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Building Internal AI Capabilities

Building Internal AI Capabilities: What to think about when hiring a Chief AI Officer

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Business leaders must take the time to educate themselves on what artificial intelligence technology is and is not. AI is as transformative as it is overhyped. This is a huge problem and it takes a discerning eye to separate fantasy from reality.

You must know enough to confidently identify the use cases that apply to the business. Executives must feel competent and empowered to lead the strategy development. A well-defined strategy should map the intersection between corporate strategy, data availability, and practical use cases for artificial intelligence.

Prioritizing point solutions on narrow use cases as low-hanging fruit will empower the business to act fast and see results sooner. You can start with a small project to build confidence and internal momentum for future projects. It’s still important, however, to have a roadmap and strategy for use cases. Your roadmap of use cases will drive other strategies for data and technology acquisition. Thinking of use cases first will cut costs and de-risk development down the road.

Many organizations cannot develop AI and data science talent internally. And attempting to build an entire team of data and machine learning engineers is costly and difficult. There’s a huge talent shortage in this space and academia cannot keep up with demand. A more effective strategy is to hire one executive that reports directly to the CEO who can set the strategy and build a team.

Below is a sample resume of what a Chief AI Officer's background might look like:


Experienced tech professional with deep background in product development and consulting. Led multi-disciplinary teams focused on product development and life cycles, data science, platform integration, and machine learning.


AI services consultancy / Co-founder & CTO

Co-founded AI services consultancy that designs, builds and deploys custom AI solutions for clients. Partner with clients to assess and identify areas ripe for AI deployment and guide teams through effective preparation, deployment and maintenance.

Personal health management applications / Founder & CEO

Founded and led a software startup building web and mobile applications to make it easier for people to access and manage their personal health information.

Cloud-based endpoint protection company / VP, Product Development

Oversaw product development, quality assurance and IT. Responsible for all aspects of the product life cycle, including strategy, roadmap, development, testing, deployment and hosting.

Professional software services / VP

Counseled clients on product roadmap and development and led teams focused on product delivery.

Online community development / CTO

Defined the vision and led the execution of the company's technology product strategy. Developed products include a multi-tiered, highly scalable social collaboration platform that integrates a full suite of technologies.

Enterprise software development / Senior product engineer

Managed team of engineers dedicated to product development for web and mobile applications.EDUCATION

MS, Machine LearningBS, Computer Science

Your CAIO should have industry experience getting AI solutions into the market. While a CAIO doesn’t solve for talent shortages, he or she will be more connected to ML engineers and the communities to recruit from. This individual will also be equipped to evaluate talent for aptitude, both internal employees as well as potential external hires, and can bring in those who are fast learners.

Ideally, your CAIO has experience launching AI products as well. Experience productizing AI solutions is harder to find but brings perspective on how the total process and technology ecosystem comes together, and where projects break down.

Need more advice on what to consider when searching for a Chief AI Officer? Download our free whitepaper here.

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