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Research Reports » Caceis April 2019

R&D, talent, impact and conclusions

How far have survey participants advanced with AI research projects and implementation? The survey found that just under 40% of respondent firms have already started their research and development efforts (fig 14).

Eleven percent said they would be launching their AI development projects during the next 12 months, with a further 17% expecting to do so in the next two years.

Timing the introduction of AIA sizeable group ( 29%) viewed AI investment as a project for the longer term and indicated that they would begin this more than two years from the time of the survey.

This cross-section of opinion provides insight into current priorities at respondent firms, but also the degree to which resources are tied up with regulatory adaptation, compliance costs and other technology projects.

Just 5% of respondents said that AI was not an area their firm was interested in.

Talent
Next, we asked respondents how they will source the AI skills that they require (fig 15).

The largest group of respondents indicated they would access specialist AI skills on a best-of-breed basis from individual partners (38%). Just over a fifth of firms said that they would recruit these skills in-house.

Getting AI skillsSome firms, as we have noted, have established their own innovation labs and are sponsoring research and development via a small group of fintech partners. Others have sponsored and partnered with research teams in the university sector. Often, firms will source talent and ideas through a combination of these channels.

The overarching theme, however, is that AI skills are important to the development and success of their business. Only 2% believed that they did not need these skills.

Given the rising demand for data scientists and data engineers, this skill set is becoming more expensive and hard to access. University computer science departments are responding to this requirement by accepting larger student numbers on data science courses – but currently this supply is not keeping pace with demand from financial services and other industries. In the face of these challenges, almost a quarter of respondents indicated that they are not clear at this stage where they will access these skills.

Impact
In the final question, we asked “What do you believe the impact of AI will be on your business?”

The largest respondent group (43%) said that it will enable their firm to eliminate manual intervention and to generate added-value services using high levels of automation (fig 16).

The impact of AISome 33% of respondents highlighted the importance of AI in driving efficient data analytics and data science applications, disciplines for many firms that are essential to their future business performance.

A smaller group (15%) identified the value of AI principally in economic terms, as a way to address falling margins within their business. Given the rising cost of compliance and regulation, and the pressure this is placing on margins, we may expect a larger number of companies to prioritise AI applications as a means to address these economic pressures in the future. This is an issue to monitor in any update of the survey.

Only 3% believed AI will be a new cost to the business that has doubtful payback.

Conclusions
There is a high level of agreement across survey participants that artificial intelligence and data engineering are tools that investment managers will employ in the future. Two-fifths indicated that they have already initiated work on their AI research and development projects. A further 28% said they would begin within the next two years.

The majority view was that the benefits of AI will be realised across the front, middle and back office. Respondents believed that equity managers in particular will benefit from use of AI techniques, enabling them to bring new insights to their investment process beyond the ideas traditionally generated by their investment and research teams.

AI will also transform the area of fixed income investment, but to a lesser extent than for equity teams. Both active and passive strategy managers will benefit from use of AI, with active managers experiencing most advantage in the near term.

High-quality data is essential to the effective application of AI. Slightly more than a quarter of respondent firms have now established specialist data engineering teams overseen by a chief data officer.

It is testament to the importance of data as a strategic asset that less than 10% of respondents have outsourced their data management to a third party.

Currently, the major constraint to applying AI is a lack of maturity in the technology. There is also a dearth of visible-use cases to demonstrate how AI can benefit asset managers and their investors.

A quarter of respondents indicated that key decision-makers with asset management companies do not have a clear understanding of how AI will benefit their business.

For the next part of the report, click here.

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