Measuring and Optimizing AI Investments in the Enterprise
Published on Nov 26, 2024
Legion’s AI summary
Understand the key factors that contribute to the total cost of ownership (TCO) of AI investments.
- Compute resources: Balance speed, scale, and cost when selecting AI infrastructure
- Human capital: Optimize headcount investments for strategic AI development
- Foundational elements: Prioritize governance, education, and analytics for AI success
Investing in artificial intelligence (AI) has become crucial for enterprises aiming to stay competitive. However, the key to a successful AI strategy lies in effectively measuring and optimizing the return on investment. In this post, we’ll focus on the investment side of that calculation. The key concept executives must understand is the total cost of ownership (TCO). Here's a guide to help you navigate this complex landscape.
Understanding Total Cost of Ownership (TCO)
The TCO encompasses several factors beyond the initial purchasing decision. Key among these are the compute resources you select, the talent needed to support these resources, and essential components like governance and education.
Compute: Balancing Speed and Scale
External API Approaches: If your security posture allows it, using externally hosted models via APIs is a great way to hit the ground running. However, costs can grow as your project scales.
Host Internally: Hosting models internally gives you control over security and performance but requires careful evaluation of your infrastructure capacity.
Right-size Your Model: Always align your AI capabilities with your primary use-cases to avoid over-investing in complexity. Bigger isn’t always better but is almost always more expensive.
Funny story: Legion performed a bakeoff between two competing models for a customer needing to self-host. The smaller model won on many tasks, proving that size doesn’t always correlate with performance.
Optimizing Headcount Investment
Support Staff for Compute: Hosting internally requires various technical personnel to oversee development and resource allocation. Factor these costs into your overall investment.
Strategic Development: Focus your team’s efforts on projects that align with your enterprise's core competencies and consider off-the-shelf solutions for everything else.
Don’t Overlook the Essentials
Successful AI implementation requires spending resources on:
- Internal Awareness and Adoption: Ensure stakeholders understand the value AI brings.
- Ongoing Education: Broaden workforce knowledge about AI's applications and limitations.
- Analytics and Proof of Value: Measure the impact of AI investments.
- Governance: Implement oversight mechanisms to manage risks and comply with regulations.
Conclusion
Investing in AI involves strategic planning and understanding the full TCO. By considering compute resources, human capital, and foundational elements, you can optimize your investments to deliver sustainable value. For more advanced strategies on maximizing ROI in your AI investment, check out this blog.
Frequently asked questions
What are some common pitfalls to avoid when investing in AI for enterprises?
Common pitfalls include underestimating TCO, over-investing in overly complex models, and failing to engage stakeholders.
How can enterprises ensure their AI investments align with their strategic goals?
Enterprises should conduct thorough needs assessments and involve stakeholders in planning.
How should an Enterprise "right-size" their AI models?
Right-sizing involves matching model complexity to specific tasks, with assessments to determine the minimal viable model that meets objectives.