Perhaps the single most important ingredient for AI is data. I’ve lost track of how many times we’ve heard firms complain that their AI project would have been successful if only they had a Data Strategy. For a data-driven investment manager, data is foundational for almost any initiative. However, the adage of, “Garbage In, Garbage Out” rings exceptionally true for AI projects.
AI opportunities are everywhere. Recent advancements with GenAI have enabled dreams to become reality. Increasingly, AI is sourcing, cleaning, and incorporating data into your data platform. For example, alternative asset managers are scaling private market operations thanks to leveraging AI for streamlining the handling of unstructured and non-standard documentation.
An AI Strategy is essential for fostering AI adoption throughout the firm, which is increasingly critical for staying competitive. A firm’s AI Strategy has a symbiotic relationship with its Data Strategy. A well-executed Data Strategy enables and fuels your AI Strategy.
AI Strategy vs Data Strategy
A firm’s Data Strategy and AI Strategy are deeply interconnected and interdependent. The Data Strategy is a foundation upon which the AI Strategy is built.
- Data Strategy Enables AI: High-quality, well-governed, and accessible data is essential for successful AI implementations. AI algorithms learn and make decisions from data. The quality and structure of the data directly impact the performance and reliability of AI solutions. A robust data strategy ensures that the right data is available in the right format for AI initiatives.
- AI Strategy Drives Data Requirements: The specific goals and use cases outlined in the AI strategy dictate the types of data needed, the volume required, the frequency of updates, and the necessary data quality standards. For example, developing an AI model for predicting market movements will have different data requirements than building a chatbot for client support.
- Data Governance is Crucial for Responsible AI: A strong data governance framework, a key component of the data strategy, is vital for ensuring the ethical and compliant use of data for AI. This includes managing data privacy, security, and bias, all of which are critical considerations in AI development and deployment.
- Data Infrastructure Supports AI Capabilities: The data strategy must address the infrastructure needed to support AI workloads, including data storage, processing power, and data pipelines. AI often requires handling large datasets and complex computations, necessitating a scalable and efficient data infrastructure. Modern data platforms like Snowflake and Databricks offer rich AI capabilities.
- Insights from AI Inform Data Strategy: As AI models generate insights, these can, in turn, inform the data strategy. For example, AI might identify new data sources that could be valuable or highlight areas where data quality needs improvement.
Data Strategy Lessons for AI
Experience doing Data Strategies has taught us many lessons about how best to execute an AI Strategy. For example:
- Culture Change: Both strategies are aimed at the broad enterprise to evolve culture and mindset. Raising AI Literacy is like raising Data Literacy. Organizational change management effort is required to educate and align the organization because success requires teamwork.
- Enterprise Leadership: Both require a tone from the top and an enterprise perspective around championing literacy, adoption, evolving capabilities, and removing obstacles.
- Ecosystem: Data and AI capabilities come from a portfolio of interconnected systems. Many vendors offer new AI capabilities and are rapidly adding enhancements for data and AI. It can be a challenge to keep up with the pace of change and rationalize the available capabilities. Distributing responsibility and transparently sharing capabilities and decisions are key.
- Democratization: Staff must feel empowered to leverage data and AI to do their jobs better and shift to value-added activities. Access to trusted data, tools, and a self-service mindset enables agility and stokes innovation.
- Federated Model: We learned a Command-and-Control approach does not work well for enterprise-wide initiatives that aim to empower subject matter experts and teams to scale. Use lightweight guardrails and processes to avoid suffocating bureaucracy. A Federated model that emphasizes decentralization, collaboration, and distributed governance is a better approach. It allows for a degree of independence and localized control while still participating in a larger, unified system.
- Talent Development: New skills are required to stay competitive. Upskilling talent makes you a desirable employer that helps attract, retain, and compete for top talent.
Both strategies improve efficiency, intelligence, and innovation which leads to gaining a competitive advantage.
Conclusion
Ignoring the data strategy while pursuing AI is like trying to build a skyscraper on weak foundations – it’s unlikely to be stable or sustainable in the long run. In essence:
- A well-defined Data Strategy is a prerequisite for a successful AI Strategy. Without a solid foundation of data management, AI initiatives are likely to falter due to data quality issues, lack of access, or governance challenges.
- The AI Strategy provides direction and purpose for the Data Strategy by outlining the specific data needs and priorities driven by AI applications.
A strong and integrated data and AI strategy is the key to unlocking the full potential of AI.
Tinkering with a few AI projects is not going to cross the chasm between narrow tactical AI solutions and widespread AI adoption. Developing a strategy is essential for efficiently and safely scaling across the organization. Industry leaders know that having a Data and AI strategy is non-negotiable.
It can be overwhelming to construct a Data or AI Strategy for your organization. Olmstead has frameworks for data and AI that accelerate building a tailored strategy for your organization. They are comprehensive yet lightweight and are ultimately customized to fit your needs. Contact us today to learn more.