> Markdown version of [/jobs/ext/4160040-ai-transformation-lead](https://www.wearedevelopers.com/jobs/ext/4160040-ai-transformation-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Transformation Lead - **Company:** The Economist Group - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, AI-enabled Products, Data Processing - **Published:** October 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=08b8dd80b3679c1b ## About the Role * 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 our business. * 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 ## Description 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. We are hiring several AI Transformation Leads to help turn that ambition into measurable results. These are hands-on, commercially minded transformation roles for people who combine strong stakeholder skills with practical fluency in building with AI. You will work closely with business 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 business, product, engineering and platform colleagues to deliver sustainable change. This is not a role focused on pilots for their own sake. You will help prioritise opportunities with clear strategic and commercial value, take successful initiatives into day-to-day use, and track the benefits they deliver., * Partner with business areas (e.g. Editorial, Finance, EIU Research & Analysis, Enterprise Sales, B2C, Back-office) to identify, assess and prioritise AI automation opportunities. * Understand current workflows and reimagine them end to end, rather than simply digitising existing processes. * Define problem statements, scope, success measures, delivery approaches and benefits cases; park (or give back for self-implementation) low-value use cases early * Build working prototypes, hands-on models, agents, integrations and get something testable in front of users quickly. * Take proven use cases through to production, utilising 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 into the AI Internal Automation programme rhythm * Stay current on AI tooling, translate new developments into AI opportunity areas and judge honestly where AI does not fit.