clients still need context.
by penny breslin
it’s not just the numbers
in 1994, in a small hotel meeting room in colorado springs, 50 accounting firms were up in arms when the presenter mentioned quickbooks. they shouted angry comments: “quickbooks is destroying my business.” “they want to take my clients.” “they are giving people a false sense of security with their commercials that say, ‘if you can write a check, you can do quickbooks.’”
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the presenter was nervous and asked me what he was to do. i said i use quickbooks for your company, and i have no idea what i am doing, and i could use an accountant to teach me.
the next two days were dedicated to showing them how much help was needed, and a quickbooks training company was born. i seem to remember a lot of accounting people made some nice income from that.
we have the same kind of hysteria today. “robots will take our jobs.” accountants fear that automation means clients will no longer need us to keep their books.
there is nothing to fear and a lot of work that is yet to be done. the loss of data entry is by no means a loss of required human interaction. artificial intelligence (ai) is a marketing term. truly what we have, because of faster processors, blockchain security and multiple data-points, is automated machine learning (ml).
so what is machine learning?
machine learning can be defined as “computers’ use of algorithms to find statistical patterns in massive amounts of data, which can then be used to make predictions” (the brookings institution).
with machine learning, accountants will have more time to spend on review of incoming data. the decision on what to do with any given piece of data can be done by what was traditionally a data entry bookkeeper. errors can be minimized by using machine automation, yet often a human still has to recognize and act on two types of failures:
- alpha failure: probability of failure to detect an error
- beta failure: false negatives
those errors can occur with ai as well as with humans. think of all the intersection points that can occur in any given data file in the cloud. the intersecting data is typically the first point of failure, especially when you put three to five apps on top of your general ledger and allow them to sync in real time. and that is just a review of simple writeup. here are some of the ways that human discernment and judgment are still needed when apps intersect:
- splitting transactions into varying amounts
- duplicate transactions when multiple data points cover the same information
- new bank or credit card account
- business owner mixing personal and business income and/or expenses
- reimbursable expense income mixed with regular income
- multiple people working on the same app file, allowing for duplication of effort and transactions
that is all initial surface stuff. we could go on and on with the problematic items that crop up when apps intersect.
machine learning will adapt and improve over time. along the way, information provided by humans to check the failure factors will be critical to that improvement. however, that is all just binary-level information. small business owners still need advisors for the things that machines can’t do.
the average small business owner most likely did not take courses in expense management, cost accounting, tax law, valuation, financial analysis or projections. or reading the myriad of u.s. or state regulations and then keeping up with all the changes in those regulations. a machine will not take a call from an anxious or worried business owner late on a friday afternoon and provide anything more than that binary support. ai does the data entry, but your value comes when you provide the context.
providing back office support (bos) is how you provide that context and demonstrate your value and expand your opportunity for client advisory services (cas). controlling the basic inflow and organization of data for a business puts you in the driver’s seat for expanding the value add.
a future with binary-level automation is inevitable, but how you approach that future, get ahead of it and respond to it is a choice. so as bos it behooves us to be flexible, to collaborate, to move along with the changes in technology and, most importantly, to keep open the lines of communication with clients.
when atms first started popping up around the u.s., there was an outcry that tellers would no longer be necessary and therefore a whole job market would be gone. contrary to that idea, what occurred is that banks hired more people, restructured the work type of current tellers and hired more employees to work on other customer-facing functions. it is not always true that automation moves one to another job within the same framework, but in the services arena, it is not uncommon.
the move away from data entry has been ongoing and mentioned over and over at accounting conferences, training classes and from the app developers for the last decade at least. get prepared, this is coming, and here is how you can re-engineer how you work using these new tools. this has been presented and spoken about over and over. there is a myriad of apps for the accountant/bookkeeper to assist, track, manage and analyze data in a general ledger. producing reports that provide understood and actionable information for clients is a very important function and one many small businesses need. assisting them in understanding and actioning is where value lies.
as much as we can feel overwhelmed by fast changes, new technology and all the data, think how your small business owner clients must feel. changes are not just occurring in accounting. these business owners also must be nimble and react quickly to changes within their industry. this certainly is a good time for those who smartly look at a small business niche for their firms.
get in front of ai. it is not the enemy, it is an opportunity. just like desktop accounting software before it, which gave rise to the professional bookkeeper/accountant needed to fix “stuff,” ai continues the adventure with a human at the helm.