Conversations at SS&C Deliver EMEA 2026 show a clear shift: firms are no longer debating whether to modernize; they are trying to determine how to do it without destabilizing existing operating models.
Asset managers are operating in an environment defined by compounding complexity.
Regulatory expectations continue to rise, while AI has reset the bar for speed, insight, and responsiveness. At the same time, stitched-together infrastructures continue to limit how quickly firms can respond, adapt, and scale.
At Deliver, these pressures converged around a single question: How do firms modernize in a controlled, practical way while maintaining stability?
At the conference, I led a session titled From Complexity to Clarity: Your Strategic Blueprint for Modern Investment Operations.
The goal of this session was to address that question directly and open a dialogue with asset managers about the realities of modernization — what’s working, where firms are getting stuck, and how they are thinking about change.
Our conversations reflected a common tension between recognizing the need to modernize and determining how to move forward without introducing unnecessary disruption.
Most firms understand what needs to change. Their constraint is executing.
AI Adoption is No Longer the Question: The Divide Between Experimentation and Execution
During our recent session, we surveyed representatives from UK and European asset management firms about their progress in AI integration.
Not a single firm in attendance claimed to have completely mastered the technology.
Most participants identified themselves as still in the early stages of their AI journey, engaging either in exploratory use cases or running controlled pilot programs. A smaller group had advanced to production environments where AI was generating measurable results, yet none reported achieving significant return on AI investment.
The main takeaway was unmistakable: the conversation around AI has evolved. Firms are no longer debating whether to adopt AI; the focus has shifted to whether their adoption efforts are yielding tangible operational benefits.
The dynamics within the room emphasized this shift. The real divide isn’t between those who adopt AI and those who don’t; it’s between those who are experimenting and those who are executing. And, in this setting, those actually executing were in the minority.
Priorities for 2026 Reveal a Familiar Tension
When asked about priorities for the year ahead, firms most frequently pointed to front-office modernization and AI adoption, with data quality and access following behind.
That ordering is telling.
Across conversations, data fragmentation, quality issues, and limited accessibility consistently emerged as some of the most persistent operational challenges.
Yet when firms allocate attention and investment, they continue to prioritize visible transformation initiatives over foundational constraints like data.
This is a recurring pattern: firms gravitate towards investing in what is most visible, even when the binding constraint sits elsewhere.
The Real Constraint Beneath Modernization
Throughout the discussion, two structural issues repeatedly surfaced as sources of operational friction in asset management: disconnected operating environments and fragmented data.
These are not new problems. But in conversations with attendees, it was clear they now directly constrain three things that matter most:
- the scalability of AI initiatives
- the ability to control cost-to-serve
- the creation of a consistent operational view across the enterprise
Modernization efforts increasingly stall not because of technology choices, but because the underlying data and operating model were never designed for integration at scale.
Most firms know their foundation needs to work. The discussion made clear that the challenge is less awareness than action: deciding when, where, and how to invest in that foundation.
AI Does Not Fix Data Problems—It Amplifies Them
The discussion at Deliver reinforced a difficult reality: AI accelerates the visibility of data issues rather than resolving them.
As adoption increases, firms are shifting their focus beyond experimentation toward governance, explainability, interoperability, and operational control.
In regulated environments, the priority is no longer standalone AI tools. It is AI embedded within controlled workflows and trusted data environments.
Two principles now define effective adoption:
- Governance by design. AI systems must operate within defined controls, particularly where regulatory and operational risk is high.
- Human accountability. Critical decisions remain with domain experts, supported by AI rather than replaced by it.
Both depend on a consistent data foundation and clearly defined operating model. Firms moving fastest on AI understand this: front-office ambition only scales when the operational foundation beneath it is designed to support it.
Clarity and Trust: Rooted in the Operating Model, Not Just the Technology Stack
At SS&C Advent, achieving clarity in modern investment operations begins with alignment: a unified data foundation, a consistent operating model, and seamless interoperability across the entire investment lifecycle—from portfolio management and trading to compliance, accounting, operations, and investor relations.
Our Genesis platform embodies this vision by providing a cloud-based operating environment that connects every facet of investment operations through a single source of truth. This approach helps firms minimize fragmentation and enhance decision-making throughout their operating model.
By streamlining information flow across the organization, firms can adapt their operating models without disrupting daily functions.
Crucially, this framework also addresses the execution gap highlighted in our discussions. The true value of AI adoption lies not just in its implementation but in how effectively it is integrated into workflows.
When AI is embedded within cohesive systems – leveraging a vendor like SS&C’s domain expertise and a knowledge and context layer that enriches data – it drives substantial value for asset managers. This integration ensures that AI is not merely an add-on but a fundamental part of the operational fabric, empowering firms to realize measurable benefits.
Looking Ahead: Closing the Execution Gap
Across the discussions at Deliver, a consistent thread emerged. Firms are no longer debating the direction of change—they are grappling with how to execute it within the realities of existing operating environments.
That was the central tension running through the session: ambition is high, clarity on what needs to change is growing, but execution remains constrained by fragmented data and disconnected systems.
The takeaway was not about technology in isolation, but about readiness—whether firms have the underlying foundations in place to turn intent into sustained operational change. In that sense, the real question is no longer what comes next for the industry, but how effectively firms can move from understanding what needs to change to actually delivering it.
Learn how investment technology is helping firms move from ambition to execution.
