Are you reframing the future of asset management or is it reframing you?

Our modeling shows that the five-year outlook for aggregate industry profit margins is clearly to the downside. Weaker income and rising costs are already eroding profitability; representative margins fell by 3.2 percentage points during 2022. Some firms have entered a negative feedback loop, with lower revenues making it harder to invest in the talent and technology needed to boost growth. AI’s ability to automate processes and augment human capabilities means that it will play a crucial role in enhancing efficiency, productivity and profitability. Examples of AI applications explored in our report include:

1. Accelerating digitalization

AI will accelerate the digitalization of asset managers’ relationships with investors and advisors. AI will power the client contact center of the future with self-driving operations by digitizing the majority of cross-channel interactions. As we look to realize the vision, clients can work toward AI-assisted operations, guiding staff with insights during client interactions and deploying personalized chatbots across engagement channels that can provide contextual answers to client queries. A recent EY survey found that 62% of consumers trust AI-generated responses to their questions (via chatbots or automated responses1). Furthermore, GenAI could be combined with customer data and human insights to enhance personalization and build engagement through next generation hybrid distribution models.

2. Portfolio management

There is huge scope for AI to augment human skills, achieving a step-change in firms’ ability to derive risk and performance insights from internal and external data sets. Potential applications could include generating investment signals from large volumes of unstructured data; carrying out advanced pattern recognition; and calculating time-weighted return predictions or risk outcome simulations. For example, analysts could use a human language interface to search satellite images for investment insights.

3. Automation and efficiency

AI is enhancing processes across the front, middle and back offices, with practical applications that include automating investment operations and client onboarding, performing compliance reviews and generating reports, and improving the performance of sales and marketing teams. For example, AI can be used to significantly enhance the detection of money laundering and financial crime. AI will eventually transcend the industry’s current technology silos, with dramatic effects on efficiency and productivity.

Despite this potential, many asset managers are discovering that there is a big difference between experimenting with AI and implementing it at scale. Nor is this a purely practical challenge. If asset managers are to succeed with AI, their clients and staff will need to trust the technology and understand that its goal is to augment people, not replace them.

To get full value from AI, asset managers will need to get three things right.

First, firms need to establish a strong risk and governance infrastructure around their AI-driven activities. This includes:

• A tailored oversight framework that monitors AI performance and risks, minimizing errors and ensuring full compliance with standards and regulation.
• A particular focus on the heightened risks — such as data security, hallucinations, “explainability,” confidentiality breaches or copyright infringement — that could arise from the use of GenAI.
• Strong operational guardrails that ensure AI governance controls are embedded in the processes, including setting-up operational playbooks to ensure AI does what it’s supposed to do.
• Appropriate staff training, including coaching employees who use GenAI on what questions to ask, how to ask them and how to interpret the responses.

Second, asset managers need to incorporate AI projects into differentiated strategies tailored to individual asset managers’ objectives. Some key steps of an AI-specific implementation approach could include:

• Rooting AI use cases in an implementation process that starts with a strategic vision, identifies the required operating models, then optimizes solution selection
• Using that top-down approach to prioritize AI investment where it will have the greatest impact — maximizing ROI by focusing on proof of value, not proof of concept
• Leveraging innovation learnings to boost agility and sharing the benefits of AI widely across the organization and its external ecosystem

Third, asset managers need to reverse their usual approach to innovation and embrace a transparent approach to AI. Openness about the potential benefits and risks of AI is key to building trust, including transparency over the data sets, methodology being used and traceability of outcomes to source. Firms must educate and empower their own staff, enabling them to communicate confidence and trust to clients – and to elevate investors’ own understanding of the role of AI.

Without the right support, asset managers risk committing a lot of time and resources to AI without generating sustainable improvements. They could even see their use of AI halted altogether by regulators or shareholders, setting back their transformation goals and creating significant reputational damage.

AI will truly be a game changer in asset management during the 2020s. The steps we have set out will be vital to keeping firms’ AI applications focused on the needs of the business; to maximizing the impact of investments in AI; and to delivering tangible benefits to investors, staff and other stakeholders. Even so, no one can be sure exactly what changes it will bring about. To explore the possibilities, along with seven potential 2030 scenarios for the industry, consult our full report on Reframing Asset Management.