Your AI Roadmap: Who Else Should Be on the Journey?

AI’s business use cases

AI has been around for about 70 years, but advances in computational capabilities and the more recent focus on generative AI tools such as ChatGPT and Google Gemini have made it a part of mainstream conversation. And over the last several years we’ve all been encountering AI in one form or another in our daily lives, whether it’s the auto-correct feature on your mobile phone’s texting app, what-to-watch-next recommendations on streaming TV services, digital personal assistants, or smart home devices. These tools are largely designed for convenience.

From a business perspective, Pfeiffer said AI's potential benefits are largely focused on three areas: cutting costs, mitigating risk, and increasing revenue. To achieve these outcomes, he explained that various industries use AI to provide insights for making better decisions.

It helps you understand the patterns you’re seeing in the data more quickly, with more detail and with more nuance, and then getting those insights from management to customers in terms of spotting trends or making recommendations.
-- Marc Pfeiffer, Head, Data Foundations, Innovation and AI, BMO --

O’Donovan noted that while many companies are leveraging generative AI's automation capabilities to improve efficiency in certain tasks, such as document review, the next step in AI’s evolution will be the transition from a tactical tool to a strategic enabler. “The question that remains is that once these tools are in place and these efficiency gains start to get realized, how do you turn that into hard dollars and cents in terms of an impact to your bottom line,” O’Donovan said.

AI’s limitations

The potential benefits of AI are significant, but so are the possible pitfalls. Pfeiffer pointed out that responsible use starts with a foundation of quality data.

“If you have poor data, if it’s not captured properly, if it’s somehow biased, those are things that are going to lead to inaccurate predictions or faulty decisions,” Pfeiffer said. “One of the things we think a lot about with AI is explainability. You don’t necessarily need to know why the AI decided that a certain Netflix show was a good recommendation. But in other situations, it would be critical to understand why that recommendation or outcome was made. You have to be careful about which AI model you use; some have good explainability, some don’t. It’s something you need to think about, particularly in regulatory or internal compliance situations.”

O’Donovan noted that the risk of biased data is especially important to keep in mind.

These are complex systems that have a lot of black box elements to them. Understanding how your data is moving within the system, how you’re making sure it’s not treating customers or employees in different ways across different protected classes is critical.
-- Paul O'Donovan, Head, Data Analytics and AI Risk Management, BMO --

Upskilling treasury departments

That’s why as more companies adopt AI, some upskilling will be required within corporate treasury departments. Everyone won’t need to become computer scientists, but data literacy, critical thinking and ethics awareness will become essential skills.

As an example, O’Donovan said treasury teams will need the ability to spot where AI functions are not working as expected and know how to relay that information to the development team. “I do see some need for training folks within treasury departments to be able to appropriately interact with the AI system and be able to interpret and flag the output,” he said.

Pfeiffer also cautioned against overreliance on AI, particularly in situations that require nuanced communication, such as customer service.

If you have a chatbot communicating with a client, you need to be able to recognize when an AI isn’t able to handle a question and that it needs to go to a human who has a more nuanced understanding of the business
-- Marc Pfeiffer, Head, Data Foundations, Innovation and AI, BMO --

In its current state, AI functions well when it supports human efforts and work, so the lines of accountability are clear. “In certain cases where you're dealing with sensitive business processes or sensitive information, it’s critical that AI is seen as an enabler in the decision-making process and not the ultimate deciding factor,” O’Donovan said.

Who should be at the table?

As businesses look to leverage AI to support strategic needs, involving key stakeholders early in the process and communicating your goals are crucial to a successful implementation.

Many parts of the organization can be impacted. Ensuring that you have that buy-in early on is going to be a critical factor.
-- Paul O'Donovan, Head, Data Analytics and AI Risk Management, BMO --

To that end, O’Donovan listed the key groups that should be engaged in these discussions:

Risk management
Procurement
Legal

IT
Risk and compliance
Human resources
Third-party technology providers

Along with getting the right stakeholders involved, cross-collaboration among these groups is also crucial. “What has really worked well is collective engagement,” O’Donovan said. “There’s a lot of overlap between the legal group and the third-party group, the technology group and the risk group. It’s important to have folks in the same room at the same time to make sure that all the different perspectives can be heard.”

Involving all your key stakeholders will help you navigate all the necessary considerations for a successful implementation. That includes making sure you’re using AI to solve for the right use case and that you’re using high-quality, structured data. It also means understanding how AI may impact your operations.

“The AI model is half the challenge,” Pfeiffer said. “The other half is making sure that what you're building will meet the needs of your business. Making sure you understand how it will be used, how the user—whether it's an employee or customer—is going to receive it, and how that's going to change their own daily operations as they interact with the company. Employee training and change management are critical pieces to making sure you understand the post-implementation functionality.”

Ultimately, knowing what you want AI to help you accomplish will drive all the other decisions that your key stakeholders will be involved in. “Companies tend to be less successful when there's not a clear set of criteria for success at the outset,” O’Donovan said. “Being clear on what constitutes a good outcome needs to be clear from the beginning so that all the different partners can be aligned on what the ultimate success looks like.”

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Oscar Johnson

U.S. Head of Commercial Sales for Treasury and Payment Solutions, BMO Commercial Bank

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