Modernizing Without Disrupting Critical Processes

There is obvious enthusiasm for innovation in ALTs investing. GPs around the world surveyed by Dynamo this year reported that leveraging AI-powered tools to enhance decision-making was their top strategic priority for the coming year.

In our conversations with investors, including those who participated in this year’s GP roundtable at Dynamo University (DU), the eagerness to modernize operations was quite clear—but, even still, they were proceeding with caution.

Not because of a lack of optimism about an AI-powered future. Many of the hesitations were about the road to get there, which often requires significantly overhauling processes. The question on the minds of many GPs is how to accomplish it without completely disrupting day-to-day operations, which carries its own set of risks.

Here are the best practices that emerged from that conversation with GPs and the implementation experiences of the Dynamo Client Services team.

Sprint-Based Implementations

Many firms today are showing a preference for modernization in more manageable increments instead of the “big bang” approaches of the past. Firmwide adoption requires extensive planning and preparation, and even still, it can be difficult to absorb that much change all at once.

Sprint-based implementations allow firms to focus on a more defined set of objectives over short time frames. This way, firms can validate progress along the way and make adjustments based on user feedback. It reduces implementation fatigue and also minimizes risk because the scope of change is limited at any given time.

For GP firms evaluating new platforms or end-to-end solutions, the implementation strategy deserves as much attention as product functionality. A strong implementation plan combined with expert client service can have just as much impact on adoption success.

Phased Rollouts by User Group

Another consideration is whether every team needs to change at the same pace. Often, they don’t. Many GPs at the DU roundtable realized a lot of value from rolling out new capabilities to specific user groups before expanding them across the organization.

Investor relations, accounting, operations, and compliance teams often have different workflows and priorities. With a phased approach, each group can test new processes and identify training needs.

This strategy also helps build internal momentum. Early successes build confidence and produce internal champions who can help accelerate adoption more widely.

Adopting new technology is as much a people initiative as much as a tech initiative. Thoughtful change management an important ingredient for long-term success when it comes to modernizing.

“AI systems are only as effective as the quality and structure of the data supporting them.” — Steve Tobio, VP, Product

Set the Data Foundation

As firms get ready for operational AI deployment, many are discovering that one of the biggest obstacles is underlying data readiness. More firms are assessing and preparing their data before committing significant internal resources.

In practice, this means having visibility into:

• What stored data is outdated?
• Which data fields are actively being used?
• What level of cleanup or mapping is required to effectively introduce automation?
• Do day-to-day workflows result in consistent data capture?

Once firms lay the right data foundation for modernization, moving to an integrated technology platform can eliminate data fragmentation going forward and make intelligent data management automatic.

Understand AI Readiness

Many firms already have access to powerful general-purpose AI tools and are experiencing the benefits. There is especially strong interest across the industry in pre-built AI tailored for the ALTs industry that can simplify repetitive operational tasks, surface relevant insights faster, and reduce the need for users to engineer prompts manually.

But, this enthusiasm doesn’t translate directly to “ready for AI.” What they need to understand is whether their firms have properly prepared to make tailored AI capabilities usable within their day-to-day workflows. One of the biggest mistakes is treating modernization as an AI feature rollout rather than true operational transformation.

Dynamo’s AI Readiness Assessment provides a concrete readiness score in less than three minutes. It will help to identify where AI can create measurable business value, which workflows are best suited for automation, and what changes to people and processes are required to support adoption.

Make Modernization Ongoing

Perhaps counterintuitively, the ALTs firms most likely to make the fastest progress in the coming years aren’t the ones pursuing the most aggressive tech transformation timelines. Creating a solid structure that can support continuous adaptation is an approach with lower friction and risk, and it yields cumulative results over time.

Intentional pacing, clear roadmaps, and expert support are crucial pillars for successful modernization without major disruption. It’s not a race to deploy the latest AI capabilities, but an ongoing strategy that allows firms to evolve alongside new technologies and a changing investment market.

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