This will begin to change in 2026. AI will become less of a buzzword and more of the backbone of business operations. Processes that previously took hours or days will be reduced to minutes or even seconds. Success will not be brought by flashy tools, but by embedding AI into the everyday fabric of work to drive real revenue, cut costs, and outpace competitors. Additionally, at the core of any successful AI implementation is high-quality data. The companies that thrive will be those that clean up their data and embrace AI as a true business partner, not just a side project or marketing slogan.

What industries will feel this change first?

While the impact will be far-reaching, several industries are expected to benefit from the transformative shift earlier. For example, AI will enable healthcare providers to diagnose disorders faster and more accurately. Predictive analytics uses historical and real-time data to forecast future health outcomes, identify at-risk patients, and optimize operations. Shifting care from reactive to proactive will improve public health and increase the effectiveness of treatments.

The logistics industry will receive a huge boost from data analytics and AI. Businesses can use AI to aid in smarter route planning, resulting in fewer delivery delays. AI transforms raw supply chain information into actionable insights, and this data-driven approach moves logistics from guesswork to precise, real-time management for better resource use and competitive advantage. 

Likewise, banks and financial companies are increasingly using AI-powered tools to improve fraud detection and risk management decisions. Retailers, especially in e-commerce, will be able to give customers better product suggestions and automatically adjust prices in response to fast-changing market conditions.

Predictive analytics will take back the spotlight from generative AI

Generative AI is the most discussed form of AI, accompanied by various discussions on its ethics and some degree of controversy. However, in the coming year, predictive analytics will become more widely known to the public, as its effects become more visible. AI-powered predictive analytics tools help businesses plan further ahead than previously possible, giving a more accurate picture of future demand, sales, and risks. Additionally, predictive analytics programs are more reliable, cheaper, and easier to explain to a layperson. Businesses want results and that puts predictive analytics back on top. Despite that, generative AI should not be totally discounted. Businesses that can successfully use both are more likely to succeed, with predictive analytics providing guidance and generative AI enabling automatic action.

Data security to become a higher priority

One of the most important topics in the new frontier of AI is data security. While AI offers powerful benefits, it also introduces new vulnerabilities and is increasingly weaponized by threat actors. As a result, in 2026, AI companies will lean more toward tighter control of data. In the past decade, there have been many high-profile data breaches, demonstrating the huge risk posed by poor data security. Moving forward, data and AI companies will adopt a need-to-know approach, ensuring that individuals will only get access to specific data based on their role.

AI companies will build safer systems that allow people to use data without exposing all sensitive information. This will be very important in highly regulated sectors, such as banking, healthcare, and government. While other sectors such as tech or retail may be more flexible, security is still crucial as violations can result in major penalties and reputation loss. In 2026, companies will have a goal of widening access to data but with more safeguards in place.

Automation will change how organizations work with data

In past years, the novelty of AI and misunderstanding of its capabilities have caused many organizations and individuals to misuse it, often outsourcing too much of the thinking to AI. In 2026, many businesses will correct their course, using AI to do repetitive tasks while having humans think using their superior capacity for creativity, emotional intelligence, and contextual understanding.

The development of agentic AI will introduce AI helpers that will take over routine and ‘boring’ tasks such as cleaning data or fixing errors. Data teams will spend less time on coding pipelines, with AI freeing up their time to solve business problems. AI will also make various digital tools easier to use, which means even people without advanced degrees and credentials can work with data analytics and AI. However, companies will still need specialists for complex AI, security, and architecture. These experts’ roles are not disappearing but shifting towards high-value strategic oversight.

Businesses will see through the hype and focus on results

With more businesses gaining a better understanding of data analytics and AI, 2026 won’t be about chasing the next shiny AI trend. Instead, it will be about delivering measurable business impact. Companies that integrate AI into their core operations, clean up their data, and strike the right balance between predictive and generative capabilities will lead the pack. On the other hand, those that cling to old models or treat AI as window dressing will fall behind. The future belongs to businesses that see AI not as a tool, but as a strategic partner that accelerates decisions, safeguards data, and frees humans to focus on what matters most: thinking big and solving complex problems.

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By Matthew Nassau, Business Architect, Treasury & Capital Markets at Finastra

Markets move in cycles. Each generation experiences most of the things that previous generations have endured (bull or bear markets, natural disasters, geopolitics, …) punctuated by turning points from which the future takes a distinct path (powered flight, the transistor, The Beatles, …). These highlights are often recognized early on as important in their day and seem to appear ‘overnight’, and yet have taken years of development and formation to appear in our consciousness, while the lasting extent of their transformative power is not fully appreciated.

Generative AI (GenAI) fits the model described above, poised as it is to revolutionize treasury and capital markets by markedly altering decision-making processes for market professionals. From conversational finance to predictive analytics, AI is evolving from a mere assistant to becoming a crucial decision-making tool. In Gulf Cooperation Council (GCC) countries, GenAI could add between USD 21 billion and 35 billion each year, on top of roughly USD 150 billion that existing AI technologies are expected to contribute. That represents about 1.7 to 2.8% of the region’s current non-oil GDP.

To deliver on this potential, it is essential that financial institutions have access to high-quality data, upon which GenAI can infer connections, deliver insights and enable actions.

Data has never looked so good

Data has long been treated as one of the most important assets in financial services. Vendors have built major businesses supplying real-time market feeds, and institutions invest heavily to safeguard customer information in every form. The value is clear. What is changing is how much more that value can grow as GenAI gains access to richer and more precise datasets. Large language models can spot relationships and trends that were previously buried, turning raw information into forecasts, alerts and actions that support commercial and risk decisions.

Unlocking that potential requires broader access to the information that treasury teams already rely on. Data lakes and warehouses form part of the picture, but they rarely capture everything. Treasury management systems are a prime example. Their reporting evolves constantly and plays a central role in liquidity decisions, yet much of it remains confined within the system. By making these reporting histories available to GenAI, banks can reveal patterns over time, flag emerging opportunities or risks and prompt timely intervention.

Timing is everything

To show how quickly things have shifted, consider a discussion I had with a major European bank a few years ago. The team was exploring how to treat treasury and capital markets data as a strategic asset without forcing everything into one central system. Their vision was a unified data layer where information could stay within existing applications yet still be accessed, combined and analyzed by staff using low code tools. The goal was to shift toward more data-driven decision making across the business and to uncover new sources of commercial value.

The concept was sound, but the technology required to deliver it at scale was simply too expensive and complex at the time. The bank had to narrow its ambitions and proceed with smaller, tactical initiatives. Artificial intelligence was not even part of the conversation. It felt experimental and far removed from daily operations.

Looking back, the idea wasn’t premature in strategy, only in timing. GenAI now makes this kind of agile, distributed data insight far more realistic.

‘Go big or go home’ – not any more

Expectations have moved on as technology has matured and become easier to access. The old way of classifying data projects as either short-term tactical fixes or long-term strategic overhauls no longer applies. GenAI changes the conversation. It shifts focus from where data lives to how much value it can generate. Deploying AI in specific functions like operations, the front office or reconciliation isn’t a stopgap. It’s a practical way to unlock intelligence quickly.

What will determine success is an institution’s ability to surface a wide range of data, ensure its accuracy and let AI learn from it. This doesn’t require a massive transformation program from day one. Starting with focused use cases can improve efficiency, reduce manual work and reveal valuable insights straight away. As more processes become AI-enabled, those individual wins begin to connect, creating a stronger and more intelligent foundation across the entire organization.

Outcomes lead to incomes

When a technology is still emerging, no one can predict with certainty how far its influence will reach. The best indicators often come from those willing to adopt early and test ideas in the real world. Many concepts compete for relevance, and only a few will ultimately reshape how people work.

The organizations that benefit most are the ones comfortable experimenting, moving quickly and learning as they go. GenAI encourages exactly that mindset. It allows teams to explore and refine new approaches by tapping into the data they already hold. The results show up in lower costs, stronger client value and healthier margins.

This shift is not about replacing existing business models but enhancing them. Each step forward can deliver outsized returns for firms confident enough to start now.

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