Chapter 1: Charting a Practical AI Strategy for Growth


The AI race is officially underway in asset management. But amid the excitement, one truth stands out: firms don’t need more tools—they need a plan. Managers consistently tell us they see AI’s potential to improve sales coverage, marketing precision, and compliance speed. What they lack is a clear, focused path that connects strategy to execution and produces results they can measure.

For growth-stage asset management firms (<$100B AUM), the need for focus when it comes to AI strategy is acute.  Smaller size means fewer at bats, and the impact of each missed opportunity or misstep is magnified.  

Our new series, The AI Playbook, distills how emerging asset managers (<$100B AUM) can use AI to strengthen their go-to-market (GTM) strategy, sharpen advisor engagement, and create measurable lift within weeks, not years.

In Chapter 1 — Charting an AI Strategy for Growth — we’ve distilled into a simple, repeatable framework how we work with clients to chart a successful course to their north star and where AI fits in:

  • Diagnose where friction in GTM is pushing results off course

  • Rank friction points according to impact on maintaining course 

  • Map heaviest impact friction points to potential AI solutions

  • Select high-impact use cases with potential to deliver meaningful value near-term

  • Execute, then measure results

Most emerging managers see meaningful lift when they apply this framework in focused six-week sprints. They balance day-to-day demands while generating visible improvements in sales and marketing performance within months, not years.  

Start with Strategy, Not Software

Every winning AI program begins the same way:

Firm Strategy → Go-to-Market Strategy → Use Cases → Technology

This looks deceptively simple. In reality, it’s where leading emerging managers begin to separate themselves. The industry’s instinct is often to modernize through large platform upgrades or automation projects. But technology alone doesn’t create advantage. Clarity does.

The firms making the most progress—many of them small to mid-sized managers we work with—start with a firm grip on fundamentals: how they grow, which advisors matter most, and where friction slows momentum. Only then do they identify AI use cases that remove those bottlenecks. Tools come last, not first.

Diagnose the Friction 

Our Sales System Framework helps asset managers locate the gaps between strategy and execution by asking simple but powerful questions:

  • Are we meeting growth expectations in key channels?

  • What are advisors telling us about coverage and value?

  • Where do sales teams spend their time, and does it match opportunity?

  • Does our structure still align with how advisors make decisions today?

Answering these questions before adopting new tools ensures every investment targets a real business problem, not a theoretical one.

Across our survey data, consistent friction emerged: limited pipeline visibility, inconsistent advisor segmentation, underpowered marketing, and slow compliance workflows.

The best firms don’t view these as failures they see them as design opportunities. In fact, firms that have implemented even partial automation of lead scoring or advisor segmentation report 20%+ improvements in sales-coverage efficiency, and those applying AI to marketing analytics see shorter sales cycles and higher conversion rates.

Flex to Win: Strategy in an AI World

AI is evolving too quickly for static roadmaps. The firms that pull ahead embrace flexibility. As outlined in Technology Strategy in an AI World from The AI Edge , leading managers use three complementary approaches:

1. Compounding Core

Strengthen high-frequency, repetitive processes first (e.g., meeting prep, forecasting, content review). These small wins accumulate quickly and build organizational momentum.

2. Perpetual Beta

Continuously test new AI pilots, measure quickly, and scale only what works. Treat your GTM engine as a living system, not a static program.

3. Moat Building

Use proprietary datasets to generate insights competitors can’t replicate advisor behaviour models, personalized product recommendations, or firm-specific growth signals.


Most sub-$100B firms start with Compounding Core (where ROI appears fastest) and grow into Moat Building as data pipelines mature.

That evolution brings its own challenge: while 58% of firms subscribe to external datasets from providers like Envestnet, custodians, and wirehouses, fewer than one-quarter say they use those datasets effectively.

Data abundance is not the issue. It’s data without purpose.

Once AI use cases are defined, data becomes a means rather than an obstacle. External datasets suddenly have jobs: fueling segmentation models, powering next-best-action systems, and improving forecasting. Firms that make this shift consistently report lower integration costs, higher adoption, and clearer accountability around data.

Shift Mindset : Purpose Over Prediction

Creating an AI strategy isn’t about predicting the future it’s about building the agility to thrive as the future unfolds. Firms that treat AI as a capability, not a project, create advantages that compound: better prioritization, more personalized engagement, tighter alignment between sales and marketing, and a GTM engine that gets sharper with every cycle.

The AI Playbook is designed to help emerging managers build that capability — practically, measurably, and with a clear link to growth.