Museum IT departments have traditionally served as support functions to manage security and systems. But the National Museum of Korea goes further, identifying and pursuing AI initiatives to preserve cultural heritage, broaden its use, and improve the visitor experience.

The National Museum of Korea uses technology to expand the possibilities to preserve and display physical artifacts, and its digital museum division (DMD) plans and oversees that transformation. A dedicated team responsible for digital strategy and content took shape in 2019, and in 2020, the museum informatics division adopted its current name.

“The director at the time placed great importance on investing in technology and felt we needed a department that could bring a strategic direction to work that had been carried out separately,” says Choi Seungae, former DMD head. “The digital museum division was created as a way to prepare for the next generation.”

A cultural transformation

In just over five years, the DMD has delivered a range of services, including AI guide robots, smart kiosks, and multilingual and sign language services. It’s also developed immersive experiences that use large-scale imagery and sound to recreate cultural heritage.

More recently, its focus has shifted toward data. The aim is to move beyond storage and preservation, making data previously confined to the museum available for wider use. Physical artifacts face restrictions on movement and display because of security concerns and risk of damage. Digital data can help overcome those limitations.

One example is an immersive content project featuring the Denny Taegeukgi, Korea’s oldest surviving national flag, undertaken last year to mark the 80th anniversary of Korea’s liberation.

The project displayed images of the 130-year-old flag once held by Owen Denny, a diplomatic adviser during the late Joseon period, on a large outdoor media screen in front of a department store in central Seoul.

At first glance, the project might appear to have involved little more than photographing an artifact and displaying it. But it required highly detailed data since enlarging existing photographs would’ve produced visible pixelation on such a large display.

Therefore, the division created a new, ultra-high-res dataset containing 10.8 billion pixels at a two-meter scale from the original flag, which measures approximately one meter. Precision scanning captured even the fine texture of both sides of the fabric.

Plan of action

Earlier this year, the museum was selected for the government-led K-Science initiative, through which it plans to pursue data projects beginning in 2027. The cross-ministerial R&D program combines Korea’s historical, cultural, and environmental assets with advanced science and technology to create economic, social, and cultural value.

Through the initiative, the museum will digitize over 2.5 million artifacts and develop technologies to expand the commercial potential of Korean cultural assets. The central goal is to connect the entire process, from archaeological exploration and preservation, to digitization and content development.

The resulting technologies are expected to help overcome technical limitations in detailed surveys of ancient tombs, for example. In preservation and collection management, AI could also automatically control temperature, humidity, and lighting in museum storage facilities.

Never a single right answer

Data presents challenges across both private and public sectors. For museums, however, they’re particularly complex because artifact data contains layers of interpretation, facts, and history.

Consider an AI system designed to answer questions about a particular artifact. It needs data to support its responses. But what should it say if records from Baekje and Silla, two ancient Korean kingdoms, offer conflicting accounts of the same event. Ideally, it would adequately represent different perspectives, but that’s not always possible since the same artifact can tell different stories depending on who used it, when, and for what purpose.

Choi says current LLMs don’t adequately account for these layers of context. When users ask for an accessible explanation, models can produce answers that overgeneralize or oversimplify. Reducing an event with multiple interpretations to a single sentence can obscure or distort its context.

But ensuring accuracy is another challenge. According to Choi, historical discrepancies introduced during initial training can prove difficult to correct, even when a model is subsequently asked to revise them.

The DMD is, therefore, concentrating on building data with clearly identifiable sources. The aim is to let users verify if an AI response is based on material from the National Museum of Korea to assess its reliability.

With these characteristics of artifact data in mind, the division is working with external experts to redesign its database structures around both the nature of the artifacts and the purposes for which people use the data.

Upgrade with AI

Where earlier databases focused on registering and managing objects, the new approach seeks to capture their different meanings and relationships while making relevant materials easier to find. Images, 3D data, videos, and related literature previously scattered across separate records are being connected. Metadata describing file sizes, formats, and how the data was created are also being expanded to support reuse so linking different names for the same artifact makes searching easier, for instance.

“Cultural heritage can’t be standardized like manufactured goods,” Choi says. “We need to organize and structure the data and materials we’ve accumulated in ways that reflect the character of our artifacts, while at the same time, identify connections between them. Once that foundation is in place, curators and external users like content creators, researchers, and educators will be able to find and use materials suited to their needs.”

Leaving room for discovery

After nearly two decades spent researching artifacts and developing exhibitions, Choi didn’t expect to find herself working in the DMD. Now director of the Jeju National Museum, she looks back on her digital work where there were many times her previous experience alone couldn’t guide her decisions. Exhibitions have clear opening and closing dates, and experience helps curators judge what further research and preparation will improve outcomes.

Digital projects offer fewer such reference points, however. It can be difficult to assess how far an initiative has progressed, or anticipate how emerging technologies will affect work already underway. Even so, her expertise provides a foundation to decide where change should lead, and help propose ideas to assess what new tech serves the museum’s purpose.

“Whether it’s curatorial or digital work, I hope the many things we do at museums can make a small contribution to help visitors think for themselves and arrive at their own insights,” she says.

The most expensive quantum mistake a CIO could make today may not be investing too early. It may be investing in the wrong capabilities, for the wrong reasons, without understanding the architectural and economic consequences.

Over decades of leading enterprise technology, architecture, modernization, and innovation in complex, highly regulated environments, I have seen a recurring pattern: technological enthusiasm advances faster than institutional readiness. Capital gets committed. Platforms become entrenched. Dependencies multiply. Business value remains difficult to demonstrate.

Quantum computing risks repeating that pattern, but with an important distinction: The cybersecurity consequences demand action today, while the timing and economics of broader commercial quantum advantage remain uncertain.

That is the strategic tension CIOs must resolve.

Quantum readiness and preparedness is not a race to acquire qubits. It is a discipline of protecting enterprise trust, preserving strategic options and directing scarce capital toward demonstrable value.

The fundamental question is no longer whether quantum computing deserves executive attention. It is whether CIOs and enterprises can distinguish what must be protected now, what deserves experimentation and what should remain an option rather than a financial commitment.

Two quantum clocks. One enterprise balance sheet

Quantum presents two fundamentally different timelines to a CIO.

The first is the security clock.

In August 2024, the National Institute of Standards and Technology (NIST) published its first three finalized post-quantum cryptography standards, establishing practical mechanisms for protecting information against anticipated quantum-enabled threats.

This is critical because cryptography is not an isolated cybersecurity component. It underpins identities, financial transactions, digital signatures, software integrity, communications, connected infrastructure and trusted business relationships.

A sufficiently capable future quantum computer could compromise certain widely deployed public-key cryptographic methods.

The October 2026 U.S. Government Accountability Office (GAO) report, Quantum Computing: Federal Actions Needed to Prepare for Emerging Cyber Threat, reinforces the urgency. None of the 24 federal agencies examined had fully addressed all three assessed preparatory practices for migrating vulnerable cryptography.

The lesson extends beyond government: you cannot migrate cryptographic dependencies you have not discovered.

The second is the commercial advantage clock.

Quantum computing may eventually offer meaningful advantages in selected areas of chemistry, materials science, simulation and optimization. But promising research does not automatically translate into repeatable enterprise economics.

IBM’s published quantum technology roadmap targets a fault-tolerant quantum system in 2029. That is an important technological ambition, not a guaranteed date for widespread commercial advantage.

These two clocks should not share the same investment assumptions.

Security readiness is justified by exposure and risk tolerance. Commercial adoption must be justified by demonstrated advantage and economic returns.

Confusing the two creates either dangerous underinvestment or speculative overspending for the CIOs.

Architecture before acquisition

The enterprise architecture challenge is more consequential than the quantum procurement question.

For CIOs, post-quantum preparation begins with initial understanding of where vulnerable cryptography exists across their enterprise, how it supports critical business services and whether those dependencies can be changed without destabilizing operations.

For example, consider a globally distributed financial institution.

Cryptographic dependencies may span payment platforms, customer identities, authentication services, certificates, mainframes, cloud environments, third-party APIs and decades of retained information.

Replacing an algorithm is not necessarily equivalent to replacing a library.

Some changes affect protocols, hardware security modules, certificate lifecycles, application compatibility, vendor contracts and operational resilience.

This demands cryptographic agility: the ability to replace or adapt cryptographic mechanisms without rebuilding the enterprise.

The same architectural discipline applies to future quantum workloads.

Enterprises should favor modular integration, well-defined interfaces, workload portability and interoperability across classical computing, high-performance computing, AI and emerging quantum services.

Quantum will not make classical computing obsolete. The more credible architectural direction is selective integration where a quantum capability demonstrably improves a specific computational outcome.

The architectural objective therefore is not quantum dependence. It is quantum optionality.

An enterprise should be able to adopt a useful breakthrough without surrendering control of its technology economics or becoming captive to a single provider.

The missing equation: Quantum workload economics

The quantum conversation becomes strategically meaningful when CIOs stop asking what a quantum computer can theoretically perform and start asking what business outcome it can improve.

The OECD’s 2026 research, Building Business Readiness for Quantum Computing, identifies barriers including technological immaturity, uncertainty surrounding business applications, access and training costs, and shortages of specialized expertise.

These are not simply engineering obstacles. They are capital allocation constraints. This means that a quantum experiment may demonstrate technical progress while failing to establish commercial value.

For example, a proposed optimization workload should be evaluated against competitive classical algorithms, realistic datasets, operational constraints and the full cost of execution.

The comparison must include more than processing time. It should consider problem preparation, data movement, orchestration, error mitigation or correction, integration, specialized talent, assurance and recurring operating expenses.

Even an impressive laboratory result may offer little economic benefit if the end-to-end enterprise workflow becomes slower, costlier or less dependable.

I would therefore introduce a more demanding investment metric: Cost per Trusted Quantum-Enabled Business Outcome.

This proposed measure evaluates the total attributable cost of achieving a validated business result, including the controls necessary to make that result operationally acceptable.

The denominator is not the number of quantum circuits executed. It is the number of useful, verified business outcomes delivered.

Where no defensible quantum advantage exists, the responsible decision may be to continue using classical computing.

That is not technological conservatism. It is capital discipline.

Introducing the quantum capital discipline framework

I propose a six-dimensional executive framework for separating quantum preparedness from quantum speculation.

1. Exposure: What must we protect? Identify vulnerable cryptographic dependencies, long-lived sensitive information, critical services and contractual or regulatory obligations. Prioritize remediation by business consequence, not technology fashion.
2. Relevance: What problem warrants quantum? Establish a specific business problem whose computational characteristics plausibly justify quantum investigation. Scientific curiosity alone is not an enterprise investment thesis.
3. Advantage: What evidence proves improvement? Demand reproducible comparisons against strong classical alternatives. Evaluate solution quality, performance, operational constraints and independent validation.
4. Architecture: Can we adapt without lock-in? Assess portability, integration complexity, vendor concentration, reversibility and the ability to change technology choices as the market evolves.
5. Economics: What is the full cost of value? Measure total lifecycle costs, opportunity costs, talent requirements and credible returns. Distinguish research expenditure from production investment.
6. Authority: Who decides to advance, defer or stop? Assign accountable decision makers, measurable milestones, investment thresholds and explicit exit criteria. Together, these dimensions form the Quantum Capital Discipline Framework.

Its governing principle is straightforward:

Capital follows evidence. Architecture preserves options. Governance owns consequences.

Importantly, this framework does not subject essential cryptographic remediation to the same commercial-return requirements as speculative quantum workloads. Security obligations and innovation opportunities require different investment justifications.

What boards should demand from their CIOs

Quantum strategy should enter Board discussions through enterprise risk, capital allocation, resilience and competitive positioning — not promotional demonstrations.

I would encourage directors and executive committees to demand answers to five key questions:

1. Which critical business services and information assets face material quantum-related cryptographic exposure?
2. Can we modernize vulnerable dependencies without unacceptable operational disruption?
3. Which proposed quantum applications outperform credible classical alternatives?
4. What evidence would justify expanding, delaying or terminating quantum investments?
5. Who is accountable for the financial, security and architectural consequences?

These five questions create a practical governance boundary between preparedness and speculation. They also expose a broader institutional weakness: enterprises often approve emerging technology investments without establishing the conditions under which those investments should be reconsidered.

Quantum requires a different approach.

Investment decisions should be revisited when technical capabilities, benchmark results, standards, security exposure or business economics materially change.

Boards should insist on measurable progress but avoid manufacturing artificial deadlines for breakthroughs nobody can reliably predict.

The CIO’s next mandate: Preserve the freedom to act

Quantum computing will not transform every industry simultaneously, deliver equal advantage across workloads or reward every early investor.

Some enterprises may eventually realize significant value from specialized quantum applications. Others may encounter quantum’s most immediate consequences through cryptographic modernization, technology dependencies and the protection of long-lived information.

Both outcomes demand leadership today.

The CIO’s responsibility is not to predict which quantum architecture will prevail or which vendor will dominate. It is to ensure that today’s decisions do not compromise tomorrow’s ability to compete, adapt and protect enterprise trust.

That requires investment discipline, architectural foresight, credible experimentation and accountable governance.

The next quantum advantage may belong not to the enterprise that invests first or spends the most, but to the one that knows precisely when to act, where to invest and when to walk away.

Quantum readiness is not measured by the technology an enterprise owns. It is measured by the strategic freedom it retains, the risks it responsibly reduces and the business value it can demonstrably create.

That is the difference between quantum enthusiasm and quantum leadership.

Quantum leadership is not about predicting when the future arrives. It is about architecting the freedom to embrace it, exercising the discipline to invest in it and preserving the authority to walk away when promise fails to become value.