Editorial AI Transformation Lead
Editorial AI Transformation Lead
Location: London
Reporting to: Editor and Executive Editors
The Role
The Economist Group (TEG) is investing in AI to transform how we work, serve our customers and create value across the organisation.
Our AI Transformation Programme was established by the executive leadership team to partner with departments across TEG - including Editorial, EIU, Enterprise Sales, B2C and central functions - to identify high-impact opportunities, redesign workflows and embed new, AI-enabled ways of working at scale.
As part of this AI Transformation Programme, we are hiring an AI Transformation Lead for Editorial to help turn that ambition into measurable results in The Economist’s newsroom. This is a hands-on transformation role for people who combine strong stakeholder skills with practical fluency in building with AI.
You will work closely with our editorial teams to understand how work is done today, identify where AI can make a meaningful difference, shape the right solution and lead it through implementation and adoption. You will be comfortable getting close to the technology yourself, but equally effective at bringing together editorial as well as business, product and engineering colleagues to deliver sustainable change.
This is not a role focused on automating what our journalists do –The Economist will remain a quintessentially human product. You will help prioritise opportunities that allow the newsroom to function more efficiently, take successful initiatives into day-to-day use and track the benefits they deliver.
Key Responsibilities:
Work within Editorial and partner with other business areas (e.g. Finance, EIU Research & Analysis, Enterprise Sales, B2C, Back-office) to identify, assess and prioritise AI opportunities.
Understand current editorial workflows and reimagine them, rather than simply digitising existing processes.
Define problem statements, scope, success measures, delivery approaches and high-value use cases; park (or give back for self-implementation) low-value cases early
Build working prototypes, models, agents, integrations and get something testable in front of users quickly.
Take proven use cases through to production, using the necessary resources (self-built or working with engineering and platform teams)
Drive adoption with the business owner through training, communications, embedding into daily workflow.
Track usage, outcomes and realised benefit; use evidence to refine, scale or stop initiatives.
Report progress, benefit and blockers to the Editor and the Executive Editors
Stay current on AI tooling and translate new developments into AI opportunity areas and judge honestly where AI does not fit.
Key Skills and Competencies:
Essential:
Vitally, we are looking for candidates with a deep passion and interest in AI as a field, who can demonstrate curiosity about how emerging AI capabilities can be applied responsibly and effectively within Editorial. Experience working with an Editorial team/department is vital for this role.
4+ years’ experience in transformation, consulting, product, operations, business analysis, technology delivery or a similar cross-functional role.
Practical experience applying AI, automation, data or digital tools to improve business processes. This could include prototyping, configuring platforms, shaping delivery or advising teams on implementation.
Meaningful, demonstrable hands-on experience building with AI and agentic tools.
Commercial judgement: able to assess value, make trade-offs and focus effort on opportunities with meaningful impact.
Strong ability to understand operational processes, identify root causes and redesign workflows around user and business needs.
Comfortable leading discovery activities to understand user needs, uncover opportunities and define target-state processes.
Experience supporting the adoption, enablement and embedding of new technical capabilities and tools into users’ daily workflows. You care whether what you build gets used.
Confident working with senior stakeholders, able to influence decisions and bring clarity where priorities or expectations differ.
Also useful:
Experience taking AI-enabled products, automations or services into production, including evaluation, monitoring, governance and cost management
Data wrangling and engineering - experience working with messy data spread across multiple systems
Experience in media, publishing, research, subscription businesses or professional services
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