Why Your Business Needs an AI ‘Magician’ Over a Chief AI Officer
▼ Summary
– There is significant debate over who should lead corporate generative AI strategy, with suggestions including the CIO, Chief Data Officer, or a new Chief AI Officer (CAIO).
– While many companies are appointing CAIOs, some leaders, like at Thomson Reuters, believe AI should be integrated into all roles rather than siloed under one executive.
– Insurance firm Howden created a “director of AI productivity” role to bridge its data and IT teams and drive effective adoption of AI tools across the business.
– This specialist ensures employees effectively use enterprise AI licenses (like Copilot and ChatGPT) and “sweats” these technology assets to maximize productivity gains.
– The role allows Howden’s data team to focus on proprietary machine learning projects for competitive advantage, while IT manages the rollout of standard AI tools.
The debate over who should lead a company’s artificial intelligence strategy is intensifying. While many organizations are creating the position of Chief AI Officer (CAIO), some industry leaders are finding greater success with a different, more specialized role focused on practical application and adoption. This emerging position, often called a director of AI productivity, acts as a crucial bridge between technical teams and business users, ensuring that investments in AI translate into real-world efficiency and competitive edge. The core argument is that effective AI integration requires a dedicated champion for adoption and practical use, not just high-level oversight.
At insurance firm Howden, Group Chief Data Officer Barry Panayi advocates for this focused approach. The company employs a director of AI productivity who sits between the data and IT departments. This specialist ensures collaboration and drives the effective use of AI tools across a global workforce. This role is designed to “sweat” the company’s technology investments, making certain that enterprise licenses for platforms like Copilot, ChatGPT, and Claude are used to their full potential to enhance daily work.
A primary function of this role is connecting disparate parts of the organization. Many employees are confused about the different responsibilities of technology and data teams. Panayi and the CTO established a clear demarcation: IT owns and runs purchased tools that need integration, while the data team builds bespoke models. The director of AI productivity operates in the “sliver in the middle,” where both activities intersect, such as using an API for large language model processing and writing custom code on top. This specialist ensures people are adopting new tools correctly, moving beyond the assumption that personal AI use translates seamlessly to professional productivity.
Beyond adoption, the director ensures that valuable AI assets are fully exploited. Their job is to demonstrate how enterprise-grade generative AI can solve specific, time-consuming problems. Panayi likens the specialist to a “magician” who shows brokers, who often deal with thousands of pages of documents, how to get answers quickly. By providing nuanced guidance on using AI safely and effectively, this role generates powerful testimonials, such as employees reducing week-long tasks to 20 minutes. This practical demonstration of value is critical for widespread buy-in and tool utilization.
Finally, this structure allows the organization to focus on its competitive advantage. By having a dedicated person manage the demand for general generative AI tools, the core data team is freed to concentrate on proprietary projects. Panayi notes that while generative AI creates productivity, the real business differentiation comes from exploiting unique data with machine learning. His team can therefore focus on high-impact areas like risk assessment and pricing models, which “supercharge” brokers with new insights. This separation prevents data teams from drowning in general support requests and lets them focus on creating unique value.
The director of AI productivity, therefore, becomes a force multiplier. They handle the crucial work of training, support, and cultural adoption for common tools, which is a significant operational burden. This enables the technical experts to dedicate their energy to building custom solutions that competitors cannot easily replicate. In an era of similar off-the-shelf AI, that proprietary edge, built on a foundation of widespread and effective tool use, is where lasting business advantage is found.
(Source: ZDNET)
