TECHNOLOGY AND POWER | BIG INNOVATION CENTRE

How emerging technologies reshape economic power, governance and global competition.

Privacy Is Not the Opposite of Innovation – It Is What Makes AI Possible

The core issue

For much of the past decade, privacy and innovation have been cast as uneasy rivals.
On one side stand fast-moving AI systems, trained on vast quantities of data and celebrated for their promise of efficiency, prediction and scale.
On the other stand data protection, consent, safeguards and limits – frequently portrayed not as enablers, but as sources of friction, cost and delay.

That framing is increasingly wrong — and strategically dangerous, because it encourages the false belief that innovation requires weakening the very safeguards that make people willing to participate in data-driven systems.

Artificial intelligence depends on data.
Data depends on people and organisations being willing to share it.
And that willingness depends on trust.

Privacy is therefore not the price of innovation.
It is what makes innovation possible.

Power implications

• Trust is becoming core innovation infrastructure in the AI economy
• Privacy frameworks shape who can access data and innovate at scale
• Loss of trust reduces data flows, slowing technological and economic growth
• Countries that build trusted data systems will lead economically and technologically
• Lack of transparency and accountability is a bigger barrier to AI adoption than regulation.

Introduction: trust as the foundation of technological adoption

As the UK marks 40 years of modern data protection institutions (e.g., the ICO, Information Commissioner’s Office), it is worth revisiting a more fundamental question: what enables new technologies to take root in society and move from experimental promise to everyday reality?

The answer is not technological capability alone.
It is legitimacy.

AI systems today are powerful and increasingly embedded across economic and institutional systems. Yet public confidence in their use remains fragile. This fragility does not arise because the technology does not work. It arises because people are not always confident that they are protected by it.

When individuals do not trust how their data is used, they withdraw.
When organisations fear legal or reputational exposure, they hesitate.
When legitimacy weakens, innovation slows.

The behavioural truth at the heart of AI

Artificial intelligence depends on data.
Data depends on people and organisations sharing it.

People and organisations share data only when:

• they trust safeguards,
• they feel protected,
• they believe misuse is unlikely,
• and they know accountability exists.

If trust disappears:

• data access shrinks,
• consent weakens,
• resistance grows,
• regulation tightens,
• innovation slows.

The willingness to share data is not automatic.
It is earned through credible protection and clear accountability.

Privacy is therefore not simply a legal safeguard.
It is the condition under which the data required for AI systems to function is made available at all.

Without trusted data environments, artificial intelligence cannot scale sustainably.

Innovation does not fail because technology is weak

AI already outperforms humans in many domains of speed, pattern recognition and optimisation. Yet adoption often falters when systems move from pilot stages into real-world deployment.

The reason is rarely technological failure.
It is loss of confidence.

When automated decisions cannot be explained, when personal data flows into opaque systems, or when surveillance and biometric technologies appear without clarity or consent, the social licence for innovation erodes rapidly. Institutions hesitate to deploy systems that may provoke backlash. Governments face pressure to intervene when public resistance intensifies.

History shows that innovation rarely stalls because regulation exists.
It stalls when legitimacy disappears.

Privacy, in this sense, is not a constraint on progress.
It is the social infrastructure that allows progress to be sustained.

Privacy is how legitimacy is engineered

A quiet but significant shift is underway. Privacy has moved from the margins of compliance into the centre of AI strategy — not as box-ticking, but as design.

Well-governed data systems do three things simultaneously:

• they protect individuals,
• they give organisations confidence to innovate,
• and they create clarity about responsibility when things go wrong.

This is particularly important for AI systems that infer, predict or classify individuals. Without strong privacy foundations, efficiency gains can quickly be accompanied by suspicion. Suspicion is corrosive to adoption.

Trust does not emerge automatically from technological progress.
It must be ‘engineered’.
Privacy is one of the primary mechanisms through which that engineering occurs.

Privacy as power in the AI economy

A deeper structural shift is also taking place.
Privacy is not only infrastructure for innovation.
It is becoming a form of power.

Those who establish trusted data environments increasingly shape who can innovate, at what scale and under what conditions. Jurisdictions and organisations that create confidence in how data is governed attract participation, investment and experimentation. Where trust is weak, data flows contract and innovation fragments.

Control over how data is collected, used and protected therefore shapes competitive advantage, institutional legitimacy and technological leadership.

Privacy is not the opposite of innovation. In the AI economy, it is a source of power.

In the AI economy, the ability to generate trust will determine the ability to innovate.

The real risk is opacity, not regulation

A persistent myth suggests that privacy slows growth. In reality, lack of trust is what slows growth. People disengage from systems they do not understand. Institutions hesitate to deploy technologies that feel legally or ethically brittle. Governments retreat when public backlash becomes politically costly.

Opacity is the common factor. AI systems that cannot be explained, audited or challenged tend to fail publicly and visibly. When that happens, trust does not just evaporate from one system; it spills over into the wider technological environment.

Privacy frameworks force necessary questions:

• what data is being used,
• for what purpose,
• with what safeguards,
• and with what accountability.

Far from obstructing innovation, these questions anchor it in reality.

Privacy-by-design as strategic advantage

In the AI era, privacy cannot be retrofitted. Systems trained on inappropriate data cannot easily be corrected later. Governance assumptions embedded early shape how systems behave at scale. The cost of weak privacy foundations rises sharply as adoption expands.

Organisations that embed privacy by design move faster over time. They spend less time managing crises and more time innovating. They also earn something increasingly valuable: public confidence.

Privacy is therefore not merely compliance.
It is becoming a source of competitive and institutional advantage.

Conclusion: what makes AI possible

Artificial intelligence will shape how we work, govern, communicate and decide. Whether it succeeds will depend less on model size or computational power than on whether people believe their data is handled fairly and responsibly.

Privacy is not the price we pay for innovation.
It is what allows innovation to exist in the first place.

In the AI economy, trust is not a by-product of technological progress.
It is the foundation on which future innovation will depend.

TECHNOLOGY AND POWER | BIG INNOVATION CENTRE

How emerging technologies reshape economic power, governance and global competition

Professor Birgitte Andersen is Professor of the Economics and Management of Innovation and leads research on the political economy of emerging technologies.