In recent years, artificial intelligence (AI) and machine learning (ML) have been projected by some to eventually assume almost 99 percent of the investing industry. Now, in light of this technology’s continued ascent towards complete sentient functionality, there remains one vital question: “how far will AI go?”

With 2018’s first quarter nearly behind us, it is safe to assume that investing-based AI/ML will, in fact, go very far, confirming the aforementioned projections in the process. An overarching corporate shift to the cyber sector has resulted in subsequent emphasis on automated asset management tactics, and now, AI/ML is slowly becoming an accepted norm across firms worldwide.

Industry implications for AI/ML remain both promising and problematic as firms work to hone their automated approach, mitigate risk, and align key drivers into a progressive extension of what they already know and practice. With these notions in mind, here are a few projections for AI/ML on asset management’s immediate horizon.

 

Driving change

Accenture recently identified AI/ML technology as one of the “Big Three” of asset management in 2018 (alongside the cloud and the industry’s current operating model). This observation is consistent across many commentators within the industry, and this is not surprising considering an acceleration in firms’ efforts to examine their current workforce strategies. Accenture specifically predicts that firms will “add a range of integrated automated technologies to their resource mix” this year, focusing not only on AI, but on robotic process automation (RPA) to concurrently streamline distribution strategizing, employee engagement, and risk and cost reduction — among other vital areas.

These projections are fair, as cognitive technologies have allowed firms to jumpstart their business models in exciting new ways.

 

Achieving change: the tools

To actually bring these changes to fruition, many funds are employing a variety of tools and strategies to push the threshold of technological possibility. These resources include, but are not limited to Deep Learning networks, which can help to boost profits, navigate complex markets, and cut costs via artificial neural networks; Natural Language Processing (NLP), a branch of ML specifically focused on human-to-machine interactions (e.g., digital assistant software); and Computer Vision, an especially prominent concept, in recent years, dedicated to the “high-level understanding of digital images or videos” with intentions of reaching parity with the human visual system. These three concepts in particular have been implemented to further bridge the gap between human cognition and technological automation — all for the sake of making asset management both trustworthy and convenient.

 

What will change look like?

An enduring anxiety surrounding the shift to AI/ML is a perceived threat to employment. As the product of longstanding conditioning from science fiction, unfounded mythos, and fear of the unknown, portions of the human race continue to exhibit unease towards the concept of an automaton taking full control of a human-occupied position. The reality is that, despite a growing pool of research suggesting their expansive implementation in the near future, ambitious AI/ML initiatives remain a big bet for firms to make at this point in time, and for now, their place in the industry mosaic remains a work in progress — albeit it a very promising one.

However, many firms expect to soon make these technologies ubiquitous conductors of key repetitive tasks (client onboarding, customer data input and analysis, FAQ-related response, etc.), allowing asset managers to allocate their focus in constructive new ways (for example, on the strengthening of competitive advantage).