Prepared for Paul Davis and the WW2 Headquarters leadership team
WW2 Headquarters AI Lab
Building the World's Leading AI-Enabled History Production Platform
A phased AI Lab designed to help WW2 Headquarters expand its global reach, increase content output and reduce production overhead while preserving historical accuracy, editorial judgement and human control.
Problem-first automation. Human-led creativity. Continuously improving production.
Chris Bradshaw, Founder, Collective Intelligence · July 2026
A message from Chris Bradshaw
Four outcomes that matter
Global Reach
Identify new international audiences and adapt distribution around real market intelligence.
More Content
Generate more long-form, short-form and platform-specific output from every production.
Lower Production Burden
Reduce repetitive editing, publishing, administration and channel-management work.
Editorial Trust
Keep historians, producers and editors visibly in control of facts, context, tone and final approval.
Five things the discovery call confirmed
Growth must sit alongside efficiency
Reducing editing and publishing costs matters, but cost reduction alone will not solve the business challenge. The programme must also support audience growth, discovery, conversion and sustainable revenue.
“Efficiency will not solve the problem on its own. Growth and discovery must be built into the programme.”
Global discovery is a major opportunity
Second World War history has a genuinely global audience. WW2 Headquarters should use audience and social intelligence to understand which topics, formats and stories are most likely to perform in different territories.
Editorial accuracy is non-negotiable
The system cannot rely on generic internet fact-checking or unsupervised AI output. Historical claims, archive usage, editing decisions and contributor meaning must remain grounded in approved sources and expert review.
“We cannot compromise historical accuracy, context or tone.”
Leadership time is limited
The programme must minimise demands on a small, time-poor team through focused workshops, targeted interviews, clear decision requests and asynchronous collaboration.
ROI must guide prioritisation
The programme should distinguish between what AI could do and what is commercially worth doing. Growth potential, cost reduction, risk, effort and speed to value must all be considered.
“The opportunity is to scale reach without scaling headcount at the same rate.”
The intelligence that wins is not the most powerful — it is the most organised.
From initial brief to a fuller programme
The discovery call expanded our understanding of what the programme needs to achieve. The original brief covered production efficiency. The revised direction addresses both growth and efficiency.
- Publishing automation
- Long-form editing
- Short-form clips
- Social distribution
- Global audience intelligence
- International market discovery
- Content and distribution strategy
- Editorial trust architecture
- Long-form production automation
- Short-form content at scale
- Membership and community support
- Organisational memory
- Back-office automation
- Commercial growth measurement
Six priority areas
Each priority is independently scoped and sequenced based on commercial value, technical feasibility and editorial safety. Click any card to expand.
Global Audience Discovery and Social Intelligence
Build an intelligence layer that identifies promising audiences, territories, themes, formats, competitors and emerging content opportunities.
Capabilities
- Territory-level audience analysis
- Competitor and adjacent-channel research
- Topic and format trend detection
- Content-performance analysis
- International distribution recommendations
- Localisation and translation testing
- Evidence-led content planning
Outcomes
- Stronger international reach
- Reduced guesswork
- Better content decisions
- Faster identification of growth opportunities
- Territory-specific publishing plans
Short-Form Content and Distribution Engine
Turn approved long-form content into platform- and territory-specific short-form assets.
Capabilities
- Clip identification
- Virality or engagement scoring
- Multiple aspect ratios
- Captions and titles
- Platform-specific copy
- Territory variations
- Approval workflows
- Scheduling and distribution
Outcomes
- Increased weekly output
- More audience entry points
- Greater value from existing footage
- Reduced manual channel-management effort
AI-Assisted Long-Form Editing
Test how much of the first-pass edit AI can complete safely.
Capabilities
- Speaker detection
- Camera-selection logic
- Transcript editing
- Silence and repetition removal
- Rough-cut assembly
- B-roll recommendations
- Captions and chapters
- Human correction capture
Outcomes
- Reduced editing hours
- Lower cost per film
- Faster publication
- Reusable editing rules
- A measurable human-review model
Podcast and Website Publishing Automation
Automate repetitive work from source ingestion through to draft publication.
Capabilities
- RSS or source detection
- Transcript and chapter generation
- Website-ready copy
- SEO and structured metadata
- CMS draft creation
- Human review
- Publishing logs
- Organisational memory
Outcomes
- Faster turnaround
- Reduced administration
- Fewer errors
- More consistent metadata
Membership and Community Support
Prepare for membership growth without requiring linear growth in support headcount.
Capabilities
- Approved membership FAQs
- Onboarding
- Account guidance
- Content recommendations
- Community support
- Human escalation
Outcomes
- Faster member responses
- Lower support workload
- Better retention and engagement
- Scalable membership operations
Back-Office Administration
Explore contained automation around invoices, expenses, financial preparation, reporting and recurring administration.
Useful but should sit behind opportunities that drive growth or remove major production constraints.
Capabilities
- Invoice and expense processing
- Financial report preparation
- Recurring administration
- Supplier communications
Outcomes
- Reduced administrative overhead
- Fewer manual errors
- More time for production
Note: Useful but should sit behind opportunities that drive growth or remove major production constraints.
Every word broadcast to millions began as a decision made by one careful editor.
Human control at every stage
No AI output reaches an audience without expert review and explicit human approval. Historical accuracy, archive rights and contributor meaning are protected at every step.
Approval Workflow
Built-In Safeguards
How the Lab works
The Lab is not a one-time delivery. It is a continuously improving production system built on five founding principles.
Problem first, technology second
Every workflow starts with a real operational constraint or commercial opportunity, not a technology capability.
Human creativity, AI execution
Historians, producers and editors make creative and editorial decisions. AI executes, accelerates and scales.
Build and validate in levels
Nothing moves to production until it has passed quality review and met confidence thresholds defined by the team.
Visible human approval
Every AI output that reaches an audience carries a clear approval record. Nothing is published without human sign-off.
Build once and learn continuously
Each workflow generates data, corrections and improvements that feed back into the system and reduce future effort.
The Continuous Improvement Loop
Each cycle of the loop generates data, corrections and improvements that reduce future effort and increase reliability.
The archive is not the past. It is the foundation of everything that comes next.
Discovery, alignment and AI Operating Model
Phase 1 produces the foundations everything else depends on: a clear operating model, a prioritised portfolio, a measurement framework and the right first pilot.
What happens in Phase 1
- Leadership alignment workshop
- Current workflow mapping
- Current cost and headcount analysis
- Audience and social-intelligence research
- Global growth opportunity analysis
- Editorial-control design
- Human-review requirements
- Technology and data assessment
- Agent opportunity mapping
- Governance design
- Client-time model
- ROI scoring
- Operating-cost forecasts
- Selection of the first pilot
Deliverables
- Signed-off AI Operating Model
- Prioritised opportunity portfolio
- Global audience-discovery plan
- Editorial accuracy and approval framework
- Current and future-state workflow maps
- Recommended first pilot
- Six-to-twelve-month roadmap
- Refined implementation costs
- Twelve-month operating-cost model
- Human-versus-agent cost comparison
- Measurement framework
ROI Scoring Model
Every opportunity is evaluated across eight dimensions to identify the highest-value, lowest-risk starting point.
Minimal time on your side
The programme is designed around a small, time-poor team. Workshops are focused. Review requests are clear and bounded. Asynchronous input is maximised wherever possible.
Kickoff workshop
Half dayIn person or remote
Leadership and key decision-makers
Stakeholder interviews
1–2 hours eachIndividual or small group
Targeted and structured — 3 to 5 people
Review and feedback
1–2 hoursAsynchronous or short call
Draft review of operating model and findings
Blueprint sign-off session
2 hoursLeadership team
Final review and approval to proceed
Phase 1 and beyond
Phase 1 — Six Weeks
- Stakeholder intake
- Kickoff workshop
- Workflow mapping
- Cost analysis
- Editorial-risk mapping
- Audience research begins
- Opportunity scoring
- Social-intelligence findings
- Operating-model design
- Editorial-control framework
- Technical assessment
- First-pilot definition
- Blueprint completed
- Roadmap agreed
- Budget and operating-cost model
- Pilot plan signed off
- Low-risk proof-of-concept work may begin
Twelve-Month Horizon
Discovery and operating model
First proof-of-value pilot
Production automation
Content and distribution engine
Governed end-to-end platform
A balanced scorecard
The programme measures success across four dimensions. No single metric tells the full story. Growth without editorial quality is not success. Efficiency without sustainability is not success.
Growth
- International audience growth
- Views by territory
- Engagement by territory
- Membership conversion
- Audience retention
- Revenue per content asset
- Localised-content performance
Efficiency
- Editing hours saved
- Publishing time reduced
- Administrative steps removed
- Cost per film
- Cost per episode
- Cost per short
- External production cost reduced
- Additional headcount avoided
Editorial Quality
- Percentage accepted without correction
- Number and severity of factual corrections
- Archive and provenance compliance
- Contributor-meaning accuracy
- Human review time
- Approval turnaround
Operating Health
- AI and infrastructure cost
- Reliability
- Failure rate
- Leadership time required
- ROI by workstream
- Time to measurable value
Phased. Reviewed. Approved at each stage.
Investment is structured in phases. Each phase is independently reviewed and approved. Nothing proceeds without your explicit sign-off.
Discovery, alignment and AI Operating Model
Administration and publishing automation
AI-assisted long-form editing
Short-form content factory
Automated distribution and continuous learning
Ongoing AI Lab — Optional
£3,000 / month
Optional retainer covering monitoring, improvement, new workflow development and strategic support.
Technology and Infrastructure
Paid directly to providers. Not a margin item.
Programme Totals
Each phase is independently reviewed and approved. Payment is staged to each phase milestone. Ranges reflect scope variables confirmed during Phase 1.
Start lean. Build evidence. Invest further when the returns justify it.
We understand that committing to a programme of this scale requires confidence — in the return, and in the timing. Cash is not abstract. Every pound spent on this programme is a pound that could go elsewhere. That is exactly why the programme is designed the way it is.
£8,000 – £12,000
investment
You get the AI Operating Model, ROI scoring for every opportunity, a twelve-month cost model, and a recommended first pilot with a refined implementation budget. Before committing a single pound to Phase 2, you will know what it costs, what it saves, and what it returns.
Exit with full financial clarity before committing further.
£12,000 – £20,000
investment
Administration and publishing automation. Transcript generation, CMS drafts, metadata, scheduling and publishing logs are handled by the system. Human time is freed for creative and editorial work.
£18,000 – £35,000 / year
Estimated savings from reduced administrative and publishing overhead — typically 8–12 hours per week across the production team.
£20,000 – £35,000
investment
AI-assisted long-form editing. First-pass edits assembled automatically. Editors review, correct and approve rather than building from scratch. The system learns from every correction.
£30,000 – £70,000 / year
Estimated reduction in editing hours across your production schedule. Varies with volume of films produced.
£15,000 – £25,000
investment
Short-form content factory. Each approved long-form asset generates multiple short-form clips at scale — without proportional headcount. More audience entry points from the archive you already own.
£20,000 – £40,000 / year
Equivalent cost of producing comparable short-form output manually or through external agencies.
£20,000 – £40,000
investment
Distribution automation and continuous learning. Savings from earlier phases compound. The system improves itself, reducing the marginal cost of each new workflow.
Compounding
Phase 5 amplifies returns from all previous phases. Savings measured at programme level.
Building the evidence that supports your next round.
Each phase generates measurable data: hours saved, cost per film reduced, content output increased, administrative headcount avoided. By Phase 3, you will hold concrete operational evidence — not projections — showing what the programme costs and what it returns. That evidence is exactly what strengthens an investor conversation. We are not asking you to fund a promise. We are building the proof, phase by phase, that makes the promise credible.
What makes this different
Six reasons organisations choose Collective Intelligence.
We start with problems, not technology
We do not arrive with a pre-built AI product and look for a place to put it. We start by understanding what is costing you time, money and missed opportunity. Technology decisions follow.
We understand editorial environments
We have worked inside media organisations, publishing environments and content-led businesses. We know that accuracy, tone, archive rights and contributor meaning are not constraints to work around — they are the product.
We build for human control, not AI autonomy
Our Lab model is built on visible human approval, confidence thresholds, audit trails and correction loops. AI handles scale and repetition. Your team handles judgement.
We think commercially, not just technically
The programme measures success by audience growth, cost reduction and revenue impact — not by how many AI tools are deployed. Every workflow must justify its ROI before it is built.
We deliver working systems, not reports
Phase 1 produces a plan. Every subsequent phase produces working, tested automation that your team uses in production. Nothing is theoretical.
We build the capability into your organisation
The Lab is yours. We design it so that knowledge, prompts, workflows and improvements accumulate inside your team — not in our heads. You are not dependent on us indefinitely.
Begin with Phase 1
Phase 1 answers four critical questions before any implementation begins. It is the foundation every subsequent phase depends on.
Where can AI create the strongest commercial growth?
Where can it remove the greatest operational cost or constraint?
What editorial and human controls are required?
Which first pilot gives WW2 Headquarters the fastest credible proof of value?
Discovery and Operating Model
£8,000 – £12,000
3–5 weeks