Hyper-personalisation in enterprises reshaping decision-making

Hyper-personalisation in social networks is not just a technological trend. It is a phenomenon quietly reshaping how societies think, how businesses operate, and how individuals make choices.

For most of the twentieth century, communication was defined by scale. A single message was broadcast through newspapers, radio, or television and consumed by millions. Marshall McLuhan’s phrase, “The medium is the message,” captured that era well.

Today, the medium is fragmented. Social networks no longer address the crowd; they communicate with individuals. Algorithms track likes, pauses, and moods, curating individual worlds based on engagement rather than truth or chronology.

Personalisation, once associated mainly with Facebook, TikTok, or Instagram, has now crossed into enterprise information systems. In organisations, personalisation has moved beyond an abstract premise to a concrete application.

At Siemens, predictive analytics modules flag contracts and projects at risk of delay. General Electric uses maintenance systems that generate automated alerts for equipment likely to fail across its industrial operations. Unilever uses procurement dashboards to provide supplier-specific information that supports sourcing decisions. At IBM, leadership platforms track costs, approvals, and project progress in real time.

These cases show that personalisation has shifted from newsfeeds to decision intelligence in enterprises. What social networks achieve with attention, enterprises accomplish with action.

The Evolution of Personalisation

Unlike what many may believe, hyper-personalisation is not the creation of artificial intelligence (AI). Instead, its roots lie in human communication. Throughout history, leaders have adapted their messages to the audience’s mood, fears, and hopes. For example, Winston Churchill inspired courage during wartime by matching his words to the nation’s emotional state: “We shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets.”

Shakespeare’s Mark Antony demonstrates how messages evolve with audience sentiment, beginning with “Friends, Romans, countrymen, lend me your ears” and gradually transforming audience grief into fury.

Today, algorithms read micro-expressions, analyse tone of voice, and evaluate sentiment at a scale unimaginable in earlier times. Alvin Toffler’s observation in Future Shock, “Technology feeds on itself. Technology makes more technology possible,” remains strikingly relevant.

This observation resonates quite aptly with the rise of new technologies in hyper-personalisation. Generative AI now creates dynamic content tailored to individual preferences, while natural language processing engines such as chatbots calibrate tone and recommendations in real time through continuous conversation.

Similarly, computer vision is reshaping retail by recognising customer behaviour in physical stores, and wearable devices, along with IoT sensors, feed health platforms with streams of personal data. At the same time, edge computing and federated learning enable personalisation without exposing sensitive data, enabling enterprises to deliver customised interactions while maintaining privacy.

Balancing Innovation and Responsibility

Such technological power comes with profound responsibility. For instance, in social networks, hyper-personalisation can shape elections, polarise societies, and deepen echo chambers. In enterprises, AI influences creditworthiness, pricing, and vendor selection. In public policy, algorithms affect subsidies, infrastructure priorities, and energy pricing.

Shoshana Zuboff, in The Age of Surveillance Capitalism, asks three critical questions: “Who knows? Who decides? Who decides who decides?” These are central to preserving trust in institutions.

The convergence of social AI and enterprise AI points to algorithms that not only curate information but also anticipate actions—predicting supply disruptions, guiding investments, and shaping citizen-centric policies.

The risks are significant: will these systems empower better decision-making, or will they quietly eliminate choices? Peter Drucker’s warning, “The greatest danger in times of turbulence is not the turbulence; it is to act with yesterday’s logic,” is particularly relevant across industries.

At the end of the day, hyper-personalisation must prioritise people, society, and ethics before efficiency. Technology, algorithms, and abundant data already exist; the challenge lies in responsible governance.

Opportunities are immense, but so are the challenges. Filter bubbles and echo chambers narrow perspectives. Privacy concerns intensify as every action becomes fuel for personalisation engines. Algorithmic manipulation can invisibly nudge consumption, preferences, and even beliefs. Bias in AI models risks reinforcing structural inequalities in access to loans, jobs, or healthcare. Governance and fairness are therefore not optional—they are essential.

Spotting Emerging Trends in Hyper-AI

Three emerging trends are expected to define the future of hyper-personalisation. First, personalisation will shift from reactive to proactive, anticipating needs before they are expressed, whether in preventive healthcare alerts or optimised travel recommendations.

Next, privacy-preserving personalisation, enabled by frameworks such as federated learning, will allow customisation without exposing raw data. Third, regulation—through frameworks such as the EU’s AI Act and global data-privacy laws—will shape the equilibrium between innovation and human rights.

Hyper-personalisation offers immense opportunities for value creation, enhanced experiences, and innovation. Yet it also raises critical questions about privacy, fairness, and trust. The responsibility lies with researchers, technologists, policymakers, and consumers to ensure that personalisation empowers rather than exploits.

Ultimately, technology does not define humanity; it reveals it. At its best, personalisation amplifies human potential, broadens perspectives, and strengthens the foundations of society rather than weakening them.

The author is a Professor of Business Analytics at IIM Ranchi and Visiting Fellow at Hong Kong Poly University, and Ural Federal University, Russia.
(The views expressed are those of the author and do not necessarily reflect official policy, position, or endorsement of the organisations or institutions he works with.)