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The Secret to Surviving the Tech-Led Revolution

The Secret to Surviving the Tech-Led Revolution

Automation has been at the forefront of the digital revolution for decades, primarily because it maximizes efficiency, reduces costs and accelerates service levels. But the cloud, mobile and other innovative technologies – coupled with an ever-growing volume of raw data – have led to dramatically more complex IT environments.

According to ESG’s IT Spending Intentions Survey from 2018, 68% of those surveyed said their IT infrastructures are significantly more complex than they were just two years ago. Furthermore, 39% of respondents listed automated IT operations as a critical component of survival in today’s digital age.

In response to this increasing complexity, organizations are beginning to make the shift toward the next generation of automation – from basic to intelligent. This new level of automation involves technologies like machine learning and artificial intelligence to orchestrate workflows across a multitude of tools, systems and processes.

In fact, with the right platform, it is now possible to fully automate L2 and L3 tasks – functions which have traditionally required the use of human judgment. Now, those insights lie within the data itself and can be extracted, interpreted and leveraged autonomously by AI.

Embracing intelligent process automation is also enabling enterprises to lay the foundation for AIOps, a focus area that experts predict will boom over the next five years or so.

AI and ML: Augmenting IT Operations

AIOps is helping IT teams manage the increasing challenges created by data and digital disruption, leveraging intelligent process automation and orchestration to gain competitive advantage. Thanks to the powerful processing capabilities of artificial intelligence, IT can sort through mind-boggling amounts of data points to find the proverbial needle in a haystack.

The role of humans in this increasingly tech-driven environment is still present, though it too is evolving. Rather than relying on error-prone employees to handle the bulk of the processing work, human cognition and advanced skillsets are being used to define that proverbial needle.

In response to this, more organizations are focusing their efforts on reskilling and upskilling their existing staff to bring them up to speed on ML and AI technologies.

Making the Switch to Autonomous Operations

Autonomous operations (AO) utilizes advanced AI to deliver unassisted responses to IT incidents across the entire infrastructure. Thanks to the self-learning capabilities of ML algorithms, AO is able to continuously improve its ability to identify patterns and carry out the appropriate actions.

Again, human workers are still needed in an AO-driven environment, but in the role of supervisor as opposed to operator. Yet as the software continues to evolve and improve, and as errors consistently decrease over time, full autonomy and a zero-touch IT operations environment will one day become a very real possibility.

The Role of Data

The key to success with intelligent automation is accurate data, as this enables users to write more impactful rules. There is little to no value in static data. These days, it’s all about dynamic information which comes from things like descriptive metadata as well as relational and behavioral data.

In order to harness this dynamic data and gain adequate insights from it, organizations need to develop software-defined IT environments. Intelligent process automation is about the ability to not only proactively identify anomalies, but to also remediate those issues automatically without causing any business disruption.

The Right Way to Automate Intelligently

In today’s competitive landscape, automation is no longer an option but a necessity. That said, there’s a right way and a wrong way to leverage this game-changing technology. Start by weighing the time, effort, complexity and frequency of a given task and then benchmarking these factors against the cost of transitioning that task to intelligent process automation. From there, create a prioritized list. This will help you maximize ROI and harness the full potential of intelligent IT operations.

Not sure where to start? Why not give intelligent process automation a test drive free for 30 full days? Click here to launch your Ayehu trial today.

5 Ways Intelligent Automation is Shaping the Future of Work

5 Ways Intelligent Automation is Shaping the Future of Work

In terms of disruptive technology, intelligent automation has gained tremendous ground. In fact, according to Statista, more than half of today’s business leaders say they expect to implement automation in the coming years. And for good reason. While technologies like traditional workload automation, cloud computing and Software-as-a-Service (SaaS) reduce costs and provide the flexibility to perform routine tasks and workflows, artificial intelligence (AI) brings these benefits to a whole new level with the capability of performing tasks that normally require human intelligence.

Intelligent automation software enables businesses to perform much more diverse and complex activities without the need for human intervention. Furthermore, thanks to machine learning algorithms, this type of platform is capable of learning and improving entirely on its own based on data from past experience. Artificial intelligence can also provide valuable insight and decision support for management. But how does all of this translate into actual, tangible return on investment? Let’s take a look.

Drastically Saving Time and Money

When a good portion of business processes are shifted from human to machine, the operation runs far more efficiently. Work is performed faster and more accurately, which equates to greater productivity and higher service levels. Fewer man hours results in tremendous savings for the organization. (In one recent case study, one global enterprise slashed man hours by 1,500 in less than a year simply by adopting intelligent automation. That reduction resulted in an overall savings of nearly $500k.)

Distinct Edge Over the Competition

Staying a step ahead of the competition is the key to success – especially in today’s global marketplace. Every company is chasing digital transformation and hoping to claim their spot at the head of the pack in their respective industry. The use of intelligent automation can facilitate this transformation, not only be streamlining processes, but by empowering human workers.

When the mundane tasks and workflows no longer require human input, employees are able to apply their skills, time and effort toward more important business initiatives. The freedom to be creative breeds innovation which can provide the competitive advantage companies are striving for.

Agility and Scalability

The ebb and flow of business has long been a challenge for organizational leaders. Scaling up as needed based on sudden changes in market demand is not only difficult, but it’s also quite costly. Conversely, in situations when finances are lean, such as during economic recessions, the ability to maintain an expected level of production on a limited budget is incredibly problematic.

The deployment of intelligent automation resolves both of these issues by enabling businesses to scale up or down at a moment’s notice. Seasonal or other business influxes can be met seamlessly thanks to the ability of software robots to take on some of the workload. And when it comes time to tighten the belt, automation can help skeleton crews operate as if they were fully staffed. Every business leader understands the importance of agility like this.

Maximizing Uptime

Another way intelligent process automation can deliver tangible benefits to a company is through improved system operability. According to Gartner, the average cost of IT downtime is $5,600 per minute. Due to variations in how businesses operate, experts estimate that on the low end, downtime can cost as much as $140k per hour, while at the high end, can run upwards of $540k per hour.

Regardless of which end of the spectrum a business happens to fall on, system outages can be, without question, downright disastrous. Enter intelligent automation and suddenly there’s an army of robots monitoring the infrastructure 24 hours a day, 7 days a week, 365 days a year. Furthermore, artificial intelligence is capable of identifying threats that could take days, weeks or longer for humans to spot. When incidents can be pinpointed quickly and the platform itself is capable of addressing and remediating those issues, downtime can be dramatically reduced and, in many cases, prevented altogether.

Data-Driven Decision Support

Because intelligent automation is powered by AI and machine learning, it is inherently capable of analyzing massive amounts of data and extracting value. Furthermore, AI-powered automation can then turn that data into actionable insights that can be utilized by business leaders to make better decisions.

Incorporating advanced business automation technology into the mix enables the analysis of overall organizational performance. With these intelligent analytics, business leaders can more effectively identify and implement the right approaches to achieve improved performance over the long-term.

Could your organization benefit from any of the above? If so, adopting intelligent automation should be on your list of priorities for the coming year. Get a jump start by taking Ayehu for a test drive today.

How AI Can Reduce Service Desk Ticket Costs from $20 to $4 [Webinar Recap]

Author: Guy Nadivi

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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Ayehu’s New Advanced Features in NG v1.5 [Webinar Recap]

Author: Guy Nadivi

In response to growing user requests to add more flexibility to the Ayehu NG automation platform, Ayehu has released NG v1.5. This release will significantly expand the scope of what you can automate in your environment, all from a single pane of glass, and we think that makes it a real game changer in the IT orchestration and automation market.

If you’re an existing user of Ayehu NG, or even if you’re just thinking about trying us on for size, you probably know that one of the core strengths of our solution is how easy and quickly you can plug Ayehu into various ITSM platforms, cyber security tools, operating systems, messaging and notification tools, and increasingly chatbots and AI services. Almost all of these integrations can be activated seamlessly without writing a single line of code.

And the purpose of providing you with all these pre-built integrations and connectors that make up our ever-expanding ecosystem, is to simplify your ability to orchestrate automation across any platform in your environment. All from a single pane of glass!

So, here’s what’s really exciting about this new version of NG. We’ve added a “Do It Yourself” capability to allow you to build your own platform-specific activities without the need for Ayehu to do it for you.

From the feedback we’ve received, that’s really going to appeal to those of you who aren’t afraid to roll up your sleeves, do a little coding, and craft your own specific intelligent IT automation activities.

In fact, when you see how easy we’ve made it to build your own activities, we think some of you non-coders might even be tempted to take a crack at it yourself and perhaps fulfill some aspirations on your personal automation wish list.

Without further ado then, let’s dive into what’s new in our latest release of NG, v1.5:

  • Activity Designer – This is the big one. It’s a new feature designed to give users the option to build their own activities, which marks the first time they’re not relying on us to build an activity. You already know we provide an Out-Of-The-Box library of more than 500 no-code, pre-built activities. With the Activity Designer though, customers can now independently develop or modify existing activities in Python, C# or .Net to extract further value through customization that meets specific needs.
  • GitHub Community Repository – Ayehu now has a new community on GitHub that contains more than 100 of Ayehu’s workflow templates, as well as source code for built-in activities. Customers can use this in conjunction with the Activity Designer to create custom activities based on existing pre-built workflows. The GitHub Community Repository also provides free access to other peer-developed workflow templates and activities which have already been created and contributed to the community. 
  • Ayehu Academy Advanced Courses – We now have two new Ayehu Automation Academy courses – Activity Designer Essentials and Advanced Activity Designer. Together, these courses help train and certify developers in creating new activities using the Activity Designer. The Academy has already certified nearly 1,000 IT automation engineers since its inception earlier this year.

Let’s talk a bit more about the Activity Designer.

Typically, when building a workflow you simply drag and drop activities onto a canvas, and position them in the order you want them to execute. There’s no coding, scripting, or programming of any kind required. All you have to do is configure any particular activity by entering some parameters into a popup window, as shown in the image below:

With the new Activity Designer, you can build your own activities from scratch, in Python, C#, or .NET. We believe this will typically be for a system we haven’t integrated yet, perhaps some home-grown in-house application. But it can also be used to create new custom activities for an existing integration, like ServiceNow or SolarWinds. This is a big deal because now organizations will be able to take previously unintegrated systems and incorporate them into enterprise-wide orchestration and automation via Ayehu’s single pane of glass. The Activity Designer interface is shown in the image below:

Ayehu’s GitHub Community Repository marks an expansion of our presence on GitHub’s open-source community, and can be seen at this link: – https://github.com/Ayehu

At the repository, you’ll find:

  • 100+ Ayehu workflow templates
  • Source code for built-in activities

There are many benefits to our users from this new repository, including:

  • Shorter time to value through reuse of existing, pre-built workflows
  • Shorter time to value thru customization of open source activities
  • Free access to peer-developed workflow templates and activities

Here’s an example. If we want to see what kinds of workflow templates are already available for Cisco devices, we can just click on the Cisco category, and drill down to all the workflow templates you can access that are Cisco-specific, as seen in the image below:

These new features are also accompanied by new advanced courses created for the Ayehu Academy, which can be found on our website ayehu.com under the Customers menu.

The two new Ayehu Automation Academy courses are:

  • Activity Designer Essentials
  • Advanced Activity Designer

Together, these courses help train and certify developers in creating new activities using the Activity Designer. 

Ayehu recommends getting certified because your new knowledge will enhance 2 areas of interest:

  • Your organization’s automation capabilities
  • Your own personal professional standing.

Furthermore, as this market continues to grow, we anticipate new income opportunities will be created for Certified Activity Designers. The Academy has already certified about 1,000 IT automation engineers despite only opening earlier this year. That’s a reflection of the growing interest in automation, and if you’re one of those IT automation engineers, you’ve positioned yourself very nicely for the growth curve ahead.

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Harnessing the Cognitive Capabilities of Intelligent Automation

In order for business leaders (and ultimately their teams) to meet the growing demands of maximum operational efficiency, organizations across the globe and in just about every industry have been turning to automation for decades. We have reached a time, however, in which basic automation is no longer sufficient. In response to this, enterprises are taking things a step further by utilizing the power of AI and cognitive technology through intelligent automation to address the increasingly complex challenges they’re facing.

The Next Generation of Automation

In its simplest form, machine learning is a technology through which machines (software robots, if you will) are capable of learning from data and applying what they’ve learned to either provide insight to key decision-makers or resolve problems independently. Rather than being programmed to follow a specified series of steps, intelligent bots can now complete tasks without the need for human intervention. The results have been impressive, particularly in terms of efficiency gains.

Logically speaking, automated tasks that are carried out by basic automation are fastest when they are repetitive. With non-intelligent automation bots, workflows that follow a set of predefined rules are most effective at producing effective results immediately. For instance, employees who waste hours a day manually copying and pasting data would realize instant value by moving that task to basic automation.

There are instances, however, when data is incomplete, requires multiple sources or needs to be enhanced in order to carry out a particular task. For example, resolving a single issue for a customer may require accessing half a dozen different systems and data sources. To bridge this gap, organizations need intelligent tools. AI-powered automation ties together siloed systems and provides independent resolution while reducing errors and ensuring compliance.

The key to this? Cognition. Leveraging automation tools that feature advanced cognitive abilities that are similar to humans, organizations can create a more unified infrastructure, improve business-related outcomes and dramatically accelerate and enhance customer service. In addition to basic automated tasks and workflows, companies can deploy virtual support agents to further automate processes, including those that require context and conversation.

What Should Be Automated?

To determine what to automate, decision-makers should begin by evaluating whether or not the tasks, workflows and processes could be improved or possibly even eliminated prior to introducing automation into the mix. Some things will be obvious – the mundane, repetitive and low-value busywork, such as data management or reporting. These things can (and should) be easily transitioned from human worker to automation robot.

Looking past these rote tasks, however, some work is currently considered to be more suited for humans. For instance, activities that require understanding of text, complex decision-making or matching of patterns. Additionally, tasks that rely on emotional intelligence and/or require interaction and collaboration with other human workers have previously been considered too difficult, if not impossible, to automate. That’s where intelligent cognitive automation technology has become a game-changer.

This type of advanced automation incorporates machine learning to facilitate decision-making, natural language processing for understanding and contextualizing written and verbal communications, and state-of-the-art predictive analytics and pattern matching to handle process exceptions.

Collaborative Process Optimization

Of course, even with all of these technological breakthroughs, humans are still required to choose, apply and manage automation – at least for the time being. One area where artificial intelligence and humans are already working in tandem is in the way of decision-making support. For instance, next generation tools like Ayehu combine cutting-edge automation and cognitive technologies to determine how processes are configured and/or currently operating. From there, automatic suggestions can be made to further improve workflows and optimize processes.

While many still worry about intelligent bots taking over human jobs, particularly due to the rapid evolution and development of artificial intelligence, a more accurate reality will be humans and intelligent bots working side by side to add value and collaboratively achieve optimal business outcomes.

Experience the power of intelligent automation for yourself by starting a free 30-day trial of Ayehu. Click here to get started!

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Helping the IT Help Desk – What you Need to Know about Virtual Support Agents

What you Need to Know about Virtual Support Agents

This post was originally published as a guest article on InsideBIGDATA.

IT help desks everywhere are handling a growing number of requests from multiple channels every day. And the more time the service desk spends putting out fires by phone, through email, or in person, the less time they have to focus on resolving the bigger issues and applying their cognitive skills to more meaningful projects.

Are chatbots or virtual support agents the answer? The success of virtual support depends on several key factors. Here’s how to identify those factors and evaluate whether or not VSAs are right for your organization.

Chatbot vs. VSA

The first important piece of the puzzle is understanding the difference between chatbot and virtual support agent technology. While the concept is similar, there is a distinct and critical difference, particularly as it relates to use in the help desk arena. This difference can be summed up in one word: context.

If you’ve ever visited a website and used the “live chat” feature to ask a question, chances are the party you interacted with was a chatbot. And chances are even greater that the responses you received were basic and scripted based on a set of common inquiries. Simply put, chatbots are one-dimensional. They cannot engage beyond the basic communication that they’ve been programmed for.

Virtual support agents, on the other hand, when set up properly, have far greater functionality and flexibility than chatbots. Thanks to underlying technologies like artificial intelligence, machine learning and natural language processing, VSAs are capable of understanding the meaning and intent behind human communication, even if it’s vague or ambiguous.

In other words, VSAs can understand context. As such, they are able to hold realistic conversations, generate authentic dialogue and provide intelligent responses based not only on the data they’ve received (like chatbots), but also on the context of that data.

VSAs and the Help Desk

As mentioned, help desk agents field a mind-boggling volume of incoming requests, the majority of which are routine and repetitive in nature, but important nonetheless. For instance, password resets are a necessary evil in the IT support realm as they are required in order to keep others in the organization productive.

Yet, the process of manually resetting user passwords is not only a tremendous waste of human resources, but it’s also a massive waste of money. In fact, Forrester Research estimates that the average cost of a single password reset is $70. Multiply that cost by the number of times your support team executes this task and it really adds up.

That’s where virtual support technology comes in. VSAs enable the help desk to automate almost all routine, repetitive and manual tasks. Beyond this, however, is where the true value of virtual support becomes evident. In addition to automating the basics, the technology behind VSAs enables them to work alongside human agents, providing the same level of support and assistance.

How it works is remarkably simple. The virtual agent pulls data from various knowledge management resources to respond intelligently to incoming requests. Virtual agents are also capable of taking action on behalf of the end-user without the need for human intervention. This means fewer escalations and a more manageable workload so human support agents can focus their skills on more meaningful business initiatives.

The Key to Success

Of course, as with any technology, virtual support agents do require work in order to set them up properly. For instance, AI and NLP technologies are essential components to VSA functionality. The most fundamental key to success, however, is the establishment and maintenance of a comprehensive, dynamic knowledge-base. After all, this is the resource from which the VSA will draw its responses. Without in-depth and accurate data, virtual agents will not be capable of operating to their fullest potential.

Gartner predicts that by 2023, 40% of I&O teams will be using AI-augmented automation, resulting in higher productivity with greater agility and scalability. Given the current benefits, coupled with the promise of improving technology, it’s not a stretch to see that VSAs will continue to play an increasing role in making the help desk experience better for everyone.

Click here to view the original post on InsideBIGDATA.

4 Tech Trends to Watch for in 2020

4 Tech Trends to Watch for in 2020Technology has been evolving since the dawn of time. As we prepare to enter another new decade, we can expect to see even more accelerated change on the tech front. With so much happening so remarkably quickly, it can be difficult to know which trends to track. To narrow things down, we’ve rounded up the top four adaptations that we believe will bring the greatest innovation and growth in 2020 and beyond. Take a look below.

Intelligent Automation

Not surprisingly, intelligent automation topped our list of technologies that will drive progress and success over the next several years. Thanks to the growing proliferation of cloud computing, big data and increasingly “smart” robotics, the future is a place where automation will no longer be an option, but rather a necessity. Leveraging these highly advanced technologies will enable organizations in every industry to streamline operations, maximize efficiency and uptime, dramatically lower costs and remain competitive.

Intuitive AI

While artificial intelligence plays a role in the big-picture automation trend, its capabilities and ongoing advancements warrant a separate mention on this list. The computers of tomorrow will be able to learn and evolve much the same way we do, which means that in addition to increased computing power, AI will be able to carry out tasks that were once reserved for humans and at a lightning speed. Underlying technologies, like machine learning, facial recognition and natural language processing will enable AI to continue to learn and grow smarter without the need for human intervention.

Voice Command

We’ve already begun seeing rapid and advancing developments in voice technology, thanks to the increasing adoption of voice assistants, like Siri and Alexa. Over the coming months and years, expect to see voice technology continue to develop and improve, particularly in the way of its ability to interpret and understand the context of the spoken word. This is where NLP will really begin to have a significant impact on our day to day lives.

Analytics

Enterprises across the globe are already leveraging analytics as a key driver of growth and innovation. Not only can analytics confirm whether you are successful in your industry, but they can help predict which direction the market will likely head in over the coming months and years. Data processing, facilitated by AI and machine learning, will continue to be used to turn massive amounts of information into actionable insights, as well as identifying issues and recommending next steps.

Without question, we are entering an exciting era in technological advancement. The most exciting part is that you don’t have to wait until next year to experience the power of these amazing tech trends. Download your free 30 day trial of Ayehu today and put the power of intelligent automation, powered by AI and machine learning, to work for you! Click here to get started.

Transform Your Organization with AI in 5 Steps

According to IDG’s 2018 State of the CIO report, 73% of IT executives struggle with striking a balance between the need to innovate and the demand to achieve operational excellence. One of the main reasons for this is the fact that IT frequently gets bogged down with a growing list of tools and competing priorities, all of which chip away at precious time and available resources. As a result, more organizations are turning to artificial intelligence as a way to bring technology, data and people together to drive digital transformation. Here’s how you can use AI to do the same in five easy steps.

Step 1: Understand what you can and cannot solve.

While AI has the potential to transform an entire organization, machine learning technology is not yet capable of fully replacing the experience of skilled professionals. Instead, IT teams can leverage automation powered by artificial intelligence to free up skilled workers to do what they do best: apply their expertise to develop solutions for highly prioritized issues.

Machine learning algorithms can sift through mountains of data to spot trends, deliver insights and identify potential solutions. Automation can assist in resolving certain issues. But it’s up to the IT department to apply the deep analysis necessary to achieve business goals.

Step 2: Identify and prioritize problems to address.

Artificial intelligence can help address the two biggest IT challenges: maximizing operational efficiency and improving the customer experience. The role of CIO has taken on much greater importance, with 80% of businesses viewing IT managers as strategic advisors for the business. As such, these individuals, along with others in IT, are responsible for defining key areas of focus for new technology, such as AI solutions. In order to achieve buy-in, new solutions should be presented in a way that closely aligns with broader organization-wide goals.

Step 3: Pinpoint gaps in technology and skills.

The IT skills gap is an ever-present problem, and it doesn’t appear to be going away any time in the near future. In addition to the talent shortage, IT budgets are stagnating. AI solutions can help to mitigate both of these issues by empowering IT teams to do more with less, and at a much faster rate than they could on their own.

Keep in mind, of course, that key skills are still necessary in order to drive these solutions. To address this, many organizations are looking to reskill existing staff. Thankfully, today’s automation tools do not require a PhD to operate them. Regardless, decision-makers should look for a data-based platform that features AI-powered technology.

Step 4: Develop your strategy.

Once you’ve identified which problems AI is capable of solving for your organization, defined the specific challenges you’d like to overcome, achieved buy-in for adoption and assessed what resources you have to work with, the four step is to develop your strategy for deployment. This strategy should include the following main segments:

  • Roadmap – from proof of concept to continuous process improvement
  • Testing Plan – defining what you want to accomplish and what metrics will indicate progress
  • Team – investing in and arranging training for IT staff

Step 5: Prepare for scale.

Any broader AI strategy should involve mapping out data across all systems, services, apps and infrastructure. This includes both structured and unstructured data as well as data in a variety of different formats. It’s essential to select a solution that is capable of ingesting, normalizing and formatting all data sources for analysis.

Further, it’s critical to choose a platform that offers room to mature and scale. And keep in mind, also, that while the “land and expand” concept may work for some companies, others – particularly those with a higher risk tolerance – may be better off to push transformation across the entire organization at once. Generally speaking, however, stable and sustainable change begins by starting small and building on early successes. The key is leaving enough room to grow.

Want to experience some of those early successes now? Launch your free 30-day trial of Ayehu NG and put the power of AI and intelligent automation to work for your organization today!

What is AI-Powered Decision Support?

Fifty years ago, businesses relied almost exclusively on human judgment for key decision-making. While some data existed, it was professionals and their intuitions, honed over years of experience, who were central to the process of determining good vs. bad and safe vs. risky. Not exactly the most ideal solution.

From there, we moved to data-supported decision making. Thanks to the growing number of connected devices, business leaders were able to access unimaginable volumes of data – every transaction, every customer interaction, every macro and microeconomic indicator – all available to make more informed decisions.

Unfortunately, even this approach had its limitations. For one thing, leveraging such a massive amount of data wasn’t feasible, which left a summarized version. This often obscured many of the patterns, insights and relationships that existed in the original data set. Further, cognitive bias from humans still existed.

Enter stage three: AI-powered decision support. Artificial intelligence is already ahead of the game because, provided the data being used is accurate, it’s not prone to cognitive bias. Therefore, it is more objective in its decisions. Furthermore, AI is better capable of leveraging not just mountains of data, but also all the information contained within that data, allowing for a much higher degree of consistency and accuracy.

As a real-world example, decision support that is powered by artificial intelligence can determine with much more certainty what the optimal inventory levels are, which ad creative would be most effective and which financial investments would be most lucrative.

While humans are essentially removed from the workflow, however, the purpose of introducing AI into the mix is to enhance and enable better decisions that what humans are capable of achieving on their own. In other words, the ideal scenario would involve both humans and AI working in tandem to leverage the inherent value of both for the benefit of the organization. In fact, there are many instances in which business decisions depend on more than mere data alone.

Take, for example, inventory control. While AI may be leveraged initially to objectively determine the appropriate inventory levels for maximum profitability, other information that is inaccessible to AI but incredibly relevant to business decisions may also come into play. For instance, if the organization is operating in a highly competitive industry or environment, human decision makers may opt for higher inventory levels in order to ensure a positive customer experience.

Or, let’s say the AI workflow indicates that investing more in marketing will generate the highest ROI. That company may decide, instead, that it’s more important to focus on areas other than growth for the time being, such as improving quality standards.

So, where artificial intelligence offers consistency, accuracy and objective rationality, other information that is available to humans in terms of values, strategy and marketing conditions may merit a change of direction. In these cases, AI can essentially generate a number of different possibilities from which human decision makers may select the best course of action based on the whole picture at hand.

The key takeaway is that humans are no longer interacting directly with data, but rather the insights produced by artificial intelligence’s processing of that data. Culture, strategy and values still remain a critical component of the decision-making process. AI is basically a bridge to marry them with the objective rationality that cannot be achieved through human cognition. Essentially, it’s a “best of both worlds” situation. By leveraging both humans and AI together, organizations can reach better decisions than they ever could using either one alone.

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Preparing for the Future of Work: Transitioning to Intelligent Automation

These days, a growing number of organizations are making the shift toward integrating intelligent automation as a critical component of their business model. But it should never be about automating just for the sake of automating. That’s not going to help you win the AI race. The future of work will involve more of a hybrid approach that balances artificial with human intelligence for a “best of both worlds” kind of environment.

From the top down, it’s important to identify and address the challenges companies will face when implementing initiatives around critical areas, such as analytics, intelligent process automation, digital and AI. There is no shortage of lessons to be learned and common themes from which to gain knowledge. For those charged with preparing their organizations for what the future of work will inevitably become, here are five key insights to keep in mind.

Expect the unexpected.

While it’s true that business leaders can learn from others who have gone before, the fact is, every hybrid AI initiative is unique. As such, it’s important to design fluid systems that are capable of accommodating requirements, expectations and business challenges that are often unexpected. To some degree, the future of work is a moving target. Systems that can adapt and evolve will be the ultimate key to success.

Mistakes will happen.

It’s been said that failure is the key to success. This is an important mantra to keep top of mind when preparing for the future of work. Remember that when it comes to any type of change process, mistakes are inevitable. When and if you do fail, the key is to fail fast and bounce back by reflecting, regrouping and iterating your subsequent attempts with a greater understanding of what you’ll need to do in order to succeed. Decision-makers must also recognize that change is a necessary investment that requires the right communication and resources at the right time.

Start with small, measurable wins first.

Automating at scale isn’t something that takes place overnight – at least not if it’s done correctly. As you move toward the future of work and strive for digital transformation, it’s wise to start with smaller wins that can quickly generate ROI. To begin, focus on the tasks already being performed by the organization that are menial, repetitive and mature. Capturing those quick wins early will gain you buy-in and provide a solid foundation upon which to build out an organization-wide automation strategy.

Identify and communicate the ‘hows’ and ‘whys’ across the enterprise.

When it comes to good governance, it’s critical that executives carefully develop their strategic plans around intelligent automation. More importantly, they must openly and consistently communicate the hows and whys behind their decisions to everyone across the organization. As mentioned, incorporating the valuable skills of humans in with the benefits of automation and AI is the ideal scenario. As such, proactively reskilling, retraining and reorganizing employees will become essential over the coming months and years.

Automate accordingly to address business problems.

Some organizations find it necessary to enlist the help of consultants or outsourced companies to help them identify the best processes to begin the automation journey. Don’t be afraid to admit if this assistance is something that could benefit your organization. Either way, the goal is to gain a deeper understanding of business priorities so you can identify quick successes. Ideally, the automation strategy should be one that is a joint initiative between the IT department and the rest of the business.

Ultimately, what these five insights have in common is that they require executive buy-in, AI investment that is strategic and a shift toward a business model that involves a convergence of innovative technologies. Master these five steps and your organization will be much better positioned to be successful in the future of work.

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