Most Institutions Aren’t Ready for AI in Investment Management

Are institutional investors ready to trust artificial intelligence (AI) to facilitate decision-making? Not quite.

While AI in investment management promises speed, insight, and scale, most institutions are still stuck with outdated systems and siloed data. The result? Missed opportunities and mounting pressure to catch up.

In this breakdown, we analyze why so many are falling behind and what the frontrunner institutions using AI for investing are doing differently to pull ahead.

Table of Contents Table of Contents

1. Key takeaways
2. Four key reasons why institutions aren’t ready
3. The path forward: 3 actions to close the gap
4. The bottom line
5. FAQs
6. References

Show Full Guide

Table of Contents

1. Key takeaways
2. Four key reasons why institutions aren’t ready
3. The path forward: 3 actions to close the gap
Show Full Guide
4. The bottom line
5. FAQs
6. References

Four key reasons why institutions aren’t ready

The latest benchmarking survey by McKinsey and CEM Benchmarking asked institutional investors to rate the performance of their internal tech systems across a range of capabilities. The results paint a clear picture: most institutions aren’t ready to support advanced technologies like AI in investment management, because the foundations simply aren’t there.

Only a small percentage of respondents gave their systems high marks across critical areas such as data access, platform flexibility, and real-time reporting.

The chart below shows just how far most institutions still have to go:

These data points help explain why so many institutions continue to struggle with AI adoption across four main problem areas: legacy systems, poor data foundations, lack of senior-level buy-in, and a growing talent gap.

1. Legacy systems are slowing everything down

Many institutional investors are still using outdated technology, such as spreadsheet-based processes, email threads, and old internal platforms, which aren’t built for today’s fast-paced investment landscape. These legacy systems can’t keep up with the growing demands for real-time data, speed, or flexibility.

Even as interest in AI in investment management grows, most institutions can’t adopt it properly because an adequate foundation is still missing. Without the right systems in place, it’s hard to make AI work in a meaningful way.

Some of the biggest blockers include:

In fact, in 2023, fewer than half of institutions said their tech systems met core operational needs:

• Only 48% said they don’t rely on Excel for key data tasks
• Just 31% integrated public – and private-market data
• And just 17% felt their systems were ready to support AI or future technology.

Other issues were equally telling:

• Just 24% said their systems didn’t require workarounds
• Only 21% reported having a flexible architecture that wasn’t locked into a single system or vendor

Many try to fix these gaps with temporary solutions, but that often leads to more complexity. Over time, this creates technical debt – patches that make future upgrades harder, not easier.

That’s one reason why so few have moved toward automated investment management or started using reliable AI investment tools.

The institutions that are getting ahead have done the opposite:

• They’ve updated their core platforms
• Integrated their data, and
• Started using tools designed for scale and speed.

This shift allows them to make smarter decisions and adapt faster, especially regarding AI in asset management.

There’s a growing gap between those who are modernizing and those who aren’t, and with more tools becoming available, that divide is only going to get wider.

2. Data isn’t treated as a strategic asset

One of the biggest challenges holding institutions back from adopting AI isn’t just outdated systems; it’s how they think about data.

In many institutions, data is still treated as something that supports the process, rather than something that drives it. That makes it harder to build anything smarter on top.

In fact, according to McKinsey’s survey, only 18% of front-office teams could access the data they needed without delays or manual effort.

For the rest, it’s a slow process – gathering information from different systems, cleaning it up, and trying to make sense of it all. That delay makes it difficult to implement AI portfolio management tools or act quickly when markets shift.

Other indicators reinforce the problem:

• In 2023, just 41% actively measured and improved data quality
• Only 33% reconciled duplicated data sources
• And just 27% reported having a single source of truth across systems

Getting this right means starting with the basics:

Once that’s in place, institutions are in a much better position to explore AI investment management and use practical AI tools for investing, because they’re finally working with information they can trust.

3. Boardroom buy-in is missing

Even though AI gets a lot of attention, many institutional investors still haven’t made it a clear priority at the board level. Leadership teams are often unsure about where to focus or whether the AI investment is worth it right now.

In many cases, the hesitation comes from how technology is viewed. It’s still seen as an expense; something you manage, not something that drives value. That view holds institutions back from building the systems they need to move forward.

The numbers reflect this:

• Most institutions spend between 1.3 and 2.7 basis points of AUM on technology. That means they’re investing just 0.013% to 0.027% of their assets under management – the total market value of all the investments they oversee
• To put that in perspective, an institution managing $100 billion would be spending from $13 to $27 million a year on technology and AI. Given the scale and complexity of what they manage, that’s not much
• Only a small group – those in the top 25% – spend more than 3.5 basis points. These are often the institutions that are seeing real progress.

Even more telling: just 54% of institutions said their current technology helped them manage market, liquidity, or credit risks. That’s a red flag for decision-makers hoping to link tech to better investment outcomes.

So what sets successful institutions apart isn’t just the budget – it’s the mindset:

These institutions aren’t waiting for perfect conditions. They’re already exploring AI in asset management and using AI for investing as part of their bigger shift toward digital transformation in wealth management.

4. Talent shortages & siloed teams persist

Technology on its own isn’t enough. Institutions also need people who can make it work, and that’s where many are running into problems.

There’s a growing shortage of professionals who understand both investment strategy and modern technology. That blend of skills is hard to find, and even harder to keep.

To fill the gap, most institutions turn to outsourcing. It helps in the short term, but it often makes it harder to innovate. External partners can deliver tools, but aren’t always close enough to daily decision-making to help drive meaningful change.

The institutions making real progress are doing something different:

In 2023, only around 10% of tech staff were working directly inside investment teams. That’s a small proportion, especially as demand grows for AI in portfolio management.

To move forward, institutions need to rethink how they structure teams; bringing tech talent closer to investment strategy and building up skills from the inside, not just buying them in from outside.

The path forward: 3 actions to close the gap

For institutions that want to move forward, the key isn’t a full-scale overhaul. It’s about taking small, smart steps that build momentum, starting with what’s already in place.

Taking these steps now can help institutions stay ahead. They can use AI for investing in ways that are realistic, grounded, and tied to actual performance rather than waiting for the ideal setup that may never come.

The bottom line

Most institutions still aren’t ready for AI in investment management, and it’s starting to show. Outdated systems, messy data, and a lack of in-house tech skills are making it hard to move forward. But some are closing the gap by upgrading their investment management technology and taking small, practical steps.

Those who act as soon as possible will be better placed to adapt, compete, and grow in an increasingly digital market.

FAQs