What is a Chief AI Officer? Role, Responsibilities and Key Skills

The rise of the Chief AI Officer role 

However, what is unique compared to other interdisciplinary roles is AI's incredible impact on every aspect of the organization in such a short time. AI is capital and resource-intensive, so the CAIO’s end-to-end overview ensures it is implemented thoughtfully and responsibly. This means understanding other departments' roles, demands, and external pressures.

In a fast-moving environment like AI, staying on top of everything is challenging but essential. This includes not only experimenting with and researching new technology (that’s actually the fun part) but also adapting to legal, security, or regulatory boundaries and developing commercial options for delivering AI solutions that fit customers’ needs. 

In fact, the CAIO's job is to see how the company can use the newest technology to benefit the customer, not for technology's sake.

That’s why AI implementation needs to be thoughtful. AI without immediate ROI or business value for customers, which can only be achieved when deeply embedded in their existing business processes, data, and practices, could risk becoming just a costly add-on or afterthought.

A recent Gartner study found that, while over half of organizations surveyed had an AI leader, 88% of those leaders did not have the title of Chief AI Officer. So, while the title may be a relatively new one, adoption is increasing - in responsibility if not yet in name. That being said, LinkedIn suggested that the number of CAIO roles has almost tripled in the last five years. 

Source: Gartner

Customer Value: The CAIO’s Key Focus

Ensuring the customer remains at the top of everyone’s mind is critical to the CAIO role. All company functions must think from a customer’s point of view – from the initial use case and scenario selection to actually building and selling our products. 

For example, we need to always ask ourselves what benefits we can deliver out of the box without the customer having to worry about tedious integrations, prompt engineering, security and safety concerns, and so on. 

User research is another important anchor. It helps us embed AI and design products, understand the functionality of use cases, and verify our assumptions. This way, product teams can deliver valuable, trustworthy AI experiences that our users will love. 

At SAP, we evaluate the potential productivity and efficiency gains of our use cases before we start engineering – and ultimately check whether the business outcomes work for our customers. If applicable, we test those scenarios in-house. 

Like we did for our HR in SAP SuccessFactors, the consulting capabilities of our generative AI copilot Joule with our service consultants, or the latest ABAP Large Language Model we have trained with our SAP S/4HANA developers. This helps us to get early feedback and prove the business value, accuracy, and responsibility of the applied AI methods and technologies before we release such capabilities to our customers. 

Chief AI Offiers Collaborate Across Teams

The nature of AI is inherently interdisciplinary and cross-product. It should be integrated with products to augment and add value. It doesn’t exist in a vacuum. The best example is our generative AI copilot, Joule. It is being implemented across the entire organization - by the end of 2024, Joule will help handle 80% of the most frequently used tasks in SAP solutions. 

Learn more about What Leaders Need to Know About Implementing AI Responsibly in our webinar. 

It is precisely because the CAIO is the common connection through so many different AI-focused teams that they are best placed to advocate for customers. In our case, the CAIO is the driving force for engineering, which includes foreseeing a flexible architecture for future innovations. However, it also includes the responsibility for how we communicate AI’s value to the market, how we partner, and how we sell the go-to-market.

Last but not least, the CAIO must have a vision of how to use AI as a springboard to increase productivity, innovate, and grow the business.

AI Strategy and Technology

Looking at SAP, we have built a fully integrated experience through SAP Business Technology Platform (SAP BTP). Here, integration was the top priority of all our engineers. Today, we use SAP BTP from the get-go, and all SAP applications use the same services for an integrated experience. 

This is no different for AI. 

So, we are benefiting from the integration work that started about five years ago and can quickly embed AI and our AI copilot Joule across SAP’s entire cloud solution portfolio.   

Also, it’s much easier to provide value to users if AI can interact with the data critical to them doing their job. When procurement wants to extract document information automatically, AI will do a better job if it can match that extracted information to previous records. If an HR colleague asks AI to generate a job description or parse resumes, it will do a better job if it knows the company’s current job descriptions. 

Automating these processes can provide incredible value if we give that data context – one company reduced its hiring process from three to four months to a matter of weeks. 

Providing that context, especially as we move forward with more use cases, requires collaboration across products. The approach we established at SAP was to get all the various lines of businesses, whether ERP, finance, supply chain, HR, customer experience, or spend management, to agree on what we wanted to do. At the same time, we have built a technology foundation – SAP’s AI Foundation as part of SAP Business Technology Platform – that offers platform services and capabilities everybody must adopt to develop suite qualities. 

This uniform technological background has enabled SAP to embed our AI copilot, Joule, across more products. Customers can ask questions and receive intelligent answers from business data across the SAP portfolio and third-party sources while retaining reliable business context. 

This is the power of one copilot at the heart of our architecture, and the more applications you connect to it, the more powerful the assistant eventually becomes. Today, Joule offers an intuitive one-stop interface for our cloud solutions – and is moving to become an interface to AI agents in the future.

Do You Need a CAIO?

Whether or not you hire a CAIO will depend on several factors. What are your company’s strategic goals around AI? How is your organization currently structured, and how are you using AI? 

Steering an overarching strategy is helpful to ensure consistent user experiences and that customer value remains a priority. It’s important to reinstate the fact that AI is resource-intensive and carries ethical implications that must be enforced company-wide. 

Companies should not implement AI for its own sake but when it offers added value that could not be achieved in any other way. 

As AI advances, it will need people to tell its story clearly to the public in ways that help them better grasp its power and limitations. I believe this storytelling is a core skill that CAIOs will need. Do it well, and we can not only alleviate fears but also help users and decision-makers understand how AI can make their day-to-day work more enjoyable and rewarding. 

Final Thoughts

As Philipp has explained, the Chief AI Officer role has emerged as a critical role for organizations navigating the complexities of AI adoption. Tasked with aligning AI initiatives to business objectives, the CAIO ensures AI delivers measurable value by integrating it across functions, fostering collaboration, and prioritizing customer-centric innovation. This role demands a unique combination of technical expertise, strategic foresight, and leadership capabilities.

As organizations increasingly rely on AI, building a workforce that understands its potential and applications is imperative. Even without a formal CAIO, empowering teams with the right skills can accelerate AI adoption and create competitive advantage.

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