According to an EY report, the average age globally for a newly minted CFO is 42. Unsurprisingly, they’re a highly educated bunch, with 27% of CFO’s surveyed having completed an MBA and 27% holding a chartered accountancy qualification.
Highly educated talent with years of experience usually doesn’t come cheap. And for small to medium businesses, this could mean forking out a salary in the range of $130,000 to $250,000 to land themselves a C-Suite financial executive.
So with CFO’s trading at premium, is there any way the knowledge, analytical brains and strategic insights a CFO possesses could be mass produced for less? For companies turning over between $1M to $5M per year, deep learning, big data and AI is quite possibly the answer.
Medical profession leads the way
We need not look far to see a similar story of knowledge democratisation at play – medicine. In late 2015, computing powerhouse IBM acquired Merge Healthcare, a medical imaging business with a collection of over 30 billion X-rays, CT and MRI scans. These images are ready fodder for it’s hungry AI protégé Watson. IBM plans on using the data, alongside other diagnostic indicators such as pathology results, genetic data and clinical studies, to train Watson to recognise diagnostic patterns and abnormalities.
The impact of these sorts of deep learning initiatives in the medical sector is huge. When presented with a patient in the future, in order to diagnose and treat, Watson will be able to call on a database of knowledge far wider than any medical professional alone. The era of Dr House could soon be eclipsed by that of Dr Watson – and it’s hard to see how patients stand to lose.
The ‘just in time’ CFO
The parallels to financial decision making in the business sector are evident. At any one point in time, to effectively manage an organisations financial health, CFO’s must monitor and then interpret a myriad of data points. Not only must they track internal financial metrics, but they are increasingly required to keep a steady pulse check on the wider financial markets, investor sentiment, and microeconomic indicators pertinent to their sector. With data often fragmented internally and externally – and shifting fast – decision making is a complex undertaking.
For small finance teams, such as those in small businesses, the problem is compounded by a lack of headcount. In many instances, the financials are only looked at in depth once or twice a year by an external accountant. That means, for the rest of the year, business owners are left to their own devices, often unaware or unable to comprehend looming financial mishaps, like cash flow shortages or inventory issues.
But, what if a business owner or lower level finance executive could tap into an online CFO, powered by a Watson engine, and obtain insights and recommendations in a heartbeat? An engine with access to numerous financial data sources in real time – internal and external – crunching the numbers and providing ‘just in time’ answers to company specific operational and strategic financial questions? This flexibility would help small business build financial agility into their operations in an affordable way, without the need to rely on static financial planning sessions or expensive advisors.
Watson powering sales efforts already
A great use case for Watson in the business world can be seen in RedAnt’s SellSmart, an offshoot of the the IBM Watson Ecosystem. Using the cognitive engine, the app addresses the pain point of sales staff struggling to keep up to date on ever increasing swathes of product knowledge. Responding to natural language questions, the app can promptly deliver the product information required for the sales member to assist the customer, improving the in store relationship and increasing the likelihood of closing the sale.
Financial data could be delivered in a similar way. Tomorrow’s small business CFO may very well sit within an iPad, and thanks to cognitive AI, might understand what business owners are trying to ask about the financial well being of their businesses, without them necessarily having to use complex technical financial jargon in order to get there.
CFO Watson might be able to:
- Analyse sales across online competitors and advise what future inventory should be purchased to capitalise on market trends
- Forecast and recommend various pricing strategies with respect to their impact on revenue and profit
- Automatically redistribute cash across 2 or 3 term deposit accounts to maximise interest earned verses working capital on call.
- Crunch weather patterns and advise the business to renegotiate an order placed with a supplier for summer stock
- Pick up on consumer sentiment to target marketing spend and sales efforts more effectively
Today the concept of a small business ‘Virtual CFO’ is merely a case of outsourcing financial decision making to an accountant or financial expert. It still doesn’t replace the human at the end of the chain providing the advice and recommended actions after crunching the numbers and interpreting the reports.
Given the financial insights required by small business are significantly less complex than larger entities, utilising AI at the development stage it is at today is feasible. Overly engineered solutions for financial management are not required for this sector. But being able to translate financial data sets, trends and patterns into reliable and actionable insights is.
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