Rapid technology advancements, increased AI awareness and geopolitical pressures are reshaping how organisations build and scale AI
Rapid technology advancements, increased AI awareness and geopolitical pressures are reshaping how organisations build and scale AI
Many organizations have a clear Data & AI strategy, but struggle to turn it into real business value. The reason is often structural: the operating model hasn’t kept up with the pace of change. New technologies, new roles, and rising governance expectations require organizations to rethink how Data, AI, business teams, and IT work together.
Why Data & AI Operating Models Must Change
Organizations today face a combination of pressures: generative AI, agentic AI, self-service analytics, new governance requirements, and evolving skillsets.
At the same time, traditional operating models were designed for centralized data teams and highly niche expertise skills.
That gap is why organizations are losing control of their AI initiatives and failing to create tangible return on investment.
The infographic below highlights 10 key forces currently reshaping Data & AI operating models and pushing organizations to rethink how business, data teams, and IT collaborate.
The Operating Model Challenges Leaders Must Solve
As demand for AI grows, organizations face increasing pressure to deliver measurable ROI.
Business users expect autonomy, but governance and oversight must keep pace.
AI depends on trusted, well-managed and high-quality data across the organization.
New tools and technologies are reshaping roles and required capabilities.
Scaling AI requires new ways of working and emerging skill sets across teams.
AI can boost performance, but only if processes and operating models adapt.
Organizations must ensure AI is secure, compliant and ethically managed.
AI solutions must connect seamlessly with existing enterprise architecture.
Adoption depends on preparing teams and supporting organizational change.
Organizations must rethink cloud strategies and data control in a changing landscape.
Updating your Data & AI operating model helps address these challenges by clarifying how Data, AI, business teams, and IT work together, enabling organizations to scale AI responsibly while maintaining control and delivering measurable value.
Leading organizations are redesigning their Data & AI operating models - introducing federated teams, clearer ownership of data and AI products, and stronger governance to scale AI responsibly.
In our webinar with Zilveren Kruis, see how a large enterprise is evolving its operating model to balance innovation, governance, and enterprise control.
Leading organizations are redesigning their Data & AI operating models - introducing federated teams, clearer ownership of data and AI products, and stronger governance to scale AI responsibly.
In our webinar with Zilveren Kruis, see how a large enterprise is evolving its operating model to balance innovation, governance, and enterprise control.
Book a 60–90 minute consultation with Steven Nooijen or a Data & AI expert in your region.
We will explore whether your operating model is ready to support AI at scale, where governance, ownership, or architecture may be slowing progress, how leading organizations structure their Data & AI teams, and what practical steps can help align your operating model with your broader Data & AI strategy.
Head of Data & AI Strategy, Xebia
Our Ideas
Discover why traditional Data & AI operating models are no longer sufficient. And what organizations need to change to successfully scale AI.
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