The investment management industry is undergoing a significant transformation, fueled by the rapid rise of Artificial Intelligence (AI) and automation. AI applications, such as ChatGPT, Perplexity AI, or Bing AI, have revolutionised the expectations for client interactions and portfolio management. However, the increasing adoption of AI has also raised major concerns about job displacement, ethics, and data security. In particular, the recent EU AI Act impacts firms using Generative AI tools through stricter regulations, requiring developers to undergo reviews before commercial release and potentially slowing down their deployment. The Act also upholds bans on real-time biometric identification and ‘social scoring’ systems, prompting firms to reconsider their AI strategies for these purposes.

But despite valid concerns about the use of AI, investment management firms can harness the power of AI strategically to future-proof their businesses while addressing these challenges. To effectively integrate AI into their operations, investment management firms must develop a well-designed AI strategy that aligns with long-term goals and values while harnessing three key components: data, talent, and tools.

Strategic success
Data serves as the foundation for AI-driven decision-making. Investment firms need to leverage various data sources internally, externally, and in aggregate to fully capitalise on AI's potential. A wealth management firm can use internal data to streamline client servicing processes, set clear service level agreements (SLAs) for timely responses to client queries, and efficiently allocate resources across teams. AI-driven analytics can identify areas for improvement, ensuring high-quality service delivery. For example, AI-based tools can provide comprehensive client profiles, including preferences, risk tolerance, and financial goals, enabling advisers to tailor investment strategies effectively and enhance the overall client experience. Investment firms can also tap into external data sources to gain a comprehensive view of their client's financial positions. Accessing held-away assets, illiquid investments, and the overall wealth picture of clients allows firms to offer personalised investment strategies. For instance, Ultra-High-Net-Worth (UHNW) advisory firms might integrate AI-driven data aggregation tools to consolidate clients' diverse assets and identify opportunities for wealth growth.

And by aggregating both internal and external data, investment firms can make better-informed decisions across the board. AI-powered analytics can analyse market trends, client preferences, and risk profiles, optimising investment recommendations and achieving superior portfolio performance.

A comprehensive AI strategy includes a roadmap for leveraging all types of data as currency.

Talent
Second, the adoption of AI will also reshape the talent landscape within investment management firms. AI will make current talent more efficient by enhancing the productivity of existing talent by automating routine tasks, and allowing employees to focus on higher-value activities. Wealth management firms could, for example, implement AI-driven portfolio rebalancing tools, reducing the time spent on manual adjustments and enabling advisers to build stronger client relationships. And as AI becomes a core component of investment management, new roles will emerge, requiring expertise in AI integration, data science, and Machine Learning (ML). Investment firms may need to hire data scientists, prompt engineers, and other technicians to develop AI models that forecast market trends and optimise portfolio strategies. At the same time, unfortunately, some traditional roles may become obsolete with the advent of AI. For instance, brokerage firms that extensively adopt automated trading algorithms might no longer require as many human traders. The advent of AI tools could also redefine financial education for the better, both for employees and clients. Firms should anticipate roles dedicated to AI integration and management, highlighting the emphasis on continual education and training.

Tools
While harnessing AI capabilities is essential, deciding whether to build, buy, or partner with external experts can accelerate progress and provide specialised solutions. Firms should be aware that advanced AI models, especially those focusing on generative tasks and deep learning, are on the horizon, and they promise significant advantages in terms of market intelligence and competitive advantage.

Firms might consider internally building their AI solutions if the goal is complete control over the development process. For example, family offices might build an in-house Robo-adviser platform, catering specifically to their client base’s unique needs.

Another approach is to acquire an existing AI-focused firm or capabilities and run it in-house. For instance, a wealth management company may acquire a Robo-advisory startup, integrating its technology and expertise into its own operations.

Working with best-of-breed AI providers can also offer scalable and tailored solutions for firms to implement. In this case, a large asset manager could partner with leading AI-driven analytics firms to enhance their trading strategies and risk management capabilities without large overhead internally.

The optimal approach depends on individual business objectives, but we have witnessed a mix in the market. Firms can take advantage of all three strategies by integrating with what already exists, taking advantage of the wave of innovation by others while also investing in in-house capabilities that differentiate your firm supporting your unique value proposition.

Adoption

• Wealth management firms can use AI to analyse extensive client data, including historical investment performance, financial goals, and risk appetite, to create personalised and optimal investment portfolios for each client.
• Pension funds can leverage AI to analyse vast amounts of financial market data, economic indicators, and demographic trends to make data-driven investment decisions, ensuring the long-term financial security of their beneficiaries.
• Family offices can also deploy AI-powered reporting platforms to augment the capabilities of their investment teams, empowering them to analyse complex investment scenarios more efficiently and provide bespoke customisation at scale.
• Brokerage firms can use AI-powered algorithms to automate routine trading tasks, enabling their traders to focus on high-value activities such as market research and risk management.
• Independent Financial Advisers (IFAs) can focus solely on relationship management and growing their assets under management by outsourcing to a provider who provides cutting-edge technology and data analytics capabilities, gaining a competitive edge in the market.

Future-proofing
In conclusion, the rise of AI presents both challenges and opportunities for investment management firms. By defining a well-rounded AI strategy, and investing in data, talent, and strategic partnerships, investment management firms can harness AI’s potential to streamline operations, enhance client experiences, and drive business growth. Responsible AI adoption will not only future-proof these firms but also enable them to stay competitive in the ever-evolving landscape of the investment management industry.

Interested in reading the full report? You can read this edition of the UK WealthTech Landscape Report online here.