07.10.25
Ben Galyas, Associate Director on our Editorial desk, discusses the operational realities for B2B information providers.
There has been a clear shift in industry mood. Over the past year, many businesses have moved from anxiety to curiosity, from ‘show and tell’ to an environment where businesses are actively building and integrating AI-powered tools. AI is a transformation comparable to the emergence of desktop publishing, the web, and mobile. Where are businesses finding practical value? Content delivery, monetisation, and user personalisation.
Strategic Applications: Practical AI Use Cases Already in Deployment
Internal Workflow Optimisation
AI is being embedded in internal systems to:
- Respond to common customer service or HR queries via secure internal chatbots
- Accelerate onboarding documentation and knowledge sharing
- Assist editors and researchers with structured summarisation of large content sets
In most cases, this doesn’t replace staff, but brings higher leverage to existing teams. High-potential staff ramp faster, and senior staff spend more time on judgment calls rather than manual work.
Product Enhancement and Differentiation
AI is being used to unify fragmented information repositories across the reader/user journey, bringing together archived reports, data, commentary and news into single-query interfaces. In
particular:
- AI-enhanced natural language search allows users to query across disparate media and formats
- Recommendation engines trained on sector-specific context surface insights that would otherwise require hours of manual searching
- Content suggestions are increasingly being shaped by user behaviour, search queries and prompt data, leading to highly targeted editorial outputs and transforming editorial strategy
The result is improved UX and a clearer pathway to product stickiness, ultimately increasing leverage over pricing power.
Revenue Expansion
Beyond efficiency gains, AI is unlocking new commercial models:
- AI-assisted user segmentation enables more personalised pricing and upgrade journeys
- Predictive analytics are informing editorial calendars to prioritise high-traffic, high-monetisation topics
- Publishers are beginning to monetise insight by layering AI analysis onto existing news or data
The most forward-thinking organisations are no longer thinking in terms of audiences or personas but of individual subscriber journeys informed by interaction data.
Risk Management and Legal Guardrails
Publishers have found their gated content being scraped, ingested, and used to train large language models (LLMs), having been accessed via AI agents able to surpass gates and restrictions.
As a result, strategic and legal recommendations:
- Terms of Service and Subscriber Agreements should explicitly restrict scraping, training, and inferencing on subscription content.
- Middleware can be used to control which content LLMs can access, and under what terms, e.g. for training, for inference, or neither.
- Ownership of user prompts and outputs should be considered. As user behaviour becomes a data asset, publishers should manage IP and data rights.
The legal framework surrounding AI use is still immature, and M&A due diligence already reflects this: investors are examining how well content IP has been protected, and whether exposure to AI
models undermines the uniqueness or defensibility of the asset.
Distribution, Discoverability and Platform Threats
AI also challenges the traditional economics of web traffic. Early signs suggest that LLM-enhanced search, including generative answers in Google results, is reducing traffic to publisher domains. This raises a familiar but urgent question: Is your content a destination, or is it only valuable when someone lands on your site?
The response is to rethink platforms and to increasingly deliver where your user digitally operates (e.g. Excel, Slack, embedded tools, or third-party environments). Meanwhile, new strategies will
emerge for Generative Engine Optimisation (GEO), ensuring content is properly structured, attributed and surfaced by LLMs.
Any strategy must be built on a defensible core:
- Strong, differentiated editorial brands
- Proprietary data and context that generic models can’t replicate
- User experiences are formed around actionable information
Action Points
- Update your contracts: Ensure AI, LLM, and inferencing clauses are explicit in all subscriber agreements and Terms of Service.
- Invest in middleware and metadata: The value of your content is multiplied when it is properly tagged, tracked, and presented contextually.
- Move beyond Search Engine Optimisation to Generative Engine Optimisation: Understand how your content is being ingested and surfaced by LLMs.
- Make IP protection an M&A readiness issue: Buyers will increasingly want proof that your value hasn’t been leaked into public models.
- Define your position in the AI ecosystem: Are you building AI tools? Or are you using AI to elevate what only your business can do?
Article by Ben Galyas, Associate Director.
