The AI Implementation Gap in Asset Management: Practitioners vs Pontificators
The gap between talking about AI and actually implementing it effectively in asset management has never been wider. As someone deeply immersed in the intersection of artificial intelligence and financial services, I’ve observed a concerning trend: while many firms claim to be “AI-powered,” precious few have moved beyond superficial implementations.
A recent SimCorp survey revealed that 75% of investment operations leaders understand AI’s potential benefits but need more guidance on practical implementation. This statistic perfectly captures our industry’s current state—recognising the transformative power of AI while grappling with how to deploy it effectively.
Beyond the Buzzwords: What Real AI Implementation Looks Like
True AI practitioners in asset management understand that the future isn’t just about automating existing processes; it’s about transforming them through intelligent systems that can understand, learn, and adapt. Mere automation of routine tasks no longer qualifies as innovative.
Real practitioners are developing systems that:
- Genuinely enhance decision-making processes through proper data integration
- Create contextual understanding from diverse sources
- Adapt and learn from interactions with domain experts
While many rush to adopt Large Language Models (LLMs), genuine practitioners understand their limitations—particularly that LLMs cannot perform reliable mathematical calculations despite their impressive natural language capabilities. These models make educated guesses based on training data when asked to calculate complex financial metrics, which can lead to significant errors.
The Practitioner’s Approach to AI Integration
What separates practitioners from pontificators is their approach to implementation. Those genuinely implementing AI in asset management:
Create hybrid systems that combine different technologies: deterministic models for precise mathematical calculations, traditional solutions for data processing, LLMs for generating natural language explanations, and sentiment analysis tools for deeper market insights. This ensures each component handles what it does best.
The most sophisticated implementations are moving toward agentic AI—systems that allow domain expert humans and AI agents to work collaboratively toward goals or automate incoming tasks. This represents a significantly more complex engineering challenge compared to basic LLM chatbots, requiring state management and tool execution capabilities.
The Knowledge Gap
Simply directing your IT department to integrate AI is not a recipe for success. An example of unintelligent AI integration would be signing up for Microsoft Co-pilot, implementing it company-wide, and assuming your team members will figure it out independently. Strategy and a proper onboarding plan are critical to user adoption.
Practical Applications That Deliver Value
Where are practitioners focusing their efforts? The most valuable applications in asset management include:
Data Management: Advanced AI systems are helping transform challenges around data models and metadata. Modern AI excels at automating data cleaning while simultaneously identifying patterns across large datasets and generating valuable insights from unstructured data sources.
Risk Management: With many firms struggling to obtain firm-wide views of investments, risk, and performance, AI offers compelling solutions through automated assessment and monitoring, real-time compliance checking, and enhanced due diligence processes.
Market Intelligence: AI-powered sentiment and market intelligence tools are revolutionising how firms gather market information. They continuously analyse news, social media, and market data to provide actionable insights that inform investment decisions.
From Understanding to Implementation
As AI technology has matured, the focus has shifted from whether to adopt AI to how to implement it effectively. Success depends on choosing the right applications, maintaining strong governance, and ensuring human expertise remains central to the process.
For asset management firms ready to move beyond the hype, the path forward requires an honest assessment of capabilities, thoughtful strategy development, and partnerships with those who understand both the technology and the unique demands of financial services. The question isn’t whether your firm will adopt AI—it’s whether you’ll work with a pontificator, or a practitioner; timeliness and effectiveness will be determined by your choice.
If you would like to learn more about how AI can assist with financial services, please reach out to schedule a discussion.
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Our team brings significant collective experience in the operational automation of financial services. We are at the forefront of leveraging Artificial Intelligence (AI) to drive experimentation and innovation across the Banking, Financial Services, and Insurance industries (BFSI).