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CAIOs need an organized approach to change management, to understand risk management and ethical considerations and have a knack for cultivating collaboration and partnerships
In 2024, AI will be put into practice at a greater scale and will bring more AI applications than ever before. With the surge in AI usage and regulations being instituted by the Biden administration for AI guidelines and safety measures, U.S. firms must rapidly reskill and upskill talent. Plans are underway to onboard over 400 chief artificial intelligence officers (CAIOs) at the federal level, underlining the immediacy of the situation. With this backdrop, what should corporations fundamentally look for while hiring a CAIO and how can they ensure successful performance? Grounding these reflections in my experience as CAIO at Schneider Electric, here are the three skills a chief AI officer needs to possess.
Successful AI implementation has a lot to do with change management. AI will continue to bring new tools and challenges and how a CAIO manages them will define if they succeed with AI implementation; this makes the right approach to change crucial. One of the biggest challenges ahead is building confidence in AI insights. Inventing new ways of working and ensuring AI adoption is critical and building trust in insights delivered by AI is a key enabler.
Each business has a different attitude toward continuous improvement and how to cultivate a culture that prioritizes data quality. However, regardless of the company, CAIOs will need to build trust in AI-delivered data and be prepared to face obstacles related to company culture. But trust alone in AI-enabled insights cannot be taken for granted, a CAIO needs to be able to inspire, educate and influence to drive change.
Therefore, having an in-depth knowledge of the company, its track of previous successes and challenges with the implementation of digital technologies and a solid internal network, will strongly help a CAIO in his journey.
AI is exposing companies to new types of vulnerabilities, most notably by giving easier and faster access to larger quantities and diversity of data to more users. The second skill relates to risk management, compliance and developing a strong ethical code of conduct.
A CAIO must be able to recognize AI's potential but also its limitations. One of the biggest mistakes executives can make is to try to apply AI everywhere. They need to be able to assess when an AI-related project’s risk is too high, if there is too much at stake and which use cases to develop and which to stop, or never engage with.
When the CAIO starts to build the AI strategy and roadmap, it’s the customer or end-user value that should drive the AI implementation, not the hype around it. In order to properly assess which AI projects to pursue, a good CAIO should be able to create, select and adhere to responsible AI frameworks. CAIOs may need to create their own framework customized to their company to maintain the highest accountability.
With a clearly defined ethical code of conduct, robust data governance and cybersecurity measures in place, they will have the tools they need to succeed. Looking for a qualified candidate means prioritizing people who can create and adhere to regulatory and ethical insights above all else.
Whatever organizational structure the CAIO chooses to implement AI, one thing is certain, creating an environment of collaboration between data scientists and domain experts is a must. We often underestimate the importance of domain knowledge which is key at every step when working with data: from project selection, through execution, to ensuring explainability and results interpretation.
The CAIO will have to shatter many company silos and build a bridge between the core data science team and the entire organization. At Schneider Electric, we go a step further. Our squad teams who create AI applications for energy management and industrial automation are composed of AI experts and field practitioners in equal proportion. It helps us to keep the customer value central to the new application, increase the adoption and ensure any new AI feature is successfully embedded into our portfolio.
Cultivating partnerships and collaboration is one of the keys to success. Whether you decide to buy off-the-shelf AI solutions or develop in-house applications, the pace of innovation can be augmented through partnerships with vendors, research organizations, academia, start-ups and non-profit organizations.
Technical knowledge and experience are strong advantages. However, I’d argue that being a CAIO is not just about coding skills or knowing when to use which algorithm. Most importantly, being a CAIO is about having a solid grasp of the current AI landscape, which is all about understanding its potential, limitations, ethical implications, data management issues and fast-evolving technologies. Also, it is equally important to have a deep understanding of the field of operations because the ultimate value of AI projects will depend on it. So having an appetite for learning and having fundamental skills in mathematics and science are strong assets for a wannabe CAIO, more than in-depth knowledge of the specific, potentially narrow, AI domain.
The CAIO role might seem new and complex, but it can be a necessary role in our tech-saturated world. While I urge other companies to start planning and thinking about bringing a dedicated AI executive on board, it's worth noting it is not a position that businesses should create on a whim.
Taking time to identify a leader with strong AI and business knowledge, an eye for effective risk management, a solid process for insightful change management, inherent collaboration skills, some passion for technology and a mathematical background will be the key to success for any CAIO.
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