· Connectors · 4 min read
Using Proof-of-Concept AI Projects as a Strategic Springboard
AI, particularly large language models (LLMs), can be safely adopted through targeted proof-of-concept projects:
- Focus on support functions rather than core business activities
- Build internal knowledge databases to centralize crucial information
- Clean historical data to improve decision-making capabilities
- Enhance operational efficiency in internal processes
Board implications include improved data management, enhanced risk mitigation, and potential for significant operational efficiencies.
As new technology is developed, it can be difficult to figure out how to effectively implement or utilize it. Jumping in the deep end without knowing how to swim is a recipe for disaster; instead, adopting a strategic approach to implementing emerging technology - such as AI - through carefully selected proof-of-concept (PoC) projects, focusing on support functions to minimize risks and build organizational capabilities leads to much better results.
I analyzed how law firms and legal departments could effectively utilize PoCs for 273 Ventures (my legal AI company); for my post here, I’m pulling out the board-level elements for how to use PoCs to leverage strategic implementation of AI.
Adopt and Adapt Strategy
- Organizations must develop a nuanced understanding of AI to build skills and confidence
- Real-world projects in support functions provide a safe environment for learning and experimentation
Board Consideration: How can we ensure that our AI adoption strategy aligns with our overall business objectives while minimizing potential risks?
Focus on Support Functions
- Initial AI projects should target internal operations and support functions rather than core business activities
- This approach minimizes risks associated with sensitive data and regulatory compliance
Board Consideration: What criteria should we use to prioritize AI projects in support functions, and how do we measure their success?
Exemplary PoC Projects
Build an Internal Knowledge Database
- Centralize and organize crucial documents and information
- Use AI for document classification, information extraction, and comparative analysis
Clean Historical Data
- Improve data quality in existing systems and databases
- Enhance decision-making capabilities through better data integrity
Improve Operational Efficiency
- Apply AI to streamline internal processes and compliance
- Focus on areas with potential for direct financial impact
Board Consideration: How can we ensure that these PoC projects create tangible value and serve as a foundation for more advanced AI implementations in the future? Do we need to make any changes to our overall data strategy in order to capture value from AI-enhanced processes?
Future-Proofing
- These projects lay the groundwork for developing proprietary AI models
- Organizations can build competitive advantages through unique datasets and AI applications
Board Consideration: How much capital should we be investing in current technology now versus in the future, when costs may decrease? Should we be developing our own internal solutions as a new competitive advantage?
Strategic Implications for the Board
Enhanced Data Management: AI-driven knowledge databases and data cleaning processes can significantly improve the organization’s data quality and accessibility. How should we integrate these improvements into our overall data governance strategy?
Risk Mitigation: Focusing on support functions for initial AI projects helps minimize potential risks. How can we ensure that appropriate risk management measures are in place as we expand AI usage to external-facing areas of the company?
Operational Efficiency: AI has the potential to streamline internal processes and improve efficiency. How should we prioritize and measure the impact of AI-driven operational improvements? How will we ensure that our personnel are aligned with this strategic goal? What changes to our labor force do we anticipate these improvements will have, and how will we address them?
Talent and Skill Development: Adopting AI technologies may require new skill sets within our organization. What strategies should we consider for upskilling existing staff or acquiring new talent?
By starting with low-risk, high-value projects in support functions, organizations can safely build AI capabilities while creating tangible benefits. This approach provides a solid foundation for more advanced AI implementations in the future, positioning the organization for long-term success in an increasingly AI-driven business landscape.