A standing room only crowd for an industry conference’s AI session,
something seen with great regularity these days. But it's actually from an
American
Payroll Association event in Orlando. You read that correctly.
While the payroll function and services market likely weren’t among the
first AI or RPA candidates written on white boards in innovation labs, this
obvious level of interest might suggest a “can’t see the forest through the
trees” dynamic operating in some of those innovation labs. Back-office
corporate functions such as payroll are in fact fertile ground for RPA and
intelligent automation overall, given the preponderance of recurring manual
tasks and transactions not dependent on person-to-person interaction.
Innovation labs are now on the case.
The speaker for this session called “Prepare Your Teams for the Future of
Payroll: Robotics, Automation & Shared Services” was Brian Radin, President
of global payroll services provider
CloudPay
and long-time entrepreneur in the HR Tech space as well. Brian immediately got
everyone’s attention by factually reporting that the number of bank teller jobs
did not decrease in the years following the introduction of ATM machines.
Teller numbers actually went up due to shifting staff costs to support new,
higher value services within retail branches, which ultimately allowed more
local branches to open up, tellers in tow.
Using AI in the realm of HR operations, including cognitive computing and
RPA (Robotic Process Automation) or bots, has been explored in my blog posts. Radin’s session focused specifically on AI’s current
and future use in payroll operations, including via services providers like
CloudPay.
Some Easy Questions, Some Hard Ones
Radin’s talk directly addressed some key questions about “AI in Payroll”;
e.g., how can (or will) these capabilities help payroll clients spend less time
on manually intensive, routine or recurring tasks, ones that machines can often
handle with more alacrity? And are there other tasks where resourcing can be
toggled between human and bot staff depending on availability? Here the
presenter highlighted examples like data validations and checks pre and
post-payroll run (payroll has quite a few of those), machines fixing errors or
automating the consolidation of data, and of course, chatbots to answer
recurring questions like “what is my accrued PTO?” or “when will I receive my
first check?” (Questions which come up hundreds of times per year.) Allowing
RPA tools to handle these will benefit clients of providers like CloudPay and
any other vendor investing in these capabilities. And as far as highlighting a
“resourcing agnostic” (bot or person) type of activity in payroll, the example
given was using people or bot staff to train new staff.
One of the highlights of the session for me was listening to questions
attendees were posing at the podium afterward, away from the large audience.
One gentleman told Radin that training and re-skilling of staff were already
going on in his company in areas where RPA would be heavily leveraged, but it
sometimes provided only a year or so of “job runway” for employees until RPA
would impact their next job. Then re-skilling would have to start again.
Radin’s response was both admirable and accurate: “Re-skilling decisions in the
RPA era is very much a work in progress.”
Machines that Do, Do and Think, and Learn
CloudPay’s VP Marketing, David Barak, elaborated for me after the session on
Radin’s slide which highlighted these three different categories of RPA
capabilities: “Do” describes the use of RPA to move and manipulate payroll data
without human involvement, as one example. “Do and think” capabilities include
the machine flagging and fixing hundreds of data issues pre-payroll run; and
while “Learn” is an RPA capability in payroll processing that’s still being
tested and improved upon (as with machine learning in most areas), it includes
anticipating spikes in payroll processing costs based on time of year, business
cycles, new regulations, etc. This information can then guide the customer in
optimizing staffing levels.
Bottom Line:
Payroll departments and services provider clients will increasingly benefit
from emerging RPA and cognitive capabilities. It will probably be a few steps
forward and a couple backward until something akin to a “human/bot hybrid
resourcing homeostasis” is figured out – in general, and also reflecting
specific customer contexts. Predicting how far / how fast with any precision,
in any industry or discipline, is almost a total crapshoot. One thing we do
know, machines are not nearly as susceptible to errors due to work overload or
distractions.
