Up until relatively recently, the only option end-users had for
receiving IT support were phone calls, tickets or emails. Now, thanks to rapid
iterations of artificial intelligence and machine learning technology, IT
departments are able to leverage the power of intelligent bots to offer round-the-clock,
automated (read: agentless) support.
But not all bots are created equal. One of the biggest
differentiators is whether they are rule-based or true AI. Understanding the key
differences here will help organizations make more informed decisions when
adopting a virtual support agent (VSA) model.
Rule-Based Bots
Rule-based chatbots are capable of answering end-user
questions based upon a predefined set of rules that they have been programmed
for. This isn’t to say they’re necessarily basic. In fact, with the right
programming, rule-based bots can be built to be relatively complex (at least,
to some degree). And because they are built on if/then conditions, they are
much easier to train than AI bots, which means they can be implemented
extremely quickly. That being said, they are far more cumbersome to maintain
over time, as every new piece of information must be programmed as it’s needed.
Where these chatbots fall short, however, is in their inability
to understand context and learn on their own. As such, there is often a
disconnect between the end-user and the bot, which can lead to frustration and
delays. For more complex issues, bots can hand over the conversation to a human
agent who can provide a higher level of service and support. This means that
rule-based bots cannot operate completely autonomously. They must rely on human
intervention whenever anything outside of their database arises.
AI-Based Bots
While the human/computer interface of rule-based vs. AI bots
is relatively the same, the major difference between the two technologies is
their self-learning capabilities (or lack thereof). AI bots are programmed with
machine learning (ML) and natural language processing (NLP) so that they can
read and comprehend context and continuously learn and improve on their own. The
key to success with AI bots is access to rich, relevant data.
While there is certainly an investment of time, resources
and money upfront, AI-bots are generally much more cost-effective in the long
run, because they require far less ongoing maintenance than rule-based bots. They
are also more resource-efficient, since they can handle highly complex support
needs without requiring any human input. This enables organizations to optimize
their staff numbers, either trimming down or reallocating human resource to
more meaningful, revenue-generating projects. Meanwhile, end-users receive the
on-demand support they need, maximizing satisfaction levels.
Which Type of Bot is
Right for You?
The best way to determine whether rule-based or AI bots are
the right fit for your organization is to map out your needs. Generally
speaking, AI-bots offer the most “bang for your buck,” because they can do
everything a rule-based bot can do, along with much more complex and valuable
functionality. This is especially important for IT operations. The good news
is, you can experience AI bots in action free for a full 30 days. Simply download your trial
of Ayehu NG today to get started.
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Over the past several decades, the role of IT support has
evolved from basic plug-and-play transactions to handling much more complex
tasks and workflows. Unfortunately, the pace of technological change and demand
for faster, more accurate and more seamless service has also evolved – in many
cases, beyond what human agents are capable of. Furthermore, support teams are
being hindered by antiquated processes and technology silos, preventing them
from realizing their true value.
That’s why more and more organizations are turning to
emerging capabilities, like machine
learning and artificial intelligence, to help supplement and enhance the IT
support role. AI tools, like intelligent chatbots and virtual support agents,
have already proven highly effective in facilitating greater efficiency and
superior end-user service.
IT Support’s Greatest Challenges
To truly recognize the impact AI can have, it’s important to
understand just what today’s IT support agents are up against. Research has
shown that L1 and L2 IT support personnel waste hundreds or even thousands of
man hours each year simply due to time-consuming manual labor and inefficient
infrastructures. Often times, agents find themselves having to switch between
multiple systems and platforms just to accomplish a simple end-user support
request.
Another major hurdle modern IT support teams face today is a
lack of adequate access to data. Or, perhaps we should clarify this to lack of
access to quality, usable data. Agents (and their managers) need access to
insights like this in order to analyze and improve performance. Unfortunately,
these insights are not always readily available in many organizations,
crippling the support desk and ultimately impacting service levels.
When these issues occur, either individually or compounded,
not only does the end-user become frustrated, but so do the IT support desk
agents. And if they’re feeling unhappy, overworked and unfulfilled, they’re far
more likely to churn, leaving organizations with the burden and cost of
recruiting and training. It’s a never-ending cycle.
How AI Can Help
To answer the call of these costly and frustrating
challenges, organizations across the globe are turning to AI technologies. In
particular, they are leveraging the power of intelligent chatbots to handle
lower-level support needs. Imagine how much more valuable your skilled IT
workers could be if they weren’t wasting half their day resetting passwords or restarting
systems. Not only do virtual support agents resolve issues faster and improve
customer satisfaction, but they free up the IT team to focus their efforts and
skills on more value-added business initiatives. This is good for everyone
involved.
Artificial intelligence can also assist with building out
additional content resources, helping higher level agents resolve issues faster
and providing invaluable insight to management to facilitate data-driven
decision-making. Machine learning algorithms can automatically and continuously
analyze past cases to provide real-time guidance for best next steps as well as
identify and make suggestions on areas of potential improvement. It’s like
having a consulting firm working on your behalf, 24/7/365, only without the
hefty price tag.
When IT support agents have access to the most up-to-date
tools and innovative capabilities like AI, they’re jobs will be made infinitely
easier. Trained workers will be able to apply their skills to more meaningful
work, end-users will receive faster and better service, while at the same time,
organizations will realize improved satisfaction levels, higher productivity
and efficiency, and lower costs overall. That’s what we call a win-win-win!
Want to experience
this kind of breakthrough for your IT support team? It’s as easy as downloading
Ayehu NG. Click here to try it free for 30 days and put the
power of AI to work in your organization.
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https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2020/02/chatbot-concept-background-with-mobile-device_23-2147831506.jpg589626peter leehttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngpeter lee2020-02-27 18:58:352020-02-27 18:58:37How AI is Revolutionizing the IT Support Role
It’s the End of the IT Service Desk as We Know it (and We Feel Fine)
If you’ve
been paying attention the last few years, you know Digital Transformation is a
concept that’s sweeping through many organizations, and fundamentally changing
how they operate and deliver value to customers.
There’s
some very cool, but still somewhat emerging technologies underpinning this
disruption, and you’re no doubt familiar with them. Things such as:
Data Science
Machine Learning
Artificial Intelligence
But in the
last couple years, the emerging technology that seems to have garnered the most
mindshare faster than any of them is chatbots. That’s right! Chatbots are the
coolest kids on the digital transformation block, because they assimilate many
of the benefits from data science, machine learning, and artificial
intelligence into a form that can be used today, and deliver value to your
organization and customers right now. As a result, chatbots have emerged as
perhaps the most familiar digital transformation experience for end users.
BTW –
There isn’t any consensus yet on a single definition of “Digital
Transformation”. One thing just about everyone can agree upon though is that
shifting more of the laborious, repetitive tasks that people shouldn’t be doing
in the first place over to chatbots is a good idea. This becomes especially
true when you look at some numbers.
A the 2017
HDI show, Jeff Rumburg, Co-Founder and Managing Partner of MetricNet, an IT
research and advisory practice,
delivered a presentation on the results of his research into the costs of
different service desk access and communication channels. He discovered some
amazing disparities.
Jeff found
that incidents requiring Vendor Support cost on average a whopping $599 per
incident.
If you
needed to get IT Support involved (that’s level 3 support), the average cost
was $104 per incident.
Desktop
Support (level 2) was cheaper, but still relatively expensive at $69 per
incident.
Incidents
going through the Service Desk, your level 1 support tier, cost $20 per
incident. Since level 1 tickets comprise by far the highest volume at most
service desks, that’s a logical place to start applying chatbots.
If you can
push out incident resolution for level 1 tickets to your end users, enabling
them to initiate and remediate their own incidents with chatbots, the cost of
support drops down to a very economical $4 per incident. Yeah, wow!
At this
point, some more skeptical people in IT might be asking – are chatbots a
passing fad or are they here to stay? Let’s look at the objective data on that,
and see what direction the numbers point to.
Earlier
this year, Salesforce.com released a major report entitled the “State of
Service”. Nearly a quarter of their respondents (23%) said they currently use
AI chatbots and nearly another third (31%) said they plan to use them within 18
months.
That
represents a projected growth rate of 136% in the use of AI chatbots over the
next year and a half. By any definition, that’s a viral trajectory.
Spiceworks
published a report not long called “AI
Chatbots and Intelligent Assistants in the Workplace”.
One
question their survey asked was about utilization of intelligent assistants and
chatbots by department. Guess which
department uses chatbots more than any other? That’s right – IT.
Another
question in that Spiceworks survey specifically asked IT professionals if they
agree or strongly agree with a number of different statements. The statement IT
professionals overwhelmingly agreed with more than any other was that AI will
automate mundane tasks and enable more time to focus on strategic IT
initiatives.
Those IT
professionals Spiceworks surveyed were right. One of the biggest benefits of
chatbots is that they automate many of the robotic, laborious tasks that humans
shouldn’t be doing anyway. That frees up those IT professionals to work on more
strategic and far more valuable IT initiatives. Which in turn makes those
professionals more valuable to their organizations.
Why is
offloading that tedious work from IT staff so important? Because Gartner has
shown that the biggest budget item for IT Service Desks is personnel. Between
2012 and 2016, the average percentage of a service desk’s budget allocated to
labor ranged from 84% – 88%. With digital transformations driving up the demand
for IT support, there’s simply no way an organization can hire their way out of
this situation, even if they wanted to.
The
reality is that quality service desk personnel simply cost too much, and no
matter how good those personnel are, they can only keep up with so much volume.
At some point the laws of physics reassert themselves, reminding everyone that
people simply don’t scale very well. Chatbots though, have infinite
scalability.
That limited
human capacity to scale, combined with the increased volume of requests for
service desk support, is degrading end user experiences.
A 2016
Harvard Business Review Webinar titled “How to Fix Customer Service” revealed
that:
81% of consumers say it takes too long to
reach a support agent.
43% of customers try to self-serve before
calling a contact center.
What that
tells you is that waiting for human support has gotten so insufferable, end
users are increasingly willing to remediate their own issues. All they need is
for IT to enable a channel for them to do that.
What kinds
of requests are keeping IT service desks so busy?
Well if
you’ve attended any of our previous webinars you might’ve heard us cite a
well-quoted statistic from Gartner that as much as 40% of an IT service desk’s
call volume is nothing but password resets. 40%!
Another
big drain on your service desk? Requests for ticket status updates. Those can
comprise as much as 10% of a service desk’s call volume, and we’re citing
ourselves (Ayehu) as the source on that.
How do we
know? Well, Ayehu knows because our clients tell us which workflows have the
biggest impact on reducing call volume to their service desks.
Therefore,
if you can use a chatbot to automate just these two processes – password resets
and ticket status updates – you could cut call volume to your service desk in
half! That’s huge, and it will go a long way towards reducing your service desk
ticket costs dramatically.
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Due to ongoing
improvements in artificial intelligence and machine learning
technologies, we are on the cusp of an entirely new era in automation.
Not only are software robots adept at performing routine, repetitive
tasks on behalf of humans, but they are now capable of carrying out
activities that rely on cognitive abilities, such as those requiring the
use of judgment and emotion. One only needs to look at the cars we
drive to recognize just how far automation technology has come.
Does this mean that there will be no place for humans in the future?
The answer – at least for the foreseeable future – is a resounding no.
That’s because, despite the growing list of benefits, there are also a
number of drawbacks to having a system that is entirely autonomous.
That’s where hybrid AI comes into play.
The concept behind hybrid AI is remarkably simple, even if the actual
technologies and strategies driving it are incredibly complex. In basic
terms, a hybrid model integrates humans throughout the automation
process, but uses advanced technologies like deep learning and natural
language processing to make automation systems even smarter.
AI needs humans
Beyond the hype, the truth is that artificial intelligence technology is
simply not yet ready to replace humans – particularly when it comes to
mission-critical applications. Take, for example, Tesla’s autopilot
feature. While the vehicle itself is equipped with the capability to
drive on its own, the driver behind the wheel is still required to
remain alert and attentive to ensure his or her safety. In other words,
AI is capable of running unassisted, but when it comes to
mission-critical functions, it still needs humans, not only to train it,
but to make sure everything stays on track.
The truth is, when artificial intelligence gets things right,
everything is peachy. But when it doesn’t, the outcome can be disastrous
– especially for larger organizations. And while modern AI may have
some impressive cognitive capabilities, at the end of the day, it’s
still just as its name indicates: artificial. Keeping humans in the mix
ensures that the nuances of communication are present and that the
output is accurate and relevant.
Humans need AI
On the other side of the coin, humans can benefit tremendously from artificial intelligence technology. And with 37% of organizations
having already implemented AI to some degree, it’s clear that people
and machines working side by side is becoming the norm rather than the
exception. The reason being, artificial intelligence is like a force
multiplier for human workers.
For example, data mining can be handled far faster and in much more
massive volumes than any human being is capable of. Using AI,
organizations can more effectively turn data into insights that can then
be used to assist in human decision-making. This thereby drives
innovation and competitive advantage.
Bringing it all together
As we progress toward a more automated future, a hybrid approach to
integrating AI can help organizations figure out how to get from point A
to point B with as little business disruption as possible. One way
executives are handling the shift is to create automation centers of
excellence (COE) that are dedicated to proliferating automation
throughout the organization. Taking a structured approach like this
helps to reduce confusion and limit friction.
Members of the COE are responsible for planning, ongoing testing and
continuous oversight of the enterprise automation strategy. Typically,
this group is made up of individuals who possess a mix of critical IT
and business skills, such as developers, operations specialists and
business analysts. Additionally, an entirely new role of automation
engineer is being created to support the COE.
CIOs may choose to create their COEs with existing employees who are
reskilled or newly hired team members. Regardless, COEs represent a
strategic approach that is designed to drive adoption across the
enterprise while delivering key results in support of company goals.
Ultimately, choosing a hybrid approach that includes a combination of
humans and artificial intelligence, is simply the logical evolution of
any disruptive technology. It safeguards against the risks of
early-stage gaps and helps organizations get the most out of new
solutions every step of the way. Done right, technology enables humans
to focus on mission-critical applications while using AI to streamline
operations and identify the best opportunities and strategies for
ongoing organizational success.
AI is not an either/or proposition. It’s up to each organization to
determine the right mix of humans and technology that makes sense. As
new capabilities and options emerge, that mix will inevitably evolve.
And the IT leaders that fully embrace their increasingly strategic value
will know how to create the balance that will continually optimize and
elevate staff, technology and the entire future of work.
This article was originally posted as a guest piece on ITOps Times. Click here to redirect to the official publication.
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Remediating IT incidents in just seconds after receiving an alert isn’t just a good performance goal to strive for. Rapid remediation might also be critical to reducing and even mitigating downtime. That’s important, because the cost of downtime to an enterprise can be scary. Even scarier though is what can happen to people’s jobs if they’re found to be responsible for failing to prevent the incidents that resulted in those downtimes.
So let’s
talk a bit about how automation can help you avoid situations that imperil your
organization, and possibly your career.
Mean Time
to Resolution (MTTR) is a foundational KPI for just about every organization. If
someone asked you “On average, how long does it take your organization to
remediate IT Incidents after an alert?” what would your answer be from the
choices below?
Less than 5 minutes
5 – 15 minutes
As much as an hour
More than an hour
In an
informal poll during a webinar, here’s how our audience responded:
More than
half said that, on average, it takes them more than an hour to remediate IT
incidents after an alert. That’s in line with research by MetricNet, a provider of benchmarks,
performance metrics, scorecards and business data to Information Technology and
Call Center Professionals.
Their
global benchmarking database shows that the average incident MTTR is 8.40
business hours, but ranges widely, from a high of 33.67 hours to a low of 0.67
hours (shown below in the little tabular inset to the right). This wide
variation is driven by several factors including ticket backlog, user
population density, and the complexity of tickets handled.
Your
mileage may vary, but obviously, it’s taking most organizations far longer than
15 seconds to remediate their incidents.
If that
incident needing remediation involves a server outage, then the longer it takes
to bring the server back up, the more it’s going to cost the organization.
Statista recently calculated the cost of
enterprise server downtime, and what they found makes the phrase “time is
money” seem like an understatement. According to Statista’s research, 60% of
organizations worldwide reported that the average cost PER HOUR of enterprise
server downtime was anywhere from $301,000 to $2 million!
With
server downtime being so expensive, Gartner
has some interesting data points to share on that issue (ID G00377088 – April
9, 2019).
First off,
they report receiving over 650 client inquires between 2017 and 2019 on this
topic, and we’re still not done with 2019. So clearly this is a topic that’s
top-of-mind with C-suite executives.
Secondly,
they state that through 2021, just 2 years from now, 65% of Infrastructure and
Operations leaders will underinvest in their availability and recovery needs
because they use estimated cost-of-downtime metrics.
As it
turns out, Ayehu can help you get a more accurate estimate of your downtime
costs so they’re not underestimated.
In our
eBook titled “How to Measure IT Process Automation ROI”, there’s a specific
formula for calculating the cost of downtime. The eBook is
free to download on our website, and also includes access to all of
our ROI formulas, which are fairly straightforward to calculate.
Let’s look
at another data point about outages, this one from the Uptime
Institute’s 2019 Annual Data Center Survey Results. They report that
“Outages continue to cause significant problems for operators. Just over a third (34%) of all respondents
had an outage or severe IT service degradation in the past year, while half
(50%) had an outage or severe IT service degradation in the past three years.”
So if you
were thinking painful outages only happen at your organization, think again. They’re
happening everywhere. And as the research from Statista emphasized, when
outages hit, it’s usually very expensive.
The Uptime
Institute has an even more alarming statistic they’ve published.
They’ve found that more than 70% of all
data center outages are caused by human
error and not by a fault in the infrastructure design!
Let’s
pause for a moment to ponder that. In 70%
of cases, all it took to bring today’s most powerful high-tech to its knees
was a person making an honest mistake.
That’s
actually not too surprising though, is it? All of us have mistyped a keyboard
stroke here or made an erroneous mouse click there. How many times has it happened
that someone absent-mindedly pressed “Reply All” to an email meant
for one person, then realized with horror that their message just went out to
the entire organization?
So
mistakes happen to everyone, and that includes data center operators. And unfortunately,
when they make a mistake that leads to an outage, the consequences can be
catastrophic.
One well-known
example of an honest human mistake that led to a spectacular outage occurred back
in late February of 2017. Someone on Amazon’s S3 team input a command
incorrectly that led to the entire Amazon Simple Storage Service being taken
down, which impacted 150,000 organizations and led to many millions of dollars
in losses.
If
infrastructure design usually isn’t the issue, and 70% of the time outages are
a direct result of human error, then logic suggests that the key would be to eliminate the potential for human error.
And just to emphasize the nuance of this point, we’re NOT advocating
eliminating humans, but eliminating the
potential for human error while keeping humans very much involved. How do
we do that?
Well, you
won’t be too surprised to learn we do it through automation.
Let’s
start by taking a look at the typical infrastructure and operations
troubleshooting process.
This process
should look pretty familiar to you.
In
general, many organizations (including large ones) do most of these phases
manually. The problem with that is that it makes every phase of this process
vulnerable to human error.
There’s a
better way, however. It involves automating much of this process, which can
reduce the time it takes to remediate an IT incident down to seconds. And
automation isn’t just faster, it also eliminates the potential for human error, which should radically reduce the
likelihood that your environment will experience an outage due to human error.
Here’s how
that would work. It involves using the Ayehu platform as an integration hub in
your environment. Ayehu would then connect to every system that needs to be
interacted with when remediating an incident.
For example,
if your environment has a monitoring system like SolarWinds, Big Panda, or
Microsoft System Center, that’s where an incident will be detected first. The
monitoring system (now integrated with Ayehu) will generate an alert which
Ayehu will instantaneously intercept. (BTW – if there’s a monitoring system or
any kind of platform in your environment that we don’t have an off-the-shelf
integration for, it’s usually still pretty easy to connect to it via a REST API
call.)
Ayehu will
then parse that alert to determine what the underlying incident is, and launch
an automated workflow to remediate it.
As a first
step in our workflow we’re going to automatically create a ticket in
ServiceNow, BMC Remedy, JIRA, or any ITSM platform you prefer. Here again is
where automation really shines over taking the manual approach, because letting
the workflow handle the documentation will ensure that it gets done in a timely
manner (in fact, in real-time) and that it gets done thoroughly. This brings
relief to service desk staff who often don’t have the time or the patience to
document every aspect of a resolution properly because they’re under such a
heavy workload.
The next
step, and actually this can be at any step within that workflow, is pausing its
execution to notify and seek human approval for continuation. To illustrate why
you might do this, let’s say that a workflow got triggered because SolarWinds
generated an alert that a server dropped below 10% free disk space. The
workflow could then go and delete a bunch of temp files, it could compress a
bunch of log files and move them somewhere else, and do all sorts of other
things to free up space. Before it does any of that though, the workflow can be
configured to require human approval for any of those steps.
The human
can either grant or deny approval so the workflow can continue on, and that
decision can be delivered via laptop, smartphone, email, instant messenger, or
even regular telephone. However, please note that this notification/approval
phase is entirely optional. You can also choose to put the workflow on
autopilot and proceed without any human intervention. It’s all up to you, and
either option is easy to implement.
Then the
workflow can begin remediating the incident which triggered the alert.
As the
remediation is taking place, Ayehu can update the service desk ticket in
real-time by documenting every step of the incident remediation process.
Once the
incident remediation is completed, Ayehu can automatically close the ticket.
Finally, Ayehu
can go back into the monitoring system and automatically dismiss the alert that
triggered the entire process.
This, by
the way, illustrates why we think of Ayehu as a virtual operator which we
sometimes refer to as “Level 0 Tech Support”. A lot of incidents can be
resolved automatically by Ayehu without any human intervention, and thus
without the need for attention from a Level 1 technician.
This then is
how you can go from alert to remediation in 15 seconds, while simultaneously
eliminating the potential for human error that can lead to outages in your
environment.
Gartner
concurs with this approach.
In a
recently refreshed paper they published (ID G00336149 – April 11, 2019) one of
their Vice-Presidents wrote that “The intricacy of access layer network
decisions and the aggravation of end-user downtime are more than IT
organizations can handle. Infrastructure and operations leaders must implement
automation and artificial intelligence solutions to reduce mundane tasks and
lost productivity.”
No
ambiguity there.
Gartner’s
advice is a good opportunity for me to segue into one last topic – artificial
intelligence.
The Ayehu
platform has AI built-in, and it’s one of the reasons you’ll be able to not
only quickly remediate your IT incidents, but also quickly build the workflows
that will do that remediation.
Ayehu is
partnered with SRI International (SRI), formerly known as the Stanford Research Institute. In case you’re not
familiar with them, SRI does high-level research for government agencies,
commercial organizations, and private foundations. They also license their
technologies, form strategic partnerships (like the one they have with us) and
creates spin-off companies. They’ve received more than 4,000 patents and patent
applications worldwide to date. SRI is our design partner, and they’ve designed
the algorithms and other elements of our AI/ML functionality. What they’ve done
so far is pretty cool, but what we’re working on going forward is what’s really
exciting.
One of the
ways Ayehu implements AI is through VSAs, which is shorthand for “Virtual
Support Agents”.
VSA’s
differ from chatbots in that they’re not only conversational, but more
importantly they’re also actionable. That makes them the next logical step or
evolution up from a chatbot. However, in order for a VSA to execute actionable
tasks and be functionally useful, it has to be plugged in to an enterprise
grade automation platform that can carry out a user’s request intelligently.
We deliver
a lot of our VSA functionality through Slack, and we also have integrations
with Alexa and IBM Watson. We’re also incorporating an MS-Teams interface, and
looking into others as well.
How is
this relevant to remediating incidents?
Well, if a
service desk can offload a larger portion of its tickets to VSA’s, and provide
its users with more of a self-service modality, then that frees up the service
desk staff to automate more of the kinds of data center tasks that are tedious,
repetitive, and prone to human error. And as I’ve previously stated, eliminating
the potential for human error is key to reducing the likelihood of outages.
Speaking
of tickets, another informal webinar poll we conducted asked:
On
average, how many support tickets per month does your IT organization deal
with?
Less than 100
101 – 250
251 – 1,000
More than 1,000
Here’s how
our audience responded:
Nearly 90%
receive 251 or more tickets per month. Over half get more than 1,000!
For
comparison, the Zendesk Benchmark reports that among their customers, the
average is 777 tickets per month.
Given the
volume of tickets received per month, the current average duration it takes to
remediate an incident, and most importantly the onerous cost of downtime,
automation can go a long way towards helping service desks maximize their
efficiency by being a force multiplier for existing staff.
Q: What types of
notifications can the VSA send at the time of incident?
A: Notifications
can be delivered either as text or speech.
Q: How does the
Ayehu tool differ from other leading RPA tools available on the market?
A: RPA tools are
typically doing screen automation with an agent. Ayehu’s automation is an
agentless platform that primarily interfaces with backend APIs.
Q: Do we have to do
API programming or other scripting as a part of implementation?
Q: Do we have an
option to create custom activities? If so, which programing language should be
used?
A: In our roadmap,
we will be offering the ability to create custom activity content
out-of-the-box.
Q: Do out-of-the-box
workflows work on all types of operating systems?
A: Yes. You just
define the type of operating system within the workflow.
Q: How does Ayehu
connect and authenticate with various endpoint devices (e.g. Windows, UNIX,
network devices, etc.)? Is it password-less, connection through a password
vault, etc?
A: That depends on
what type of authentication is required internally by the organization. Ayehu
integrated with the CyberArk password vault can be leveraged when privileged
account credentials are involved. Any type of user credential information that
is manually input into a workflow or device is encrypted within Ayehu’s
database. Also, certificates on SSH commands, Windows authentication, and
localized authentication are all accessible out-of-the-box. Please contact us
for questions about security scenarios specific to your environment.
Q: What are all the
possible modes that VSAs can interact with End Users?
A: Text,
Text-to-Speech, and Buttons.
Q: Can we create
role-based access for Ayehu?
A: Yes. That’s a
standard function which can also be controlled by and synchronized with Active
Directory groups out-of-the-box.
Q: Apart from
incident tickets, does Aheyu operate on request tickets (e.g. on-demand access
management, software requests from end-users, etc.)?
A: Yes. The
integration packs we offer for ServiceNow, JIRA, BMC Remedy, etc. all provide
this capability for both standard and custom forms.
Q: Does Ayehu
provide APIs for an integration that’s not available out of the box?
A: Yes. There are
two options. You can either forward an event to Ayehu using our webservice
which is based on a RESTful API, or from within the workflow you can send
messages outbound that are either scheduled or event-driven. This allows you to
do things such as make a database call, set an SNMP trap, handling SYSLOG
messages, etc.
Q: Does Ayehu
provide any learning portal for developers to learn how to use the tool?
A: Yes. The Ayehu
Automation Academy is an online Learning Management System we
just launched recently. It includes exams that provide you an opportunity to
bolster your professional credentials by earning a certification. If you’re
looking to advance your organization’s move to an automated future, as well as
your career prospects, be sure to check out the Academy.
Q: Does Ayehu
identify issues like a monitoring tool does?
A: Ayehu is not a
monitoring tool like Solarwinds, Big Panda, etc. Once Ayehu receives an alert
from one of those monitoring systems, it can trigger a workflow that remediates
the underlying incident which generated that alert.
Q: We have 7
different monitoring systems in our environment. Can Ayehu accept alerts from
all of them simultaneously?
A: Yes. Ayehu’s
integrations are independent of one another, and it can also accept alerts from
webservices. We have numerous deployments where thousands of alerts are received
from a variety of sources and Ayehu can scale up to handle them all.
Q: What does the AI
in Ayehu do?
A: There are
different areas where AI is used. From use in understanding intent through
chatbots to workflow design recommendations, and also suggesting workflows to
remediate events through the Ayehu Brain service. Please contact an account
executive to learn more.
Post Views: 72
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/07/Featured-imageI-IT-Incidents-From-Alert-to-Remediation-in-15-seconds.jpg374400Guy Nadivihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngGuy Nadivi2019-07-25 21:02:362019-07-25 21:04:49IT Incidents: From Alert to Remediation in 15 seconds [Webinar Recap]
Self-service automation is becoming more of the norm rather than the exception. In fact, a recent survey by SDI found that 61% of businesses were focusing on some type of self-service initiative (up from 47% in 2015). And it’s not only for making your customers’ lives easier. Many organizations are realizing the benefits of providing self-service options to employees to eliminate the need for many of the common issues plaguing the help desk, such as password resets and system refreshes. If you’re thinking about jumping on the bandwagon, here are a few common mistakes you should actively avoid.
Inadequate Communication – If you want your employees to adopt and embrace self-service technology, you have to ensure that they understand its many benefits. This is particularly important for your IT team, some of whom may feel uneasy or even threatened by the thought of automated technology handling some of their tasks. Gain acceptance and buy-in by communicating how self-service options will actually make the lives and jobs of everyone easier and more efficient.
Lack of Knowledge – What types of activities can you – and more importantly – should you be transitioning over to self-service? Many otherwise savvy IT decision makers rush into self-service implementation before they truly have a good understanding of what tasks are most beneficial to automate. Take time to learn about what your IT team is bogged down by and also what areas the end-user might not only benefit from, but actually appreciate the ability to handle things on their own.
Not Choosing a Tool Carefully – Not all self-service automation platforms are created equal and if you don’t carefully and thoroughly do your homework, you could end up with a less-than-ideal result. Not only does implementing a faulty tool mean more headaches for your IT department, but the frustration of everyone who has to use it will ultimately lead to disengagement, resistance and/or complete lack of adoption. Make sure the platform you choose is robust, user-friendly and versatile enough to handle both full and semi-automation needs.
Setting and Forgetting It – Like anything else in technology, self-service automation isn’t something that you can simply put in place and never think about again. Not only is it important to keep up to date from a tech standpoint, but it’s equally important to ensure that the system you have in place remains as effective as possible. Conducting regular audits of both the IT department and the end-users can help you determine whether new tasks could be automated or if existing ones could use some tweaking.
Forgetting the Intangibles – Last but not least, maintaining an environment in which self-service automation is embraced and celebrated involves regular assessment and selling of the many benefits this technology provides. When calculating ROI, don’t forget to also consider the intangible ways self-service is good for your organization, particularly how it allows IT to improve its meaningful contribution to the organization. That is a value that can and should be recognized across the board.
What could self-service automation do for your company? Why not find out today by starting your free 30 day trial of Ayehu. No obligation, just enhanced efficiency and better overall operations. Get your free trial now by clicking here!
Post Views: 24
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/07/cropped-mistakes-to-avoid-with-self-service-automation.jpg294522Michal Itzhakihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngMichal Itzhaki2019-07-18 08:15:102019-07-11 00:23:035 Mistakes to Avoid with Self-Service Automation