Data is at the core of everything we do. The operationalization of our data model is built on our Data Strategy, which defines the strategic direction and principles for the use of data, along with how data is collected, stored, processed, shared, used, and governed to maximize benefit and enable reuse of data, models, and insights.

Re-engineered data-driven processes play a key role in ensuring consistent performance in end-to-end business flows. They establish a common language and standard practices to consistently maximize efficiency and effectiveness. The process framework acts as the backbone to which relevant human and machine capabilities are attached. It serves to facilitate change by acting as the route map  for new ways of working across traditional silos.

Digital transformation is not just about deploying new technology. It requires an organization to evolve its processes, roles, and culture to fully realize the benefits of automation, AI, and data-driven operations. 

After defining standard processes and the data that flows through them, serviced providers must redesign their functional models and define the right roles, skills, and behaviors needed to support digital ways of working. Our assessment of structures, governance, accountability, and competencies revealed clear gaps between the culture required for digital transformation and the current state, making a flexible people blueprint and a strong organizational change program essential.

Since around 60% of transformation effort involves people and organization, redefining roles, upskilling teams, and reshaping leadership behaviors are critical for the shift from human-led to system-led operations.

Ericsson Operations Engine supports this by mapping how every process and technology change impacts people, and by building structured workstreams for communication, training, and team formation. As many failed transformations show, technology alone is not enough. Success depends on an engaged, prepared workforce.

New infrastructure and application capabilities are required when transforming into data-driven operations. However, a common cause of failure is where transformation is centered on the adoption of a new tool platform.

Any change program is underpinned by the platforms and infrastructure working behind the scenes – that is not in dispute. However, only after the outcomes, organization, processes, and data needed are decided should you consider what applications are required and where to execute the service delivery. Of concern, though, are the high cost, the need for specialized equipment, and, maybe most intriguingly, data structure issues.

Automation and AI are essential for getting the most from our data-driven operations approach. The massive scale we manage enables global feedback loops that we leverage to constantly evolve our processes, grow our closed-loop automation and AI use case libraries, making it possible to handle a global network.