AI is all the hype these days, however, despite (or maybe, due to) all the excitement around it a high percentage of AI projects fail. Projects that are over budget, delayed or that fail to scale past the POC phase are very common. In many of these scenarios, choosing the right approach can be as critical as the solution itself. Should you build a tailored machine learning model, leverage the power of large language models (LLMs), or integrate with AI copilots? Each option has unique strengths, trade-offs, and resource requirements, and the key to success lies in aligning your choice with your business goals and technical constraints.

In this talk, we will go beyond the basic concepts and definitions to provide a practical guide that allows attendees to navigate these decisions through real-world case studies. We will examine scenarios where different AI paradigms were chosen, discuss the factors influencing those decisions, and outline the process of evaluating needs, constraints, and outcomes. From assessing data availability and team expertise to balancing cost and scalability, you’ll gain insights into the critical considerations that guide the selection process.

By the end of this session, you’ll have a clear process for determining whether machine learning, LLMs, or copilots are best suited to your AI challenges and understand the resources and strategies needed to bring your chosen solution to life. This talk is ideal for decision-makers, technical leads, and innovators seeking to demystify AI implementation and maximize the impact of their investments.