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Navigating the AI renaissance in Publishing

Graham Page by Graham Page

Navigating the AI renaissance in Publishing

Generative AI in the Publishing Sector


Generative AI is redefining the publishing sector, offering efficiencies and reshaping roles, content, and business models. Our strategic guidance helps leaders navigate this evolution, ensuring ethical AI integration while fostering innovation. As the industry transforms, Q5 stands ready to assist publishers in embracing this new chapter with strategic foresight. 


Reading time: 3 minutes


Generative AI (GenAI) is likely to present the biggest change to the creation, distribution and consumption of media since the dawn of the Internet. As such, the topic has been firmly seated at the top of the agenda for senior leaders across the publishing world for the last 12 months, as the technology has rapidly progressed and the implications for publishers have become clearer.

Whilst there are valid concerns around the impact on jobs, misinformation and impact on brand trust, and the effect on publisher business models, there is also the potential for huge gains in productivity and a rebalancing of the relationship with big tech, as quality trusted content becomes even more important.


The GenAI paradigm: balancing productivity with tradition 

Workforce transformation 

Looking at the impact on journalists, there is no doubt that GenAI tools are going to change the way that content is researched, written and published, which is likely to mean some changes to the shape of roles and organisations.

GenAI presents a tremendous opportunity to increase productivity and efficiency for many workflows and tasks (i.e. data gathering, translation and transcription of interviews/content) that journalists traditionally spent precious time and resources on.

In an industry that has been pushed harder and harder to realise efficiencies and cut costs, it is easy to understand the concern from many that such technology could see further layoffs of the workforce. There is certainly going to be an adjustment as roles change and we’re already seeing some reorganisation with this in mind (i.e. Bild). On the other hand, we’re also starting to see examples of where AI is being used to increase the capacity of existing resources (particularly in local news), allowing them to focus on the areas where humans add the most value (i.e. investigative journalism, providing context). Looking back at the history of the Internet, as an example, it is also likely that GenAI will create jobs and opportunities that don’t yet exist.

Brand integrity and reporting accuracy 

Looking at the impact on brands, GenAI is making the production of high-quality content much faster and easier, which is leading to the concern that misinformation from fake, misleading, biased or erroneous content is increasingly likely, posing a significant risk to the trust and credibility that publishers rely on.

The first part of this issue relates to GenAI tools making errors and forms part of the NYT’s lawsuit against Microsoft and Open AI. The premise is that GenAI can make mistakes, often called “hallucinations”, where an AI system generates outputs or predictions that are based on incorrect data, resulting in misinformation. In the New York Times case, the example cited where GenAI could produce factually incorrect content and attribute it to the NYT or produce correct content that it sourced from NYT and attribute it to a competitor publication. Both scenarios devalue the NYT brand.

Another scenario could result in the generation of inappropriate and distasteful content, like the recent example of Microsoft publishing an AI-generated poll speculating on the cause of an Australian woman’s death next to a Guardian article about the same topic.

Finally, beyond errors, there is the concern that depending on the content that is used to train GenAI systems, there is the risk that any content created could reflect these biases and perpetuate stereotypes (Bloomberg, Forbes).

In addition to technological errors, there is also the concern around how GenAI is being used to generate deepfake online videos and other misleading content. In a recent example, ITN were subject to one of their news presenters appearing on Instagram as a deepfake telling viewers about a new app, apparently backed by Adele. In a year of widespread elections that is billed to be one of the biggest in modern history, there are concerns that Gen AI could be used for even more damaging purposes in harming democracies across the world.


Business model implications 

Another key area in the discussion around GenAI is on the impact it could have on publisher’s business models. On one hand, while there is a concern around the decrease in traffic that search engines are likely to generate for publishers, the fact that AI systems need quality content to train their Large Language Models (LLMs) presents a huge opportunity for publishers to rebalance the relationship with Big Tech.


Strategic steps for Publishers in the age of AI

  1. Forming a GenAI working group

The initial step for publishers is to convene a dedicated AI working group with cross-functional leadership to oversee the integration of AI across various facets of the business. This collective will ensure a cohesive approach to adopting technologies, mindful of their wide-reaching impact.

  1. Establishing an AI use policy

It’s essential for this working group to perform an extensive review of AI’s current role within our organisations. Developing a comprehensive AI policy will help steer the direction and define the boundaries of AI deployment, aligning it with our core values and ethical standards.

  1. Crafting an AI vision and strategy

Before making significant investments, publishers must articulate a clear strategy detailing how AI can drive creative and innovative enhancements to their value chain and business model. At Q5, we’ve sketched out a model value chain to inspire this very strategy. Furthermore, we’ve devised a support program for those seeking guidance on how to best adapt their operating models to the age of AI. 

4. Testing, learning and iterating

Use of GenAI is still immature (despite all the stories you read). Creating the conditions for rapid prototyping, testing, learning and iteration will build the internal expertise required to innovate for years to come.


Key considerations for the road ahead 

In navigating this terrain, we must ask ourselves: 

  • How should our business and operational frameworks evolve to seamlessly integrate AI? 
  • What new skill sets will we need to foster within our teams to stay ahead in the AI era? 
  • How prepared is our existing technological infrastructure to support AI applications? 
  • Can we identify tasks currently performed by our staff that AI can augment or replace? 
  • In what ways can AI enhance the efficiency and quality of our content gathering, packaging, and distribution processes? 



Generative AI represents a sea change for the publishing industry, a change that is as inevitable as it was with the advent of the Internet. It brings with it a spectrum of opportunities—streamlined operations, novel revenue models, and a transformed content landscape. 

As we stand on the brink of this new era, the message is clear: it is not just about adapting to change; it’s about leading it. The steps we take today will define our role in the publishing world of tomorrow. 

At Q5, we are committed to helping you chart this course, offering our expertise to ensure that the AI transition is not only smooth but also strategically advantageous.

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If this topic is of interest or you have any questions. We would love to chat! 



Graham Page

Director and Sector Lead for Media, Sports, Entertainment, Telco, and Technology.

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