Publishers! Implement these AI Use cases

After over 300 experiments, I am happy to share the 15 most promising AI Use cases that make sense for digital publishers. These use cases created positive ROI in under 3 months!

The experiment participants were prominently news publishers, digital magazines (consumer and business), and sports content publishers.

I am also happy to share the whole analysis with my network. More details towards the end!

Here are the 15 use cases:

[The initial version had 11 use cases. Updated 19 May after 50 more experiments]

✍ Content Creation [Impact - Reduce the repetitive tasks by up to 70%]

1. Journalism Outlines: AI assistants automatically generate article outlines from data, such as market analysis, trend reports, and executive summaries. This is particularly useful for financial reporting, sports results, and any area where data can directly inform the content.

2. On-page SEO: Tools that analyse and optimise content for search engines in real-time, suggesting keywords, meta tags, and structural improvements to increase visibility.

3. Personalised Emails: AI agents that customise the emails for each reader based on their interests, past behaviour, and engagement patterns.

4. Redaction tool [Added on 19 May]: A Content Management System Ad-On that can read editor guidelines and provide corrections while creating a content piece.

5. Research Archives [Added on 19 May]: AI assistant that can do research on archives (and external content) to create references in the latest piece

👀 Reader Engagement [Impact - 40% more # of engaged sessions on GA4]

6. Reader Assistants: Gen-AI-driven reader assistants that answer queries. These can guide readers through content, answer questions, and satisfy user needs in different formats.

7. Recommendation Systems: Use machine learning algorithms to recommend articles, papers, and topics to readers based on their reading habits and preferences.

8. Interactive Content: Gen-AI-powered quizzes, polls, and interactive infographics that are relevant to the content, enhancing reader engagement and time spent on the site.

💳 Monetisation [Impact: Increase Average revenue per visitor by 25%]

9. Proposals drafts [Added on 19 May]: Tool that generates personalised proposals by accessing a database of advertising guidelines, past campaign performances, and advertiser preferences.

10. Dynamic Paywalls: Deep Learning paywalls which adapt based on user engagement, content type, and reader loyalty, maximising subscriptions while keeping content accessible.

11. Direct Ad Formats: NLP and Reinforced Learning that optimises ad placement, content matching and ad formats based on user behaviour and preferences, increasing CTR and ad revenue.

12. Subscription and Pricing Optimisation: Rapid AI A/B test subscription models and pricing strategies to launch membership strategies that maximise revenue and conversion rates.

📊 Generating Insights [Impact - 10X data-driven actionable insights]

13. Content Performance Analytics: Big Data Analysis to analyse which types of content perform best, identify topics of high reader interest, and inform future content strategy.

14. Churn Prediction: Churn Propensity models to predict which subscribers are most likely to churn and launch targeted interventions to retain them.

15. Data Assistant [Added on 19 May]: A Retrieval augmented generation system that connects to any data system, understands the data and can answer any data question in natural language.

Want the full analysis? I have compiled the Hypothesis, technology used, different tools needed to construct the use-case, Sprint Teams, the time needed to execute and how to measure results. Just comment "Interested" and I will send it to you!

Important considerations before planning your experiments [Added on 19 May]

Transparency and Explainability

Users and stakeholders need to understand how AI decisions are made. Without transparency, it becomes difficult to trust AI systems, and it can be challenging to address any issues that arise. Explainable AI helps in building this trust by making AI decisions more understandable and justifiable.

Culture

Automation through AI often has the perception of reduction of roles that were previously performed by humans. This can create workforce disruptions and necessitate retraining and upskilling initiatives. Publishers should plan for these transitions to mitigate negative impacts on their employees. In my experience, AI usually helps in reducing time spent on repetitive tasks and the right mindset and culture can really help!

Technical Vulnerabilities

AI systems can have technical vulnerabilities and be susceptible to adversarial attacks where malicious inputs are designed to deceive the AI. Ensuring robust security measures are in place is essential to protect against such threats.

Ethical Considerations

Publishers need to consider the ethical implications of their AI experiments. This includes ensuring that AI is used to benefit users and society, avoiding harm, and being mindful of the long-term consequences of AI deployment.

About Me

I am an experienced entrepreneur who has worked in Media and Tech consulting for many years. Apart from being a full-time parent, I am also leading a project that aims to make AI more accessible to restore the balance of power between tech providers and digital publishers in the Media ecosystem. Bridged’s out-of-the-box tools eliminate the need for extensive data processing or dedicated AI resources, making AI adoption accessible and efficient.