Beyond AI Reality in the Age of Quantum

Beyond AI Reality: How Leaders Can Prepare for Quantum

When Anna Beata Kalisz Hedegaard sat down with Danette Copestake of Hypatia Smart Technologies and Bill Wisotsky of SAS, the discussion was not about shiny machines or science-fiction promises. It was about something far more practical - and far more human. What happens when one major wave of innovation, AI, is still reshaping organisations, while another, quantum computing, is already coming into view?

That is the real story. Not hype. Not magic. Not a room full of people chanting ‘qubits’ as though they were invoking a wizard. This is a leadership issue, a governance issue, a data issue and, above all, a people issue.

Danette brought the perspective of someone deeply involved in large-scale enterprise transformation, including Citi’s generative AI rollout. Bill offered the view from the applied quantum front line at SAS, where the emphasis is not on fantasy but on testing what quantum can realistically achieve in business. Together, they made one point unmistakably clear - quantum is not arriving in an empty room. It is arriving in an organisation that is already noisy, crowded and still partly under renovation.

AI has changed the mood - and not always for the better

One of the sharpest moments in the conversation came when Anna asked for a single word to describe how leaders feel about emerging technology today. Danette’s answer was simple - ‘tired’.

That word captures a great deal.

AI has created a gold-rush mentality in many organisations. Leaders rushed to launch pilots, secure budgets and prove they were not falling behind. The result was progress, certainly, but also governance gaps, fragmented experimentation and widespread change fatigue. Put simply, many people ran very fast and only afterwards checked whether they were wearing the right shoes.

That matters because quantum may trigger much the same behaviour. Some firms will rush in because they do not want to miss the next major shift. Others will move more cautiously because they have already learnt, often the hard way, that speed without structure creates chaos.

The lesson from AI is not to slow down and do nothing. It is to build the road before sending more cars on to it.

The biggest barrier is not the technology

A common myth around emerging technology is that the machinery is the hardest part. Both speakers challenged that assumption.

Danette argued that the real constraint is not technical readiness but organisational readiness. That means the connective tissue between teams, data, security, legal, risk, compliance and operations. If those groups are not aligned, even the best technology ends up looking like a Formula 1 engine bolted to a shopping trolley.

Bill made a similar point from the customer side. Some organisations arrive with a team, a strategy and realistic expectations. Others are only just testing the waters. In both cases, the work is collaborative and often begins with a difficult but essential question - do we actually need quantum here?

That question matters more than it first appears. Much of quantum’s value today lies in disciplined exploration, not blind commitment. In some cases, classical tools remain entirely sufficient. In others, parts of a problem may eventually benefit from hybrid quantum-classical approaches. The sensible approach is not to force quantum into every use case. It is to understand where it may genuinely make a difference.

Data is the real battleground

If AI has taught organisations anything, it is that poor data can quietly derail a strong strategy.

Danette made this point bluntly. Security is often blamed for slowing progress, but the deeper problem is usually fragmented, poorly owned and unreliable data. AI struggles not because data is inaccessible, but because it is often untrustworthy, disconnected or badly classified.

That lesson carries directly into quantum.

Quantum systems, especially in cloud environments, raise new questions about where data travels, how it is transformed and who controls the surrounding infrastructure. Bill explained that, in practice, organisations already need to strip out personal identifiers, think carefully about where algorithms are executed and control how much information is sent to external quantum platforms.

Ironically, one current limitation of quantum hardware may provide a degree of reassurance. Because today’s machines are still constrained, organisations often cannot send large raw data sets to them directly. Instead, they work with transformed or reduced data representations. That is not a full answer to sovereignty or privacy concerns, but it does show that technical constraints can sometimes act as accidental safety rails.

Good governance should feel like a steering wheel, not a handbrake

This was one of the strongest themes in the discussion.

Danette drew a clear distinction between bad governance and good governance. Bad governance creates the appearance of control. It produces forms, approvals and tidy paperwork while changing very little. Good governance helps organisations move faster with confidence. It is designed in early rather than arriving late like a grumpy inspector with a clipboard.

That matters because quantum experimentation needs safe space. Sandboxes, pilot teams, controlled access, non-sensitive data and clear exposure limits all make it possible to innovate without creating unnecessary risk. The goal is not to smother experimentation. It is to isolate risk while allowing curiosity to flourish.

This is where many firms go wrong. They treat governance and innovation as opponents. In reality, poor governance is what makes innovation feel dangerous. When people trust the boundaries, they are far more willing to explore within them.

Human agency is not a soft issue - it is the central issue

By the end of the conversation, everything kept returning to people.

Danette argued that leaders do not need deep expertise in quantum physics, but they do need literacy. They must understand the decisions, the risks, the timescales and the likely consequences. In other words, they do not need to build the engine, but they do need to know whether they are driving towards a cliff.

She also made the important point that, as systems become more powerful and less intuitive, human skills become more valuable, not less. Critical thinking, creativity, communication, judgement and emotional intelligence are not optional extras. They are essential. Without them, everything else becomes harder.

Bill added another practical dimension. Quantum teams often require different habits of mind. Traditional data science approaches do not always transfer neatly. In his experience, progress often depended on stepping back from purely classical thinking and approaching problems with more of a physics mindset. That is why some organisations bring together physicists, engineers, software developers and domain specialists. Quantum is not simply a new tool. It often requires people to think differently from the ground up.

And that is difficult. Change is tiring. People do not always resist because they are stubborn. Sometimes they resist because they are overloaded. That is why safe forums, internal communities, university partnerships and open discussion spaces matter so much. People need room to ask basic questions without feeling foolish. That is not a nice extra. It is how capability is built.

The big takeaway

The most useful insight from this discussion is straightforward.

Quantum readiness is not about predicting the exact moment when a fault-tolerant machine changes everything. It is about preparing organisations now so they can absorb the consequences later. AI showed what happens when capability outpaces governance. Quantum gives leaders a second chance to do better.

That means strengthening data foundations, building cross-functional teams, creating safe spaces for innovation and improving leadership literacy before the pressure intensifies.

As Danette framed it through a John Wick quote, actions have consequences.

In business terms, that cuts both ways. If organisations act too late, there are consequences. If they act carelessly, there are consequences too. The winners will not necessarily be the ones who shout ‘quantum’ the loudest. They will be the ones who quietly put their house in order before the next storm arrives.

Key insights

1. AI has left many organisations energised, but also fatigued and operationally stretched.
2. The greatest barrier to quantum adoption is usually organisational readiness, not technical readiness.
3. Poor data governance is often a bigger problem than security controls themselves.
4. Good governance should enable faster, safer innovation, not act as a late-stage blocker.
5. Quantum experimentation works best in controlled sandboxes with clear risk boundaries.
6. Leaders do not need deep physics expertise, but they do need quantum literacy.
7. Human skills such as judgement, communication and critical thinking become more valuable as technology becomes less intuitive.
8. Dedicated quantum teams, connected to wider business expertise, may be more effective than loosely distributed efforts across the firm.

Market classification

Enterprise emerging technology strategy, quantum readiness, AI governance, cyber risk and innovation management.

Sub-markets and adjacent domains

Post-quantum cyber security, data governance, enterprise architecture, digital transformation, quantum-classical hybrid computing, AI operating models and change management.

Competitor categories

Global consulting firms, enterprise software vendors, cyber security providers, cloud hyperscalers, quantum hardware and platform companies, and specialist transformation advisers.

Market outlook

The market is shifting from awareness to preparedness. In the near term, most organisations will not buy quantum advantage off the shelf. Instead, they will invest in literacy, targeted pilots, partner ecosystems and post-quantum readiness. Over time, value is likely to move away from fascination with hardware and towards practical integration in analytics, optimisation, security and sector-specific software.

Demand drivers

1. Fear of missing the next major platform shift
2. Growing concern over post-quantum cryptography and ‘harvest now, decrypt later’ risks
3. Pressure to extract more value from complex data and optimisation problems
4. Lessons learnt from fragmented AI adoption
5. Demand for stronger governance across emerging technologies
6. The need for leadership teams that can prioritise innovation without creating chaos