Embracing Artificial Intelligence with Confidence: A Balanced Approach for Businesses

Building on a 100-year history of innovation, SIX is consciously confident in its approach to implementing artificial intelligence (AI). Let’s take a look at what lies behind this confidence.

I think many would agree that since ChatGPT was first introduced, just over two years ago, it feels as though everything has changed. The rapid pace of this change has caused some to see only the good and others to see only the bad, leading to such contradictory reflections as: “Will AI solve the global economic productivity and health crises? Or will it render us all redundant and wipe out humanity through an algorithmically generated conclusion?”

This type of polarized thinking has led to paradoxical questions like, “Too much, too little, or both?”, “Is there too little transparency and too much assumption?”, “Do we have too much confidence and too little risk management?", “Are too many trials creating too little impact?”

Keeping this paradox in mind, how can companies maximize the benefits that AI promises? In order to chart a consciously confident path toward AI adoption, companies need to understand and engage with the healthy challenge presented by these polarized perspectives – remaining aware at all times that this is a continuously evolving picture.

I have been working with data in the financial markets for decades and have lost count of the times I have heard that a new technology would eliminate the need to focus on basic data management. However, the principles of good data management, such as accuracy, timeliness, and completeness, are now more important than ever.

With AI everyone needs a data strategy that extends beyond the principles of data quality, stewardship, and governance. Quality does not just mean accuracy, timeliness, and completeness, but also ensuring the three Rs: that the data is right, relevant and representative. If you don’t keep this in mind, you will walk straight into the bias trap as we’ve seen when AI learning relies on a too limited data set.

We should also consider that AI governance needs the same level of rigor as financial compliance. This includes assigning clear model ownership, ensuring explainability, and maintaining full traceability across training data, decision logic, and outputs. It’s not just about ethics – it’s about operational resilience, regulatory alignment, and long-term accountability. With regulation evolving, building this in from the start will support scaling later on.

It is beyond me why in 2025 we still process PDFs of unstructured and inconsistent financial information, such as fixed income prospectuses. Instead of layering technology on top to try and make these easier to manage, we could be addressing the problem at its root. Avoid AI being a cosmetic answer.

What are the keys to embedding this consciously confident attitude in your organization? It is all about systems thinking. Stop thinking about rolling out AI, and start thinking about rolling AI in.

AI is not separate. It needs to be incorporated within the organization and processes. Everyone, from the boardroom to administration, needs to be aware and engaged. 

Avoid the use case trap, aka pilot purgatory. Often, when companies implement new technology, they end up with 200 trials proving that it works. But they are not able to scale and deliver tangible results and improvements that show up on the bottom line. That is why understanding your organization’s core domains and where AI best applies end-to-end is key to finding true lasting scalable benefits. Think about the entire value chain.

AI promises massive productivity gains – but only if you know what “productivity” really means for your organization. Are you measuring output? Decision speed? Error reduction? Understand and define the value clearly. 

Placing ethical leadership at the heart of sustainability and AI ensures that both work well together, embedded into reward systems and corporate values and behaviors. AI can absolutely have a positive impact on sustainability goals, this is not an either/or.

For example, in the world of data delivery it’s clear that you don’t need ultra-low-latency real-time feeds for an end-of-day price reconciliation process. Use that same kind of prioritization to build your AI infrastructure so that it meets, rather than exceeds, your needs. 

AI adoption is not only a technical journey – it’s a cultural one. Leadership must drive the shift with clarity and empathy, recognizing that fear and skepticism are not barriers, but critical sources of insight. Bringing people along on the journey – through reskilling, transparency, and shared goals – is what transforms uncertainty into alignment. The future belongs to companies that can innovate confidently while staying deeply human in how they lead change.

The fundamental challenge is to chart a consciously confident path between the too-much and too-little paradox, because no one wants to be too late.