The asset management industry is at a crossroads. Caught between pressures to optimize costs and the macroeconomic challenges of recent years, leaders are seeking innovative solutions to drive growth. 

Generative AI (GenAI) presents a compelling opportunity to reshape investing, enhance client engagement, and optimize cost structures. As many as 64% of financial services executives planned to allocate budget to generative AI as of March 2023 — and by June of that year, 45% of those surveyed already expected to double that investment, according to a recent KPMG‘s report.

However, integrating artificial intelligence (AI) for asset management requires careful consideration. Blanfkactor can help leaders plan a strategic roadmap to navigate the future. In this article, explore the key challenges, actionable recommendations, and a framework for successful GenAI adoption in the industry.

Early use cases signal AI’s potential impact

Generative AI has the potential to revolutionize asset management. Chief Economist of Northern Trust, Carl Tannenbaum, observes that AI could contribute a potential 1-1.5% annual growth factor for the industry.

Just a few examples of early and near-horizon use cases:

  • AI-powered research strategy: Assistants could review daily news and trading activity, identify trends, recommend real-time portfolio adjustments, and even generate initial research reports, freeing up valuable analyst time for deeper analysis. One example in development is JP Morgan Chase’s IndexGPT. (Bloomberg)
  • Distribution productivity: AI can optimize asset allocation based on factors like risk tolerance, investment objectives, market trends, and historical performance data.
  • Marketing and content generation with personalized messaging and product offerings, enabling the adoption of more effective, data-driven marketing approaches. 
  • Market insights and intelligence that can validate new investment and product ideas through comprehensive research, back-testing data, and regulatory checks, reducing time to market and increasing confidence in new offerings.
  • Productivity gains for workers dealing with information, writing, or creation.

This rapid use case experimentation highlights the industry’s growing awareness of AI’s transformative potential.

Risks and barriers to GenAI adoption

Despite plans to allocate resources to generative AI, financial services — including asset and investment management — face challenges with adoption.

  • Talent gap: A lack of skilled talent to develop, implement, and manage AI models and infrastructure.
  • Cybersecurity concerns: Protecting AI models and training data from cyberattacks requires robust security protocols and risk management frameworks.
  • Regulatory compliance: Navigating evolving regulations around AI use in financial services.
  • Cost and ROI: The up-front costs of AI investment and the challenge of accurately measuring return on investment (ROI).
  • Data quality and accessibility: Data strategy requirement also poses significant barriers. While the pandemic accelerated cloud adoption, data enrichment and utilization still lag in many firms. 

AI models are data-hungry, requiring access to vast amounts of clean and well-structured data to function effectively.

As Blankfactor’s VP of AI, Tom Barton, recently noted at the Northern Trust Integrated Trading Solutions Summit, “Data is crucial for both fundamental and quantitative strategies, as well as for crafting unique investment principles and processes.” 

Data access and control will be key

Beyond data quality, data access is of rising potential concern for enterprises looking to map a strategy with AI technology. Large language model (LLM) companies are acquiring vast datasets, as highlighted by the recent $60 million agreement between Reddit and Google granting Google direct access to Reddit’s dataset.

This could limit data access for enterprises or limit their ability to develop differentiating investment approaches. Financial services leaders should look out for data access concerns in relevant contracts. 

Mapping your AI strategy: Actionable steps for asset managers

The future of asset management is powered by AI. But how do you translate potential into tangible results? Consider these recommendations for what to do now and monitor for the future. 

1. Stay ahead of the curve

Generative AI and large language models (LLMs) are rapidly evolving. Keep an eye on the latest research and competitor activity to identify emerging trends and opportunities.

2. Retail as the forecast

Keep a close eye on retail financial services, as many first impacts of the technology are likely to unfold in this segment.

3. Protecting your advantage with data

Data is crucial for productive, effective generative AI systems. But as mentioned, recent deals involving data sets raise questions about future data access. Proactively monitor data providers to ensure your firm continues to benefit from the best available datasets and avoid situations where critical data becomes inaccessible.

4. Unleash internal efficiency with AI

Start your AI journey with internal experimentation. Can LLMs help your knowledge workers automate tasks and boost productivity? Explore pairing analysts with AI tools to streamline research processes and free up time for higher-value activities.

5. Agentic architectures: The future of complex tasks

Look beyond basic content generation. Agentic architectures represent a revolutionary leap in AI capabilities. Imagine LLMs working collaboratively across multiple tasks, like researching a new fixed-income issuance or conducting in-depth market research for an investment opportunity.

6. Find your AI partner

Success with GenAI requires expertise and collaboration. Partner with a firm that offers the latest research on LLMs, understands the unique challenges of asset management, and shares your commitment to cost optimization.

Beyond the roadmap: Considerations for asset managers

While the roadmap approach provides a strong foundation, asset managers should consider these additional factors:

  • Monitor the broader AI landscape: Stay informed about advancements in generative AI and LLM development by tracking industry conferences, research papers, and competitor actions to identify emerging trends and potential threats like data access limitations.
  • Focus on explainability and transparency: Building trust with clients and regulators requires ensuring explainability and transparency in AI-driven decisions. Invest in solutions that can explain the rationale behind AI recommendations.
  • The human factor: relationship building: While AI can enhance efficiency and unlock new insights, it’s unlikely to replace the human element of client interaction in the near future. Focus on using generative AI to empower your human talent and strengthen client relationships.

Partnering for success

Financial services executives want to ensure their firms can mitigate generative AI risks while capturing innovation opportunities. To move quickly while addressing challenges, asset managers should leverage an ecosystem approach.

  • Focus on core expertise: Understand what capabilities are central to your firm’s competitive advantage and what can be outsourced to specialized AI vendors.
  • Partner with co-developers and domain experts: Collaborate with leading AI firms and industry experts to accelerate the AI journey and leverage their specific knowledge.
  • Engage execution partners: Partner with solution providers who can support the implementation and execution of AI projects, turning ideas into reality.

Blankfactor’s AI expertise can help you capture value

At Blankfactor, we understand the challenges and opportunities asset managers face with GenAI. Our data and AI practices are designed to help you transform business processes from end to end.

No matter where you are in your AI journey, our teams can help. Whether you need a strategic AI assessment to review solutions or an innovation accelerator to help you rapidly develop and deploy, we can help you explore growth, efficiencies, and value capture with next-generation technologies. 

For instance, we analyzed a $10 million QA/QC contract and identified a potential 40% cost savings through AI-powered co-pilot solutions. We can help you achieve similar results and roadmap a strategic path forward with AI. 

Ready to unlock the power of AI for asset management?

Generative AI presents a transformative opportunity for asset managers to optimize operations, enhance client engagement, and drive growth. 

Partner with Blankfactor to unlock the potential and gain a competitive edge. We can help you identify the most impactful use cases, navigate the complexities of data access and security, build a custom AI model, and develop a successful AI strategy tailored to your specific needs.

Contact us today for a complimentary 60-minute strategy session on how we can help your firm lead with AI.