From Hype to Impact: How Asset Managers Can Navigate the Road to AI Excellence

Asset managers are under increasing pressure to harness the power of artificial intelligence – not just to stay competitive, but to lead. Yet the journey from experimentation to enterprise-wide transformation is anything but straightforward. 

At CrossCountry Consulting, we’ve seen firsthand how asset managers can move beyond point solutions and pilot projects to build scalable, strategic AI capabilities. Here’s what we’ve learned from working with leading firms across the industry. 

AI Maturity Is a Journey, Not a Destination 

Many firms begin their AI journey to increase productivity, crafting specific use cases to address manual processes, fragmented data, and isolated tools. However, to unlock true value, organizations must evolve toward a proactive, enterprise-wide AI strategy. This means embedding AI into core business processes, aligning it with strategic goals, and driving continuous improvement with human-in-the-loop models and integrated data platforms. 

Organizations have seen implementation success by using an AI strategy maturity model, which outlines five stages of evolution that reflect increasing levels of automation, data trust, and strategic alignment: Performed → Managed → Defined → Measured → Optimized 

AI Roadblocks Are Real, But So Are Opportunities 

AI adoption in asset management presents its own set of challenges. From legacy systems and fragmented data to regulatory uncertainty and ethical concerns, firms must navigate a complex ecosystem. Key barriers include: 

• Integration with legacy technologies that lack the power or flexibility to support AI tools. 
• Data immaturity is often present in environments with siloed systems and inconsistent governance. 
• Security and privacy risks are heightened and must be addressed when deploying generative AI and LLMs. 
• Cultural resistance occurs as employees become fearful of job displacement and place low trust in AI outputs. 

But these challenges also present opportunities. With the right strategy, firms can modernize infrastructure, enhance controls, and generate operational efficiencies that drive real business outcomes. 

A Structured Approach to AI Transformation 

CrossCountry’s approach to AI maturity is grounded in practical, phased execution: 

1. Discovery: Assess current tools, governance, infrastructure, and readiness. 
2. Prioritize and prepare: Define use cases, evaluate vendors, and align with business strategy. 
3. Deploy: Implement pilots, operationalize governance, and scale based on impact. 

This structured roadmap ensures AI investments are not only technically sound but also strategically aligned and culturally adopted. 

Real-World AI Use Cases in Private Markets 

AI is already delivering value across the asset management value chain. Some high-impact use cases include: 

• Unstructured data extraction for fund documents and due diligence. 
• Generative analytics to visualize trends and support decision-making. 
• Deal sourcing through automated research and third-party data ingestion. 
• Compliance and legal reviews that accelerate contract analysis and reduce risk. 
• Relationship intelligence that personalizes LP communications and enhances CRM insights. 

These applications demonstrate that AI is not just a back-office tool. It’s a front-line enabler of growth, efficiency, and insight. 

Next Steps With AI

AI is no longer a futuristic concept – it’s a present-day imperative. For asset managers, the question is no longer if  to adopt AI, but how  to do so in a way that’s scalable, secure, and strategically aligned. 

To maximize the value AI delivers to your organization, contact CrossCountry Consulting today.