Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
Mankind has been making tools for millions of years. Human society has not only evolved for centuries alongside these tools but has evolved because of them. In our digital age, the latest transformative tool is AI — the wheel that can learn to reinvent itself, pushing the boundaries of what a tool can do or be.
With its vast potential across industries and sectors, global AI spending is forecasted to double between 2020 and 2024, growing from $50 billion to more than $110 billion.
The financial industry is no exception: AI applications are accelerating the areas of asset management, tax analysis reporting, credit underwriting and more. The ability to strategically leverage AI has proven to be a major asset for finance teams in improving efficiency and productivity by streamlining processes and reducing errors.
And yet, some financial teams still hold a limited view of what AI can do. Many industry professionals, particularly those who have been in the industry since before AI struck the zeitgeist, see it as just another efficient tool for simply “crunching numbers.” Although AI can do that masterfully, that is just a sliver of the bigger picture — AI’s true value lies in deeply analyzing data and the people behind it.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Don’t miss the big picture
Data has the power to reveal subtle insights and elucidate trends that can be vastly beneficial for business operations.
The traditional approach to enterprise data has been goal-oriented, such as pinpointing customer churn or tracking siloed KPIs. However, when companies analyze data with only one metric in mind, they limit the scope and subsequent value of the insight the data can offer.
If a company seeks specific answers — say, the amount employees spend on coffee in December — they will find only those answers illuminated within the data set, and nothing more. This narrow approach to data analytics can only ever offer answers, as opposed to broad insights — not because the bigger picture isn’t there in the data, but because analysts don’t know what answers to seek.
It’s like looking up at the summit of Mt. Everest at night with only a spotlight. Even if you can spot the peak, its true vertical magnitude and the majestic beauty of the Himalayan surroundings will be totally lost on you.
Letting the data shine for better AI
Increasingly facilitated by AI, one new approach encourages data to speak for itself, unleashing a previously unseen spectrum of “light.” Only when organizations analyze data for what it is without any preconceived notions can they extract the comprehensive, nuanced insights into data variances that reflect reality.
So, if we think of data as a beam of light, AI is the prism that refracts all of its hidden colors.
When data can self-organize and self-supervise, the results emerge for themselves. This method is common in other realms of data analytics, such as determining data drift or anomaly detection.
But finance teams tend to view data drift as an arbitrary occurrence, ignoring the reasons behind it. That’s a shame because the seemingly small change that accounts for data drift may actually significantly impact the meaning of the data overall. AI tools offer a solution for this data-drift oversight, as they are uniquely useful in understanding the cause (and predicting the effect) of such changes.
In practice, the importance of these subtleties quickly becomes apparent. Consider the following trend: Recently, there has been a shift away from cow’s milk to plant-based milk alternatives in coffee.
Accordingly, the meaning behind ordering a ‘flat white’ has changed, along with how this expense is reported and understood on company expense reports. AI analytics tools would identify that the meaning of ‘flat white’ has changed due to data drift (that is, the “drift” towards often-pricier plant-based milk) thus providing us with insight as to why the price or employee spend has also subsequently changed.
Where do we go from here?
CFOs stand to benefit greatly from trusting their data to shed insights instead of imposing their own metrics onto it. AI-driven business intelligence tools allow them to do just that: Letting the data talk and showing CFOs any given dataset from all possible angles, not just how they think they should be looking at it.
The powerful alliance of automation and AI will only grow in importance for businesses’ financial processes. These tools are integral to producing actionable insights from corporate financial data and for keeping companies up to date on big-picture expenses.
As AI becomes more effective and affordable, expect to see more and more financial companies and departments behaving like data companies: Focusing on data quality and analysis to enhance their decision-making capabilities.
Although data analysis has long been shedding light and insights on business solutions that hide in the datasets, AI can serve as a prism that gives financial leaders the ability to see the data’s diversity of colors and shades as never before.
David Guedalia is CTO of BlueDot.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!