Most AI deployments fail to generate meaningful returns — recent large-scale organizational surveys consistently show that unprofitable deployments are the norm. Yet for a minority of companies, AI automation has produced substantial productivity growth.
This workshop introduces case studies and insights from companies that have deployed AI successfully, and from organizations that have recovered from initial deployment mistakes. Importantly, the workshop explains the structural difference between deployments that generate returns and those that don't, and what research in cognitive science and AI tells us about how to make people and AI work effectively together.
Enquire about this workshop →Companies often deploy AI systems based on guidance that is either anecdotal or driven by vendors selling the tools themselves. Without a clear framework for evaluating whether the system actually works as expected, AI deployments risk producing damaging service quality costs, which remain invisible to short-term financial metrics.
This workshop uses research-level insight into experimentation, study design, and evaluation to explain best practices that organizations can use to understand where their AI systems deliver value, and how to deploy them most effectively in conjunction with human expertise.
Enquire about this workshop →AI systems develop quickly. To stay competitive, organizations need to think about AI tool capabilities proactively: which types of work can be delegated to AI today? Which should be reserved for humans? Which will AI be able to handle in the near future?
This workshop reviews scientific research on AI capabilities, explains technical terms in intuitive ways, and introduces an actionable framework for deciding how to combine automation and human judgment to maximize product quality and financial returns.
Enquire about this workshop →AI systems can now solve problems that were open for decades. This changes what skills are worth developing within your training programs and your organization's knowledge infrastructure.
This workshop covers cognitive science research on how expertise is built to answer a practical question: what should humans learn deeply, and how? Drawing on research in learning, memory, and decision-making, the workshop introduces a framework for redesigning training and knowledge management around high-leverage expertise.
Enquire about this workshop →A non-technical 2-hour workshop covering the core concepts underlying modern AI — transformer architectures, attention mechanisms, embeddings, fine-tuning, and decision boundaries — explained without mathematics. Designed for senior leaders and decision-makers who need to ask the right questions and understand what is currently possible and impossible with AI systems.
Participants leave with a durable conceptual framework for navigating AI decisions.
Enquire about this workshop →A technical workshop for developers and researchers with strong mathematical backgrounds. Covers architecture design, attention mechanisms, training dynamics, scaling laws, fine-tuning and alignment methods, and current capability and failure mode research.
Draws on primary literature and goes beyond surface-level implementation to address the theoretical foundations of modern language models and their empirically observed limits.
Enquire about this workshop →Tell us about your organization and what you're trying to accomplish — we'll propose a format that fits.