By now everyone is aware that the transformation to processes driven by artificial intelligence (AI) and machine learning (ML) is a journey. And no one has yet been able to describe the destination.
What we do know is that the transformation to the human-and-machine workforce is inevitable. It will bring a myriad of uncertainties, not least in how it will affect people – both those already working in asset management firms and those who these firms need to think about hiring.
We saw this recently when we supported one of our clients – a long-standing, high-performance asset manager – in recruiting a data scientist with sought-after AI and ML experience for a newly created role.
It became apparent that the extensive shopping list of stringent criteria the client was looking for did not in fact exist in the marketplace. Our solution was to take a two-pronged approach, which resulted in two searches running simultaneously. This enabled us to target several skill sets both within and outside of investment management.
A further challenge was that the client did not have enough knowledge of the AI/ML skill sets to be able to evaluate the candidates’ abilities. This is a common experience when the skills are rare and the tech is not widely understood in all areas of an organisation.
We resolved this by drawing in a tech-savvy manager from a different team to assess the candidates. A project was added to the recruitment process, which further confirmed the technical proficiency of the candidates. It was a learning experience all round. Happily, the client recruited not only one but two excellent candidates.
For asset managers, choosing to not take this AI/ML journey is a dwindling option. Hesitating may come with a high competitive cost.
There are already South African fund managers using AI and advanced analytics to reduce costs and potentially increase returns. Whether firms are thinking about it, or starting their journeys with a few experiments, or deploying applications in certain business units, they are probably feeling the pressure of being behind their competitors. The lead firms are already scaling up AI applications across their organisations, impacting people in both the front and back office.
Although it might be a common fear, AI/ML is not about replacing people. Its value is in being able to hand routine tasks to a machine to free up humans – highly qualified people should not be doing routine tasks. Its capacity is to process phenomenal amounts of data quickly, which humans simply cannot do. Its aim is to undercut human biases and raise questions about ‘gut feels’ by presenting data-driven insights that achieve better results, faster.
Investment management firms deploying AI and advanced data analytics report advantages such as lower costs, rapid capacity to process huge amounts of data, improved screening of investment options and accelerated modelling. All of this presents significant advantages when it comes to the performance of the fund manager.
Having said this, transforming to an AI/ML-driven firm is not an event but a process, and leadership needs to communicate a clear vision.
It is an ongoing process that requires competencies many traditional asset managers do not currently have. It is likely they will have to add faces to their teams: younger, more tech-savvy, super-data-analytical and literate in computer languages the average fund manager does not understand.
Things are going to change
It is arguable that the asset managers of the future thrived because they had the foresight and open-mindedness to implement AI strategies and embrace change. They are the life-long learners most enthusiastic about new knowledge and new ways of doing things better. They are also the most flexible, who could engage easily with younger, digital natives.
It is unlikely that all investment decisions will be wrested from the white-knuckled grasp of asset managers by a coldhearted machine. Instead, savvy investment managers will use insights they have never been able to access before to achieve alpha.
The human-plus-machine workforce can be a battle or a win-win. The winning asset managers will understand which side to be on.