Integration, AI, and the best-of-breed moment: What we heard at SimCorp’s Global Summit
Canoe Intelligence attended SimCorp’s Global Summit to gauge the institutional investment community’s sentiments. The discussions illuminated key challenges, highlighting three major themes that reveal operational friction within the industry.
SimCorp’s annual summit draws a cross-section of the institutional investment world: asset managers, pension funds, insurance firms, and the technology vendors that serve them. This year’s event was notable for the consistency of the challenges that surfaced and for the clarity with which practitioners described the gaps between where they want to be and where their data infrastructure currently sits.
Integration was the lens through which everything was evaluated.
Across conversations on the summit floor, one question kept surfacing: how well does this connect with everything else we run? Whether attendees were evaluating portfolio management systems, data platforms, or adjacent tooling, interoperability was the primary filter.
This reflects a broader shift in how institutional technology decisions get made. Buyers are no longer assessing tools in isolation – they are evaluating ecosystems. The question isn’t whether a solution works. It’s whether it works within the specific, often complex tech stack that the institution already operates.
For firms managing significant alternatives investment allocations, this integration challenge is most acute in the data layer. Private market data arrives through a fragmented mix of GP portals, email attachments, and inconsistently formatted documents and getting that data into portfolio management systems cleanly, on time, and in a usable format is a persistent operational burden. Spreadsheet-based workarounds and manual re-keying are still common, even at sophisticated institutions.
AI ambition is running ahead of data readiness.
AI featured prominently throughout the summit; in product demonstrations, panel discussions, and side conversations. The appetite for AI-driven automation, pattern recognition, and decision support is clearly growing fast. But beneath the enthusiasm, a more sober acknowledgment was emerging in conversation.
Organisations know they need more granular, structured data to make AI tools effective, which can foster confidence that investing in data quality is essential for reliable AI outcomes.
This is a problem particularly felt in private markets. GP reports arrive in inconsistent formats. Capital call notices sit in PDFs. Performance data is delayed by weeks or months. When AI tools are deployed on top of this kind of unstructured, fragmented input, the outputs are unreliable and ROI is correspondingly difficult to demonstrate. Several attendees described the situation candidly: they had invested in AI tooling and were struggling to justify it internally, not because the tools were poor but because the underlying data wasn’t ready.
The implication is important: investment in AI tooling and investment in data infrastructure are not independent decisions. The value of one depends entirely on the quality of the other. Institutions that treat data normalisation as a precondition, rather than a downstream concern, will be better positioned to realise returns on their AI spend.
Best-of-breed architectures are the industry norm.
Most of the institutions we spoke with are running hybrid environments as a deliberate, considered choice combining specialised tools across asset classes and functions, and accepting the integration overhead that comes with choosing best-in-class capability for each domain.
This isn’t inertia or legacy reluctance. It reflects a pragmatic view that no single tool can serve every function equally well, particularly as alternatives allocations grow in complexity and scale. The firms building these architectures are doing so intentionally, and they are looking for new solutions that fit cleanly into that composable model with open APIs, reliable data handoffs, and a track record of playing well with the other components in the stack.
The implication for data management is significant. In a multi-system environment, the quality of data flowing between components determines the quality of the decisions made on top of them. A portfolio management system is only as good as the data it receives. Risk analytics are only as reliable as the inputs that feed them. The data ingestion and normalisation layer, often the least visible part of the stack, is in practice one of the most consequential.
The data gap is widening as private market allocations grow.
The conversations at SimCorp’s summit sit within a wider market dynamic that is accelerating. Across Europe and globally, institutional allocations to private markets are growing, driven by return expectations, liability-matching requirements, and in some markets, active policy pressure to direct capital toward domestic infrastructure and innovation sectors.
The mismatch between the scale of capital now moving into alternatives and the quality of the data infrastructure supporting those allocations is the industry’s central operational challenge. Funds relying on fragmented, manually assembled portfolio data are making allocation decisions based on an incomplete picture, often weeks or months out of date. In a market moving quickly, that lag carries real cost: undetected concentration risk, missed rebalancing signals, and limited visibility into where opportunities remain unfilled.
The case for modern data infrastructure in alternatives rests on governance and competitive positioning as much as operational efficiency. Funds with real-time visibility over their private market portfolios are better placed to fulfill their fiduciary obligations, respond to regulatory scrutiny, and make allocation decisions with genuine conviction.
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About Canoe Intelligence
Canoe Intelligence (“Canoe”) is the platform for smarter alts management. We redefine alternative investment intelligence with AI-driven software that directly addresses the core challenges of private markets. Our technology empowers institutions, LPs, and wealth managers to future-proof their alts infrastructure, modernizing systems and providing a scalable foundation for long-term growth and compliance. By automating manual data processing with AI-native precision, Canoe helps clients reduce operational costs and risks, significantly lowering overhead and mitigating errors. Ultimately, our timely, accurate, and comprehensive data enables investment teams to drive superior investment outcomes through deeper insights and more profitable allocation strategies. With Canoe, it’s all about making Alts, smarter. Learn more at www.canoeintelligence.com.
MEDIA CONTACT:
Betsy Miller Daitch
Canoe Intelligence
+1 443-690-6200
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