Editors should take into account emerging areas such as “agent AI,” and whether it's faster, such as sector wealth managers, private banks, and more, and accept that the benefits of using AI appear to be very different.
There is growing evidence that wealth advisors, bankers and others in our industry use AI as part of their work. Almost every day, I receive email releases and comments on how artificial intelligence applications get “desk seating” and how companies are testing how they adopt the technology.
For example, in a report from US private banks and trust companies late last week, Cerulli Associates said bank advisors and home office executives had increased the use of AI to help review data and directly support portfolio construction and asset allocation. Less than half (42%) of bank advisors report using in-practice AI capabilities, but this number is expected to soar to more than three-quarters (77%) within the next two years. Private banks have already used more than half (56%) to some extent AI support, citing “significantly high” levels of AI usage, and are predicting consolidation over the next two years.
The scope of this publication's AI stories over the past few weeks shows how busy the sector is now.
Canoe Intelligence, a financial technology company powered by alternative investment intelligence, has launched Canoe Labs, an incubator that enables investment and management experts to realize new AI capabilities.
Global fintech Broadridge Financial Solutions has acquired a minority stake in UPTIQ, an AI platform for financial services.
Advisor CRM, RIAS's client relationship management platform, has announced its AI Conference Assistant.
EnvestNet, a US head office turnkey asset management program and a provider of FinTech-led back-office services, has announced two AI innovations: Generating Business Intelligence (GEN BI) and Insights AI. This product is designed to translate the way advisors access, interpret and use data.
This only damages the surface of this news service report on A1. If you haven't written about other topics, there's still plenty of content to be found.
Variations
The overall trend appears to be moving towards more AI adoption, innovation and product/service rollout, but there are differences to watch. The themes that come up are whether companies can afford to stay in front of the pack by spending money on technology that may become outdated within a year, whether they are in that “middle space” or should end up as a lagguard. June 26th, Bloomberg Professional Services A survey in late 2024 highlighted an increase in the gap between early AI adopters and Laguards. Almost half of banks expect low costs over the next 3-5 years (half predicts a 5-10% decrease), while over 40% predicts an upward cost.
It's easy to see why companies have different approaches. These technologies can be expensive. According to European Technology Consultant Future Processing (March 27, 2024), AI project costs are affected by a variety of factors, including development, hardware, data quality, functional complexity, and integration with existing systems, leading to costs ranging from $5,000 for simple models to $500,000 for complex solutions. It's easy to see why small private banks, for example, prefer to outsource this type of work as much as possible. To give another example, the same applies to family offices. Building solutions in-house is primarily a problem with bulge bracket banking.
Also, while keeping track of internal and outsourcing calculations, managers need to keep up with rapidly changing jargon and terminology (I think that the persuasive nerds who have read a lot of science fiction recently have a clear workplace advantage). There are “co-pilot” and “virtual assistants.” The relative beginner is “agent AI” (according to the AI-driven search I used to find out about it, “an autonomous artificial intelligence system that can plan, plan and execute complex tasks with little or no human intervention”).
Foregoing Bloomberg Agent AI will be a major force, according to the report: “It can handle complex workflows such as customer queries resolution, account balance optimization, and transaction execution.” However, reaching your chosen destination is not quick. This is because it requires large-scale technology upgrades and these new systems need to be adapted to the core platform. The report states, “Scrutiny of data governance, legacy systems and regulations suggest that the path to full autonomy could take more than five years.”
Considering the space of dramatic change, look at how systems like ChatGpt grasp – 5 years is a long time, but it's like a time frame, but people should often say they are investing in private equity.
It must be difficult for regulators to respond to this. Developments such as Agent AI may have distinct benefits, but it's easy to see how this makes the watchdog tense. In the UK, for example, the UK government guidance (June 5) poses the risk of fully autonomous AI, which reduces human oversight (like regulatory nightmares and perhaps client nightmares) and prematurely deploying such technology. It goes back to the question of how quickly we deploy technology. You should be on the head of the field and try to gain the advantage of your first driving. That seems to be a very difficult question to answer.
