Change & Adoption Lead

Sheffield, United Kingdom | Posted on 11/06/2026 Level /Location GCB4/ 5 typical — Various —co-located with the FDTTech She sit within the FDT Tech team and close to users (business teams, delivery pods, transformationsquads) as the accountable Change/Adoption Lead for rapidrollout and sustained adoption of AI-enabled solutions (including GenAI wherein scope). Build and execute a change and adoption plan aligned to releaseincrements—turning real user needs into embedded ways of working quickly,safely and repeatably. This role bridges product intent, delivery execution andoperational ownership, accelerating time-to-value while ensuring solutions aretrusted, responsibly used, and aligned to enterprise controls and guidance. Reporting identify changechampions/super-users across regions/teams. Shape the narrative: “what’s in itfor me”, expected impacts on workload, risk, customer outcomes, and controls. User journey, operating model, and process change - Define the future-stateprocess including hand-offs, decision rights, and escalation paths (especiallyimportant when AI introduces “human-in-the-loop” steps). Update SOPs/knowledgearticles/playbooks and ensure operational ownership is clear. Ensure roleimpacts are managed (new tasks, removed tasks, new skills, capacity shifts). Training, comms, and enablement - Create role-based training(frontline, operations, risk, support, managers) with practical scenarios.Deliver communications: launch packs, FAQs, demo sessions, “what changed”notes, and release highlights. Build self-serve assets: short guides, videos,in-tool tips, prompt guides, and troubleshooting steps. Adoption measurement and benefits realisation - Define adoption KPIs(active users, frequency, task completion rates, time saved, error reduction,case deflection, customer outcomes). Track benefits vs baseline and work withProduct/Delivery to adjust backlog based on feedback and data. Run pulse checksand qualitative feedback loops (surveys, floor-walks, listening sessions). Trust, transparency, and responsible use - Drive user trust byexplaining AI limitations, confidence cues, and when to override or escalation..For GenAI: set safe usage guidance (what not to input, how to verify outputs,how citations/grounding works). Coordinate human-in-the-loop and “decision accountability”messaging-AI assists; people remain responsible. Readiness, cutover, and hypercare - Own business readinesscheckpoints for go-live: training completion, comms sent, SOPs updated,champions in place. Coordinate hypercare: feedback intake, incident themes,quick-fix loop with the squad, and comms for known issues. Feedback to product and continuous improvement - Convert adoption barriersinto actionable backlog items (UX friction, unclear outputs, missingintegrations, policy confusion). Ensure enhancements prioritise real user painpoints and operational realities. AI-specific responsibilities - Manage perceived risk and build confidence by addressing “black box” concerns withplain-language explainability, examples, and clear do/don’t guidance. Drivebehaviour change around verification by teaching users how to validate AIoutputs (especially for GenAI) and how to report issues (hallucinations, unsaferesponses, bias). Ensure enablement is policy-aware: guidance aligns withrequired controls (data handling, customer information, record keeping) androutes users to the correct procedures where needed. HSBC Success Profile — criticality for this role Criterion Criticality Experience Delivering change and adoption plansaligned to incremental releases Stakeholder mapping andchampion/super-user network mobilisation Training, comms and self-serve enablementtailored to roles and regions Adoption measurement and benefitsrealisation (KPIs, baselines, feedback loops) Future-state process / operating modelchange (handoffs, decision rights, escalation, ownership) AI-specific enablement(trust/limitations, verification behaviours, responsible use guidance) Capability Customer / Client Centricity Judgment and decision-making Influencing and stakeholder alignment Championing innovation through rapiditeration Strategic technical thinking Inspirational Leadership Talent stewardship Personal Attributes Leadership Span Calibration rationale This is a hands-on,user-adjacent change role optimised for speed-to-value and sustained adoptionof AI-enabled solutions. Core capabilities around customer centricity,stakeholder alignment, delivery discipline and pragmatic decision-making areEssential. Broader strategic leadership and talent stewardship are Desirable,as the role contributes to scaling patterns and mentoring but does nottypically own enterprise-wide strategy. Experience — Required Proven experience delivering change and adoption fordigital products, including stakeholder mapping, comms, training and readinessplanning. Strong ability to align change plans to incrementaldelivery/release roadmaps and operate readiness gates (training completion, SOPupdates, champions in place). Experience designing future-state user journeys andoperating model/process changes (handoffs, decision rights, escalation paths)and clarifying ownership. Experience creating role-based enablement materials (FAQs,launch packs, demos, quick guides) and tailoring messaging across user groupsand regions. Experience defining adoption KPIs and benefits realisationmeasures, establishing baselines, and running feedback loops to refine thebacklog. Comfortable operating in ambiguous environments withshifting requirements, while maintaining delivery discipline and transparency. Experience — Preferred Experience rolling out AI-enabled solutions, includingbuilding trust through explainability and “responsible use” guidance, andteaching verification behaviours. Experience delivering within regulated/controlledenvironments (risk, audit, security, data handling) and ensuring comms/trainingalign to required controls and procedures. Experience coordinating champion/super-user networks acrossregions/teams and using champions to accelerate adoption. Experience running hypercare and structured feedbackintake, converting themes into prioritised product improvements. Experience working in Value Streams / product-led delivery,transformation pods, or embedded/forward-deployed models. User-obsessed change partner; stays close to users, listenshard, and removes barriers to adoption. Pragmatic and outcomes-led; focuses on measurable adoptionand benefits, not just activity (emails sent, training delivered). Clear communicator under pressure; explains what’schanging, why, and what users need to do in plain language. Trust builder; acknowledges limitations, encouragesverification, and reinforces responsible decision accountability. Collaborative and inclusive; works well across roles,regions and backgrounds; welcomes different perspectives to improve enablement. Proactive risk management; identifies adoption, process,and policy/control risks early; escalates with options, not surprises. Derailers / red flags Treats adoption as a launch event rather than a sustainedbehaviour change effort with measurement and iteration. Over-indexes on generic comms/training and fails to reflectreal workflows, roles/entitlements, or regional realities. Avoids discussing AI limitations, leading to misplacedtrust, misuse or disengagement. Fails to connect enablement to required controls (datahandling, record keeping), creating audit or compliance issues. Lacks an adoption baseline or measurable KPIs; cannotdemonstrate benefits realisation. Fails to establish clear operational ownership, leaving a“who owns this now?” gap post go-live. Change impact assessments and stakeholder maps consistentlyproduced, improving clarity on who is impacted and how. Change users understand verification behaviours and safe usage. SOPs/playbooks/knowledge articles updated with clear operating modeland support ownership, reducing confusion post-release. Adoption dashboards established with benefits realisation trackingand a working feedback loop; measurable improvements in adoption and outcomesover time. Recognised as a dependable embedded change partner who acceleratesadoption while strengthening responsible AI use.