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.
A Practical Framework for AI-Driven Growth
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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:
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 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 bottlenecks. Tools come last, not first.
Firms implementing partial automation of lead scoring or advisor segmentation report 20%+ improvements in sales-coverage efficiency.
Firms applying AI to marketing analytics report shorter sales cycles and higher conversion rates.
The best firms don't view friction as failure they see it as design opportunity.
Flex to Win: Strategy in an AI World
AI evolves too quickly for static roadmaps. The firms that pull ahead embrace flexibility.
Compounding Core
Strengthen high-frequency, repetitive processes first (meeting prep, forecasting, content review). Small wins accumulate quickly and build momentum.
Perpetual Beta
Continuously test AI pilots, measure quickly, scale only what works. Treat your GTM engine as a living system.
Moat Building
Use proprietary datasets to generate insights competitors cannot replicate — advisor behavior models, product recommendations, firm-specific growth signals.
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.
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 sharpens with every cycle.
The AI Playbook is designed to help emerging managers build that capability practically, measurably, and with a clear link to growth.