Change & Adoption Lead

Change & Adoption Lead

• FDT Level / Location: GCB4/ 5 typical — Various — co-located with the FDT Tech Sit within the FDT Tech team and close to users (business teams, delivery pods, transformation squads) as the accountable Change/Adoption Lead for rapid rollout and sustained adoption of AI-enabled solutions (including GenAI where in scope). Build and execute a change and adoption plan aligned to release increments—turning real user needs into embedded ways of working quickly, safely and repeatably. This role bridges product intent, delivery execution and operational ownership, accelerating time-to-value while ensuring solutions are trusted, responsibly used, and aligned to enterprise controls and guidance. Reporting & stakeholders: Solid line into FDT Tech Lead / Delivery Lead. Strong day-to-day partnership with Product Owner(s), business SMEs and delivery pod leads on scope, prioritisation and trade-offs. Close collaboration with operations, frontline teams, risk/compliance, change, training, comms, and support teams to ensure readiness and sustainable adoption. Engage with regional teams to identify champions/super-users and ensure materials land effectively across geographies. Scope & authority: Accountable for shaping and owning the change strategy, adoption approach and business readiness for AI-enabled prototypes and production-viable thin-slice releases close to users. Owns local change decisions required to deliver outcomes at pace, within approved governance, controls and communications standards. Authority includes defining adoption KPIs, setting readiness gates (training/comms/SOP updates), coordinating champions networks, and escalating material adoption risks, policy/control gaps or operational readiness blockers Role description & core accountabilities This role exists because AI-enabled delivery changes behaviour, trust and accountability, not just screens and processes. Users need plain-language guidance on what AI can/can’t do, how to verify outputs, and how responsibility remains with people. The Change/Adoption Lead keeps releases moving while ensuring adoption is measurable, benefits are realised, and responsible use is embedded into day-to-day operations.
• Change strategy and stakeholder alignment
• Build a change and adoption plan aligned to the release roadmap (who changes, what changes, when, and why). Map stakeholders and impacted user groups; identify change champions/super-users across regions/teams. Shape the narrative: “what’s in it for me”, expected impacts on workload, risk, customer outcomes, and controls.
• User journey, operating model, and process change
• Define the future-state process including hand-offs, decision rights, and escalation paths (especially important when AI introduces “human-in-the-loop” steps). Update SOPs/knowledge articles/playbooks and ensure operational ownership is clear. Ensure role impacts 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 with Product/Delivery to adjust backlog based on feedback and data. Run pulse checks and qualitative feedback loops (surveys, floor-walks, listening sessions).
• Trust, transparency, and responsible use
• Drive user trust by explaining AI limitations, confidence cues, and when to override or escalate. 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 readiness checkpoints 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 barriers into actionable backlog items (UX friction, unclear outputs, missing integrations, policy confusion). Ensure enhancements prioritise real user pain points and operational realities.
• AI-specific responsibilities
• Manage perceived risk and build confidence by addressing “black box” concerns with plain-language explainability, examples, and clear do/don’t guidance. Drive behaviour change around verification by teaching users how to validate AI outputs (especially for GenAI) and how to report issues (hallucinations, unsafe responses, bias). Ensure enablement is policy-aware: guidance aligns with required controls (data handling, customer information, record keeping) and routes users to the correct procedures where needed. HSBC Success Profile — criticality for this role Criterion Criticality Experience Delivering change and adoption plans aligned to incremental releases Essential Stakeholder mapping and champion/super-user network mobilisation Essential Training, comms and self-serve enablement tailored to roles and regions Essential Adoption measurement and benefits realisation (KPIs, baselines, feedback loops) Essential Future-state process / operating model change (handoffs, decision rights, escalation, ownership) Essential AI-specific enablement (trust/limitations, verification behaviours, responsible use guidance) Essential Capability Customer / Client Centricity Essential Judgment and decision-making Essential Influencing and stakeholder alignment Essential Championing innovation through rapid iteration Essential Strategic technical thinking Desirable Inspirational Leadership Desirable Talent stewardship Desirable Personal Attributes Stamina & Resilience Essential Collaboration & influence Essential Leadership Span Essential Calibration rationale: This is a hands-on, user-adjacent change role optimised for speed-to-value and sustained adoption of AI-enabled solutions. Core capabilities around customer centricity, stakeholder alignment, delivery discipline and pragmatic decision-making are Essential. Broader strategic leadership and talent stewardship are Desirable, as the role contributes to scaling patterns and mentoring but does not typically own enterprise-wide strategy. Experience — Required
• Proven experience delivering change and adoption for digital products, including stakeholder mapping, comms, training and readiness planning.
• Strong ability to align change plans to incremental delivery/release roadmaps and operate readiness gates (training completion, SOP updates, champions in place).
• Experience designing future-state user journeys and operating 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 groups and regions.
• Experience defining adoption KPIs and benefits realisation measures, establishing baselines, and running feedback loops to refine the backlog.
• Comfortable operating in ambiguous environments with shifting requirements, while maintaining delivery discipline and transparency. Experience — Preferred
• Experience rolling out AI-enabled solutions, including building trust through explainability and “responsible use” guidance, and teaching verification behaviours.
• Experience delivering within regulated/controlled environments (risk, audit, security, data handling) and ensuring comms/training align to required controls and procedures.
• Experience coordinating champion/super-user networks across regions/teams and using champions to accelerate adoption.
• Experience running hypercare and structured feedback intake, converting themes into prioritised product improvements.
• Experience working in Value Streams / product-led delivery, transformation pods, or embedded/forward-deployed models. Behavioural skills
• User-obsessed change partner; stays close to users, listens hard, and removes barriers to adoption.
• Pragmatic and outcomes-led; focuses on measurable adoption and benefits, not just activity (emails sent, training delivered).
• Clear communicator under pressure; explains what’s changing, why, and what users need to do in plain language.
• Trust builder; acknowledges limitations, encourages verification, 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 sustained behaviour change effort with measurement and iteration.
• Over-indexes on generic comms/training and fails to reflect real workflows, roles/entitlements, or regional realities.
• Avoids discussing AI limitations, leading to misplaced trust, misuse or disengagement.
• Fails to connect enablement to required controls (data handling, record keeping), creating audit or compliance issues.
• Lacks an adoption baseline or measurable KPIs; cannot demonstrate benefits realisation.
• Fails to establish clear operational ownership, leaving a “who owns this now?” gap post go-live. Year 1 outcomes (scale & embed)
• Change impact assessments and stakeholder maps consistently produced, improving clarity on who is impacted and how.
• Change & adoption plans aligned to release increments, increasing readiness and reducing go-live friction.
• Comms packs delivered (FAQs, launch notes, demos, champion toolkit) that land well across regions and user groups.
• Role-based training delivered with completion tracking and practical scenarios; users understand verification behaviours and safe usage.
• SOPs/playbooks/knowledge articles updated with clear operating model and support ownership, reducing confusion post-release.
• Adoption dashboards established with benefits realisation tracking and a working feedback loop; measurable improvements in adoption and outcomes over time.
• Recognised as a dependable embedded change partner who accelerates adoption while strengthening responsible AI use.