As the Head of Data & Intelligence in NTT DATA EMEAL, I believe that the best way to support a company in designing such a strategy is through the implementation of an end-to-end framework for Data & Intelligence. This framework should include the methodologies, organizational principles, and technologies that, when combined, can bring to a company all the needed capabilities, at scale. We use this framework to analyze the organization from a strategic, organizational, and operational point of view - we seek to inject Data, use AI and bring innovation into all key processes, and align the efforts among the different areas.

To become a data and AI-powered organization, the end-to-end transformation must permeate all organizational layers from the C-level and senior managers throughout the hierarchy. Each level has a critical role to play in this transformation journey. For instance, the CEO is responsible for defining the strategic lines and setting the corporate objectives regarding the use of data and AI. The HR team communicates and evangelizes the new ‘data mission’ as designed from the C-level, and promotes a culture that emphasizes the use of data. The latter can be achieved by introducing training and literacy programs and certifications; or by encouraging access to the open ecosystem to keep up to date with the latest developments and new trends related to data and AI.

In parallel, business functions play a key role, as they are expected to create value through innovative use cases that leverage Data and AI and bring together operational, industry, and technology expertise. To implement solutions that support these use cases, business functions need to collaborate with technology teams – to build and scale data-driven solutions. Ideally, the first use cases to implement should have low complexity but a high return on investment. This helps obtain support from stakeholders and demonstrate the value of the technology and also helps drive the demand and readiness for more ambitious data-driven use cases across the organization.

Besides fostering multidisciplinary teams and promoting synergies between business areas, an AI-driven organization must establish training programs that help develop further the internal Data & AI talent. These programs also help to develop an innovation culture that permeates all areas, departments, and teams. Training also helps in spreading the AI knowledge across teams and enabling the organization to stay ahead of the market trends and experiment with the latest tech advancements. Through such literacy programs, the company can redefine itself as a more attractive organization.

A successful AI-powered organization also needs the right structure. Shaping the right organization is one of the most challenging problems a company can face. At NTT DATA we follow a hybrid model that combines the advantages of a Center of Excellence (CoE) and a Hub & Spoke approach. The Center of Excellence brings together all data and AI developments, defines the AI and Data Governance practices, and connects teams (e.g. Data Scientists, Data Engineers, and Data Analysts). The Hub & Spoke (H&S) consists of putting resources and technological know-how close to the business areas (Spokes) while the Hub enables the democratization of analytical capabilities.

Another important topic in the AI transformation journey is the opportunity for the organization to differentiate. But how to achieve differentiation from the competition when any company out there can access productized AI e.g. pre-trained industry-specific services such as Natural Language Processing or Computer Vision, etc.? Part of the strategy could be to not only pursue partnerships with leading big tech companies but also to adopt open-source AI frameworks that offer ready-to-use technologies and architectures. This would allow the company to harness these frameworks, use cloud AI services and open-source systems, and accelerate its AI developments. It could also introduce a new working model across the AI lifecycle: data scientists reuse standardized components and refocus from dull, repetitive tasks, to creating real business value through AI.

Once a company has adopted the culture and the technology, it is then necessary to think how they can use that combination to stand out from their competition. Companies should be looking not only to benefit from Cloud, AI, and open-source capabilities to improve efficiency, accuracy, and foster cost savings, but also to stand out and visibly differentiate their products from the competition through Data and AI-specific service and product design methodologies.