Today on our Tech for Business podcast, we're
joined by Kyle, our president and CEO, to talk
about the evolution of IT support. So we're talking
AI and implementing it into Help Desk. So Kyle,
can you give us some examples? What does this
mean for Help Desk? What does it mean to have
an AI component to your IT support? Sure. Yeah,
it's... It's a broad question, but like any other
use case of the AI side of it, it's about the
augmentative side of it. It's how do you drive
a lot of information and have it assist to speed
up resolution time is ultimately where we're
looking to leverage its side of it. As you look
through, if you think on IT support, for example,
and pretty much any other help desk side of it,
you got a wealth of information and the knowledge
is a wealth of information and past tickets.
There's also a lot of other information around
what else is going on as far as are there other
tickets like this that are coming in, more recent
side of it. For a person to go through and do
those things obviously would take a lot of time
and on the volume of tickets come in, it may
not even be practical, which it winds up in most
cases being so the sport person just deals with
pretty much what's in front of them and or they
can often research on their own. to augment that
with AI, the AI can automatically be doing those
things and recognizing it and flag and identify
trends. It can flag those, say we have other
instance tickets have come in that have the same
problem that are all currently open. There's
information in the relevant knowledge base that's
passed tickets. This has been the resolution.
So just to bring things much faster with with
the ultimate outcome of being quicker and better
service for or for the requester of the ticket.
And that's what we're all trying to aim for.
Yeah. So I've experienced in the help desk before,
I've experienced a knowledge base and a chat
bot. So what is really bringing it to that next
level beyond just a little helpful chat bot?
What really moves it into that? AI that different
higher level. Yeah. Yeah. So there's there's
that end and end user consumable side of the
eye to kind of self service side of it. It's
kind of what you're alluding to. And that's that's
that's an obvious use case where you can have
virtual agents and those other sides combined
to it. But it's the augmentative side, again,
of of the person on the support desk or the people
on the support desk being. assisted by the AI
with the data insight side of it. It's that data
aggregator that we're really, really leveraging
the most value side of it. To give you like a
use case example for the new support desk side
of it, AI is involved on every ticket that comes
through. And whether it's... known or unknown.
It is having an active role that's participating
in every support ticket at this point. So as
a ticket comes in, it'll look at the information
in the ticket as far as what is the summary and
then what is the details of the ticket. It's
doing AI insights into that ticket automatically
and presenting to to the support agent side of
it. And hey, these other tickets are out there.
Here's possible resolutions for the ticket that
I've found. And once even once they service at
the eyes looking at tonality it's trying to understand
is is the is the user frustrated or the neutral
are they happy. those sides of it. So it's cluing
into a number of things that gives deeper insight
that, again, a typical person working on the
sport ticket traditionally wouldn't have gone
through all those iterations or spent all that
time, but it's automatic. It's happening within
seconds. It's running against those sides of
it. And then even once a ticket close, the AI
is doing analysis of the ticket for us to determine
should this be a charge of style service or not?
And we have the AI reviewing those things to
say, if the agent updated ticket, for example,
to say, well, I left a voicemail, if they errantly
flag that as a charge to the customer, the AI
will see that and it will automatically change
it to say that is not billed. So it's learning
and applying thinking to those sides that again
would require a supervisor or somebody else to
catch on those sides. It's just a tremendous
time saver, really, at the end of the day. And
it is giving you that high -level assistance
with just a lot of information. Yeah. That's
company. Oh, yeah. For sure. You kind of talked
a little bit about the benefits, especially for
the agent, the person who's working. You know,
things are working faster for them. They're getting
that extra support and that insight. And then
also the customer with a little bit more transparency
in their billing. What are some unexpected benefits
as you're going through this process? What are
some things that came up you're thinking, wow,
I never considered that as a benefit, or I never
thought it could do that? Initially, one of the
biggest, most pleasant surprises I've seen from
it is the ability to survey the ticket and essentially
do a satisfaction. survey side on every ticket
that goes through the system, whether we still
send out surveys to the users themselves. And
that's, that's an immensely valuable side of
it. But like any other business there, you know,
the, the percent of people that fill those out
is not always, you know, that's nowhere near
a hundred percent. And, and it's, and it's usually
in the, in the 20 % or sub 20s and most industry
sides of that. So you don't get You want to have
a bigger sampling overall. So the AI does give
us its summary and the satisfaction of the ticket
across all the tickets. So why not 100 %? And
it obviously can be errant on some things. It
gives you a good idea, a much, much broader idea
of how well things are going. overall, and if
there's trends and those other sides of it, it's
like any other risk. You want awareness. The
sooner you get the awareness, the better, because
you can make changes and you want to be able
to measure those things. And so there's just
a build on that. So yeah, just being able to
help with those things and recognize it, because
it'll pick up on things like they're reopening
the ticket multiple times. We are infringing
on the SLA, you know. times, whether it's response
time or resolution time or whatever it may be
side of it, it's picking up on those and it's
giving us those ratings. So even if the person
didn't give us a neutral or a negative or even
a happy, you know, it was excellent side of it.
We're seeing that, you know, we are getting some
good samplings on that. I think that's going
to be immensely valuable to help us identify
the overall trend. Yeah, I never considered that.
And, you know, we have an amazing customer success
and strategic engagement team. So now Help Desk
is no longer just... your IT support, it can
also be like your customer and company support,
you know, it's really expanding or it gives a
an opportunity to expand that customer journey.
That's really cool. Yeah. Yeah. And those those
are active on, you know, not only the support
desk side, but that's also on, you know, our
our service delivery team for project work and
reoccurring ticket work side of it. So those
are. Just to get an idea, again, how well are
we doing or in those particular areas where we're
not seeing as high as we want and should be investigating.
And the nice thing is that it can be proactive
on our own necessarily and not in a vacuum side
of it or fall to the, boy, it feels like, or
I hurt. Like any other business, just trying
to give you some data so you can make a better,
more intelligent. decisions of where to spend
your time or make changes. I have to ask about
data privacy because I think that's everyone's
question about if you have an AI that's looking
at all these different tickets from all these
different companies that you're working with,
is there a concern about privacy? Is there a
concern about any you know, leakage and data
and all that conversation that the AI is having
on the back end, what does that sort of look
like from a security perspective? Sure, yeah,
it's a very legitimate question side of it. What
we've done at CIT side of it, and this is certainly
nothing comes out their side, is our support
desk allows us to integrate into our own Azure
OpenAI instance side of those, which again, is
fed ramped and that stays within our, you know,
our Azure subscribed tenant space side of it,
as well as the vectorized database that is all
the information that the AI is using side with
it. So from a few different sides of it, one,
it's private. We're not using the general model
that you go into chat GPT to, uh, we are no way
turning that side of it. So, you know, we're
happy to supply any information side to say that
we are Even if you're under compliance, we are
on a very private instance side of it. The other
side of it from a general user, as you go through
those sides, the way that the AI accesses and
uses the data is in a vectorized database, which
is it's not searching by words and letters of
those other sides of it. It is in a numeric,
you know, vectorized. database. It's not clear
tags. It's not anything you can make sense of.
It's looking for pattern recognition and patterns
of all inside of it within that data set that
goes through those things. So, you know, it's
not like a password or something like that is
vectorized database side of it. So why I'll be,
you still want to want to train a public model
side on those sides. It's It is inherently very,
very, very secure on how that's handling through
those sides on it. When a user is coming into
our chat bot side of it, they're not seeing any
information inside that vector side, outside
of their own role -based access side within those
sides. They can see our knowledge base. They
can see their ticket information side of it.
There's an authorization side within those things
that limits would be responded to. Of course.
Do you see any limitations to AI? I mean, where
does that human element still need to come into
play? Yeah. The humans still need to make the
undecisioned side of it. I mean, the AI's most
powerful role is that of the assistant. I mean,
it is to bring that information side of it, but
then to ultimately make the contextual call of
what needs to happen is best served by a person.
You know, I think that there are, there are,
there's a multitude, there's, there's two different
sides to the help that are to the AI. And sometimes
they get rolled together as there's AI and there's
automation and they work hand in hand in a lot
of ways, but still ultimately we want to, we
want a person driving them both. So for example
side, we're, we're, we're engaged in a lot of
automation that is layered with AI to to help
identify that an automation is available, but
then it's triggering automation of, say, a new
user onboard. We have a new person starting,
and then when that comes through, there's typically
a lot of new steps on every new user onboard
side of it. You've got to identify what security
permissions they need, what group they need to
be in, do they need a shared mailbox? There's
a whole bunch of things. What applications need?
There's a number of different steps on it. Some
of them can be very complex and very elaborate,
and they can take a lot of time, which again,
It can be expensive for all parties involved
side with it. So by using AI to identify this
is a new user onboard request and then triggering
that information side of it based on information
at the time of request, you can ask those questions.
Do you want, what group do they need to be in?
Do I need to copy them from an existing user
side of it? Are they in the same roles as somebody
else? So I can use that example. Do they need
access to the shared mailboxes? When is the start
date? we can have an AI trigger automations to
start that process, to automatically log in and
provision and create and do all those other steps
where you'd have to typically go in and log in,
find credentials, go through all these steps.
We can have it automatically happen. Then it
comes back to the engineer to say, here's everything
I do confirm that I should review it. They can
review it, say this is correct, execute on that
side of it. Done. It updates the ticket. Be a
responding user. It looks great. And if there's
anything that needs to be changed, then the support
technician can do that. But if you can take something
that is an hour or two hours in time and make
that 20, 30 minutes, I mean, that's huge for
our customers. That's also huge for us because
the Those technicians now are freed up for more
complex troubleshooting. Again, when it doesn't
go well or when they have a more complex software
application that needs to be installed, they
can spend their time focusing on that and not
clicking through the group memberships and all
the tedious things that have to be set up and
trying to track a set of checkbox sheet that
they may forget a step on it and people do those
things. So there's a There's a multitude of ways
that I think it's just a tremendous value. And
it's identifying those to keep those people focused
on where there is far more difficult and it's
not as a cookie cutter through the project side
of it. And take those cookie cutter things and
automate and have AI identify and help execute
those automations and just present those to the
technicians and then everybody's in a much better
spot. Definitely. Well, I want to make sure that
we get a chance to talk about anything that you
want to share about this topic, but I'm interested
in zooming out a little bit because we've got
this AI. We're implementing it. It's beautiful.
How is the workforce really going to change?
I mean, what are skills that these IT professionals
are going to need now? when they're using more
AI and they're using more automation. What does
that look like going forward? Yeah, that's it.
It's a great question, side with it. And it's
ever evolving. So, of course. But again, I think
as you look at these roles, and this isn't just
even the sport desk, I think it's pretty much
all of our roles in general sides on those. If
you're doing any kind of knowledge based work
side of it is you have to learn how to be. the
manager of the AI, you need to be an AI orchestrator
or AI manager side of it. So as as things evolve,
it's going to become very likely that everybody's
going to be you're going to be in charge of a
number of different AI agents. And that's what
you're going to to manage and orchestrate how
they perform those tasks. And then you're ultimately
at the result to come out of those things. So
more results focus are focused on your on your
responsibilities and job and not just like a
job description side of it. I think it's going
to come through to say this is the outcomes expected
from your position side of it. Here's the tools
that you can manage and run through. And these
tools will help you create those outcomes. So
it's it's it's less about you having to do all
of those things, but just make sure that they're.
accomplishing those end games and making the
correct contextual and right decision sides for
them, as well as evolving with it. Because that's
the other important side of this AI is I think
a willingness to learn and continuously learn
and evolve with the capabilities of these new
tools that are rapidly evolving and continuously
enhancing on what they can do. you have to continuously
challenge how it can help us do the job even
better and do it faster and get the better outcomes
coming through those sides of it. There's the
need for human API. If you're just taking information
one screen and plugging in another, that's just
going to go away. It's not necessary because
the AI is just that's what it does incredibly
well. But AI can't, it doesn't have the contextual
side that humans do. And that's, again, having
the human in charge of the outcome is incredibly
important. You know, the AI isn't going to be
able to make that, at least not yet, and it's
gonna be a while, I think, before it can do those
things. But I think there's, you wanna put yourself
and be comfortable. understanding what the AI
can do and learn how to maximize the results
of those tools. And you find yourself in a very
strong position, not only in those roles, but
pretty much, I think, in the job market going
forward. I think that's definitely where it's
going. For sure. Is there anything else you wanted
to share on this topic of AI and help desk or
IT? You covered all the really, really good questions
for this. There's a lot there. I could obviously
go on for hours about where I can go from it.
But again, the end result for me is, again, the
augmentative ability of having these tools deliver
the service better and faster and more predictably
side of it, and then allowing us to do data insights
into how we can continuously improve and evolve
it to do better. is the immediate outcomes that
we're looking for and actively leveraging and
continuing to learn how to leverage even more
and continue to adjust as it comes through. You
know, I'm just super excited about what it can
do. Very happy with what I've seen so far, but
even more excited, I think, for what we haven't
got to yet of what it can uncover and help us.
Yeah, for sure. I'm excited, too. I'm excited
to see how things will grow and change as we
move forward. Thank you for joining us today.
If you enjoyed this topic, please let us know.
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