Learner stories
Pillar one: Digitally enabled organisation, marked by agility and innovation
Eve Niblock Programme - Structure and Planning Assistant
Department
Academic Registry (Registrar and COO)
Programme
AI-Powered Productivity L3
Stage
Delivered
Why they did it (problem)
SharePoint instruction guides were being created and updated manually by different contributors, so structure, formatting and quality varied and each edit took longer than it needed to.
How they did it (approach)
Built a locked Word template to enforce a standard structure, plus a reusable Microsoft Copilot prompt (used inside Word) to review and refine drafts against that structure before publishing to SharePoint.
Impact
Adopted by the whole team - improved document consistency, more efficient collaboration and reduced revision time.
Our recommendation: how this could scale
Package the Word template and Copilot prompt as a shared Registry-wide asset (SharePoint template gallery) so any admin team drafting guides — not just this one — starts from the same standard. Low effort, high reuse: this is a template or prompt, not a bespoke build.
Manager (validation)
Andrew Philip
ROI agreement
Strongly agree
Manager quote
"The continued use of AI for producing minutes and agendas for our cross team meetings has continued to save much time and effort."
Catherine Brady - Student Experience Officer
Department
Faculty of Society and Culture
Programme
AI-Powered Productivity L3
Stage
Delivered
Why they did it (problem)
Staff kept needing the same explanations repeated for SCP14C risk assessment forms and processes, wasting time and producing inconsistent outcomes across the faculty.
How they did it (approach)
Built a one-page completion guide and a Copilot prompt list so staff can self-serve the most common questions, drafted with input from the Health and Safety team and tested with real queries.
Impact
Cut staff query time on the form from 15 to 30 minutes to five minutes. Staff say the guide is less overwhelming and improved consistency.
Our recommendation: how this could scale
SCP14C is a UK-wide sector form, not LJMU-specific — the guide format (one-pager plus Copilot prompt list) could be adapted for other high-volume compliance forms across the university (for example other H&S or procurement forms) with the same "explain once, reuse forever" logic.
Manager (validation)
Rachel McCloskey
ROI agreement
Agree
Manager quote
"Cathy has demonstrated a proactive approach to applying skills developed through her apprenticeship to improve existing processes and support colleagues across the faculty."
Amanda Stewart-Reilly - Business Manager
Department
Faculty of Society and Culture
Programme
AI Strategy and Leadership L5
Stage
In progress
Why they did it (problem)
As AI adoption grows in Faculty Operations, there was no governance framework covering data protection, accuracy, "shadow AI" use, inclusion, or cyber resilience — leaving the faculty exposed on compliance and equity.
How they did it (approach)
Produced four governance documents — an AI regulatory impact analysis, a business continuity plan for AI-enabled operations, an AI risk assessment, and a sustainability and inclusion integration document — using Microsoft 365 Copilot to draft and structure the analysis.
Impact
Expected (not yet delivered): stronger operational resilience, compliance and inclusivity as AI use scales across the faculty. Included to show what strategic-level L5 work looks like at month three.
Our recommendation: how this could scale
This is a faculty-level governance framework, not a departmental tool — the natural scale path is upward, not sideways: position it with the university AI governance and IT Services group as a candidate template for other faculties, rather than rebuilding the same framework four times.
Manager (validation)
Timothy Nichol
ROI agreement
Agree
Manager quote
"Use of AI to manage emails" — on-track rating: Exceeding expectations."
Pillar two: Increase operational efficiency and financial resilience
Nicola Mullineux - Subject Recruitment Marketing Manager
Department
Student Recruitment, Marketing and Admissions (Registrar and COO)
Programme
AI-Powered Productivity L3
Stage
Delivered
Why they did it (problem)
Budget allocation across schools was manual, debate-driven and time-consuming, risking inefficient spend, diminished ROI and missed recruitment opportunities.
How they did it (approach)
Built a Gen-AI-powered process and template (Excel, Copilot and Power BI) to segment the central recruitment marketing budget by school, using recruitment, application and conversion data, with a repeatable SOP for future cycles.
Impact
Substantially cut time spent on budget allocation and related discussions; improved fairness and transparency of spend and freed time for strategic analysis.
Our recommendation: how this could scale
The SOP and template are explicitly built to be repeatable — the immediate scale step is next year's budget cycle using the same template. Beyond that, the same segmentation logic (recruitment, application and conversion data by group) could apply to other centrally-held budgets that get split across schools.
Manager (validation)
Carolyn Williams
ROI agreement
Agree
Manager quote
"Nikki used the skills learned to plan this year's expenditure for subject recruitment marketing and the areas of focus... this should increase return on investment."
Ildus Akhmetov - Reader in Genetics and Epigenetics
Department
Sport and Exercise Sciences (Faculty of Health, Innovation, Technology and Science)
Programme
AI-Powered Productivity L3
Stage
Delivered
Why they did it (problem)
Drafting, updating and responding to emails across multiple academic research projects was repetitive and time-consuming, with constant context-switching and inconsistent tone or structure between projects.
How they did it (approach)
Built a GenAI-assisted workflow (ChatGPT, Outlook and Word or OneNote) with reusable project-context templates so the AI has the right background for each project before drafting.
Impact
Reduced time and cognitive load per email, with clearer, better-structured communication across research projects.
Our recommendation: how this could scale
The reusable project-context template is the reusable part — it could be shared as a starting pattern for other academic staff juggling multiple research projects and funders, not just within Sport and Exercise Sciences.
Manager (validation)
David Low
ROI agreement
Agree
Manager quote
"Ildus has made good progress with the skills he has learnt from the apprenticeship through improving his PowerPoint slides he uses for teaching lectures as well as improving his manuscripts."
Susannah Waters - University Librarian
Department
Library Services (Registrar and COO)
Programme
AI Strategy and Leadership L5
Stage
In progress
Why they did it (problem)
Academic Liaison reports were built manually from data held in disparate systems, taking staff time away from advocacy, liaison and planning, and making it hard to see where to prioritise Library resources.
How they did it (approach)
Mapped the manual process end-to-end (via a Copilot-built process map), then began piloting an automated data collection, cleaning, analysis and reporting workflow using Power BI and Copilot ahead of a planned full roll-out.
Impact
Predicted (not yet delivered): an 80% reduction in staff time on data processing within 6 months, and more frequent reporting. Included to show a large, quantified L5 business case in the pipeline.
Our recommendation: how this could scale
The pilot is scoped to Academic Liaison data first — the plan already flags expansion to building and resource usage data, which is the natural next phase once the pilot proves out. Worth tracking as a delivered example for the next QBR.
Manager (validation)
Julie Sheldon
ROI agreement
Strongly agree
Manager quote
"Susannah has used AI derived insights to review a number of team activities and spans of control. These will tighten up some of the slack in areas and harmonise team efforts where there are inconsistencies."
Pillar three: Removing silos
Paul Doherty - Senior BI Developer
Department
IT Services (Registrar and COO)
Programme
AI and Machine Learning Fellowship L6
Stage
Delivered
Why they did it (problem)
The university had no systematic, data-driven way to identify at-risk first-year students early enough for effective intervention, and Admissions and Student Support risked duplicating effort and working from misaligned data.
How they did it (approach)
Ran a full CRISP-DM project: landscape review, an automated data pipeline (Azure Synapse and Microsoft Fabric) with embedded GDPR and bias-mitigation governance, then a predictive model deployed in Fabric and surfaced via Power BI dashboards for daily risk scoring.
Impact
Now used jointly by Admissions and Student Support for daily intervention decisions — breaking down a data silo between two teams that previously worked from separate information.
Our recommendation: how this could scale
The pipeline and dashboards are already built for shared use — the scale opportunity is extending the same risk-scoring approach to later-year retention risk (not just first year), and formally handing the model over to a business-as-usual BI owner so it survives beyond the apprenticeship.
Manager (validation)
Christopher Dyche
ROI agreement
Agree
Manager quote
"Paul is applying stakeholder management skills within his role in the development of the student recruitment reports. He is already forward thinking on how this data can be used in a machine learning context to better support students."
Paul Reece - Library Space and Experience Manager
Department
Library Services (Registrar and COO)
Programme
AI-Powered Productivity L3
Stage
Delivered
Why they did it (problem)
Weekly gate counter data was cleaned, analysed and presented manually, which was repetitive and slowed down operational decisions about staffing and space use.
How they did it (approach)
Built an automated Copilot, Excel and Power BI workflow to clean and visualise the data, plus a Copilot Chat-generated narrative summary, with dashboards shared live via Teams and SharePoint.
Impact
Real-time dashboards now shared across Teams and SharePoint give multiple teams the same live view of footfall data, replacing siloed manual reports.
Our recommendation: how this could scale
The same gate-counter approach (Copilot, Power BI and shared dashboard) is a template for any other manually-reported operational metric across campus estates and facilities teams — the tooling is generic, only the data source changes.
Manager (validation)
Susannah Waters
ROI agreement
Strongly agree
Manager quote
"Paul is applying his learning to evaluate and identify improvements to our AI chatbot service. This will have a positive impact on user experience and improve staff workflows relating to customer service provision."
Amanda Mannion - HR Policy and Projects Manager
Department
Human Resources
Programme
AI for Business Value L4
Stage
In progress
Why they did it (problem)
Different departments interpret HR policies inconsistently, so employees struggle to get accurate answers quickly, leading to confusion, inconsistent practice and compliance risk.
How they did it (approach)
Mapped how staff currently access policy guidance to find the interpretation gaps and delays, then began scoping an AI chatbot to give approved answers to common HR policy questions, with escalation routes for anything outside agreed boundaries.
Impact
Expected (not yet delivered): consistent HR guidance across departments and less administrative burden. Included to give AIBV L4 representation on this pillar.
Our recommendation: how this could scale
By design this already targets cross-department consistency — the scale step is rolling the chatbot out university-wide once piloted, rather than department-by-department, since inconsistency-between-departments is the problem it exists to solve.
Manager (validation)
Gregory Thompson
ROI agreement
Agree
Manager quote
"Amanda has continued to deliver clear business impact by increasing her productivity through the integration of Microsoft Copilot into her day-to-day work — particularly in drafting and summarising policies."