AI has been at the center of discussions in the news industry for the past few years, but the nature of those discussions is shifting. Up until 2025, many of our clients were primarily focused on identifying effective AI use cases across editorial, reader engagement and monetization. Our AI Launchpad programs in 2024 (1st and 2nd edition) and 2025 reflected this phase, helping publishers explore, identify and deploy AI use cases.
Publishers are still experimenting with new products and use cases, but we are now seeing more attention turn to scale. Instead of AI remaining in the hands of small groups of specialists or AI-savvy employees, publishers increasingly want more journalists and teams across the organization to benefit from the technology.
This shift is also reflected in the evolution of FT Strategies' work. In 2026, our AI Lab, supported by the Google News Initiative, broadened the focus from individual use cases to assessing AI readiness across the organization, looking at eight dimensions: Leadership commitment, Vision and Alignment, Technology Infrastructure, Content and Data Infrastructure, AI Product and Tooling, Ethical and Legal Risk Management, Adoption, and Governance. Through our discussions with clients across these eight dimensions, Governance emerged as a particularly important area of focus, with many publishers highlighting the need for clearer definitions of who makes decisions about AI, which decisions sit centrally, and how risks, standards and learning are managed.
So, what governance models are we seeing in the industry, what are the trade-offs between them, and why is a “Centre of Enablement” becoming particularly relevant?
AI governance determines how decisions about AI are made: who sets strategic priorities, who can manage experiments or tool deployment, how risks are assessed, and how knowledge is shared across the organization.
At FT Strategies, we group AI governance into three broad models depending on how centralized decision-making and delivery are: Centre of Excellence, Centre of Enablement and Decentralized / Embedded Teams. There is no “one size fits all” governance structure. Publishers can combine elements of more than one model or evolve their structure over time. The framework helps publishers understand the trade-offs of each approach and consider which elements best fit their organization.
This is the most centralized of the three models. There is usually a dedicated AI lead and team that manages AI strategy in line with leadership priorities and wider business goals. The central team often oversees AI tool deployment, the AI product roadmap and prioritization, and gathers ideas from departments across the organization.
At the other end of the spectrum, decision-making and delivery sit predominantly within individual departments or teams. Embedded AI expertise is used to identify, prioritize, develop or procure solutions for local needs. Organization-wide strategy, policies or guardrails may still exist, but teams have greater autonomy over day-to-day AI experimentation and implementation.
This is different from unmanaged AI use that emerges simply because an organization has no formal governance structure. A decentralized model can still be deliberately designed, with clear responsibilities and organisation-wide standards.
Sitting between the two models is the “Centre of Enablement.” It combines a central hub for strategy, governance, shared standards and specialist expertise with AI capability embedded across different departments. Local teams have room to identify and explore use cases relevant to their needs, while the central function provides guidance, connects expertise and may retain responsibility for higher-risk approvals.
Representatives from different teams can exchange learnings and ideas, raise technical or compliance questions, and take relevant information back into their own departments. The central function does not own every experiment but provides a route for teams to access expertise and maintain visibility over what is happening.
The right structure depends on factors such as organizational size, AI maturity, internal capabilities, culture and risk, to name a few. In our work with publishers, however, we are increasingly seeing interest in elements of the “Centre of Enablement.” Its appeal is practical. Local teams can move quickly and identify use cases that fit their workflows, while central expertise, shared guardrails and cross-functional coordination help maintain alignment.
This does not make the “Centre of Enablement” universally relevant. For a publisher at an earlier stage of its AI journey, a more centralized model may provide useful clarity. For an organization with strong AI capability in teams, greater decentralization may be appropriate. The important point is that governance should support the organization's current needs.
Scaling AI changes publishers’ operating model, creating the need for sufficient central coordination to manage risk and avoid duplication, but enough distributed capability for teams to innovate in their own workflows. For many organizations, this points to a federated model in which a central function sets the guardrails and connects expertise, with distributed teams owning experimentation, adoption and knowledge sharing.
Through our work assessing AI readiness with more than 50 publishers, we are building a growing view of how these governance choices play out in practice, alongside first-hand experience from the Financial Times.
If you are reviewing how AI is governed across your organization, please get in touch.
At FT Strategies, we help publishers move beyond AI experimentation to responsible, organization-wide adoption. Drawing on our experience assessing AI readiness, we can assess your current capabilities, identify gaps in your operating model and design an AI governance approach that fits your strategy and culture. Get in touch to explore how we can help your organization scale AI with confidence, ensuring innovation is balanced with effective oversight.
