The AI race is on but it may create new bubbles.<\/p>\n
Artificial intelligence (AI) has penetrated many areas, and the asset management industry hasn\u2019t been spared. Seven in 10 asset managers now use artificial intelligence in their front office, up from about one in 10 a year earlier, according to SimCorp, a provider of investment management software and services for the buy-side.<\/p>\n
Dean McIntyre, chief commercial officer at the firm, said AI adoption is \u201cimproving productivity, helping users to focus more on value-adding tasks\u201d, being deployed across investment teams, research workflows and client operations.<\/p>\n
Across the industry, there is an increasing number of AI use cases, and they are becoming ever more specific.<\/p>\n
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How the industry is using AI<\/p>\n
At BNY, an internal AI platform is used across the business, including sales and client onboarding, said Gerald Rehn, head of EMEA distribution at the firm.<\/p>\n
\u201cClient onboarding steps have moved from days to hours,\u201d he said. More than a quarter of its staff use the system daily, with many building their own tools on top of it.<\/p>\n
But it goes further than that. At Man Group, AI is embedded in the research process, as Tushara Fernando, head of data and AI at the firm, explained.<\/p>\n
\u201cWe can onboard and evaluate datasets faster than ever,\u201d he said. \u201cWe\u2019ve found that leveraging both humans and machines has led to diversifying and uncorrelated content.\u201d<\/p>\n
On top of that, generative AI is used to analyse company filings, financial reports and news flow, allowing investment teams to cover more securities and spend more time on portfolio construction and risk management.<\/p>\n
Elsewhere, BlackRock\u2019s multi-asset range My Map combines systematic (80%) and discretionary (20%) investing, with machine learning introduced selectively rather than reshaping the process.<\/p>\n
Chris Ellis-Thomas, co-manager of the range, said: \u201cWe carefully introduce elements that lean into machine learning or large language-model (LLM) platforms. We won\u2019t switch things on without a thorough understanding of how they operate\u201d.<\/p>\n
One of the more successful use cases for him is sentiment analysis.<\/p>\n
\u201c[AI] is a good way of digesting news sources and understanding broader economic and market sentiment,\u201d he said, describing how these insights can feed into broader risk-on and risk-off positioning.<\/p>\n
Other firms are pushing further into generating investable signals. Aberdeen has built a model that reads central bank speeches and quantifies how hawkish or dovish policymakers are, generating an AI-built index that \u201chas a lead on government bond yields\u201d and \u201ccould be used as a trading input\u201d.<\/p>\n
AI allows investment teams to screen more securities, test more ideas and expand the range of inputs that feed into portfolios. In wealth management, it is already being used to identify portfolios that may need rebalancing, allowing managers to focus on the accounts most likely to require attention.<\/p>\n
Agentic AI is also starting to be used.<\/p>\n
\u201cAI-powered agents can take multi-step actions, monitor changing market conditions and trigger workflows without needing constant human prompts,\u201d said McIntyre.<\/p>\n
For investment teams, this means routine tasks can be automated rather than handled step by step, freeing up time for portfolio decisions and client work.<\/p>\n
AI agents are emerging both at Aberdeen and BNY, where they answer portfolio managers\u2019 questions, carry out analytical tasks, edit research content and automate workflows.<\/p>\n
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The limitations<\/p>\n
But simply adding AI tools does not create an advantage.<\/p>\n
\u201cGenuine AI use means rethinking business processes, not just asking AI to summarise a document,\u201d Fernando said. The key question is whether AI changes how teams operate or simply increases the pace of existing workflows.<\/p>\n
The quality of the underlying data is another constraint. \u201cAI delivers the most value when it boosts decision-making and efficiency. That requires a unified, governed data layer,\u201d McIntyre said. Without that foundation, outputs risk becoming generic, particularly as many models are trained on similar pools of public information.<\/p>\n
This is why firms are investing heavily in proprietary systems and internal datasets. Man Group, for example, has built what Fernando described as an \u201cAI-ready\u201d data environment so its tools can operate in the context of its own positions, research and investment processes. Without this, the outputs of large language models are unlikely to differ meaningfully across firms.<\/p>\n
There are also limits to how far AI is allowed to influence decisions.<\/p>\n
\u201cWe do not have AI as a direct decision-maker in our critical processes,\u201d Fernando said. Models used in signal generation are subject to testing, review and ongoing monitoring before being deployed.<\/p>\n
BlackRock\u2019s approach reflects a similar caution. The result is a hybrid process in which AI supports decision-making but does not determine it.<\/p>\n
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Is AI any good at investing?<\/p>\n
AI does little to change the fundamentals of investing, according to Nick Clay, manager of the Redwheel Global Equity Income fund. \u201cUltimately, it makes life more efficient, but it doesn\u2019t replace making the final investment decision\u201d.<\/p>\n
While useful, it may also introduce new risks, for example, widespread adoption could lead to more uniform behaviour.<\/p>\n
\u201cAI didn\u2019t make picking stocks easier. Valuation drives total return. If everybody uses the same tools, they end up in the same stocks and create bubbles,\u201d he said.<\/p>\n
\u201cWhen everyone was taught the same way to analyse and value companies, they all bought the same \u2018cheap\u2019 stocks and those stocks stopped being cheap. If we all use the same AI to pick stocks, we will all end up in the same place and prices will be too high.\u201d<\/p>\n
Ellis-Thomas noted that investors are simultaneously focused on the scale of investment going into AI and the risk of disruption it poses to companies, creating volatility that can present opportunities.<\/p>\n