The personalisation economy: how is AI affecting businesses and markets?

The quantity of consumer data to which many companies now have access means that they can personalise their goods and services, as well as optimise their operations. In this way, artificial intelligence is changing big parts of our lives – from how we shop and learn to our banking and healthcare.

Artificial intelligence (AI) is transforming how consumers shop, engage with content and interact with businesses. From recommendation engines that suggest products based on browsing history to AI-powered chatbots offering instant support, personalisation has become a core feature of modern commerce.

Companies like Amazon and Alibaba use machine learning to refine product recommendations, while streaming platforms such as Netflix and Spotify curate content to match individual preferences. This evolution can give consumers a greater sense of control and influence over their interactions. At the same time, it enhances the online experience in a way that strategically increases spending and engagement.

How is AI-powered personalisation working across industries?

With access to vast amounts of consumer data, businesses are increasingly using AI-driven insights to create products and services that feel more relevant and intuitive. This trend is evident in many sectors, where personalised technology enhances efficiency, deepens user engagement and improves outcomes.

In finance, for example, AI-powered advisory tools are redefining personal wealth management by offering customised investment strategies based on real-time risk assessment. Fintech companies are moving away from static, one-size-fits-all financial plans towards AI-driven models that dynamically adapt to life changes, such as career shifts or family expansion. Their aim is to deliver tailored financial roadmaps instead of rigid, inflexible plans.

Start-ups like Betterment combine AI-driven financial planning with the option to speak to a financial expert when needed, ensuring that users receive data-backed insights that enhance personalised guidance and support.

Similarly, the healthcare sector is witnessing a major AI-driven transformation, where patient-centred personalisation takes precedence (Athey et al, 2023). Predictive analytics and AI-generated health insights allow medical professionals to customise treatments based on an individual's medical history, genetic makeup, lifestyle and behaviour.

Companies like IBM Watson Health and Tempus are using these tools to deliver more targeted healthcare solutions. For example, Tempus uses machine learning to personalise cancer treatment plans, while Ada Health provides an AI-powered symptom checker that offers individualised health insights. In addition, pharmaceutical companies – such as Pfizer and Moderna – make use of data-driven research to accelerate drug development. The rapid development of Covid-19 vaccines is one such example.

Education has been affected too. AI-driven platforms adapt learning pathways to individual students. For example, Duolingo and Coursera adjust to users’ learning speeds, helping to increase engagement and value. Other AI-powered tools, like Knewton, refine learning paths based on student progress. Duolingo goes even further, using strategies that respond in real time to students’ strengths and weaknesses, creating a more adaptive learning experience.

These platforms make education more accessible and effective for students with different learning styles and needs. But educators, mentors and other designers of learning experiences remain essential. In particular, teachers can ensure that AI-supported education fosters critical thinking, creativity, emotional growth and ethical considerations, rather than mere memorisation.

One sector that has arguably been revolutionised most by this technology is marketing. AI has enabled hyper-targeted advertising, personalised email campaigns and automated social media engagement. These tools help brands to build stronger customer relationships and boost loyalty by delivering relevant and timely content.

In the retail sector, AI is improving the shopping experience. Sephora, for example, uses AI to offer beauty recommendations, with their virtual artist tool allowing customers to try on make-up digitally. This accompanies in-store beauty advisers who are still available for human interaction.

Likewise, entertainment platforms, including Netflix and Spotify, use AI-driven recommendations to tailor their content to viewers and listeners. They also combine automated personalisation with thoughtful human curation through editorial playlists and carefully selected watchlists.

Netflix, for example, fine-tunes its recommendation algorithms based on users’ viewing habits, but it also allows them to rate content and adjust their preferences. This interactive approach ensures that personalisation is both automated and user-driven, deepening engagement and enabling users to shape their own content experiences.

Is the personalisation economy changing markets?

AI-driven personalisation is not just a new business trend, but also a fundamental shift in how markets operate. We are entering the era of the ‘personalisation economy’, where companies that harness AI effectively gain a competitive edge by delivering tailored, data-driven experiences. Firms that have invested in the creation and use of data are poised to benefit most from these evolving trends.

Many consumers now expect hyper-personalised interactions, from curated shopping recommendations to real-time pricing adjustments. But some may resist dynamic pricing, especially when paired with marketing tactics that create urgency to ‘BUY NOW’ due to fluctuating prices or limited availability, as seen with airline tickets and concert seats.

What was once a strategic advantage has now become a necessity for businesses to remain competitive in an increasingly dynamic market. Today, using these tools is critical to maintaining relevance, enhancing efficiency, deepening consumer engagement and achieving long-term success in an increasingly agile, data-driven world. Companies that fail to adapt risk falling behind, while those that integrate AI thoughtfully – balancing technology with human touch – are setting new industry standards.

Beyond refining recommendations, AI also transforms how businesses anticipate trends, tailor experiences and engage with customers in ways that once seemed unimaginable. Retailers study shopping patterns closely, using AI to recommend products that match customers’ unique preferences.

Similarly, car companies have developed smart vehicles that adapt to a driver’s preferences for comfort and performance. In customer service, AI-driven chatbots learn from past interactions to provide more intuitive and human-like support. Meanwhile, in banking, AI-driven financial advisers adjust investment strategies to individuals’ evolving financial goals, adapting recommendations as personal circumstances change. Nonetheless, like human advisers, they may also promote excessive portfolio turnover to generate higher fees.

Businesses also now develop products that align more closely with market needs, driving innovation and informing smarter strategic decisions. Nike develops custom footwear using biometric data, while L’Oréal adapts skincare products to individual skin types.

Pricing strategies have also become increasingly dynamic. Companies like Uber and airline ticketing platforms use AI to adjust prices in real time, factoring in demand fluctuations, competitor pricing and consumer behaviour.

This continuous processing of vast amounts of data allows firms to identify emerging trends, optimise their offerings and maximise profitability while maintaining a competitive edge. These innovations are fuelling business success across multiple industries.As AI continues to reshape industries, companies that embrace its potential will not only meet rising consumer expectations but also drive innovation, efficiency and long-term success.

The rising demand for AI-powered personalisation is reflected in the rapid expansion of the customer experience (CX) personalisation industry, which is projected to grow by 65%, from $7.6 billion in 2020 to $11.6 billion in 2026 (see Figure 1).

Figure 1: CX personalisation and optimisation revenue, worldwide

Source: Statista, 2024

This surge underscores the widespread adoption of AI across e-commerce, software as a service (SaaS), financial services, healthcare and marketing, reinforcing the importance of investing in AI-driven strategies to maintain competitiveness.

How are transactions within organisations evolving?

AI-driven personalisation is no longer limited to broad economic trends: it is actively reshaping economic organisation by optimising operations, refining strategies and driving innovation (Baily et al, 2023).

Companies are increasingly turning to AI to improve inventory control, forecast market trends and streamline their supply chains to ensure that products reach customers exactly when and where they are most needed. Consequently, production has become more responsive and better integrated with strategic business objectives.

Siemens, for example, has revolutionised manufacturing by use of AI and automation to create smart factories with highly integrated production processes. By seamlessly coordinating previously fragmented components, the company has enhanced efficiency, reduced waste and optimised its supply chain operations. This not only improves customer satisfaction but also strengthens organisational capabilities, demonstrating the growing impact of AI-driven decision-making in organisational economics.

Organisational processes, such as teamwork and internal communications, are also increasingly driven by personalised interactions enhanced by AI. For example, virtual collaboration tools and interactive whiteboard technologies now adapt to individual preferences and communication styles.

What are the challenges?

Despite its advantages, AI-driven personalisation brings a number of challenges. Algorithms that are not meticulously designed can inadvertently perpetuate biases and discrimination.

For example, the e-commerce giant Amazon had to shut down an AI hiring tool after it showed gender bias: the system, which ha been trained on past hiring data, favoured male candidates. Facebook has also faced criticism for AI-driven ad targeting that reinforced social inequalities: its algorithms were found to show job and housing ads disproportionately based on gender and race.

These two examples raise concerns about the risks of algorithmic bias and discrimination. This issue also underscores the ethical challenges of AI in digital platforms and the need for greater transparency in automated decision-making. Further, as companies collect vast amounts of personal data, concerns over privacy and security continue to grow.

Similarly, unlike large corporations such as Amazon or Meta, small retailers often lack the resources to develop complex data-driven tools. But cloud-based services from companies like Salesforce and Google Cloud are making advanced personalisation more accessible to businesses of all sizes.

Technology giants like Apple and Google have also introduced stricter privacy policies to ensure transparency in data usage. And governments have implemented regulations like the general data protection regulation (GDPR) to protect consumer rights. Further, as AI may have a negative impact on labour markets through job displacement, companies and governments should take proactive measures to mitigate these effects.

Where will this technology go next?

Personalisation is becoming even more refined. Beyond online platforms, this trend is extending into in-store experiences, smart home devices and customer service interactions.

For example, Dyson’s AI-powered air purifiers use the technology to monitor air quality, humidity and temperature in real time and automatically adjust settings to optimise the indoor environment. Fast-food chains like McDonald’s are experimenting with AI-driven digital menu boards that change based on the time of day and the weather.

Further, as AI becomes more immersive, its role in virtual environments like the metaverse will expand. Firms like Gucci, Nike and Samsung are already experimenting with digital personalisation, allowing users to design custom virtual clothing and accessories. Meanwhile, AI-driven stores like Amazon Go offer checkout-free shopping experiences, using automation to enhance convenience.

But these innovations also raise important concerns about privacy and data security in public spaces. The widespread use of AI-powered surveillance and facial recognition systems has sparked debates over potential misuse and the erosion of personal freedoms. Without proper regulations and safeguards, these technologies could lead to mass data collection, increasing the risk of breaches and unauthorised tracking.

As AI continues to advance, firms must strike a balance between automation and ethical responsibility. Leading companies like Google, IBM and Microsoft are investing in research to minimise bias and ensure transparency.

The challenge here lies in using AI as a supportive tool rather than a controlling force in consumer decision-making. Companies that proactively adopt ethical AI principles, such as data minimisation and bias mitigation, will not only comply with evolving regulations but also gain a competitive edge by promoting consumer trust.

Data minimisation ensures that only the necessary amount of personal data is collected and stored, reducing the risk of breaches and unauthorised access. Bias mitigation involves identifying and correcting algorithmic biases that could lead to unfair treatment, ensuring that AI-driven decisions are transparent and equitable. By prioritising these principles, businesses can enhance accountability and build more inclusive, secure AI systems.

Conclusion

Consumer insights and data analytics have evolved beyond mere marketing tools – they now shape how companies design, build and deliver their products and services. AI enables businesses to predict trends, customise offerings and enhance customer engagement in ways that were previously unimaginable.

Even in traditional industries like banking, AI-powered financial advisers craft bespoke investment plans that evolve with a person’s life changes. Consequently, AI-driven personalisation is not just transforming markets, but also fundamentally redefining consumer expectations.

Across industries such as healthcare, finance, education and entertainment, AI helps businesses to anticipate consumer needs and adapt in real time rather than offering one-size-fits-all solutions.

What’s more, companies are weaving personalisation into every step of their operations, using consumer data and analytics not only for marketing but also to inform product design, service development and business strategy. AI-driven pricing strategies have also revolutionised markets through dynamic pricing. This development highlights the importance of human-AI collaboration in ensuring that automation is used both responsibly and effectively.

As AI continues to evolve, businesses that harness its potential thoughtfully – balancing automation with human expertise – will set new industry standards. The future of AI-powered personalisation is not just about efficiency, but also about creating meaningful, adaptive experiences that deepen customer relationships and drive innovation across sectors.

Ultimately, AI-driven personalisation should empower consumers rather than make decisions for them. The most successful enterprises will be those that view AI as an enabler of richer experiences rather than just a predictive tool.

By striking the right balance between innovation and ethics, businesses can build a personalisation economy that respects individual autonomy while delivering unparalleled value. AI-powered customisation is not merely a trend but a paradigm shift that will continue to shape industries and redefine consumer expectations for years to come.