5 FEB 2025 | 5 min read

Within financial institutions, a sophisticated web of automation and AI-powered capabilities increasingly extends from back-office operations to customer-facing services. This marks a stark departure from IT environments once constrained by legacy technologies and processes that dramatically limited what could be accomplished in a single day.

Generative AI is the next frontier in this continuum of progress. Technology leaders are eager to maximize this new, powerful lever for efficiency, as reflected by the speed of generative AI adoption. The fervor is striking; there’s a rush to consume solutions touted as silver bullets for reducing costs and increasing productivity. Daily headlines only complicate these claims with breathless projections of growth and global prosperity — or else existential warnings of disproportionate risk and negative outcomes.

The truth, as is often the case, lies between extremes. Generative AI has the true potential to transform business operations in banking and other industries, adding undeniable value to the global economy and resource constraints. Moving beyond theoretical implications, however, depends on embracing thoughtful and fresh approaches to technology deployment.

How leaders manage the change involved in implementing generative AI will be just as important as the technological capabilities it provides. As organizations integrate this valuable tool into their technology estates, their success in shepherding behavioral change will determine their ability to seize the immense opportunities ahead.

When cloud transformation became an urgent priority for businesses navigating the increased digital demands precipitated by a global pandemic, companies were quick to invest. Technology leaders hoped to gain operational agility and speed in bringing new services to customers as they also laid a strong foundation for emerging AI capabilities.

Some CIOs, however, struggled to see a return on their cloud investments. Capabilities were often spread between different providers, resulting in increased complexity and costs. As companies looked to rein in their cloud spending, some CIOs paused transformation efforts or even moved workloads back on-premises.

Companies that overcame these challenges drove simplification, prioritizing data optimization and integration. But the root of the problem often extended all the way to operations teams, who were supporting cloud infrastructure as if nothing had changed from on-premises computing. A clear lesson emerged: Cloud benefits were predicated on matching the pace of change in the workplace on how we work to that of technology implementation.

At the National Bank of Canada, we started like many the journey to the cloud some time ago, we learned though some difficult experiences that a cloud-first strategy required putting people first and be supported by robust change management. As we took the opportunity to address technical debt and built a streamlined environment, we had to realign and mobilize a workforce of thousands of people to embrace new technology new ways of working. Migrating from a traditional infrastructure management to infrastructure as code was a shift for the technology workforce. Only then did our transformation become possible and ultimately successful, increasing employee satisfaction, velocity, achieving cost savings, and delighting our customers.

If we are to avoid rolling the same boulder up the same hill, these lessons should apply to generative AI: technology transformation must proceed in parallel with behavioral change but this time beyond the technology community, but for the entire organization .

For CIOs and business leaders, now is the time to get organized. Managing the change inherent to generative AI implementation extends beyond operations teams and includes every end-user. To avoid chaos, you need clear frameworks and guiding principles that enable your workforce to simultaneously maintain trusted customer services — and transform.

Begin by establishing trust. Individuals need assurance that they can both use generative AI solutions and protect customers’ data. This imperative is even greater in highly regulated sectors such as financial services, where strong data privacy, IP, and security controls are essential to success.

Practice effective communication to help build this trust. Do you have clear, concise and accessible guidelines on responsible use? Are you closely monitoring compliance to quickly address issues? You may also consider forming a centralized office to address use, deployment or development concerns. In my leadership, I prioritize two-way communication, ensuring every team understands how our strategy connects directly to our customers. I also seek feedback from multiple perspectives to achieve buy-in, ensuring we can move forward together as we evolve.

As you proceed, be careful to strike the right balance between pushing boundaries and equipping individuals with the training they need to succeed. After investigating inaccurate results associated with a new generative AI capability the bank deployed, we learned the issue stemmed from prompt inputs, meaning that rather than removing this capability, we needed to refine our training to achieve desired outcomes.

Finally, consider that this training may involve developing traditionally overlooked skills. As generative AI democratizes technology by expanding access to technical competencies like coding, it places greater demand on skills associated with the humanities like critical thinking and curiosity. These skills help employees identify problems and ask the right questions. They are also among those most projected to grow in importance in the coming years, according to the 2025 World Economic Forum’s Future of Jobs Report. By fostering a culture of learning where curiosity thrives, you can create an environment conducive to embracing new technology.

As you implement generative AI, prepare to face resistance to change. Individuals may worry technology will replace, rather than evolve, their roles. We must also contend with an inconvenient reality: people are creatures of habit.

While it’s true that generative AI is transforming the skills required in the modern workplace, the technology is also creating a greater need for human validation. Employees must also understand the implications of falling behind peers who are faster to hone their skills in new competencies associated with generative AI, such as prompt engineering. Building this expertise will be central to remaining competitive.

When teams understand these truths, they’re more likely to welcome the change that comes with generative AI implementation. And once they’re equipped to fully explore generative AI tools, the technology’s value becomes evident: daily work transforms, and opportunities start to emerge to pursue more rewarding endeavors.

Prioritizing change management can help your organization progress efficiently. It is important for technologists to remain forward-looking, stay curious, and enhance their understanding of ongoing digital developments while balancing technological proficiency with business transformation. As emphasized at National Bank, it is crucial to manage both operational activities and transformational initiatives simultaneously; We need to be able to walk and chew gum at the same time. This fluency will be critical to implementing generative AI, as you accelerate the behavioral change today that will deliver value far into the future.