Technical considerations for business leaders implementing AI

The rise of generative AI in business: Generative AI is rapidly becoming a critical technology for businesses, requiring CEOs and other leaders to make informed decisions about its implementation and use.

• Generative AI involves two core phases: training, where the model learns from curated data, and inference, where the model applies its learning to analyze, recognize, and respond.
• Business leaders must balance cost considerations with the potential value that generative AI can deliver to their organizations, people, and customers.

Choosing the right foundation model: Selecting an appropriate foundation model (FM) or large language model (LLM) is crucial for determining the cost and capabilities of generative AI applications.

• There is no one-size-fits-all approach to choosing a model, necessitating rigorous evaluation to balance price and performance.
• Criteria for selection include latency, scalability, and suitability for specific organizational needs.
• Experimenting with multiple models and involving stakeholders from various departments can lead to more informed technical and business decisions.

Customization techniques and their implications: Model customization is an important business decision that affects the accuracy and utility of generative AI applications.

• Fine-tuning modifies the model to make its responses more relevant to specific use cases.
• Retrieval-augmented generation (RAG) is a simpler, more cost-effective technique that optimizes output accuracy by retrieving data from external sources without modifying the model.
• The choice of customization technique impacts both cost and complexity of implementation.

Leveraging data as a competitive advantage: Integrating organizational data with generative AI applications can transform generic tools into powerful, company-specific assets.

• Customization techniques like RAG help models draw from diverse data stores to provide accurate, relevant results and personalized recommendations.
• Business leaders may need to consider investing in upgrading data infrastructure to better fuel generative AI applications.
• The condition and availability of data significantly affect the relevance of results, success of applications, and implementation costs.

Mitigating risks associated with generative AI: Implementing proper risk mitigation strategies is crucial for protecting an organization’s finances, brand reputation, and customer loyalty.

• Context grounding is a customization technique that checks model output against verifiable sources, helping reduce bias and hallucinations.
• Implementing effective guardrails and testing results against defined policies helps ensure accurate, relevant, and unbiased outputs.
• According to Gartner, organizations that implement transparency, trust, and security in their AI models may see a 50% improvement in adoption, goal achievement, and user acceptance by 2026.

Holistic cost considerations: Business leaders need to consider all costs associated with generative AI implementation, beyond just the model itself.

• Factors affecting cost include model choice, customization methods, testing, data preparation, and the anticipated volume of user interactions after scaling.
• Inference costs can increase significantly for customer-facing applications available on the internet.

Creating value through generative AI: Business leaders should focus on the potential value that generative AI can deliver to their organization.

• • Value can manifest as higher revenue, improved customer experiences, or breakthrough innovation.
• • Consistently asking “What is the business value here?” throughout the generative AI journey can help keep organizations on track.

Collaborative approach to AI implementation: Business leaders who actively engage with their tech-focused colleagues are better positioned to guide their organizations through the generative AI journey.

• This collaboration can lead to the creation of a viable generative AI roadmap.
• It also helps in guiding the organization from initial experiments to production-grade applications that deliver significant value at scale and at the right cost.

Balancing technical and business perspectives: As generative AI continues to evolve, business leaders must strike a balance between technical considerations and broader organizational goals.

• Understanding the technical aspects of generative AI allows leaders to make more informed decisions about its implementation and use.
• By focusing on both the technological capabilities and the potential business impact, organizations can maximize the benefits of generative AI while minimizing risks and costs.