MAIA × IIMI
Maintaining the independence advantage without operational constraint
Boutique and mid-sized asset managers face a familiar challenge: the mandates that drive growth are often the same mandates that can place increasing pressure on operations. During an workshop hosted by MAIA in collaboration with the Independent Investment Management Initiative (IIMI), operations leaders explored how firms can continue to scale without compromising the service, responsiveness and operational edge that differentiates them.
A clear consensus emerged. Sustainable growth in assets under management (AUM) must be decoupled from proportional growth in operational headcount. Achieving this requires more than relying on AI. It is directly anchored to stakeholder alignment a unified data foundation and the right operational infrastructure that drives the necessary outcomes and efficiencies.
As for the ever-increasing premise of AI, participants unanimously concurred that itis a necessary enabler that should be positioned to collaborate with people rather than replace them. The most prominent early-adopter scenarios, unsurprisingly, centered on client servicing, reporting and operational efficiency, where AI-enhanced process automation allows teams to focus on higher-value activities.
This article summarises the workshop’s findings and highlights the practical steps investment managers can take to enable efficiencies and drive scale within their respective operating model while preserving the independence advantage that clients value most.
Scaling AUM
Growth should never come at the expense of high-quality client service. Automation should enhance, not dilute, the client experience.
Trust in automation
Adoption depends on understanding and confidence, particularly among non-technical users. Education should precede large-scale deployment.
Leadership sponsorship
The ambition to ‘double AUM without doubling operations’ must come from leadership-driven alignment. Technology investment is no longer a cost center but an enabler to scale.
Data foundations
Data quality, security and governance are prerequisites for successful AI adoption. A structured, comprehensive data strategy should come before an AI strategy.
Delivery model
Optimal outcomes come from combining operational expertise with engineering capability. Cross-functional collaboration and simpler technology estates drive better results.
The scale challenge: Growth without compromise
Participants consistently described the same challenge. As AUM grows, so too does operational complexity. More mandates create more reporting requirements, reconciliations and client requests. Unless operating models evolve, headcount increases at a similar pace, placing pressure on margins and limiting scalability.
The discussion made clear that automation is not about reducing the quality of client relationships. Boutique managers differentiate themselves through responsive, high-touch service. The objective is to remove operational friction so teams can maintain that standard of service as the business grows.
Client service as the first opportunity
Reporting and client enquiries emerged as the most practical starting point for AI. These activities are frequent, structured and time-consuming, making them well suited to automation supported by governed data.
Improving these processes delivers visible benefits. Clients receive faster, more consistent responses, while firms generate measurable operational improvements that help build confidence for broader adoption.
Closing the trust gap
Participants acknowledged that trust remains one of the biggest barriers to adoption. Many non-technical users are uncomfortable relying on technology they do not fully understand.
Rather than overlooking these concerns, firms should invest in education. Understanding how AI works, where it performs well and where human oversight remains essential creates confidence and encourages responsible adoption.
Use cases should always begin with a genuine operational challenge. Technology should solve a clearly defined business problem rather than being introduced simply because it is available.
Leadership must set the direction
Successful transformation requires visible sponsorship from senior leadership. Framing the objective as ‘double AUM without doubling operations’ gives technology investment a clear commercial purpose and aligns operational initiatives with long-term business growth.
Leadership ownership also prevents strategic projects from being continually delayed by competing operational priorities
Data is the Foundation
Technology is only as effective as the data that supports it. Participants repeatedly identified data quality, governance and security as the foundations of any successful automation strategy.
Firms that establish trusted, well-governed data before expanding AI capabilities create a far broader range of opportunities while reducing implementation risk.
Bringing operational and technical expertise together
The workshop highlighted the importance of combining subject-matter expertise with engineering capability throughout every project. Solutions developed collaboratively are more likely to reflect the realities of investment operations and deliver measurable value.
Participants also advocated simplifying technology estates wherever possible. Reducing system complexity and consolidating data creates stronger foundations for automation, collaboration and future growth.
Key Recommendations
- Anchor the transformation programme at leadership level with a clear objective of scaling AUM without proportional operational growth.
- Prioritise client reporting and on-demand requests as high-value initial use cases.
- Invest in AI literacy across operational teams before scaling adoption.
- Position automation around resilience, consistency and the removal of operational bottlenecks rather than headcount reduction.
- Ensure every AI initiative addresses a clearly defined business or client need.
- Assess data quality, governance and security before developing an AI roadmap.
- Build cross-functional project teams that combine operational expertise with engineering capability.
- Reduce unnecessary technology complexity through greater system harmonisation and unified data.
Conclusion
The workshop did not position AI as a silver bullet. Instead, participants described a more measured and practical approach to operational transformation. Technology delivers lasting value when it strengthens client service, simplifies operations and enables firms to scale with confidence.
For boutique and mid-sized investment managers, the opportunity is not simply to introduce new technology. It is to build an operating model founded on trusted data, strong governance and unified infrastructure that supports sustainable growth. As participants concluded, success should not be measured by whether operations grow alongside AUM, but by whether that growth becomes unnecessary.
