Innovating Financial Processes with Intelligent Automation

IA FORUM MEMBER INSIGHTS: THOUGHT LEADERSHIP INTERVIEW

Claire Grosjean, Former Senior Director, Business Process Management, TECHNOLOGY CREDIT UNION

What key skills and capabilities are essential for successfully implementing intelligent automation solutions in financial

processes?

It is interesting to me as I have seen intelligent automation programs starting within IT or Finance. That highlights that the skills required are a mix of technical expertise and domain knowledge of financial processes. Technical expertise would be around RPA, AI, machine learning, and data analytics tools. It is also very helpful to understand ERP systems and the different finance platforms to integrate automation seamlessly into existing systems. These skills should also be complemented with strong project and change management as well as the ability to work cross-functionally.

How can organizations foster a culture of innovation to maximize the impact of intelligent automation?

In order for a culture of innovation to be prominent, it has to start with the leadership team and a clear vision. This should include a ‘fail-fast’ mindset to allow employees to experiment. There should also be some training and upskilling opportunities to increase digital fluency and comfort. Finally, there should be a forum to learn from failures, reward innovation, and celebrate successes.

Beyond efficiencies and cost savings, what are some overlooked benefits of adopting an intelligent automation program?

There are many benefits of adopting an intelligent automation program beyond efficiencies and cost savings. I would break them down in 2 categories:

1. Tangible benefits such as improved accuracy and compliance, especially in terms of having a standardized process. Automation also increases employee’s satisfaction by eliminating mundane tasks.

2. The second category is more around gaining valuable insight from data capture and analytics, enabling better and faster decision making. I also see an IA program as a steppingstone and solid foundation to more complex automations such as with unstructured data and ones leveraging Agentic AI or Generative AI.

What common challenges do organizations face when implementing intelligent automation & how can they address these effectively?

The main challenge I have seen is the lack of alignment with strategic goals and stakeholder expectations. Companies should ask the question: what does success look like? What is our budget and who will work on this program? Other challenges as you start the program are often around data quality, systems integration, and having a solid backlog of processes ready to be automated.

How do you ensure that intelligent automation initiatives align with broader strategic goals in financial services?

Great question and one that must be answered before starting an IA program! The strategic goals need to tie to key organizational objectives. These objectives need to be prioritized based on capacity to ensure focus and alignment across the organization. It is best practice to use KPIs (key performance indicators) metrics to link IA program outcome to business goals and measure the benefits.

What role does leadership play in driving successful adoption of intelligent automation across teams?

The program needs a leader who can act as a champion, providing vision, resources and advocacy. Without a champion there will always be the ‘next shiny object’ that distracts the program. The champion needs to be able to articulate the ‘why’ across the organization and help foster a culture of learning and innovation and reduce the fear of change.

How do you measure the long-term success of intelligent automation programs beyond the initial implementation phase?

Goals must be in place at the beginning of the program to understand what success should look like. Ideally the program should measure tangible benefits such as value realization (cost savings or avoidance, additional revenues, etc.), accuracy & speed. I also recommend measuring on-going benefits as volumes change, and not just when an automation gets deployed in production or over the first 12 months. Employee satisfaction should also be measured including adoption rates & user feedback to ensure longevity of the program and continuous improvement.

In summary, what are your key lessons learned & what advice would you share with others tackling similar objectives or challenges?

In my experience, it is better to start small to build confidence and demonstrate success early on with simple processes; learn what works and what doesn’t work, pivot if need be. I have also learned that creating short demos of automation in production can go a long way in fostering adoption and support teams embracing automation. Finally, teams need to adopt a continuous improvement mindset to regularly evaluate and optimize processes as well as the technologies being used.

Author Disclaimer: The views and opinions expressed herein are those of the Author alone and are shared in a personal capacity, in accordance with the Chatham House Rule. They do not reflect the official views or positions of the Author’s employer, organization, or any affiliated entity.