How Finance Leaders Can Turn AI Optimism Into Real Results

Alexander Hagerup

Alexander Hagerup

Co-founder & CEO

Optimism is what drives leaders to explore and embrace the latest technologies, with the hope that those innovations will result in higher productivity and profits.

April 11, 2024

4 min read

How Finance Leaders Can Turn AI Optimism Into Real Results

This article originally appeared on Author Alexander Hagerup is CEO and co-founder of, and a member of the Forbes Technology Council.

Optimism can be a powerful tool. In business, optimism is what drives leaders to explore and embrace the latest technologies, with the hope that those innovations will result in higher productivity and profits.

However, when it comes to deploying these leading-edge technologies, enthusiasm doesn’t always translate to action. In a recent Gartner survey, 79% of corporate strategists said that technologies such as analytics, AI and automation will be critical to their success over the next two years. In contrast, a recent study by open-source AI solutions firm ClearML, in partnership with the AI Infrastructure Alliance (AIIA), revealed that 59% of top-tier executives believe they have inadequate resources to fulfill their AI innovation ambitions.

How can businesses have more success in bringing their AI visions to fruition in 2024? In conversations with fellow leaders in fintech, a few themes have emerged.

Start small

First, business leaders should start small. In their eagerness to adopt AI, business leaders who bite off more transformation than they can chew are setting themselves up for a false start. No one should be trying to solve every challenge in their company all at once with AI.

Instead, leaders should narrow down their initial AI project to a small area with high potential upside and very low risk. My fellow fintech leaders and I have witnessed real success when organizations have carved off one small division or operation at a time and implemented AI in an area that quickly creates efficiencies for their teams—usually by taking over a manually-intensive, repetitive task. By launching these small-scale pilots, business leaders can gain quick wins, learn valuable lessons and keep up the momentum in their AI transformation—moving from traditional analytics on to more advanced capabilities like predictive modeling, line-of-business process improvement, efficiency gains and more.

Don't recreate the wheel

Second, business leaders should never recreate the wheel when they don’t have to. A common hesitation around AI deployment is the fear of needing to create an AI tool from scratch, requiring an advanced mathematics degree or deep competency in coding.

For most organizations, this fear is unfounded. The needs of businesses, especially smaller organizations, do not require bespoke AI tools. In most applications, where a pain point is in a common business workflow, leaders can look toward existing and emerging AI solutions that have already solved that problem. More and more SaaS applications have some level of AI functionality built-in, integrating smoothly with a business’s ERP and other systems, removing the risk inherent in building a tool from scratch.

360-degree buy-in

Lastly, leaders should gain 360-degree buy-in from their teams. With any transformation, leaders should respect that not everyone in an organization might share the same level of optimism. Board members might be wary of unproven investments, and employees might expect difficulties with AI implementation, or doubt a technology’s effectiveness, or even fear for their own jobs.

Wise business leaders will anticipate those hesitations and address them head-on. Starting small will mitigate fears among the higher-ups. Trusted industry reports demonstrating the success of AI implementation in similar use cases can do this as well—especially where those successes translate into dollars.

Among employees, leaders should clearly explain the reasoning behind the transformation—why a certain division or operation was chosen, why the selected AI solution will be a good fit and how the change will improve employees’ work experience as well as their relevance in the marketplace. Communicated correctly, a technological transformation might even feel akin to a retention bonus.

Leaders should also gauge individual teams’ appetite for new tech. Some teams will prefer an “easy button” solution with minimal disruption. Others, however, will appreciate both the journey and the end result of a transformation, seeing both as opportunities for growth, and will be eager to continue adapting as new AI capabilities emerge.

Finally, in order to correctly anticipate what it will take to move on from the design phase into production requires business leaders to bring together three essential stakeholders: the business leaders themselves, the data scientist or IT professional (who could be internal or external) and the functional team leader who will be deploying the end solution. Having these three players in the room for ongoing, collaborative discussions around design, build, experimentation, deployment and maintenance is essential for success.

Leaders are right to be optimistic about AI. By focusing on an incremental approach, sensible technology choices, careful collaboration with stakeholders and careful communication across the organization, business leaders can turn that optimism into a successful AI transformation.

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