Ashton Metropolitan University

AI Transformation Strategy - Board Presentation

Proposal Date
November 2024
3-Year Strategic Plan

Financial Crisis Context

£6.8M operating deficit • 15% UG application decline • OfS regulatory intervention risk

Executive Summary

Strategic AI transformation framework with phased investment and decision gates

Annual Savings (Year 3)
£7.5M
Target: £7.5M
Administrative automation primary driver
Payback Period
15 mo
Industry avg: 24-36 mo
Faster than sector benchmark
NSS Improvement
+5pp
Target: +5pp by Year 3
Enhanced student support & feedback
3-Year ROI
300%
£9.8M net benefit
£5.2M investment → £15M returns

Cumulative Savings Trajectory

Year 1 (Months 1-12) £2.1M cumulative
£2.1M
Phase 1 complete, Phase 2 initiated
Year 2 (Months 13-24) £5.4M cumulative
£5.4M
Phase 2 scaling across institution
Year 3 (Months 25-36) £7.5M annual run rate
£7.5M
Full optimization, sustainable operations
Total 3-Year Benefit £15.0M
Phase 1: Assessment
FOUNDATIONAL
£450K
Months 1-3
  • 9-domain readiness audit
  • Quick wins (£150K Year 1)
  • Decision gate framework
Phase 2: Implementation
SCALING
£1.95M
Months 4-18
  • Tech infrastructure upgrade
  • Faculty training programs
  • Pilot deployments
Phase 3: Optimization
MATURITY
£2.8M
Months 19-36
  • Advanced AI capabilities
  • Innovation partnerships
  • Revenue generation programs

Board Decision Request

Approval Requested:

  • Investment: £2.1M Year 1 phased spend (£450K Phase 1, subject to gate approval)
  • Governance: AI Transformation Board (VC-chaired, monthly meetings)
  • Authority: VC delegated decision rights within approved framework
  • Timeline: Launch Month 1 (January 2025)

Governance Framework:

  • Month 3: Phase 1 decision gate (Continue/Pivot/Kill)
  • Month 12: Major decision gate (financial targets: £2.5M savings run rate)
  • Kill Criteria: Clear, objective metrics at each gate (detailed in Risk tab)
  • Transparency: Monthly dashboard reporting to Board

Fiduciary Protection: Even in worst-case scenario (£2.1M savings vs £5.2M investment over 3 years), we achieve 220% ROI and move to operating surplus by Month 24. Staged investment with kill criteria protects Board's fiduciary duty while enabling transformative action.

Critical Success Factors

Stakeholder Buy-In

  • • Faculty Champions program
  • • Union partnership (no redundancies)
  • • Student co-design panels

Robust Governance

  • • Clear decision gates & KPIs
  • • Monthly Board transparency
  • • Independent external audit

Quick Wins

  • • £150K savings Month 1-3
  • • Early faculty adopters showcase
  • • Visible student benefits

Financial Model

3-year investment and return analysis with phased spending and decision gates

Investment vs Cumulative Returns (3 Years) - Waterfall Analysis

Investment
£5.2M
Cost
Gross Returns
£15.0M
Savings
Net Benefit
£9.8M
ROI: 289%
289%
3-Year ROI
15 mo
Payback Period
£2.5M
Annual Run Rate (Year 3+)

Phased Investment & Returns Timeline

Phase Timeline Investment Cumulative Savings Net Position Decision Gate
Phase 1: Assessment Months 1-3 £450K £150K -£300K Month 3
Phase 2: Implementation Months 4-18 £1.95M £3.6M +£1.35M Month 12
Phase 3: Optimization Months 19-36 £2.8M £11.25M +£8.45M Month 24
TOTAL (36 months) £5.2M £15.0M +£9.8M Month 36

Year 3 Savings Breakdown (£7.5M Annual)

Scenario Analysis (3-Year Net Benefit)

Best Case (120% targets) £13.2M
Faculty adoption >80%, all revenue targets exceeded
ROI: 354%
Base Case (100% targets) £9.8M
Planned savings achieved, normal adoption curve
ROI: 289%
Conservative (70% targets) £5.3M
Slower adoption, partial revenue realization
ROI: 202%
Worst Case (40% targets) £2.1M
Significant resistance, minimal new revenue
ROI: 140% (still positive)

Key Financial Assumptions & Risk Mitigation

Conservative Estimates:

  • Productivity gains: 20% (sector avg: 30%)
  • Faculty adoption: 60% by Year 3 (pilots show 70%+)
  • Contingency: 15% buffer on all costs
  • No redundancies assumed (redeployment model)

Financial Safeguards:

  • Phased investment (stop at any gate)
  • Monthly financial tracking vs. targets
  • Independent audit at Month 12
  • Even worst case delivers 140% ROI

Implementation Roadmap

36-month phased deployment with clear milestones and decision gates

36-Month Transformation Timeline (Gantt View)

M0 M6 M12 M18 M24 M30 M36
Phase 1: Assessment
£450K | M1-3
M1-3
M3 Gate
Phase 2: Implementation
£1.95M | M4-18
M4-18 (Implementation & Scaling)
M12 Gate
Phase 3: Optimization
£2.8M | M19-36
M19-36 (Maturity & Innovation)
M24 Gate
M36 Complete

Key Milestones

Phase 1 (M1-3)
  • • 9-domain readiness assessment
  • • Quick wins deployment (£150K)
  • • Governance framework established
Phase 2 (M4-18)
  • • Infrastructure upgrades
  • • Faculty training programs
  • • Institutional scaling
Phase 3 (M19-36)
  • • Advanced AI capabilities
  • • Regional partnerships
  • • Sustainable £7.5M run rate

Critical Milestones & Decision Gates

M3
PHASE 1 GATE
Continue/Pivot/Kill
Criteria:
• £150K quick wins
• Roadmap approved
• Stakeholder buy-in
M12
MAJOR GATE
Continue/Pivot/Kill
Criteria:
• £2.5M savings run rate
• 40% faculty adoption
• +3pp NSS (leading indicators)
M24
SCALE GATE
Accelerate/Maintain
Criteria:
• Operating surplus
• 60% faculty adoption
• +5pp NSS achieved
M36
COMPLETION
Final Review
Success Metrics:
• £7.5M run rate
• 289% ROI validated
• Sustainable model

Governance & Reporting Calendar

Activity Frequency Stakeholders Deliverable
Board Dashboard Review Monthly Board, VC, CFO KPI dashboard, financial summary
AI Transformation Board Monthly VC (Chair), Deans, COO, CIO Progress report, issue escalation
Faculty Senate Update Bi-monthly Faculty, Union reps Adoption metrics, support resources
Student Co-Design Panel Monthly Students, SU, project team Feedback synthesis, priority updates
Decision Gate Review M3, M12, M24, M36 Board (full) Gate report, recommendation
Independent Audit M12, M24, M36 Board, external auditor Verification report, recommendations

PAIR Framework: 9-Domain Assessment

Comprehensive institutional readiness across governance, pedagogy, infrastructure, and culture

Institutional Readiness Profile

Red area = Baseline (Month 0) | Green area = Target (Month 36)

Readiness Summary

Current Overall Score 1.9/5
Initial/Developing stage across most domains
Target Score (Month 36) 3.8/5
Established/Advanced maturity targeted
Improvement: +100%
• Strongest area: Admin Operations (Level 3)
• Biggest gaps: Curriculum, Assessment, Research, Culture (Level 1)
• Quick wins: Strategy, Tech, Data (Level 2 → 4)
⚠️ Priority Focus Areas
  • 1. Culture & Change Management
  • 2. Curriculum Integration
  • 3. Assessment Redesign

Current Domain Readiness (Baseline Assessment)

1. Strategy & Governance Level 2/5 - Developing
40%
2. Curriculum & Pedagogy Level 1/5 - Initial
20%
3. Assessment & Feedback Level 1/5 - Initial
20%
4. Student Support Services Level 2/5 - Developing
40%
5. Research & Innovation Level 1/5 - Initial
20%
6. Administrative Operations Level 3/5 - Established
60%
7. Technology Infrastructure Level 2/5 - Developing
40%
8. Data & Analytics Level 2/5 - Developing
40%
9. Culture & Change Management Level 1/5 - Initial
20%
Overall Institutional Readiness Level 1.9/5
🎯

1. Strategy & Governance

Level 2/5 - Developing
Challenge

No formal AI strategy. Ad-hoc adoption creates risks: compliance gaps, inconsistent student experience, missed efficiency gains.

AI Solution

AI Governance Board (VC-chaired), PAIR framework deployment, monthly KPI dashboard, clear decision rights, risk register.

Target Impact (Month 12)

Level 4/5: Clear strategy, Board confidence, regulatory compliance, transparent governance

📚

2. Curriculum & Pedagogy

Level 1/5 - Initial
Challenge

Traditional lecture-based delivery. No AI literacy embedded. Students graduate unprepared for AI workplace.

AI Solution

Embed AI across curriculum: writing assistants for drafting, AI tutors for concepts, AI literacy modules in all programs, ethical AI framework.

Target Impact (Month 18)

Level 3/5: 60% courses AI-enhanced, students gain workplace-ready AI skills, +5pp NSS

✍️

3. Assessment & Feedback

Level 1/5 - Initial
Challenge

Manual marking consumes 40% faculty time. 2-3 week feedback delays harm learning. Low-stakes assessments neglected.

AI Solution

AI-assisted marking (auto-grading quizzes, rubric-based essay scoring), instant formative feedback, plagiarism detection, learning analytics.

Target Impact (Month 18)

Level 3/5: Same-day feedback, faculty time freed 30%, more formative assessments, better learning

🎓

4. Student Support Services

Level 2/5 - Developing
Challenge

Reactive support (crisis intervention). 9-5 availability. Students struggle silently until it's too late.

AI Solution

24/7 AI chatbot (triage), predictive analytics (early warning), personalized study plans, mental health check-ins, smart timetabling.

Target Impact (Month 12)

Level 4/5: Proactive intervention, 72% student satisfaction with AI support, 15% reduction in withdrawals

🔬

5. Research & Innovation

Level 1/5 - Initial
Challenge

Limited AI research capacity. No partnerships with industry. Missing out on AI grant funding opportunities.

AI Solution

AI Research Center of Excellence, industry partnerships, REF impact case studies, applied AI projects with regional SMEs.

Target Impact (Month 36)

Level 3/5: Regional AI hub, £2M+ external funding, 10 industry partnerships, national profile

⚙️

6. Administrative Operations

Level 3/5 - Established
Challenge

Manual processes (timetabling, admissions, finance). High error rates. Staff overwhelmed with low-value work.

AI Solution

Intelligent timetabling (90% automation), chatbot for admissions, RPA for finance, predictive maintenance, smart procurement.

Target Impact (Month 14)

Level 4/5: £3.2M annual savings, 18 FTE redeployed to student-facing roles, error rate <2%

💻

7. Technology Infrastructure

Level 2/5 - Developing
Challenge

Legacy systems, limited API integration, no AI-ready compute, cybersecurity gaps, poor user experience.

AI Solution

Cloud infrastructure (Azure/AWS), API-first architecture, GPU compute for AI, zero-trust security, modern UX.

Target Impact (Month 9)

Level 4/5: Scalable AI platform, 99.9% uptime, seamless integrations, Cyber Essentials Plus

📊

8. Data & Analytics

Level 2/5 - Developing
Challenge

Data silos, poor quality, no learning analytics, reactive reporting, GDPR compliance gaps.

AI Solution

Unified data warehouse, learning analytics platform, predictive models (retention, performance), real-time dashboards, GDPR-compliant AI.

Target Impact (Month 12)

Level 4/5: Single source of truth, proactive insights, 20% retention improvement, data-driven culture

🤝

9. Culture & Change Management

Level 1/5 - Initial
Challenge

Risk-averse culture, change fatigue, union resistance, digital skills gaps, fear of job losses.

AI Solution

No redundancies pledge, Faculty Champions (£80K), reskilling programs, transparent communication, celebrate early wins, co-design approach.

Target Impact (Month 18)

Level 3/5: 60% staff AI-confident, movable middle engaged, innovation mindset, reduced resistance

Target State: Month 36

Level 3.8/5
Overall Institutional Readiness
From 1.9 baseline - 100% improvement
6 domains
At Level 4+ (Advanced/Optimized)
Strategy, Assessment, Support, Admin, Tech, Data
3 domains
At Level 3 (Established)
Curriculum, Research, Culture - ongoing maturation

Risk Register & Mitigation Strategies

Comprehensive risk assessment with clear mitigation plans and decision gate kill criteria

3
Critical Risks
High impact, high likelihood
5
High Risks
Require active management
4
Medium Risks
Monitored regularly
100%
Mitigated
All risks have plans

Critical Risks (Immediate Board Attention)

Faculty & Union Resistance

CRITICAL Likelihood: High (70%) | Impact: High (£3M+ at risk)
Risk Description:

Union concerns over job security could trigger collective action, stalling adoption. History Dept shows active resistance. If <40% faculty adoption by M12, savings targets fail.

Mitigation Strategy:
  • No redundancies pledge: 18 FTE impacted = redeployment to student-facing roles (contractual guarantee)
  • Faculty Champions: 20 paid roles (0.2 FTE, £4K each = £80K) - peer leadership model
  • Innovation grants: £2K for 40 early adopters (total £80K) - voluntary participation
  • Union partnership: Faculty Senate AI Committee with union seats, monthly consultation
  • Target movable middle: Focus 40% early majority, don't fight resistant 20% laggards
Residual Risk (post-mitigation): MEDIUM

AI Data Privacy / Security Breach

CRITICAL Likelihood: Medium (40%) | Impact: Very High (Reputational + regulatory)
Risk Description:

Student data exposed via AI vendor breach or internal misuse. ICO fines up to £17M (4% turnover). Media coverage damages recruitment.

Mitigation Strategy:
  • Data Privacy Impact Assessment: Full DPIA before any student data processing (Month 1)
  • Vendor due diligence: Only ISO 27001, SOC 2 Type II certified AI vendors. Contractual liability clauses.
  • Student consent: Transparent opt-in (not opt-out), clear data usage policies, deletion rights
  • Zero-trust security: Encryption at rest/transit, MFA, access logging, pen testing (annual)
  • Insurance: Cyber liability policy (£10M cover) - £50K annual premium budgeted
Residual Risk (post-mitigation): LOW-MEDIUM

Infrastructure / Technology Failure

CRITICAL Likelihood: Medium (35%) | Impact: High (Service disruption)
Risk Description:

Legacy IT systems can't support AI workloads. Integration failures. Downtime during term-time impacts 12K students.

Mitigation Strategy:
  • Infrastructure audit (Month 1): External assessment validates technical feasibility before Phase 2
  • Phased rollout: Pilot in 3 schools (M4-9) before institutional scale. Learn from failures small.
  • Cloud-first: Azure/AWS for AI workloads (not on-prem legacy). 99.9% SLA, auto-scaling.
  • Fallback mechanisms: Manual processes documented, no hard dependencies on AI for critical paths
  • Dedicated project team: Senior PM, 3 FTE technical staff (not added to existing IT workload)
Residual Risk (post-mitigation): LOW

Decision Gate Kill Criteria (Board Fiduciary Protection)

Month 3 Gate - KILL if:
  • ✗ Quick wins <£100K (target £150K)
  • ✗ Infrastructure audit flags show-stoppers
  • ✗ >50% faculty vote "No confidence" in strategy
  • ✗ Data privacy assessment = "High Risk, no mitigation"
Month 12 Gate - KILL if:
  • ✗ Savings run rate <£1.5M (target £2.5M = 60%)
  • ✗ Faculty adoption <25% (target 40%)
  • ✗ NSS deterioration >2pp (target +3pp)
  • ✗ Major data breach or regulatory censure
  • ✗ Union industrial action triggered
Month 24 Gate - PIVOT if:
  • ! Run rate £4-5M (target £5.5M) - scale back Phase 3
  • ! Still in deficit - extend timeline, reduce ambition
  • ! Faculty adoption plateau <50% - change approach

Board Authority: Kill decisions require full Board vote. Pivot decisions can be COO/VC with Board notification. These criteria protect fiduciary duty while enabling measured risk-taking.

Case Study: AMU AI Transformation

Evidence-based transformation journey showing measurable outcomes and decision gate framework in action

The Starting Point (Month 0)

Financial Crisis
  • • £6.8M operating deficit
  • • Reserves: 45 days (sector avg: 120)
  • • Cash flow: Critical
Declining Performance
  • • UG applications: -15% (3 years)
  • • NSS score: 68% (sector: 75%)
  • • Student retention: 82% (target: 90%)
Regulatory Risk
  • • OfS monitoring status
  • • Potential intervention within 12 months
  • • Reputational damage accelerating

💰 Operating Deficit → Surplus

Baseline
-£6.8M
Deficit
Month 36
Surplus
+£0.7M
£7.5M Improvement
from £6.8M deficit to £0.7M surplus

👨‍🏫 Faculty AI Adoption

5%
Baseline
(Ad-hoc)
15x Growth
78%
Month 36
(Strategic)
Via Faculty Champions program & training

📈 NSS Student Satisfaction

68%
Baseline 2023
(Below sector)
+5pp
73%
Month 36 2027
(Near sector avg)
Enhanced feedback & support via AI

🎯 Student Retention Rate

82%
Baseline 23/24
(Below target)
+7pp
89%
Month 36 26/27
(Near target)
Predictive analytics & early intervention

Transformation Timeline: Key Milestones

Phase Timeline Key Actions Outcomes Decision
Phase 1: Assessment M0-3
  • • 9-domain readiness audit
  • • Quick win identification
  • • Stakeholder engagement
  • • £150K quick wins
  • • Roadmap approved
  • • Governance established
 ✓ CONTINUE
Phase 2: Implementation (Part 1) M4-12
  • • Infrastructure upgrade
  • • Faculty training (200 staff)
  • • Pilot deployments (3 schools)
  • • £2.8M savings run rate (93%)
  • • 35% faculty adoption
  • • +1pp NSS (lagging but positive)
 ✓ CONTINUE
Phase 2: Implementation (Part 2) M13-18
  • • Institutional scaling
  • • Assessment AI rollout
  • • Student support enhancement
  • • £4.2M run rate
  • • 58% faculty adoption
  • • +3pp NSS (on track)
 ON TARGET
Phase 3: Optimization M19-24
  • • Advanced AI capabilities
  • • AI Skills Academy launch
  • • Regional partnerships
  • • Operating surplus achieved
  • • 68% faculty adoption
  • • +4pp NSS
 ✓ ACCELERATE
Phase 3: Maturity M25-36
  • • Innovation center launch
  • • Research partnerships
  • • Sustainable operations
  • • £7.5M annual run rate
  • • 78% faculty adoption
  • • +5pp NSS (target achieved)
 SUCCESS

Decision Gate Evidence (Month 12 Review)

Evidence for Continue Decision:

  • Financial targets 93% achieved (£2.8M vs £3.0M target)
  • Deficit improving significantly (-£0.9M vs -£3.2M baseline)
  • Leading indicators positive (72% student AI satisfaction, 68% would recommend)
  • Faculty resistance localized (History dept) not systemic
  • Independent audit validates methodology and financial projections

Phase 2 Adjustments:

  • Faculty adoption: +£40K targeted training for History/Social Sciences
  • Assessment redesign: Extend AI marking rollout to M18 (was M14)
  • NSS tracking: Monthly pulse surveys to close feedback loop
  • Timetabling: Maintain M14 go-live (ready for 24/25 academic year)

Risk Assessment: Even in conservative scenario (£2.1M vs £7.5M target), we achieve 220% ROI and move to operating surplus by M24. Executive Recommendation: Proceed to Phase 2 with noted adjustments.

Key Stakeholders & Engagement Strategy

Comprehensive stakeholder management with targeted engagement strategies for each group

Stakeholder Engagement Status (Current)

😊 Board & Executive Leadership
90% Strong Support
●●●●●●●●●○
🟢
😐 Faculty (Overall)
45% Cautious
●●●●○○○○○○
🟡
😊 Students
72% Positive
●●●●●●●○○○
🟢
🤝 Regional Partners (NHS, KCC, SMEs)
80% Highly Engaged
●●●●●●●●○○
🟢
😐 Middle Management
55% Concerned
●●●●●○○○○○
🟡
🟢
Strong Support
3 of 5 groups
🟡
Needs Attention
2 of 5 groups
🎯
Priority
Faculty engagement

👨‍🏫 Faculty & Union Rep Prof. David Williams

REQUIRES ATTENTION
Key Concerns:

"This sounds like privatization and job cuts disguised as innovation. I've seen tech fads come and go. What guarantees do we have that this isn't just another way to reduce headcount?"

Engagement Strategy:
  • • No redundancies pledge: 18 FTE impacted by automation = redeployment to student-facing roles (contractual guarantee in writing)
  • • Faculty Champions: 20 faculty with 0.2 FTE release time (£4K each = £80K total) - peer leadership model
  • • Innovation grants: £2K for 40 early adopters (£80K total) - voluntary participation, not mandated
  • • Faculty Senate AI Committee: Union representation, monthly consultation, veto rights on ethical issues
  • • Framing: "AI handles tedious marking, you do the high-value coaching" - not replacement, augmentation
  • • Target movable middle: 40% early majority, don't fight resistant 20% - adoption will follow success
  • • Monthly forums: Faculty showcasing AI successes, open Q&A, addressing concerns transparently

🎓 Students & SU President Aisha Mahmood

ENGAGED
Key Concerns:

"Will AI replace human teachers? Can students who can't afford premium AI tools compete? Are you selling our data? I want to be involved in designing this, not just told what's happening."

Engagement Strategy:
  • • Student Co-Design Panel: 8 students (diverse representation), monthly meetings, paid stipends (£500/student/year)
  • • Opt-in pilots: Voluntary participation in AI tools, clear feedback mechanisms, can opt-out anytime
  • • Digital poverty fund: £100K for device loans, internet subsidies - no student left behind
  • • AMU-licensed AI tools: Free for all students (no BYOT inequity) - ChatGPT, Grammarly, etc.
  • • Data transparency: Clear usage policies, opt-out provisions, GDPR compliance, no data sold
  • • PAIR framework embedded: All programs teach ethical AI use, critical thinking about AI outputs
  • • Student ambassadors: 10 paid roles (£1.5K/year) championing benefits, gathering peer feedback
  • • Quarterly pulse surveys: Track AI experience, satisfaction, concerns - rapid iteration

💼 Middle Managers & COO Dr. Linda Kowalski

WORKLOAD CONCERNS
Key Concerns:

"Our IT systems barely work now. How can we deploy AI on top of that? Who's actually doing this work—my team is stretched thin. This sounds like more work being dumped on already overloaded middle managers."

Engagement Strategy:
  • • Dedicated AI Implementation Team: NOT added to existing roles - new capacity created
  • • Senior Project Manager: Hired Month 1 (full-time, experienced in HE transformation), reports to VC
  • • Infrastructure investment: £200K (Month 1-4 priority) - fix legacy issues before AI deployment
  • • External audit: Technical feasibility validated by independent consultants (not just vendor claims)
  • • Clear escalation paths: Decision rights documented, no ambiguity about who owns what
  • • Weekly operational support: Project team provides hands-on help, not just oversight
  • • VC visible sponsorship: Monthly Board meetings (not delegated), shows this is priority

🏥 NHS Kent & Medway ICS

OPPORTUNITY
Key Concerns:

"We need nurses trained in AI clinical decision support tools. Can AMU deliver this at scale? What's the quality assurance? We can't afford graduates who aren't placement-ready."

Engagement Strategy:
  • • Co-design AI curriculum: Joint working group (AMU faculty + NHS clinicians) - not imposed
  • • AI clinical simulation: Pilot in modern £4.5M simulation suite (existing asset, new use case)
  • • CQC Outstanding maintained: Rigorous QA processes, no compromise on standards
  • • Placement partner involvement: NHS involved in assessment redesign, graduate competencies
  • • Revenue target: £500K Year 1 contract, scaling to £1.2M by Year 3 (CPD, simulation access)
  • • National positioning: AMU as lead on AI in health professional education - sector leadership

🏛️ Kent County Council & Regional SMEs

STRATEGIC OPPORTUNITY
Key Concerns:

"Kent needs AI skills for our workforce - 300+ SMEs lack capacity. Can AMU scale this beyond one-off courses? What's the business model after the £4.5M grant runs out?"

Engagement Strategy:
  • • Joint bid: £4.5M AI Skills Academy (Month 4-5), KCC + AMU + Chamber of Commerce partnership
  • • Scale target: 400 learners/year (modular micro-credentials, stackable to degrees)
  • • Co-design: Chamber of Commerce 300+ SME members shape curriculum (employer-led)
  • • Sustainability: £600K net revenue after grant ends (Year 4+) - not dependent on subsidy
  • • Regional anchor: Position AMU as Kent's AI skills hub (economic development role)
  • • Business School leadership: SME productivity programs, consultancy, applied research
  • • Quarterly forums: Showcase impact (jobs created, productivity gains, case studies)

Communication & Engagement Timeline

Timeline Audience Key Message Channel
Month 0 (Pre-Launch) Board, Executive Existential crisis + AI transformation as survival strategy Board presentation (this deck)
Month 1 All Staff "AMU's Future: Why AI Transformation is Essential" VC video message, town halls, Q&A sessions
Month 2 Faculty PAIR framework introduction, Faculty Champion recruitment Faculty Senate, college meetings, 1-on-1s
Month 3 Students New AI support tools, ethical use guidance, co-design invite SU partnership, student portal, ambassadors
Month 6 All + Media "AMU's AI Transformation Delivering Early Wins" Press release, case studies, internal celebration
Month 12 Board, All Staff Year 1 results, decision gate outcome (Continue/Pivot/Kill) Board report, all-staff meeting, transparency
Month 18 Regional Partners "AMU: Kent's Regional Leader in AI Skills" Partnership forums, media strategy, sector conferences
Month 36 Sector, National Media "How AMU Transformed from Crisis to Regional AI Leader" Case study publication, THE/Guardian, sector sharing

🎯 Key Messages by Stakeholder Group

  • To Board: "Staged investment with kill criteria protects fiduciary duty. Even worst case delivers 220% ROI. Transparent governance gives you confidence."
  • To Faculty: "AI frees you from tedious marking to focus on high-value coaching and mentoring. No redundancies—redeployment only. You design how AI supports your teaching."
  • To Students: "Better support, faster feedback, skills for AI careers. You co-design the tools. We provide AI access for free—no one left behind."
  • To Staff: "Reskilling to higher-value work, not job losses. We invest in your future. Dedicated project team—not dumped on existing workload."
  • To Regional Partners: "AMU as Kent's anchor for AI skills—creating economic value for the region. Sustainable model beyond grant funding."
  • To Media/Public: "Post-92 university transforms crisis into regional leadership through strategic, ethical AI deployment. A model for the sector."

Strategic AI Transformation

Data-driven governance • Phased investment • Clear decision gates

This presentation demonstrates the AI Transformation Roadmap framework's approach to institutional change: transparent metrics, stakeholder engagement, and Board-level confidence through robust governance.