Four Themes from Cutter Connect Scottsdale 2026

Investment managers are accelerating their investments in artificial intelligence, automation, and modern data infrastructure. Yet translating these investments into measurable operational improvements remains a significant challenge.

At Cutter Connect Scottsdale 2026, held October 28–30 in Arizona, investment management executives, technology leaders, and operations professionals came together to explore the evolving investment operating model and the technologies shaping its future.

The conference agenda reflected several priorities facing institutional investors, from modernizing investment data platforms and integrating AI into existing workflows to bridging the technology divide between public and private markets.

For Alkymi, these topics connect to a fundamental challenge facing the industry: How can investment managers turn fragmented, unstructured information into trusted data that supports intelligent automation and better decision-making?

Here are four key themes from Cutter Connect Scottsdale and their implications for the future of investment operations.

1. Building AI-Ready Data Infrastructure Starts with Trusted Data

Data infrastructure was a prominent theme throughout the conference, with sessions including “Investing in a Data Platform” and “Beyond the Data Warehouse: Building an AI-Ready Enterprise Data Platform at Scale.”

As investment managers modernize their technology environments, the focus is expanding beyond centralizing information to ensuring that data is accurate, accessible, and ready to support advanced analytics and AI-driven workflows.

For private markets, this challenge begins well before information reaches an enterprise data platform.

Critical investment information remains embedded in unstructured documents, including capital notices, capital account statements, loan activity notices, compliance certificates, and borrower financial statements. These documents arrive through investor portals, emails, and other fragmented channels, often in inconsistent formats.

Traditional data infrastructure can store, organize, and distribute information, but it does not necessarily address the complexity of extracting and validating data from these sources.

Before unstructured information can support investment reporting, portfolio monitoring, or AI applications, it must be collected, extracted, normalized, validated, and mapped to the appropriate investment entities.

An AI-ready data environment requires a reliable process for transforming unstructured investment information into trusted, workflow-ready data.

2. Moving from AI Experimentation to Operational Execution

AI was another major focus of the conference, with sessions such as “The State of Play for AI,” “From AI Promise to Performance,” and “Smarter Investment Operations: Scaling with Automation & AI.”

These topics reflect a broader shift in the industry from exploring AI capabilities to determining how they can be integrated into everyday investment operations.

While AI models can extract information, summarize documents, and assist with analysis, delivering meaningful operational value requires more than automating individual tasks.

The greater opportunity lies in connecting the entire workflow, from retrieving source documents and extracting relevant information to validating results, resolving exceptions, and delivering data into downstream investment systems.

Consider private credit operations, where investment teams must continuously process borrower financial statements, loan activity notices, and compliance certificates. These workflows involve multiple steps, often across different systems and teams, creating opportunities for manual errors, processing delays, and operational bottlenecks.

End-to-end intelligent automation can help firms reduce repetitive work, improve consistency, and make critical information available more quickly.

Emerging agentic AI capabilities are extending these possibilities by enabling AI-powered applications to interact with enterprise systems, retrieve relevant information, and initiate authorized workflow actions.

However, successfully operationalizing these capabilities requires reliable underlying data, seamless integration, and appropriate governance and human oversight.

The next phase of AI adoption will be defined by how effectively firms integrate it into their operational processes.

3. Bridging the Technology Divide Between Public and Private Markets

The session “The End of the Divide? Public, Private & the Future of Investment Technology” addressed an increasingly important consideration for institutional investors: how to support a more integrated investment environment across asset classes.

As private markets become a larger component of institutional portfolios, firms are looking for ways to improve visibility, consistency, and efficiency across public and private investments.

Historically, these asset classes have relied on different technology infrastructures and data management processes.

Public markets generally benefit from more standardized data sources and established processing mechanisms. Private markets, by contrast, continue to rely heavily on unstructured documents, investor portals, and fragmented reporting formats.

These differences create challenges for firms seeking a consolidated view of portfolio exposures, performance, and risk.

Bridging this divide requires more than connecting existing applications. It requires making private markets information accessible and usable within the broader investment technology ecosystem.

Automating the collection and transformation of private markets documents into structured, validated data enables firms to integrate this information into existing investment platforms, analytics environments, and reporting workflows.

This approach can help organizations reduce operational silos and support a more consistent view of investment information across asset classes.

The future of investment technology is not necessarily a single platform for every asset class, but an interconnected ecosystem supported by reliable, accessible, and consistent data.

4. Modernization Must Deliver Value Without Adding Complexity

Investment managers face growing pressure to modernize their technology environments while managing costs, operational risk, and competing business priorities.

Sessions including “The New Economics of Buy-Side Technology: Vendor Perspectives on Cost, Value, and Tradeoffs”and “Building Resilience Through Co-Sourcing and Managed Services” addressed the strategic considerations involved in technology investment decisions.

For many firms, modernization does not necessarily mean replacing established systems. Instead, the opportunity lies in extending the value of existing infrastructure through intelligent automation, better integration, and more efficient data workflows.

This is particularly important for organizations operating complex environments that include investment accounting platforms, portfolio management systems, enterprise data platforms, and specialized applications.

Introducing new technologies without considering how they connect to these systems can create additional fragmentation rather than eliminate it.

A more practical approach is to introduce capabilities that complement existing infrastructure, automate manual processes, and improve the quality and accessibility of data across the organization.

Interoperability is also becoming increasingly important as firms explore AI agents and more sophisticated automation.

Technologies such as the Model Context Protocol (MCP) offer new ways to connect AI applications with enterprise data and operational systems, enabling more integrated and actionable workflows.

Ultimately, the value of modernization should be measured by tangible improvements in efficiency, scalability, data quality, and operational resilience.

Building the Foundation for the Next Generation of Investment Operations

The themes explored at Cutter Connect Scottsdale highlight an important direction for the investment management industry: as firms accelerate AI adoption and modernize their technology environments, trusted data and connected workflows will be essential to achieving meaningful results.

For private markets, that foundation starts with addressing one of the industry's most persistent operational challenges: transforming complex, unstructured investment information into reliable, actionable data.

At Alkymi, we help institutional investors address this challenge through an AI-powered platform that automates the collection, extraction, normalization, validation, and delivery of private markets and private credit data.

By integrating with existing enterprise systems and supporting emerging agentic AI technologies, including MCP, Alkymi enables firms to reduce manual processes, strengthen data governance, and build more scalable investment operations.

As the industry moves from AI ambition to operational reality, the ability to connect trusted data with intelligent execution will be a critical differentiator.

Thank you to Cutter Associates and the Cutter Connect community for bringing together industry leaders to explore the challenges and opportunities shaping the future of investment technology and operations.

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