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How Automation Levels Up AIOps

automation levels up AIOps

In today’s increasingly complex digital environment, the ability to pinpoint, resolve and mitigate potential IT problems has never been more critical. And with a hybrid blend of public and private cloud, on-premises and virtual servers, a growing variety of mobile devices and a skyrocketing volume of network and application traffic, it’s also never been more challenging. To address this significant concern, organizations are turning to artificial intelligence for IT operations – or AIOps for short.

The term AIOps encompasses the use of advanced data analytics technologies, such as AI and machine learning, to automate the process of identifying and remediating performance issues. AIOps leverages the colossal volume of data generated by IT services and systems to proactively monitor the infrastructure and gain complete visibility over all system and application dependencies. These advanced capabilities enable AIOps to manage and address potential problems, often before they occur.

Organizations put AIOps in place to gather and analyze all IT operational data and simultaneously automate all main IT operations. The AIOps system then organizes and prioritizes that data, presenting it to IT managers so they can react accordingly. In short, AIOps provides IT decision-makers with the insight they need to stay a step ahead of IT operations. Gartner predicts that by 2023, the use of AIOps will increase from 5% to 30%.

The Key is Automation

The most critical component to a smooth and efficiently run AIOps is automation. This technology helps AIOps to perform ongoing monitoring while adhering to predetermined policies and dependency mapping and quickly and effectively carry out the steps necessary to resolve events or failures.

With all of these technologies operating in tandem, and automation at the center, AIOps can ultimately help to reduce the volume of potentially damaging events, provide proactive alerts to issues that could cause an outage, pinpoint the root cause of those issues and apply intelligent process automation to autonomously remediate.

AIOps is capable of increasing the effectiveness of infrastructure resources, streamlining and expediting service requests and problem resolution, and ultimately generating consistent, measurable value from its ability to support current and future business initiatives.

The Benefits of AIOps

Harnessing the power of automation in combination with AIOps delivers a multitude of benefits for IT. Firstly, it can dramatically enhance and improve the effectiveness of existing tools and services. And since it saves time while also increasing efficiency and productivity, organizations employing AIOps can also realize a decrease in overall expenditure.

Likewise, AIOps can also reduce the amount of time and effort currently required to manage service requests and remediate performance issues and outages. All of this adds up to improved service levels, a significant reduction in risk, and a quicker time-to-market for new initiatives.

Automated AIOps runs on a 3-phrase approach:

  • Identify
  • Analyze
  • Respond

In other words, it monitors the environment to detect any potential anomalies or concerns, then analyzes, validates and prioritizes those potential events before finally determining the best course of action to take to address the issue at hand. While this last step may involve escalation to a human decision-maker, in most cases, these steps can all be carried out without the need for human intervention. Therein lies the true value of AIOps.

To learn firsthand how AIOps can help position your organization for future stability and sustainable success, try it yourself for 30 days. Click here to start your full-feature trial of Ayehu NG today.

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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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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.

7 Steps for Getting Started with AIOps

Today’s IT teams are dealing with a growing mountain of data. What’s more, they’re finding themselves having to use a multitude of tools in order to monitor and manage that data. In situations of technical outages, this can make it incredibly difficult and time-intensive to identify and resolve underlying issues. Anyone in business knows that even just a tiny amount of down-time can have a serious and costly impact on the bottom line. And it’s the IT team that bears the brunt of the burden.

Take, for example, the two largest supermarket chains in Australia. Last year, both experienced severe technical issues which forced them to shut down several stores while they worked on fixing the problem. Not only did those companies lose revenue during the shutdown, but they also suffered a serious blow to their reputation. In other words, customers were not happy.

To better and more quickly identify, resolve and prevent outages and other problems, organizations are turning to artificial intelligence for IT operations (AIOps) – the long-term impact of which will be nothing short of transformational.

What is AIOps

In simplest of terms, AIOps combines data science and machine learning functionality to enhance and/or replace the majority of IT operations functions. This includes performance and availability monitoring, event analysis and correlation, ITSM and automation. To put it even more simply, AIOps platforms gather and analyze all of the data produced by IT to extract what’s of value and present meaningful insights.

How to Get Started with AIOps

Step 1: Don’t put it on the back burner.

If you really want to reap the benefits of AI for your IT operations, the time to jump on the AIOps bandwagon is now. Don’t make this an afterthought or push it out as some far-off future initiative. Even if the actual deployment isn’t imminent, start preparing yourself and others within your organization by becoming familiar with artificial intelligence and machine learning capabilities today. This way, in the event that priorities shift and you need to implement sooner, you’ll already be a few steps ahead of the game.

Step 2: Be careful when choosing your initial test case.

The concept of AIOps at scale may seem overwhelming, but keep in mind that truly transformative initiatives almost always start small. Focus first on capturing knowledge, testing frequently and iterating as needed. You don’t need to be an expert right out of the gate, and not every project you spearhead will be a resounding success. Just be mindful of what you’re starting with and work your way up from there.

Step 3: Work on developing and demonstrating your proficiency.

If you are leading the AIOps charge in your organization, you’ll inevitably be the go-to subject matter expert, at least initially. It will be up to you to communicate and convey the value of the technology to your colleagues and others in leadership. Wear your role with pride and start assembling a team of others who can champion the cause alongside you. Start by identifying gaps that exist in skills and experience, and then create a plan to address those gaps together.

Step 4: Don’t be afraid to experiment.

There are already many AIOps platforms on the market that are incredibly complex and subsequently cost-prohibitive. As with any tech product or solution, it’s wise do experiment and test the waters. Keep in mind that more features doesn’t necessarily equate to a better product. Your organization may not need all those bells and whistles. If possible, take advantage of product demos and free trials. This will enable you to evaluate AIOps uses and applications specific to your business needs without having to invest too heavily or commit to one particular solution.  

Step 5: Expand your vision beyond the IT department.

Data management is a massive component of AIOps. Take a step back and examine your organization. Chances are very high that your existing teams are already skilled in this area and that there are data and analytics tools already present within your organization. Resist the urge to reinvent the wheel and be willing to expand your vision to look beyond the IT department. It could save you tremendous time, effort and money.

Step 6: Standardize whenever possible and modernize wherever it makes sense.

You can prepare your existing infrastructure so that it is capable of supporting an AIOps implementation in the future by developing a consistent automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Step 7: Consider build-vs-buy.

Understand that there are a number of variables involved in making a shift to AIOps. Likewise, the platforms available on the market today will continue to evolve, as will the infrastructure and applications for which you are responsible currently. Be mindful of this as you weigh whether to purchase a solution or build one of your own. Ideally, the best answer will likely be a combination of the two, so be prepared to figure out which approach best applies where and by how much.

Over the past few years, AIOps has developed from an emerging category to an IT necessity. Successful companies are beginning to leverage AIOps to automate and improve IT operations by applying machine learning to their data. Furthermore, forward-thinking organizations will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.

If AIOps is on your to-do list (and it certainly should be), the steps outlined above should help you to, at the very least, lay the groundwork so that when the time comes to implement, the process will go faster and much more smoothly.

Why wait? Experience the next generation of IT automation, powered by machine learning and artificial intelligence and get started on the fast track to successful AIOps deployment. Start your free 30 day trial of Ayehu today!

3 Ways AI is Revolutionizing ITOps

It’s hard to imagine that while just a short time ago, people were asking what AI was all about and whether it was something worth investing in, yet pretty soon we’ll be asking how we ever lived without it. Artificial intelligence and machine learning have radically improved the lives of many, in particular, IT managers, enabling them to optimize their precious time and add legitimate value to their organizations. Here are three specific ways AI is redefining the role of ITOps.

Reduction/Elimination of Time-Consuming Tasks

Each and every day, IT managers are burdened with tasks. Some of those tasks are complex, tedious and mission-critical. Others are menial, mundane and time-consuming – administrative tasks, such as scheduling, setting deadlines and alerts, establishing priorities, directing daily operations, coordinating project activity, managing and analyzing workflow…the list goes on (and on, and on).

Here’s where AI is already making a remarkable impact on the lives of ITOps managers. By shifting most or all of these time-sapping administrative issues to intelligent automation, these leaders are freed up to allocate their skills and expertise to what’s most important. AI is particularly beneficial in terms of managing any process that is repeatable, such as preparing reports (and we all know how much IT managers adore reporting). Many organizations worldwide are already taking advantage of AI’s quantitative data analysis capability to generate analytical reports.

Increased Knowledge/Skill/Judgment Work

Only an IT manager would understand how a so-called promotion can actually be more of a headache and disappointment. Think about it. You’re awesome at what you do – ITOps work – so you get moved up the ladder, into a role that is filled with endless administrative tasks (as mentioned above). Feels more like a demotion than anything else. Many IT managers in this position would much rather be focusing their time, energy and expertise on things like:

  • Developing standards and best practices
  • Overseeing complex workflows
  • Identifying duplication and waste, quantifying outcomes and providing analysis
  • Coordinating and managing operational budgets and initiatives
  • Analyzing data
  • Developing departmental and interdepartmental goals
  • Evaluating proposals to determine requirements and feasibility
  • Consulting with users, stakeholders, technicians and vendors to determine needs and requirements

Artificial intelligence has gifted IT managers with the ability to perform more of the knowledge, skill and judgment work that they love, not only because it frees up time, but because the technology is inherently designed to support enhanced decision-making within ITOps (and beyond). That’s not to imply that human expertise and insight will be replaced by AI. To the contrary, cognitive skills and critical thinking will become even more prevalent because IT managers will have more time.

More Room for Creativity

The flood of demanding day-to-day tasks that ITOps manager face leaves little to no room for the creative thinking that is necessary to drive innovation. Some of the areas where these leaders can add value include brainstorming to improve future IT initiatives, strategizing on organizational goals and objectives, keeping up with tech developments, acting as champion for the IT department, just to name a few.

Integration of artificial intelligence is facilitating more creative thinking by enabling better experimentation and collaboration. When IT managers are able to spend more time strategizing, the human element of creativity will flourish for the betterment of ITOps and the organization as a whole. What’s more, IT managers will be able to take on more advisory roles thanks to AI’s presence. The ability to hone social skills will continue to open up new and exciting opportunities.

Conclusion

ITOps managers across the globe are already experiencing drastic changes with the growing adoption of AI technology. The relief from administrative tasks alone have made it well worth the investment, with the promise of even more widespread benefits that will change the scope of everyday life for the better.  

Want to experience some of these incredible, life-changing benefits for yourself? Give Ayehu a try completely free for 30 full days. Click here to start your free trial today!

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Pros and Cons of IT Chatbots

When IT chatbots were first introduced, admittedly, they were less than impressive. They were slow, clunky and in many cases, it was painfully obvious that a robot was on the other end. Advances in artificial intelligence technology, however, have addressed these concerns and the virtual agents of today are becoming more human-like by the minute. And their popularity is growing, with Gartner predicting that by next year, 50% of medium-to-large enterprises will be using chatbots.

Should you be one of them? Let’s take a look at a few of the ups and downs of using virtual support.

Pros of IT Chatbots

Frees Up Humans Resources – The first and most obvious benefit to deploying IT chatbots for the helpdesk is that it shifts a significant portion of the workload from human to machine. This frees up human agents to be able to focus their high-level skills and cognitive talents on more complex and important business initiatives. This is a much more optimal allocation of resources.

Enables 24/7 Support – Most organizations can’t afford to pay for round-the-clock IT support, and for those that do have the budget, justifying it can be a challenge. IT chatbots are available 24/7, which means if a problem arises at 2am, there’s a good chance it can be automatically addressed and resolved without human intervention. A higher level of support without having to pay live agents? Yes, please.

Advanced Interaction – With the right software solution, IT chatbots can be fully customized. Furthermore, thanks to advanced AI technologies like machine learning and natural language processing, virtual agents can be so “intelligent” that the end-user doesn’t even realize they’re interacting with a robot and not a fellow human.

Cons of IT Chatbots

Volume – The overarching goal of any IT chatbot implementation is to automate routine, manual and recurring tasks. Logically, if the volume of recurring activities for your IT team is low, the benefits of introducing virtual agents may not outweigh the cost and effort it takes to implement.

Time/Resources – While it’s true that chatbots free up existing IT staff, the technology isn’t something you simply plug and play. It requires oversight and maintenance by skilled human workers who can make sure the software has all the information it needs, can add new services, applications and processes for the end-user as needed and that the bots are properly tested.

Fear/Resistance – Lastly, as with most automation technology, chatbots often elicit feelings of fear and resistance from human workers who may be concerned that they are being replaced. Additionally, if the technology is not up-to-par, the end user may push back against the idea of working with virtual agents vs. human help desk support.

These issues can be overcome, provided the right people, technologies and policies are in place. In fact, if you take your time to fine-tune your chatbot platform, it can easily become an effective and realistic channel for supporting end-users, making it well worth the time and investment.

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7 Key Business Benefits of Intelligent Automation

Intelligent automation is being adopted and implemented in businesses of every industry. Still, there are some decision makers who are on the fence about whether it’s worth the investment. If you are among those who aren’t 100% certain, let’s take a look at a few of the quantifiable benefits AI-powered automation can provide to your organization.

Cost Savings

One of the biggest advantages of intelligent automation is the immediate and significant reduction in expenditure it can deliver. When work is automated, not only is it completed faster, but it also can be performed round-the-clock at a much lower rate. So, you get greater output for less, which results in a better bottom line.

Quality, Accurate Work

Let’s face it. Even the most careful human can and will make an occasional mistake. Multiply those errors by the number of people you have performing routine tasks for your company, and you could be looking at a pretty costly problem. With artificial intelligence, the work is performed error-free. Better quality means higher satisfaction rates, which – again – is good for your company’s profitability.

Enhanced Cycle Time

How long does it take a human worker to perform a given task such as completing a web form? Even if it’s mere minutes, an intelligent robot could shave that time down to just a few seconds. Over time and multiplied by dozens of tasks and several staff members, this savings really begins to add up.

Employee Empowerment

Intelligent automation does not require any special technical skills. That’s why it’s an ideal application for the end-user. The ability to deploy robots to perform certain tasks without having to enlist the help of someone from IT empowers the end-user to get their jobs done more efficiently and effectively. Meanwhile, it frees up IT to focus on more important tasks and projects.

Simplicity and Flexibility

Automating tasks and workflows through Ayehu’s AI-powered automation does not require coding or script-writing. That means even complex processes can be transferred from human to machine with little effort. The faster these tasks and workflows can be automated, the sooner your organization will begin reaping the benefits. In other words, intelligent automation delivers quick returns.

Better Control

Many companies choose to outsource so-called busy work to external parties. This, of course, comes with inherent risk. Intelligent automation can provide a better solution and since the work remains in-house, the business maintains maximum possession, control and visibility.

Insights and Analytics

Learning from the past can help your business leaders make better decisions for the future. Machine learning algorithms offer the ability to gather, organize, track, analyze, report on and store valuable data. That information can then be utilized to improve on current operations, address and correct issues in a timelier manner, accurately forecast and develop best practices.

Still not convinced that intelligent automation is a beneficial solution for your business needs? Why not try it for yourself? Click here to start your free trial of Ayehu NG today.  

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Is Intelligent Automation Really Replacing Human Workers?

When intelligent automation first hit the market, some thought it was too far-fetched to ever become a reality. But as more and more organizations began recognizing the many benefits – from increased productivity and efficiency to lower costs and fewer errors – people started worrying, wondering whether this technology would spell the end of the human workforce as we knew it. Would artificial intelligence really start taking over jobs? To answer that question, those asking it must look inward.

In reality, the impact automation has on the workforce will depend largely on how humans themselves respond. When faced with the rising adoption of AI, workers will likely take one of two paths. The first group will continue to focus on the type of work they’ve always done, but do so more efficiently thanks to the assistance of machine learning. The second will take this as a golden opportunity to pursue their ambitions, further their education to broaden their skill sets, put their creativity and innovation to work and move on to more value-added, meaningful work. In either case, the organization will benefit, as will most of the employees.

In particular, roles that have a primary focus on people, such as customer support and HR, have the potential to benefit greatly from intelligent automation. Instead of being bogged down by repetitive, menial tasks that can easily (and more quickly) be handled by software, agents will be freed up to tackle more complex issues requiring a human touch. Furthermore, the improved allocation of resources afforded by AI will enable agents to prevent issues from occurring in the first place. This can dramatically improve both customer and employee satisfaction rating.

This concept can also be applied to the IT help desk. Rather than waiting until system problems arise and scrambling to fix them in a timely and effective manner, help desk agents can use the extra time automation provides them with to monitor and proactively address technical issues before they occur. Imagine how impressed the CEO will be when he gets a call from IT letting him know his hard drive was about to fail, but it’s been taken care of.

In both of these scenarios, the human worker is enhancing their interactions with their colleagues and/or customers. And since intelligent automation is there to take on the routine, manual tasks, the human agents themselves are also able to improve.

The reality is, very few organizations are focusing on using AI to eliminate jobs. Instead, they are focused on automating tasks, which in turn will improve productivity, streamline how work is completed, eliminate errors and cut costs. In other words, companies implementing automation are not doing so to replace human workers, but rather to augment and make their lives easier. As a result, everyone benefits – from employees and management to clientele and ultimately the organization’s bottom line.

Still not completely sold on the idea of intelligent automation and the value this technology can bring to your business? Don’t take our word for it. Try it for yourself. Click here to download a free 30 day trial of Ayehu. You have nothing to lose!

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3 Business Areas that are Ideal for Machine Learning

At the current rate, AI systems worldwide are on pace to hit nearly $50 billion in revenues by the year 2020. The proof is in the pudding. And if you’re not yet leveraging the power of machine learning, you can bet your competitors are. The good news is, you don’t need a massive budget or a team of experienced data scientists to start putting machine learning to use in your business. In fact, to follow are three practical areas where almost any organization can get started with ML technologies.

Internal/External Support

If you have an IT help desk for employees or a support team dedicated to customer inquiries, you have a great opportunity to leverage machine learning technology. Chatbots can be trained to handle everything from the most basic FAQs to complex issues, working in tandem with human agents.

Not only will a chatbot strategy free up your support staff to focus on more important business initiatives, but it’ll also improve service levels, so it’s a win-win. (Not sure where to start? Here’s a step-by-step guide to implementing bots along with some tips for what not to do.)

Cybersecurity

According to research by Ponemon, the average cost of a single ransomware attack is $5 million. And that’s just one strategy hackers use. If you think cybersecurity is not a big deal, think again. The problem is, cyber criminals are becoming savvier and using more sophisticated methods by the day. Staffing enough people to handle the onslaught isn’t just challenging. It’s next to impossible.

The good news is, machine learning can be used to augment your IT security team, providing an added layer of protection against potential breaches. Intelligent automation can work around the clock, constantly monitoring and analyzing mountains of data and identifying/addressing anomalies before they have a chance to wreak havoc.

Human Resources

While there are certainly areas of the human resources function for which a human touch is still needed, such as discussing sensitive matters with employees, the reality is, the vast majority of today’s HR processes and workflows can easily be automated.

For instance, machine learning algorithms can be used to weed through job applicants, saving recruiters time and aggravation, while intelligent automation can handle new employee onboarding far faster and more efficiently than a human agent could. To get you started, check out these 5 tips for optimizing HR with automation.

Of course, none of these things will be possible without the right technology. Thankfully, you don’t have to be an AI guru to leverage machine learning, nor do you have to hire a team of experts. In fact, you don’t even have to know how to code. Experience the power of plug-and-play intelligent automation by requesting an interactive demo of Ayehu or jump right in with your free 30-day trial today!

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5 Surefire Ways to Derail Your Machine Learning Project

The process of implementing something new typically involves making mistakes, heading in the wrong direction and then figuring out a way to right those wrongs and avoid those risks in the future. Adopting machine learning is no exception. If you aren’t careful, the mistakes you make can become encoded, at least for the time being, into your business processes. As a result, these errors will occur at scale and will be difficult to control.

On the other hand, when you proactively detect errors and take the steps to address and correct them right away, you’ll have much more success with the technology. To follow are five common pitfalls that can wreak havoc on your machine learning project so you’ll know what to watch for.

Lack of clear understanding.

Simply put, you cannot adequately solve a problem that you don’t fully understand. The same can be said for machine learning initiatives. If you don’t completely understand what problem you are actually trying to solve, the risk of errors goes up exponentially.

To avoid this, begin with a hypothesis statement. Ask what the problem is that you are trying to resolve and which models you plan on using to address that issue. This is key, because if it’s not done correctly from the start, things can go wrong very quickly.

Poor data quality.

The old adage, “garbage in, garbage out” can easily be applied to machine learning projects. If the quality of the data you are supplying isn’t up to par, the outcome will inevitably suffer. In fact, poor data quality is one of the top concerns of data managers, as it can impact analytics and ultimately influence business decisions in the wrong direction.

The result of these poor decisions can negative affect performance and make it difficult to garner support for future initiatives. Exploratory data analysis (EDA) can help you proactively identify data quality issues so you can prevent problems before they occur.

No specific purpose.

Another common contributor to machine learning failure is implementation without a clear purpose. In order for machine learning to produce ROI, it must be applied properly – not simply because it’s the cool thing to do. In fact, using machine learning when it’s not the best solution to a problem and/or not completely understanding the use case can ultimately cause more harm than good.

In addition to addressing the wrong problem, doing so can involve wasted time and resources, both of which come at a cost. To avoid this, identify the precise problem and desired outcome to determine whether machine learning is the appropriate solution. 

Insufficient resources.

It’s easy to underestimate the amount of resources required to do machine learning right, in particular as it relates to infrastructure. Without adequate processing power, successfully implementing machine learning solutions in a timely manner can be a difficult, if not impossible feat. And without the resources in place to allow for its deployment and use, what’s the point?

To address the expense and complexity of deploying a scalable infrastructure, leveraging a cloud service that can be provisioned on-demand may be the better option. Those wishing to keep things in-house should look for a lightweight, plug-and-play solution that doesn’t require coding and can be deployed across on-premises and private cloud platforms.

Poor planning and lack of governance.

It’s not unusual for a machine learning project to start off with tremendous enthusiasm only to lose momentum and ultimately end up grinding to a halt. When this happens, poor planning and lack of governance is most often to blame. For those projects that don’t cease, a lack of guidelines and limits can result in an exorbitant expenditure of resources without the beneficial end results. 

To keep things moving in the right direction, machine learning initiatives must be continuously monitored. In the event that progress begins to wane, it can be wise to take a break and reevaluate the effort. Keeping people engaged in the process is the key.

Machine learning can be a tremendous asset to an organization, but only if it’s planned, implemented and managed properly. By avoiding the five common pitfalls listed above, you can place your company in a much better position and improve your chances of long-term, sustainable success.

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