The Convergence of Finance and AI (Artificial Intelligence)

I was recently asked ‘How can the office of finance better line up people, processes, and technology to score high-value data combinations?’

Despite the ever-present demand for financials and reports, the role of finance has always been to move beyond reporting.  Its fundamental role is to use the resulting data to find answers to questions of strategy and direction.  Even though many finance personnel feel driven to move past report generation and into value-added insight, many aren’t sure exactly how to do so.  And paradoxically, the convergence of finance, data visualization, and data science – the very movement that will provide the tools necessary to add value – leaves many in the field of finance feeling threatened.

Fundamentally, according to Hilary Mason in a recent HBR article (How AI Fits Into Your Data Science Team), data science is

“counting things cleverly, predicting things, and building models on data.”

We finance people embrace and embody that theory.  But when data science becomes machine learning and artificial intelligence, then we begin to quiver and worry about our jobs.  But machine learning is still only data science with the ability to “incorporate feedback loops” which still requires human programming and the machinations of the human mind.

Even cleverly assisted by machines, our organizations and CEOs still expect us to answer the question of ‘so what?’  And for the foreseeable future, the human mind is still required here as well.  The continuing role of finance will be to embrace software and technology, release the ownership of repeatable processes to machines, and leverage their capabilities to assist in answering the ‘so what’.

Artificial intelligence and technology, rather than undermining finance’s role in the C-suite, are enhancing its ability to shine.