowledge easier to access in real time. That is valuable because advisers do not just need information - they need the right information at the right moment, often while sitting in front of a client.
One of the most important tools is an internal assistant that allows advisers to ask questions in ordinary language and retrieve answers from Morgan Stanley’s research and knowledge base far faster than manual searching would allow. For a large firm with a deep research library, that is a practical advantage.
The firm has also introduced tools that summarise meetings, draft follow-up notes, and organise action items with client consent. These are not investment engines in the classic sense. They are workflow tools that help advisers be more responsive, better prepared, and more consistent in the way they serve clients.
For investors, that is still meaningful. Better adviser productivity can support stronger client relationships, and stronger relationships can help retain assets over time. So even if the AI is not directly “picking stocks,” it may still support the economics of the business.
What this means for investors
The most useful way to think about AI in asset management is not as one broad trend, but as three separate strategies.
Goldman Sachs is using AI to support investment research and decision-making. BlackRock is using AI as both an internal operating tool and a commercial platform. Morgan Stanley is using AI to improve adviser productivity and client service.
That distinction matters because the headline “AI” can hide very different investment implications. One firm may be trying to improve returns. Another may be trying to build a more valuable technology business. A third may be trying to deepen client relationships and improve operational efficiency. Those are not the same thing.
In any event, the figures involved are staggering. Goldman Sachs spends approximately $2 Billion every year on data and technology, which is the budget that supports their AI work. JP Morgan, which operates one of the largest asset management businesses in the world, has a total technology budget of around $18 Billion (!!!) in 2025, allocated roughly $2 billion to AI. BlackRock does not disclose its AI spend separately, but generates revenues in excess of $4 Billion a year from selling the Aladdin platform to other firms, which gives some indication of the scale of the underlying investment.
These institutions are not spending at this level to keep up with the industry. They have a vision of the world to come and the capacity to shape it. The investment industry will have to keep up with them.
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