One media empire.
One intelligent core.
A reference architecture for a sovereign-backed, newly merged studio and network group — with AI at the centre of ad sales, content, rights and streaming.
- $110B
- combined deal
- ~$24B
- sovereign equity
- 8
- architecture layers
01 · The client
Who we are designing for
Our assessment of the sovereign-funded media company most likely behind this engagement.
Paramount Skydance + Warner Bros. Discovery
A $110B combination of two of Hollywood's five largest studios, dozens of networks from CBS to TNT and two subscription streamers. ~$24B equity came from PIF (Saudi Arabia, ~$10B), QIA (Qatar) and Abu Dhabi's L'imad Holding — all non-voting.
Core problem
Merging two ad sales orgs and two OMS estates
OmniMedia fit
Converged linear + digital ad sales (Boostr, Audacy)
Real driver
Merger integration — sovereign capital is passive
MBC Group
PIF agreed to buy a 54% controlling stake in one of the Middle East's largest broadcasters. If MBC is the client, the architecture shifts to Arabic-first models, Shahid, and in-Kingdom sovereign compute.
No public source confirms either engagement — client name to be verified.
02 · Reference architecture
Eight layers. One shared intelligence.
Every domain of a modern media group — from camera to cash — on a common data foundation and AI core. Select a layer to inspect it.
L2 · Intelligence
AI Core Platform
- Model gateway (multi-vendor, cost metering)
- Agent runtime + governed tool registry
- Media knowledge graph
- RAG / vector search
- Evaluation & red-team pipeline
- Responsible-AI guardrails
Models are interchangeable. The media knowledge graph — titles, talent, franchises, rights windows, audience segments — is the moat.
Connected operations
From advertiser brief to collected revenue
- 01
Advertiser brief
Objectives, audience & budget
- 02
AI proposal
Forecast avails & assemble package
- 03
Sales approval
Human review of price & terms
- 04
Cross-channel delivery
OMS → linear, CTV & streaming
- 05
Reconcile & bill
Delivery evidence → order-to-cash
03 · AI at the core
Capabilities with measurable outcomes
One model gateway, one agent runtime and one evaluation pipeline — so the merger never produces two parallel AI programs.
Value signals
What success looks like
Directional proposal targets · not measured results
Relative time index · baseline = 100
Unified forecasting and pricing · relative uplift
Inventory forecasting
<10% MAPE
Forecast error target across linear and streaming inventory.
All targets require client baseline validation.
01
Unified inventory forecasting
Predicts avails across linear and streaming in one model
Forecast error (MAPE) < 10%
02
Proposal agent
Builds cross-portfolio packages from a brief in minutes
RFP turnaround: days → hours
03
Yield & pricing models
Dynamic rate cards, makegood minimisation
CPM yield +3–5%
04
Order-to-cash agent
Reconciles orders, delivery and billing across both stacks
Disputes & DSO ↓
05
Metadata enrichment
Scene, face, logo, topic & mood tagging across the combined library
Search-to-clip time ↓ 80%
06
Localisation & dubbing
AI subtitles, voice with consent workflows
Cost per language-hour ↓
07
Rights extraction
Contracts parsed into a single rights & avails catalog
Rights query: weeks → minutes
08
Unified recommendations
One audience graph ahead of platform consolidation
Churn ↓, engagement ↑
Ad revenue convergence
Highest-value, fastest-payback wedge — two sales forces, overlapping inventory, one sellable portfolio.
Shared AI core
Gateway, agents, knowledge graph and evals serve every domain; no duplicated programs.
Content & rights automation
Captures cost synergies once the revenue wedge proves value.
04 · Sequencing
From wedge to platform in 24 months
Revenue pilot
0–3 months
Revenue scale
3–6 months
Content & rights
6–12 months
Platform convergence
12–24 months
Data foundation, AI governance and security continue across every phase.
0–90 days
Prove the wedge
- — Ad sales data unification
- — Forecasting pilot on one network group
- — AI Core v1: gateway, graph seed, eval harness
90–180 days
Scale revenue
- — Proposal & order-to-cash agents in production
- — Converged OMS cutover plan
- — Clean-room & programmatic unification
6–12 months
Extend the core
- — Content ops: metadata, localisation, compliance
- — Rights & avails catalog
- — Unified recommendation layer
12–24 months
Operate as one
- — Streaming platform consolidation
- — Cloud playout migration
- — Autonomous ops with human oversight
05 · Risks & guardrails
Designed for trust from day one
Integration precedes intelligence
Budget 40–50% of year-one effort for the data foundation; AI fails on two unreconciled estates.
Editorial firewall
Architecturally separate agent scopes for CNN and CBS News to protect newsroom independence.
Sovereign-investor optics
Auditable data-access boundaries: passive investors have zero operational data access.
Guild constraints
WGA & SAG-AFTRA limits — generative AI focused on operations, metadata and localisation with consent.
Delivery scale
OmniMedia leads the ad revenue wedge and blueprint, partnered with a hyperscaler or SI for platform build.
Integration first. Intelligence everywhere.
Start with a 90-day ad sales convergence pilot, then extend one AI core across the whole group.
Talk to OmniMedia Solutions Group