Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success<\/em>. Learn More<\/a><\/em><\/p>\n

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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<\/a> is AI \u2014 the wheel that can learn to reinvent itself, pushing the boundaries of what a tool can do or be.<\/p>\n
With its vast potential across industries and sectors, global AI spending is forecasted to double<\/a> between 2020 and 2024, growing from $50 billion to more than $110 billion.<\/p>\n
The financial industry is no exception: AI applications<\/a> are accelerating the areas of asset management, tax analysis reporting, credit underwriting and more. The ability to strategically leverage AI has proven<\/a> to be a major asset for finance teams in improving efficiency and productivity by streamlining processes and reducing errors.<\/p>\n
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 \u201ccrunching numbers.\u201d Although AI can do that masterfully, that is just a sliver of the bigger picture \u2014 AI\u2019s true value lies in deeply analyzing data and the people behind it.<\/p>\n
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Event<\/h3>\n
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Transform 2023<\/span><\/p>\n
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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.<\/p>\n<\/div>\n<\/div>\n

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Don\u2019t miss the big picture<\/h2>\n
Data has the power to reveal subtle insights and elucidate trends that can be vastly beneficial for business operations.<\/p>\n
The traditional approach to enterprise data has been goal-oriented, such as pinpointing customer churn<\/a> 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.<\/p>\n
If a company seeks specific answers \u2014 say, the amount employees spend on coffee in December \u2014 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 \u2014 not because the bigger picture isn\u2019t there in the data, but because analysts don\u2019t know what answers to seek.<\/p>\n
It\u2019s 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.<\/p>\n
Letting the data shine for better AI<\/h2>\n
Increasingly facilitated by AI, one new approach encourages data to speak for itself, unleashing a previously unseen spectrum of \u201clight.\u201d 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. <\/p>\n
So, if we think of data as a beam of light, AI is the prism that refracts all of its hidden colors.<\/p>\n
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<\/a> or anomaly detection.<\/p>\n
But finance teams tend to view data drift as an arbitrary occurrence, ignoring the reasons behind it. That\u2019s 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.<\/p>\n
In practice, the importance of these subtleties quickly becomes apparent. Consider the following trend: Recently, there has been a shift<\/a> away from cow\u2019s milk to plant-based milk alternatives in coffee. <\/p>\n
Accordingly, the meaning behind ordering a \u2018flat white\u2019 has changed, along with how this expense is reported and understood on company expense reports. AI analytics tools would identify that the meaning of \u2018flat white\u2019 has changed due to data drift (that is, the \u201cdrift\u201d towards often-pricier plant-based milk) thus providing us with insight as to why the price or employee spend has also subsequently changed.<\/p>\n
Where do we go from here?<\/h2>\n
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.<\/p>\n
The powerful alliance of automation and AI will only grow in importance for businesses\u2019 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.<\/p>\n
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<\/a> to enhance their decision-making capabilities.<\/p>\n
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\u2019s diversity of colors and shades as never before.<\/p>\n
David Guedalia is CTO of BlueDot<\/em>. <\/p>\n
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\n David Guedalia, BlueDot<\/p>\n","protected":false},"excerpt":{"rendered":"
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Event<\/h3>\n\nTransform 2023<\/span><\/p>\n