Most companies know they need to respond: 73% of executives believe AI will be critical to their organization's future. However, the pressure to turn early pilots into tangible, scalable results continues to mount.
Even so, progress is not homogeneous. While many companies have already identified promising use cases, structural obstacles such as fragmented technology systems, lax organizational cultures, and still insufficient data management remain. Although AI adoption is spreading along the value chain, a widening gap between ambition and execution persists. Some companies pursue AI initiatives with a clear purpose and governance, while others remain constrained by small, unscalable experiments.
A sector that has overcome experimentation, but is advancing at uneven rates
To understand these differences, Grant Thornton collaborated with ThoughtLab on a global study involving 500 financial institutions in 16 markets. The results show an industry that has outgrown experimentation, but remains divided between companies that are structurally ready for AI and those that are not.
The sample covers the entire spectrum of investment providers, including asset managers (15%), wealth managers (14%), private banks (13%), hedge funds or private equity firms (12%), family offices (12%), stockbrokers (11%) and fintechs (12%). The responses are evenly distributed between general management and technology profiles, with geographical representation led by Europe (41%), Asia-Pacific(31%) and the United States (20%).
The report, The AI-Powered Investment Firm, which is available for download at the bottom of this page, looks at how the most advanced organizations are integrating AI into their workflows, hardening their databases, and preparing their teams for a new generation of Agent AI, capable of autonomously planning, executing tasks, and making decisions. with human supervision only when necessary. The new study also shows what's holding companies back and what sets leaders apart: clear strategy, tiered delivery, and strong governance.
As Alejandro Sánchez, Managing Partner of Business Process Solutions and expert in the Asset Management sector, points out:
"The companies that have the greatest impact are not rushing to adopt every new technology tool. Their approach is based on aligning AI with their strategy, moving forward in phases and keeping the focus on their business objectives."
AI is advancing, but internal complexity is slowing down its impact
The perception of AI's transformative potential is clear: almost two-thirds of the executives consulted in the research expect it to profoundly alter the way the asset management sector operates. Adoption is already visible in multiple areas, from automating regulatory compliance to improving customer insights and operational efficiency.
However, the study highlights that organisational complexity remains one of the main obstacles. Technological fragmentation, difficulties in accessing quality data, and regulatory uncertainty are slowing down transformation in many entities.
In this context, AI is no longer just a technological project but a cross-cutting management challenge, especially now that agent AI solutions are beginning to be deployed. Its adoption requires strategic alignment, support from senior management, and strong governance frameworks that integrate AI into the business model from the start.
According to Jorge Tarancón, Partner of Financial Advisory – Transaction Advisory Services at Grant Thornton Spain:
"AI is going to substantially increase daily productivity and integrate intelligent agents into operations. This forces us to rethink operating models and oversight mechanisms."
Where are companies focusing?
The majority of organizations are laying the groundwork for transformation: 77% already have a defined AI strategy and roadmap. Traditional and generative AI continue to be priorities, supported by mature technologies such as machine learning or natural language processing. In fact, 71% planto adopt generative AI solutions in the nextthree years.
In the front, middle, and back office, adoption is strongest in predictable, high-volume tasks.
Administrative functions—such as code development, business processes, and custodial services—have been prime candidates for early AI deployment because of the efficiency and productivity gains they can generate. At the administrative level, 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 use AI to automate compliance checks to quickly identify any violations. Many are also improving data security and privacy by using AI to detect anomalies in real-time and respond immediately to potential threats. In total, 57% use AI for regulatory and tax oversight and 52% for data security.
In the front office and customer service, almost six out of ten companies now use AI to deepen customer analysis (59%). Slightly fewer offer AI-enabled chatbots and self-service portals to offer their customers 24/7 personalized support (58% for conversational support and 54% for self-service portals).
What is holding companies back?
Despite the advances, cultural and technological challenges remain important when deploying artificial intelligence in the asset management sector. More than half of banks identify undynamic cultures and limited access to trusted data as the main barriers to scaling AI.
Gaps in data management are particularly significant: approximately half of companies have not yet implemented robust processes to cleanse, standardize and enrich internal data, or to incorporate external sources of quality. As a result, the return on investment remains uneven, with two-thirds reporting modest returns, and 12% seeing no clear improvements or even negative impacts.
Yields are also mixed. Two-thirds report only a modest ROI from AI, and 12% are not seeing returns or negative outcomes.
What do AI leaders do differently within Asset Management? Five Best Practices
ThoughtLab's analysis identifies five key practices that distinguish the most advanced organizations:
- Define a clear vision and AI-oriented culture, aligning technology with strategic objectives.
- Build AI-ready IT and data platforms with modern, scalable infrastructures.
- Integrate governance from the beginning, guaranteeing transparency, control and responsible use.
- Prepare people for new ways of working, enhancing strategic and supervisory skills.
- Redesign processes with agent AI in mind, anticipating more autonomous workflows.
While less than 10% currently use agent AI, 18% plan to implement it in the nextthree years, pointing to a profound shift in the industry's operating models.
The age of agents
The evolution towards more autonomous processes opens a new stage in which AI is no longer a one-off assistant but a true operating partner.
As Jorge Tarancón points out:
"Professionals will coexist with AI agents who will work in an equivalent way and will be responsible for the results. Human teams must learn 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 supervision.
From ambition to execution
The conclusion is clear: success in integrating AI into the asset management industry depends not so much on adopting the latest tools, but on building solid foundations. Quality data, consistent governance, well-defined use cases, and prepared teams make all the difference.
Organizations that act now with strategic clarity and accountability will be better positioned to scale AI safely and effectively. Those who delay this process risk being left behind in a fast-moving environment.
