News Corp adopts Symbolic.ai to streamline research, writing, and publishing
News Corp is partnering with Symbolic.ai to bring an AI-native publishing platform into its newsrooms, starting with Dow Jones Newswires. The goal is simple: compress production time while protecting editorial standards. Early results at Dow Jones show up to 90% productivity gains on complex research tasks, with editors channeling time back into higher-value reporting and analysis.
The move targets a global fact-based communication market topping $100 billion annually. It also signals a shift many PR and communications teams are making: consolidating fragmented AI tools into a single, auditable workflow.
"At the onset of the AI revolution, we have the opportunity to define a new way of working, and a new commercial model, for professionals and publishers who create critical content," said Devin Wenig, Co-founder & CEO of Symbolic. "A future where technology streamlines research and production, freeing people to focus on the creative, analytical, and investigative work that truly sets their content apart."
"The Symbolic team's deep editorial roots are obvious in their sincere appreciation of provenance, and their patent desire to create products that enhance, not deface, demean or devalue journalism," said News Corp Chief Executive Robert Thomson.
Why this matters for PR and Communications teams
Most teams juggle transcription services, search tools, drafting assistants, and manual QA. That fragmentation leads to inconsistent voice, slow turnarounds, and preventable errors. Symbolic's pitch is to unify the entire workflow and cut production time by more than half, without losing the nuance of house style or the confidence of verified facts.
For comms leaders, higher throughput can translate into more tailored content for stakeholders, faster issue response, and measurable cost control. It's not just speed-it's consistency, traceability, and fewer mistakes.
What the platform includes
- Sophisticated research infrastructure: Collect and organize content across media types, transcribe audio and video with strong accuracy, and keep context intact across tasks.
- Voice and format-aware writing: Drafts and edits that respect brand voice, format, and style guides across channels (press releases, internal memos, newsletters).
- Publication-ready tools: Headline refinement, one-click summaries, and SEO guidance for professional publishing needs.
- Accuracy assurance: Fact-checking that verifies claims against research materials and authoritative sources, with traceable support for editors.
- Enterprise-grade protection: IP controls, library management, and privacy-first design to keep content and data secure.
- Team-based collaboration: Multi-user workflows from ideation to final publication with shared context.
Under the hood: semantic search, agentic workflows, model routing, and token-usage tracking. The architecture reduces dependency on any single AI model while respecting publisher IP.
How to run a focused pilot in your org
- Pick high-friction use cases: Transcription, document extraction, newsletter drafting, first-pass fact-checks, and headline testing.
- Define success metrics: Time-to-first-draft, revision cycles, factual corrections per item, adherence to style, and cost per deliverable.
- Set your standards: Upload style guides, voice examples, approved source lists, and compliance policies.
- Require traceability: Every claim should link to source material with logs you can audit.
- Keep editors in the loop: Human review for sensitive claims, market-moving statements, or regulated content.
- Train the team: Short sessions on prompts, verification, and workflow handoffs. Document the playbook.
- Start small, scale fast: Pilot with one desk or client vertical, then expand by template once KPIs are met.
Questions to ask any AI content vendor
- How is data handled, stored, and deleted? Can we restrict training on our content?
- Can the system show citations and fact-check logs for every claim?
- How do we set and enforce brand voice and formatting across teams and languages?
- What audit trails exist for edits, approvals, and publishing actions?
- How is model routing managed, and what happens if a provider changes behavior or pricing?
- How do we cap spend and track token usage at team and project levels?
- Does it integrate with our CMS, SSO, and asset libraries without manual glue work?
What this signals for the industry
AI is becoming a standard assistant across research, drafting, and QA-not a replacement for editorial judgment. The competitive edge will come from unified workflows with strong guardrails: verified claims, consistent voice, and clear audit trails. Teams that standardize on a single, collaborative stack will reduce rework and publish with more confidence.
About Symbolic.ai
Symbolic.ai is an AI-native platform built for professional communicators across news, corporate communications, and public relations. It combines research tools, voice-aware writing assistance, and publication-ready features in one workflow. The company was founded by Devin Wenig and Jon Stokes and is used by leading media and communications teams.
Learn more about News Corp at newscorp.com and Dow Jones at dowjones.com. If you're uplifting team skills for AI-assisted publishing, see our curated resources by job role at Complete AI Training.
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