Artificial Intelligence (AI) has become one of the most transformative technologies of our time, and its impact is being felt across a wide range of industries, including journalism. AI is changing the way news is produced, distributed, and consumed, offering new opportunities for more efficient and effective journalism. However, it also presents unique challenges and risks, particularly in terms of accuracy, objectivity, and trust.
This article will explore what AI is, how it impacts journalism, and the opportunities and challenges it presents. We will also discuss the role of journalists, news organisations, and policymakers in ensuring that the impact of AI on journalism is positive and that the quality of journalism is maintained. Whether you are a journalist, a news consumer, or simply interested in the intersection of technology and society, this article provides a comprehensive overview of the current state of AI and its impact on journalism.
What is AI?
Artificial Intelligence (AI) is a branch of computer science that deals with creating intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to learn from data and experience, making them increasingly capable of performing more complex tasks over time.
There are two main types of AI: narrow or weak AI and general or strong AI. Narrow AI systems are designed to perform specific tasks, such as facial recognition or speech recognition, and are currently the most widely used form of AI. On the other hand, general AI systems are designed to perform any intellectual task that a human can and are still in the early stages of development.
AI has already begun to transform a wide range of industries, from healthcare and finance to retail and transportation, and its impact on journalism is becoming increasingly significant. AI in journalism offers new opportunities for more efficient and compelling journalism but also presents unique challenges and risks that must be addressed.
The use of AI in journalism has a relatively short history, dating back to the early 2000s. Initially, AI was mainly used for data analysis and information-gathering tasks, but its role has expanded in recent years. Today, AI is used in various aspects of journalism, including content creation, fact-checking, and news personalisation.
Benefits of AI in Journalism
Artificial Intelligence (AI) has already begun to revolutionise the way journalists gather and process information, but its impact on journalism is far from over. In the coming years, AI is expected to play an even more significant role in journalism, shaping the way news is reported and consumed. From automating news production to enabling more in-depth analysis and personalisation, the future of AI in journalism is poised to be both exciting and transformative.
Automated News Production
One of the most significant impacts of AI in journalism is the automation of news production. This includes using algorithms to generate news articles and summarise information, freeing up journalists to focus on more high-level tasks, such as analysis and storytelling. AI-powered tools can quickly gather and process large amounts of data, allowing journalists to identify trends and patterns that would otherwise be difficult to detect.
Improved Accuracy and Speed
AI has the potential to improve the accuracy and speed of journalism significantly. With the ability to process large amounts of data and cross-reference information in real time, AI can help journalists produce more accurate and reliable news articles. In addition, AI can help journalists work more efficiently by automating routine tasks and enabling them to access relevant information quickly.
Personalised News Experiences
Another exciting aspect of AI in journalism is the potential for personalised news experiences. By leveraging user data and machine learning algorithms, AI can help journalists deliver personalised news content tailored to individual user's interests and preferences. This can include recommendations for articles, personalised news summaries, and even custom-made news broadcasts.
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Improved Analytics and Insight
AI can also help journalists gain deeper insights into their audience and the topics they cover. By analysing data from social media, search engines, and other sources, AI can help journalists understand what topics and stories resonate with their audience and why. This can help journalists make better decisions about the types of content they produce and how they present it to their audience.
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Are publishers and editorial teams equipped enough to use the full power of AI for their work?
The degree to which publishers and editorial teams are equipped to use the full power of AI for their work varies widely. Some organisations have invested heavily in AI technology and have the expertise and resources to fully leverage its capabilities. These organisations may have dedicated teams of data scientists and AI experts who work closely with editorial teams to develop and implement AI-powered tools for content creation, distribution, and analysis.
However, many publishers and editorial teams may not have the resources or expertise to fully leverage the power of AI. Implementing AI technologies can require significant investments in infrastructure, software, and talent, and it may be challenging for smaller organisations to keep up with the rapid pace of technological change. Additionally, there may be a learning curve associated with using AI tools, and some team members may require additional training to make the most of these technologies.
That being said, there are also many off-the-shelf AI tools and platforms available that are designed to be easy to use and require little technical expertise. These tools can be used to automate routine tasks and perform basic analysis, providing value even to organisations with limited resources.
Overall, while some publishers and editorial teams are well-equipped to use the full power of AI for their work, many organisations may face challenges in fully leveraging these technologies. However, as AI technology continues to advance and become more accessible, it is likely that more publishers and editorial teams will find ways to incorporate AI into their workflows and improve the quality and efficiency of their work.
Do publishers have the necessary first-party data to benefit from AI?
AI systems typically require data in order to function effectively. In the context of digital publishing, first-party data can be very helpful in training AI algorithms to better understand the type of content that is being produced and consumed by the target audience. This data can include information such as the topics and formats of articles, the language used, the demographics of the audience, and their behaviour patterns (such as reading habits and engagement levels).
By using first-party data, digital publishers can train AI algorithms to generate content that is more relevant and appealing to their audience, and to better understand the impact of their content on their audience. For example, an AI system could analyse data on what types of articles are most popular with a certain audience, and then use that information to suggest similar articles or topics for the publisher to cover in the future.
However, it's important to note that AI systems can also be trained on other sources of data, such as third-party data or publicly available data, if first-party data is not available or is limited. The quality and diversity of the data used to train AI algorithms will impact the accuracy and effectiveness of the AI system, so it's important for digital publishers to carefully consider the sources of data they use.
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Evolok AI and Machine Learning
We can measure users and their level of engagement through machine learning algorithms. From their visit history, spend, frequency, content affinity, all of these are used by algorithms to calculate a unique score in real time for each user and those scoring close to those conversion points, will be challenged or encouraged to convert. For content owners, the paywall becomes automated and dynamic - removing any manual intervention and allowing a conversion strategy to be truly dynamic.
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At Evolok we have redefined content recommendations to offer the most advanced, fully automated 'user to content recommendations' instead of content to content suggestions. Evolok collects data about the content, such as keywords, categories. and match users with the right content based on their likes and interests. This gives you increased engagement, click throughs, less bounce rates and sticky users resulting in increased engagement, brand loyalty and overall conversion growth.
AI has the potential to greatly augment the work of journalists and improve the quality of journalism. But before making the leap, digital publishers that are thinking about utilising AI for their digital publication must carefully evaluate a number of aspects.
- How prepared, clean, and fresh is your first-party data?
- Are the website and CMS features in line with Editorial expectations ?
- Does your staff have the necessary training to analyse data and provide important information to Editorial?
- Is the internal data architecture streamlined enough for AI to work?
In conclusion, while AI can be a valuable tool for digital publishers, they need to carefully consider multiple factors before adopting AI to help with their digital publishing strategy. By doing so, they can ensure that they are using AI in an effective and exhaustive manner that benefits both the publisher and their audience.
Evolok helps online publishers increase their revenues and drive audience engagement using Evolok’s end-to-end SaaS solution, which provides paywalls, subscription management, user segmentation and identity management. Evolok delivers a selective ecosystem to drive user engagement and mobilization. Evolok helps its clients increase readership and revenue by engaging and personalizing content, protecting valuable content through paywalls, utilizing login and social data to incrementally know customers and finally targeting products and pricing to boost subscriptions.
If you need any help with your subscription journey or you are thinking of migrating your publishing business to the subscription business model contact us today.