Enterprise AI decision-makers are at a critical crossroads. With the rapid evolution of foundation models (FMs) powering generative AI (GenAI) applications, selecting the optimal model is increasingly complex—and increasingly vital. A new IDC Spotlight Report, sponsored by Appen, entitled “Choosing the Right Foundation Model for Your Generative AI Application: Navigating the Abundance of Options,” highlights key insights and practical steps to successfully navigate this critical decision.
Strategic Insights from IDC
IDC underscores that the best foundation model isn't necessarily the largest or most general-purpose one available. Rather, it’s the model that best aligns with your specific business use case, resources, and strategic objectives. A structured, formal LLM evaluation process can significantly enhance decision-making, ensure cost efficiency, and optimize performance.
The Spotlight Report reveals key enterprise trends:
- 44% of organizations heavily invest in cross-corporation employee productivity use cases.
- Performance (41%), cost (35%), and computational efficiency (29%) are the primary criteria influencing foundation model selection.
- 61% of enterprises prefer open-source models over proprietary options, valuing customization and adaptability.
How Human Evaluation Aligns Models to Your Needs
While automated foundation model benchmarks offer a helpful starting point for selecting foundation models, they often fall short in capturing real-world business needs. Human evaluation plays a critical role in filling this gap by assessing dimensions that standard metrics overlook—such as contextual relevance, brand alignment, fairness, and usability under ambiguity. Incorporating structured human assessments, particularly with domain experts, enables organizations to go beyond leaderboard rankings and evaluate how a model will actually perform in deployment settings. By integrating human evaluation into the model selection process, enterprises can ensure that chosen models are not only technically sound but also aligned with operational goals, regulatory standards, and user expectations.
Why This Matters to AI Professionals
As machine learning engineers, data scientists, and AI strategists, your role is not only to implement AI solutions but also to maximize their strategic impact. Selecting the right foundation model directly influences your project's scalability, cost management, adaptability, and long-term success.
IDC recommends a structured four-step process: define your GenAI use case, short-list aligned foundation models, thoroughly evaluate and test them, and seamlessly integrate the optimal choice into your GenAI lifecycle with continuous refinement.
This structured approach ensures that your foundation model investments yield robust returns, remain adaptable to rapid technological advancements, and align closely with your organization's strategic and operational realities.
Appen: Your Trusted Partner in FM Evaluation
Navigating these complexities requires more than internal expertise—it requires an experienced partner. Appen, a global leader in AI training data and model evaluation solutions, collaborates with 80% of the world’s leading foundation model builders. Leveraging decades of machine learning experience, Appen helps ensure your chosen foundation models deliver accuracy, adaptability, ethical integrity, and cost-effectiveness.
Through customized evaluations, proprietary data sets, and human-led assessments, Appen mitigates risks such as model biases, security vulnerabilities, and compliance challenges, ensuring your foundation models are both responsible and robust.
Take the Next Step
Download the full report today to explore these insights in greater depth and leverage it as your foundational guide for making informed, strategic AI model decisions.
For personalized support and expert guidance in selecting and implementing foundation models, contact Appen today. Our team is ready to help you navigate the complexities and ensure your GenAI projects succeed.