Roula Khalaf, the Editor of the FT, shares her preferred articles in the weekly newsletter.
The individual holds the position of president at Queens’ College, Cambridge, and serves as an advisor to Allianz and Gramercy.
The significant impact of generative AI as an innovative force is undeniable, bringing about both job displacement and advancement. The current focus on the equilibrium between these two aspects has placed asset management in the spotlight, unwittingly becoming a “natural experiment” in this ongoing debate.
The deployment of the generative AI revolution, also known as Gen AI, within the industry not only sparks discussions on job-related issues but also sheds light on broader organizational and regulatory challenges that will extend beyond finance, health, and other sectors.
A notable feature of the Gen AI revolution is its nascent stage, with its primary drivers—computing power, data, talent, and funding—growing exponentially in scale and speed, intensifying its disruptive effects. It comes as no surprise that it has become a top priority for CEOs across various companies and industries.
Within asset management, Gen AI presents significant opportunities, signaling a wave of transformations in operational processes and organizational structures. Leading companies are already leveraging it to enhance operational efficiency, improve communication, and bolster cybersecurity measures, marking just the beginning of its potential.
Both investment teams and client-facing departments can now effortlessly create powerful presentations using Gen AI to showcase capabilities and validate new trading concepts. Tasks such as communicating returns and performance attribution to clients, traditionally labor-intensive, are now executed swiftly and accurately. Moreover, technology teams have a wider array of tools to counter the escalating number of cyber threats.
In each of these scenarios, Gen AI acts as a force multiplier, enhancing employees’ capabilities and enabling them to add more value. While there may be job displacements in routine tasks requiring low skills, the overall impact on employment is positive, especially with the increasing hiring of engineers. Proficiency in interacting with AI systems is becoming a crucial skill for both new recruits and existing staff.
Looking ahead, envisioning a scenario where Gen AI engines play a pivotal role in high-skill tasks like asset allocation, model portfolio management, security selection, and risk mitigation is not far-fetched. These engines will be trained on vast untapped datasets within the industry, paving the way for innovative applications.
As technology progresses, it is plausible that Gen AI tools will assist in creating and structuring new asset classes, drawing insights from a blend of real and virtual data. Over time, successful segments of asset management will integrate Gen AI-powered tools with novel capabilities that are inherently Gen AI-centric. This evolution will enable a more personalized approach in tailoring individual investment portfolios to match clients’ risk preferences and behavioral tendencies more accurately.
However, the journey ahead is bound to be challenging. Existing tools have imperfections, talent distribution is uneven, biases may influence their application, and questions linger regarding internal AI governance and the regulatory frameworks governing their use domestically and potentially internationally. The growing technology divide between China and the US further complicates matters, leaving those in between uneasy.
This transformative path will also reshape the industry landscape significantly. Entities slow to grasp the disruptive potential of AI and its diverse applications, particularly concerning talent, managerial agility, and data management, will struggle to keep pace. Failure to capitalize on early opportunities for advancement may widen the gap further.
In essence, this dynamic shift will drive the industry towards a structure dominated by a few major players and a multitude of smaller specialized firms. Mid-sized asset managers, overseeing assets ranging from \(100 billion to \)500 billion, and firms trailing in Gen AI adoption, will face pressure to consolidate or risk becoming obsolete. This is where the job displacement is most pronounced.
The challenges confronting asset management are indicative of broader trends affecting various sectors, including finance and healthcare. Ignoring these developments poses risks for firms, while regulators, historically focused on banks, must recalibrate their oversight to address the evolving landscape of non-banking entities.
