AI is transforming the asset and wealth management industry, from investment decision-making to regulatory compliance and client relationships. This progress is significantly raising expectations in terms of speed, accuracy, and the level of personalization.
Most companies know they need to respond: 73% of executives believe AI will be decisive for the future of their organization. However, pressure is still increasing to turn early pilot projects into tangible, scalable results.
Even so, progress is not uniform. Although many companies have already identified promising use cases, structural obstacles persist, such as fragmented technology systems, less agile organizational cultures, and still insufficient data management. While AI adoption is spreading across the value chain, a growing gap remains between ambition and execution. Some companies pursue AI initiatives with a clear purpose and governance framework, while others remain limited to small, non-scalable experiments.
An industry that has moved beyond experimentation, but is advancing at uneven speeds
To understand these differences, Grant Thornton has collaborated with ThoughtLab on a global study involving 500 financial institutions across 16 markets. The results show an industry that has moved beyond experimentation but remains divided between firms that are structurally ready for AI and those that are not.
The sample covers the full spectrum of investment providers, including asset managers (15%), wealth managers (14%), private banks (13%), hedge funds or private equity firms (12%), family offices (12%), brokers (11%), and fintechs (12%). Responses are evenly split between executive and technology leadership profiles, with geographic representation led by Europe (41%), Asia-Pacific (31%), and the United States (20%).
The report, The AI-Powered Investment Firm, analyzes how the most advanced organizations are integrating AI into their workflows, strengthening their data foundations, and preparing their teams for a new generation of agentic AI capable of planning, executing tasks, and making autonomous decisions, with human oversight only when necessary. The new study also shows what is holding firms back and what distinguishes leaders: clear strategy, phased delivery, and strong governance.
As Alejandro Sánchez, Managing Partner of Business Process Solutions and Asset Management expert, notes: “Companies that achieve the greatest impact do not rush to adopt every new technological tool. Their approach is based on aligning AI with strategy, progressing in phases, and maintaining focus on business objectives.”
AI is advancing, but internal complexity is limiting its impact
The perception of AI’s transformative potential is clear: nearly two-thirds of executives surveyed expect it to fundamentally reshape the Asset Management industry. Adoption is already visible across multiple areas, from regulatory compliance automation to improved customer insights and operational efficiency.
However, the study highlights that organizational complexity remains one of the main barriers. Technology fragmentation, difficulties in accessing high-quality data, and regulatory uncertainty are slowing transformation across many firms.
In this context, AI is no longer just a technological project but a cross-functional management challenge, especially as agentic AI solutions begin to emerge. Its adoption requires strategic alignment, strong executive sponsorship, and robust governance frameworks that integrate AI into the business model from the outset.
According to Jorge Tarancón, Partner of Financial Advisory – Transaction Advisory Services at Grant Thornton Spain: “AI will significantly increase daily productivity and integrate intelligent agents into operations. This requires rethinking operating models and oversight mechanisms.”
Where are companies focusing their efforts?
Most organizations are laying the foundations of transformation: 77% already have a defined AI strategy and roadmap. Traditional and generative AI remain priorities, supported by mature technologies such as machine learning and natural language processing. In fact, 71% expect to adopt generative AI solutions within the next three years.
Across front, middle, and back office functions, adoption is strongest in predictable, high-volume tasks.
Administrative functions—such as code development, business processes, and custody services—have been early candidates for AI deployment due to the efficiency and productivity gains they can deliver. In this area, 46% already use AI to write or edit code, 42% for business processes, and 39% to support custody services.
In the middle office and risk management, most firms use AI to automate compliance checks and quickly identify violations. Many are also enhancing data security and privacy by using AI to detect anomalies in real time and respond immediately to potential threats. Overall, 57% use AI for regulatory and tax supervision, and 52% for data security.
In the front office and customer service, nearly six in ten firms now use AI to deepen customer analysis (59%). Slightly fewer offer AI-enabled chatbots and self-service portals to provide 24/7 personalized support (58% for conversational support and 54% for self-service portals).
What is holding companies back?
Despite progress, cultural and technological challenges remain significant barriers to AI deployment in asset management. More than half of firms identify slow-moving cultures and limited access to reliable data as the main obstacles to scaling AI.
Data management gaps are particularly relevant: approximately half of companies have not yet implemented robust processes to clean, normalize, and enrich internal data or to incorporate high-quality external sources. As a result, return on investment remains uneven: two-thirds report modest benefits, and 12% see no clear improvements or even negative impacts.
What do AI leaders in Asset Management do differently? Five best practices
The ThoughtLab analysis identifies five key practices that distinguish the most advanced organizations:
Although fewer than 10% currently use agentic AI, 18% expect to implement it within the next three years, signaling a profound shift in industry operating models.
The era of agents
The move toward more autonomous processes opens a new stage in which AI evolves from a point-in-time assistant to a true operational partner.
As Jorge Tarancón highlights: “Professionals will work alongside AI agents that operate on an equivalent level and are accountable for outcomes. Human teams will need to learn how to activate, supervise, and intervene in these processes, managing exceptions.”
The most advanced firms are already designing models in which technology drives innovation and efficiency without losing focus on people and human oversight.
From ambition to execution
The conclusion is clear: success in integrating AI in asset management does not depend so much on adopting the latest tools, but on building solid foundations. High-quality data, coherent governance, well-defined use cases, and prepared teams make the difference.
Organizations that act now with strategic clarity and responsibility will be better positioned to scale AI safely and effectively. Those that delay risk falling behind in a rapidly evolving environment.
cloud technology axon
Xcalibur Smart Mapping, recognised as the global leader in airborne...
