AI-driven finance department needs technical and business skills

To build an AI-driven finance company, it will need both technical and business skills to help them meet global demand.

Gartner highlighted the new AI-focused roles and skill sets finance leaders will need to incorporate into their teams, the importance of keeping people in the loop when using AI-based solutions, and other AI best practices.

Mark D. McDonald, senior director analyst in the Gartner Finance Practice said he is seeing a profound shift in how finance teams work based on the accelerating use of AI to help them handle the increased complexity of their daily activities and become more productive.

“Just like any disruptive technology throughout history, AI will inevitably displace and replace some roles and skills, but new roles, skills, and opportunities will also emerge,” he said.

“Leading finance teams will learn to position AI-driven tools and solutions as co-workers that help them do their jobs better. Using AI as a co-worker instead of a replacement also ensures that finance leaders avoid delegating responsibility to machines that should be owned by a person.”

In the future, Gartner analysts said they expect finance and accounting teams to look more like software development organisations, and finance leaders should begin introducing aspects of this paradigm shift into their teams.

According to Gartner, there are three factors that will play an integral role in finance teams in the future.

Firstly, citizen data scientists, these are current finance and accounting staff that will learn basic data science skills to help them automate tasks and make better decisions.

Citizen data scientists will not have the expertise to build complex solutions that extend beyond their immediate scope of work. They will need help building professional-grade solutions.

Secondly, a centre of excellence, these are roles new to an organisation and are comprised of technical professionals who can build and maintain technically-solid AI solutions.

These include professional data scientists, software engineers, data engineers, statisticians, and other non-traditional finance roles. A centre of excellence often starts with a single professional data scientist and grows with demand.

Lastly, company leadership, building teams of the future requires leaders to balance the challenges of introducing new skills and processes while making sure that the organisation continues to support daily business operations.

By taking advantage of the complementary strengths of people and AI technology, finance teams can build a collaborative partnership in a human-machine learning loop where each relies on the other.

This loop establishes a new pattern of working that taps into AI’s power without removing people from the equation.

McDonald said the loop formalises what each is responsible for so that leaders don’t over-rely on machines or delegate human responsibilities to machines.

“The loop ensures human feedback and judgement remains front and centre,” said McDonald.

“Leverage AI for what it does best – automating manual tasks. Be cautious about allowing machines to take actions based on their own advice. By leveraging the strengths of both people and their new machine counterparts, organisations can reach new levels of productivity and value without big risks.”