Opening Remarks with Lisa Fernihough
summary
Scaling AI is framed as an operational and organizational challenge not a research one with data showing 70% of leaders see AI as a priority and only 5% deliver consistent returns and examples include testing AI voice agents for government and deploying AI across KYC compliance and ERP and forensic work. The core bottlenecks are concrete: fear, friction, focus — recalibrating controls, redesigning workflows end to end, and shifting from pilots to productionized “new production lines.”
AI-written summary · about
related talks
59mPanel Discussion: Strategies for Scaling AI in Quality Engineering
16mHow can leaders turn operational struggles into strategic value through the integration of AI?
19mScaling & Industrialization of AI
19mScaling AI: From Experimentation to Transformation
26mFireside chat: Challenges with AI implementation in organizations
22mThe AI Adoption Gap – How We Accelerate AI Use in Enterprises
11mSuccessful AI adoption
21mCutting through the hype. How enterprises can apply a strategic lens to leveraging AI to achieve ...
