Behind the Ballot: Candidates on Upskilling in AI, Data Analytics, and Digital Resilience

Behind the Ballot: Education and Skills - How the BCI Can Upskill in AI, Data Analytics, and Digital Resilience

30 Oct 2025

Board election candidates were asked a direct question: What should the BCI do to build AI, data analytics, and digital resilience capability across its members and staff? The responses line up into a clear, practical playbook for educators and L&D leaders. Below is a distilled plan you can implement without adding noise or delay.

What candidates agree on

• Integrate AI, data, and digital resilience into core standards and certification (GPG, CBCI).
• Create tiered learning pathways: microlearning, short courses, and advanced certification.
• Prioritize hands-on practice: labs, simulations, and real case studies.
• Form strategic partnerships with universities and tech providers for relevant content and discounts.
• Continuously update content and research; keep it simple, practical, and current.
• Build internal capability so staff can lead credibly and support members.
• Establish governance, ethics, and good-practice guidance for AI use.
• Foster a learning community with webinars, peer exchange, and shared toolkits.

These themes show up across contributions from Atiq Bajwa, Desmond O'Callaghan, Federica Maria Rita Livelli, Gregory Descamps, Kelly Blakeley, Maura Santunione, Mohamed Hassan, Rajesh Pillai, Sanjay Vijayaraghavan KV, and Simon Contini.

Bake AI and data into the standards

Update the Good Practice Guidelines and CBCI to include AI-driven risk assessment, predictive analytics for BIA, and modern cyber-resilience frameworks (Bajwa, O'Callaghan). This ensures baseline competency for everyone who earns or maintains certification. Make updates predictable and visible so educators can plan curriculum changes with confidence.

Build tiered pathways with CPD recognition

Offer microlearning for rapid wins, 2-3 day courses for applied skills, and advanced pathways for specialization with CPD alignment (Contini, Bajwa, Rajesh). Include AI ethics, prompt literacy, automation techniques, and data storytelling for decision support. Validate outcomes with assessments mapped to recognized standards.

Teach the tools, then practice

Focus on hands-on labs, scenario simulations, and facilitated drills where participants apply AI tools to risk modeling, impact analysis, and crisis response (Rajesh, Contini). Show real implementations from member organizations and tech partners. Replace generic demos with data sets and situations that mirror BCM realities.

Form partnerships that deliver

Partner with universities and leading tech organizations to co-develop content and offer recognized certifications at discounted rates (Livelli, Santunione). Invite member specialists to teach modules on standards, regulations, and practical AI usage. Bring in solution providers for show-and-tell sessions anchored in outcomes, not sales (Descamps, Hassan).

Keep content current and easy to find

Curate a central hub of tools, reading lists, and decision guides with AI-assisted search to surface what matters fast (Descamps). Refresh webinars, research outputs, and course content on a set cadence so relevance never lags (O'Callaghan). Start with prompt literacy for all staff to raise overall fluency (Blakeley).

Invest in internal capability

Upskill staff in AI governance, data analytics, and instructional design for emerging tech (Santunione). The guidance, courses, and events are only as strong as the people producing them. Make internal training a non-negotiable line item.

Build community and culture

Launch an online collaboration space for member stories, peer guidance, and quick help across sectors (Hassan, Sanjay). Use webinars and local events to normalize AI usage and share what worked-and what failed-so learning compounds. Keep the tone human-first: technology supports people, it doesn't replace them.

Set clear guardrails: ethics, risk, and policy

Publish AI good-practice guidelines that cover benefits, risks, evaluation methods, and responsible adoption (Hassan, Rajesh). Align with recognized frameworks like the NIST AI Risk Management Framework. For cyber resilience, reference current guidance from ENISA and clarify how BCM teams apply it.

Choose tools with intent

Pilot practical assistants (internal copilots, retrieval-augmented chatbots) on real BCM workflows (Contini). Consider a BCI AI assistant trained on expert content to speed research and coaching (Hassan). Use a skills maturity tool so members know where to start and how to progress (Contini).

90-day action plan for the BCI education team

• Survey members and staff to map skill gaps and immediate needs.
• Publish a one-page AI usage policy and good-practice checklist.
• Launch a prompt literacy micro-course for all members and staff.
• Stand up a curated content hub with AI-assisted search and tagging.
• Run a monthly webinar series: AI for BIA, risk modeling, and incident response.
• Pilot a 2-3 day applied course combining analytics, automation, and cyber-resilience.
• Host a simulation lab using anonymized data from member case studies.
• Secure two academic partners and two tech partners for co-badged content and discounts.
• Train staff in AI governance and data literacy; set quarterly refresh goals.
• Publish a roadmap to embed AI modules into GPG and CBCI, with CPD alignment.

Sample curriculum menu

• Microlearning (30-60 min): AI ethics, prompt literacy, data quality basics, model limits.
• Short courses (2-3 days): AI-driven risk assessment; predictive analytics for BIA; cyber-resilience frameworks in BCM.
• Workshops: Building assistants with retrieval, safe automation for reporting, dashboarding for executives.
• Simulations: Incident response with AI-supported decision aids; continuity plan testing with synthetic data.
• Pathways: Digital Resilience Specialist; AI in BCM Practitioner; Data Analytics for Continuity Leaders (with CPD recognition).

Implementation notes for educators

• Start where learners are: pre-assess, then auto-route to microlearning or deeper tracks.
• Use common BCM scenarios to anchor practice; avoid abstract exercises.
• Measure skill gain with before/after tasks and portfolio artifacts, not just quizzes.
• Keep content modular so updates are cheap and frequent.

Helpful resources

• NIST AI Risk Management Framework
• ENISA guidance on cyber resilience
• AI courses by job role (curated learning paths)

Suggestions above synthesize themes from candidates: Atiq Bajwa, Desmond O'Callaghan, Federica Maria Rita Livelli, Gregory Descamps, Kelly Blakeley, Maura Santunione, Mohamed Hassan, Rajesh Pillai, Sanjay Vijayaraghavan KV, and Simon Contini. Their collective message is simple: make learning practical, continuous, and grounded in BCM realities.

Voting is now open and closes on 12 November 2025 (midnight GMT).