AI tsunami revamps competitive strategy of news media

By Greg Piechota


Oxford, United Kingdom


In this newsletter, I am discussing a new Web landscape flooded with cheap AI-generated content and a strategic conundrum faced by news executives.

If you have questions or suggestions, e-mail me today at or meet in person at the INMA Media Innovation Week in September in Antwerp, Belgium.

Amid the tsunami of AI-generated online news, seek an edge in real journalism and personalisation

The rise of AI in content production may disrupt the relationship between quality and its competitive edge. “Those repurposing content, not those creating it, could reap the benefits,” warned Professor Dariusz Jemielniak in an interview with INMA.

By the end of June 2023, U.S. news rating service NewsGuard identified 301 Web sites that operated with little or no human oversight and published articles written largely or entirely by AI bots. The number of AI-generated news Web sites increased six-fold since May, when NewsGuard identified 49.

Many of those Web sites appeared to be entirely financed by programmatic advertising, and some churned out huge volumes of articles — one such site produced on average 1,200 articles a day! For comparison, France’s Le Monde publishes about 100 articles a day, The New York Times, up to 250 articles a day. U.K.-based Mail Online, 1,700 articles per day.

This is just the beginning. “Flooding the market with cheap information, AI can lead to decrease in overall quality of the Web and misinformation,” said Jemielniak, who studies an intersection of management and the Internet for Harvard and Poland’s Kozminski University.

Together with Professor Aleksandra Przegalinska, he authored a new book, Strategising AI in Business and Education, published in 2023 with Cambridge University Press. 

The fourth wave of disruption: In Jemielniak’s opinion, AI represents the fourth wave of disruption to the news media industry, following the digital upheaval brought by news portals, search engines, and the prosumer revolution on social media.

With AI drastically driving down production costs, businesses relying today on low-cost content might lose their cost advantage while struggling to differentiate. 

“Off-the-shelf AI tools will revolutionise the production model for all,” explained Jemielniak in the interview with INMA. “But they won’t provide a competitive edge for anybody. We may see thousands of generic Web sites for which the only difference will be branding.

“This is a major disruption, a potential tsunami, that actually our current ecosystem doesnt really have a good answer to, said Jemielniak, who also complained that AI companies were training algorithms on original content from media sites without attribution, nor licensing. 

He knows the problem first-hand as a trustee of Wikimedia Foundation. Its popular online encyclopaedia Wikipedia is routinely scrapped by AI companies.

Human edge over AI: I asked for possible scenarios. Apart from the negative developments, Jemielniak imagined the positive ones, too: “AI tsunami might soon enough cause users to get frustrated by low-quality content and put a premium on journalism, including information verification and investigative reporting.”

In such an optimistic scenario, reputable publishers would survive the AI tsunami due to their brand trust built over years and established quality control processes. 

“AI cannot really send anybody to witness events. It cannot gain trust of sources revealing hidden information for investigations. It doesn’t question the official sources. These journalistic processes are difficult to substitute with AI,” believed the academic.

News customised with AI: Professor Jemielniak anticipated a new dawn for personalised content and advertising: Generative AI is the marketing Holy Grail.”

While the backbone of journalism is likely to remain human-based, the delivery might be increasingly handled by machines. As generative AI gets better, Jemielniak envisions full news customisation based on a reader’s language and preferences: "The cost of delivering news tailored specifically for you is now negligible. My guess will be that in the end, we all will be getting fully customised news written in the style and language that readers like to read.

“Today, many media outlets follow a mimicry strategy, copying what others are doing, to maximise its content’s appeal and reduce the risk of investment in niches. AI may push the media away from this paradigm to increase differentiation through personalisation to many narrow segments. Once the paradigm shifts, everybody might follow.”

This idea is described in Jemielniak’s book as the likely shift from two classic strategies positioning the business on low cost or differentiation to serving multiple niches at scale. 

Investing in AI: Not developing AI competencies may lead to a business downfall, as others could outperform in terms of cost, speed, and quality. “Developing an AI strategy, starting with modest goals, and creating an AI-conscious business culture is crucial,” urged the professor.

The AI implementation process should be iterative, with many goal adjustments and experiments along the way. Whether AI is used to increase effectiveness, efficiency, or both, it’s critical to prioritise accordingly.

While large companies may build their own AI models, small- and mid-sized will likely rely on off-the-shelf solutions from vendors. Although these are already widely available and easy to use, they provide no competitive edge.

As AI technology matures and becomes widely accessible, competition will likely shift to areas like marketing and branding. Developing unique AI solutions may become less important than the ability to brand and market existing solutions effectively.

In the meantime, measuring ROI from AI implementations can be challenging due to the significant upfront investment and potential for zero outcome. “Its important to set clear goals and directives and align data scientists with business teams and management to avoid misalignment,” he recommended.

Training for work with AI: Jemielniak advised aggressive training for employees on AI-driven tools to help people prepare and adapt to working alongside machines: “Some jobs will inevitably disappear, but there will always be a need for professional skills. For example, while everyone can now take photos, professional photographers are still in demand due to their unique skills.”

How to survive generative AI-driven search? Read my original analysis.

How AI alters news media industry competition and strategy

AI will redefine how companies compete, claim MIT and Harvard fellows in a new book. AI can help firms to reduce costs, charge more, or both thanks to scaled personalisation.

Strategy explains how a company, faced with competition, creates superior profits. In 1980, Harvard scholar Michael Porter described three main ways a company can achieve it:

  • Operate at a lower cost than rivals so it can offer lower prices to all consumers.
  • Command a premium price through product differentiation.
  • Focus on a narrow segment and differentiate by serving its needs for a premium price or at a lower cost.

Porter’s model applies to the news industry. For example, U.S.-based HuffPost uses blogging to reduce production costs and build an audience for advertisers. The New York Times offers in-depth journalism to stand out and charge consumers for subscriptions. TechCrunch concentrates on content and community for technology startups.

Empirical studies confirm both low cost and differentiation positioning drives performance, but differentiation leads to more sustained performance over time.

AI may significantly redefine Porter’s classic strategies, anticipate Aleksandra Przegalinska and Dariusz Jemielniak in a new book, Strategising AI in Business and Education, published in 2023 with Cambridge University Press.

Strategising AI: Professors Przegalinska and Jemielniak study the intersection of management and the Internet for Poland’s Kozminski University, Massachusetts Institute of Technology, and Harvard University. 

In the book, they analysed the impact of AI on Porter’s model:

  • Cost leadership: “[This approach] can be transformed through automation of tasks, optimisation of resources, and increase in effectiveness.”

  • Differentiation: “Starts to rely on personalised, or even hyper-personalised, services and products through machine learning-enabled customer segmentation and targeted marketing.”

  • Focus: “May become the dominant strategy that organises both cost leadership and differentiation approaches.”

They argue: “[AI techniques] allow for very refined analytics of people’s purchasing decisions and shopping preferences to the degree where new products and services become hyper-personalised. Such products can lean toward premium, but they can also be very affordable.” 

Redefining operations and sectors: Przegalinska and Jemielniak’s hypothesis challenges our understanding of competitive strategy trade-offs.

Porter believed: “Sometimes the firm can successfully pursue more than one approach as its primary target, though that is rarely possible.” This mirrors the concerns of news executives whether a company can successfully pursue both advertising and reader revenue models in digital or optimise for both volume of readers and their value at the same time. 

The devil is in operations — a successful implementation of the strategic position requires different resources, skills, organisation and management. 

  • For example, cost leadership-focused companies aim for the lowest production and distribution costs, which demands efficiency, scale, and tight operational control. 

  • Differentiation-focused companies strive to offer unique, high-quality products or services, requiring innovation, customer centricity, and a culture of creativity.

Companies that pioneer AI applications, observe Przegalinska and Jemielniak, are rapidly transforming not only their operations but the whole sectors. For example, healthcare increasingly relies on AI to design and customise drugs and therapies. 

In the news media, AI pioneers, such as India’s Times Internet and Norway’s Schibsted, view data and tech platforms as the backbone of their business. Collecting data, segmenting users, and targeting content, offers and ads based on predictions support both advertising and reader revenues. These are the cornerstones of news media transformation from product- to customer-centric.

Porter warned in 1980: “The firm stuck in the middle [between generic strategies] is almost guaranteed low profitability.” 

In 2023, Przegalinska and Jemielniak expect that middle ground to be the new winner, as AI allows companies to serve multiple market segments at once and at scale: “The costs of running unsupervised machine-learning models are dropping.”

New strategy playbook: How can companies apply AI to pursue “focus-oriented cost leadership” or “focus-oriented differentiation”? The academics recommend:

  1. Identify the most valuable customers using data on your audiences’ behaviours and transactions for predictions. 

  2. Create a differentiated experience by applying predictive and generative AI to customise the product in ways that cannot be easily replicated by competitors.

  3. Improve effectiveness of your marketing and sales as different types of AI can tailor your campaigns to the target segments narrowly — down to individuals.

  4. Reduce costs by automating tasks with AI. This may improve speed and accuracy of decision-making, but also free up human workers’ time for creativity and innovation.

Looking for strategic insight on generative AI? Read a summary or watch this INMA Webinar with Professor Thomas Davenport, author of All in on AI

About this newsletter

Today’s newsletter is written by Grzegorz “Greg” Piechota, INMA’s researcher-in-residence and lead for the Readers First Initiative. In his letters, Greg shares original research, analysis, and best practices in growing reader revenue.

E-mail Greg at, message him on Slack, meet him at the next online meet-up or in person at the INMA Media Innovation Week in September in Antwerp, Belgium.

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