Posts

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.

Want to experience the power of AI to create a force-multiplier for your business decisions? Try Ayehu NG absolutely FREE for 30 days. Click here to start your trial.

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.

Free eBook! Get Your Own Copy Today

4 Tips for Successful Adoption of AI at Scale

Utilizing artificial intelligence on a smaller scale is relatively simple in nature. At the enterprise level, however, it isn’t always so straightforward. This may be a contributing factor to the recent survey results from Gartner indicating that while 46% of CIOs have plans for implementing AI, only a mere 4% have actually done so. Those early adopters no doubt have faced and overcome many challenges along the way. Here are four lessons that you can learn from that will make adopting AI at scale easier.

Start small.

Desmond Tutu once said that the best way to eat an elephant is “one a bite at a time.” Just because your end goal is to have enterprise-wide adoption of AI doesn’t mean you have to aim for that large of an outcome right off the bat.

Most often, the best way to initiate a larger AI project is to start with a smaller scope and aim for “soft” rather than “hard” outcomes. In other words, rather than primarily seeking direct financial gains, focus instead on things like process improvement and customer satisfaction. Over time, the benefits gained by achieving these smaller “soft” goals will lead you to your larger objectives anyway.

If decision-makers in your organization require a financial target in order to start an AI initiative, Gartner VP and distinguished analyst Whit Andrews recommends setting the target as low as possible. He suggests the following: “Think of targets in the thousands or tens of thousands of dollars, understand what you’re trying to accomplish on a small scale, and only then pursue more-dramatic benefits.

Focus on augmentation vs. replacement.

Historically, significant tech advances have often been associated with a reduction in staff. While cutting labor costs may be an attractive benefit for executives, it’s likely to generate resistance amongst staffers who view AI as a threat to their livelihood. A lack of buy-in from front line employees may hinder progress and result in a less favorable outcome.

To avoid this, shift your approach to one that focuses on augmenting human workers as opposed to replacing them. Ultimately, communicating that the most transformational benefits of AI lie in the technology’s ability to enable employees to pursue higher value and more meaningful work. For instance, Gartner predicts that by 2020, 20% of organizations will have workers dedicated to overseeing neural networks.

Make an effort to engage employees and get them excited about the fact that an AI-powered environment will enhance and elevate the work they do.

Prepare for knowledge transfer.

The majority of organizations are not adequately prepared for AI implementation. In particular, most lack the appropriate internal skills in data science and, as a result, plan on relying heavily on external service providers to help bridge the gap. Furthermore, Gartner predicts that 85% of AI projects initiated between now and 2022 will deliver erroneous outcomes due to inaccurate or insufficient data and/or lack of team knowledge/ability.

In order for an AI project to work at scale, there must be a robust knowledge-base fueled by accurate information and there must be adequately trained staff to manage it. Simply put, relying on external suppliers for these things isn’t a feasible long-term solution. Instead, IT leaders should prepare in advance by gathering, storing and managing data now and investing in the reskilling of existing personnel. Building up your in-house capabilities is essential before taking on large-scale AI projects.

Seek transparent solutions.

Most AI projects will inevitably involve some type of software, system, application or platform from an external service provider. When evaluating these providers, it’s important that decision-makers take into account not only whether the solution will produce the appropriate results, but also why and how it will be most effective.

While explaining the in-depth details of something as complex as a deep neural network may not always be possible, it’s imperative that the service provider be able to, at the very least, provide some type of visualization as to the various choices available. At the end of the day, the more transparency that is present, the better – especially when it comes to long-term projects.

For more information on how to incorporate artificial intelligence into your strategic planning for digital transformation, check out this resource from Gartner. And when you’re ready to move forward with your AI initiative, give Ayehu NG a try free for 30 days. Click here to start your complementary trial.

Your Top Artificial Intelligence Adoption Questions, Answered

According to Gartner, the number of organizations implementing some type of artificial intelligence (i.e. machine learning, deep learning and automation) has grown by 270% over the past four years. One big reason for this boost is the fact that executives and decision makers are beginning to recognize the value that these innovative technologies present.

That’s not to say they’re all on board. Are CEOs getting savvier about AI? Yes. Do they still have questions? Also yes – particularly as it relates to the adoption/deployment process. Let’s take a look at a few of the top questions and answers surrounding the topic of artificial intelligence below, along with some practical advice for getting started.

Is a business case necessary for AI?

Most AI projects are viewed as a success when they further an overarching, predefined goal, when they support the existing culture, when they produce something that the competition hasn’t and when they are rolled out in increments. At the end of the day, it’s really all about perspective. For some, AI is viewed as disruptive and innovative. For others, it might represent the culmination of previous efforts that have laid a foundation.

To answer this question, examine other strategic projects within the company. Did they require business cases? If so, determine whether your AI initiative should follow suit or whether it should be standalone. Likewise, if business cases are typically necessary in order to justify capital expenditure, one may be necessary for AI. Ultimately, you should determine exactly what will happen in the absence of a business case. Will there be a delay in funding? Will there be certain sacrifices?

Should we adopt an external solution or should we code from scratch?

For some companies, artificial intelligence adoption came at the hands of dedicated developers and engineers tirelessly writing custom code. These days, such an effort isn’t really necessary. The problem is, many executives romanticize the process, conveniently forgetting that working from scratch also involves other time-intensive activities, like market research, development planning, data knowledge and training (just to name a few). All of these things can actually delay AI delivery.

Utilizing a pre-packaged solution, on the other hand, can shave weeks or even months off the development timeline, accelerating productivity and boosting time-to-value. To determine which option is right for your organization, start by defining budget and success metrics. You should also carefully assess the current skill level of your IT staff. If human resources are scarce or if time is of the essence, opting for a ready-made solution probably makes the most sense (as it does in most cases).

What kind of reporting structure are we looking at for the AI team?

Another thing that’s always top-of-mind with executives is organizational issues, specifically as they relate to driving growth and maximizing efficiencies. But while this question may not be new, the answer just might be. Some managers may advocate for a formal data science team while others may expect AI to fall under the umbrella of the existing data center-of-excellence (COE).

The truth is, the positioning of AI will ultimately depend on current practices as well as overarching needs and goals. For example, one company might designate a small group of customer service agents to spearhead a chatbot project while another organization might consider AI more of an enterprise service and, as such, designate machine learning developers and statisticians into a separate team that reports directly to the CIO. It all comes down to what works for your business.

To determine the answer to this question, first figure out how competitively differentiating the expected outcome should be. In other words, if the AI effort is viewed as strategic, it might make sense to form a team of developers and subject matter experts with its own headcount and budget. On a lesser scale, siphoning resources from existing teams and projects might suffice. You should also ask what internal skills are currently available and whether it would be wiser to hire externally.

Practical advice for organizations just getting started with AI:

Being successful with AI requires a bit of a balancing act. On one hand, if you are new to artificial intelligence, you want to be cautious about deviating from the status quo. On the other hand, positioning the technology as evolutionary and disruptive (which it certainly is) can be a true game-changer.

In either case, the most critical measures for AI success include setting appropriate and accurate expectations, communicating them continuously and addressing questions and concerns with swiftness and transparency.

A few more considerations:

  • Develop a high-level delivery schedule and do your best to adhere to it.
  • Execution matters, so be sure you’re actually building something and be clear about your plan of delivery.
  • Help others envision the benefits. Does AI promise significant cost reductions? Competitive advantage? Greater brand awareness? Figure out those hot buttons and press them. Often.
  • Explain enough to illustrate in the goal. Avoid vagueness and ambiguity.

Today’s organizations are getting serious about AI in a way we’ve never seen before. The better your team of decision makers understands about how and why it will be rolled out and leveraged, the better your chances of successfully delivering on that value, both now and in the future.

Need Scalability? Intelligent Automation is the Answer

Imagine how much your company could accomplish if you had a veritable army of employees at your disposal. More importantly, what if these employees were perfectly happy waiting in the background for the next time you needed them? Believe it or not, that’s precisely what you’ll get with intelligent automation. Let’s take a look at the surprising way artificial intelligence can provide your business with the scalability you need to stay on top of your game.

Instant Access

With human workers, it’s not feasible to dramatically increase your workforce whenever the need arises, nor is it easy to decrease your numbers when things get slow. There’s a complicated process behind all of this and time is not on your side. With intelligent automation, however, you have a team of robots who are ready, willing and able to get the job done at any given moment.

Consistency

Bringing different employees up to speed via on-boarding and training can be challenging and time-consuming, especially in today’s fast-paced, digital age. Not to mention the fact that you have to initiate the entire process over again every time someone new joins the team. Robots, on the other hand, can be “trained” in groups of any size with the outcome being routine and perfect consistency across the board.

Cost Savings

Recruiting, hiring, training and retaining talented employees costs money. In addition to intelligent automation providing the ability to scale up or scale down instantly as well as train and deploy thousands of bots while maintaining complete consistency, all of this can be done at a reduced cost to the business.

Now, let’s take a look at a few real-world applications of these benefits.

Scenario 1

Your business is launching a new product and, as a result, will incur a substantial increase in transactions. Your current workforce is already maxed out and you don’t have the time or the ability to hire any additional employees. Intelligent automation can step in and bridge the gap, handling the influx of work at any capacity necessary without the major hassle and expense of staffing. Then, once things settle down, you can scale back down to normal as needed.

Scenario 2

Business has been particularly lucrative as of late and you’ve had to increase output significantly to meet the increased demands of your customers. Suddenly, the market takes a turn for the worse and your numbers start to rapidly decline. With intelligent automation in place, you won’t have to face the possibility of laying employees off. Rather, you could just scale back the number of robots.

Scenario 3

One of your biggest competitors has launched a new product or service and you’re scrambling to develop and implement something similar. Chances are you can’t afford to hire a slew of new employees to help bring your comparable product or service to market and doing so would take too long. Conversely, putting too much pressure on existing team members could result in costly mistakes and QA issues. AI, on the other hand, is available at the ready to take on whatever is necessary for you to remain competitive.

Without question, intelligent automation has the potential to bring your business to the next level. Are you ready? Give us a call today or download your free 30 trial now to get started.

New Call-to-action

5 Things to Avoid for a Successful Intelligent Automation Rollout

For all the talk we do here at Ayehu about how to make intelligent automation work for your organization, one area we don’t usually cover is how and why these types of projects often fail. Sometimes even though the reason for adopting automation is on target, the outcome isn’t quite what one had hoped for. This can lead to costly double-work and the frustration of having to start over again. To improve the chances of your automation project going off without a hitch, here are the 5 most common mistakes so you’ll know exactly what to avoid.

Focusing on tools and tasks instead of people.

It may seem ironic, particularly given the widespread opinion that artificial intelligence is somehow out to replace humans, but one of the biggest reasons an automation project fails is because it was designed around a task or tool instead of the people who it was ultimately designed to help. The fact is, intelligent automation is meant to streamline operations and make the lives of your IT team better, not worse. Focus on how the project will benefit your human workers and the results will be much greater.

Failing to adequately calculate and communicate ROI.

For an automation project to be carried out successfully, the projected benefits and long-term gains must be determined and demonstrated upfront. This includes taking into account the early costs associated with adopting a platform and helping decision makers understand the time-frame for seeing positive returns. Without this, you risk upper management pulling the plug too early due to lack of results. (If you’re not sure how to calculate ROI on an IT automation project, here’s a helpful guideline.)

Not setting appropriate expectations.

Sometimes an intelligent automation project is deemed a “failure” simply because it did not meet the (often unrealistic) expectations of certain stakeholders. That’s why it’s so important that those in charge of planning, testing and implementing any AI project include communication of the expected time-frame as well as the potential for issues and delays that may inevitably arise. When “the powers-that-be” know what to expect ahead of time, there are no surprises to have to deal with during the process.

Automating broken processes.

Another common cause of an automation project failure occurs when those in charge attempt to automate a process that isn’t working properly in the first place. Not only is this a huge waste of time and resources, but it simply won’t work, which means backtracking, adjustments and a whole host of other delays will ultimately occur. Before starting any automation project, be certain everything you’re planning to automate is relevant and ready.

Not using the right platform.

Just like most things in IT, not every automation platform is created equal. Some organizations fall into the trap of purchasing the cheapest tool they can find only to learn that, as usual, you get what you pay for. Others make the mistake of investing in a product that they think is top-of-the-line, only to discover that it has way more features than they really need, making it a complex waste of money. The key to successfully carrying out an intelligent automation project is to do your research and select a platform that is robust but easy to use and scalable to fit your specific needs.

Thinking about trying automation but not sure where to begin?

Check out these common tasks and processes that can and should be automated and then download your free 30 day trial of Ayehu NG to experience it for yourself.

eBook: 10 time consuming tasks you should automate

And the Employee of the Year Award goes to: A robot?

Imagine: you’re in the annual company meeting and the CEO announces who has been the busiest, most productive employee over the past 12 months. Now, imagine if he completes that statement not with the name of you or any of your co-workers, but instead points to your intelligent automation platform. In other words, a robot.

Can a robot really take the place as employee of the year? The answer is an emphatic yes. Here’s why.

It’s no secret that the IT department is continually being asked to do more with less. In other words, be as productive and efficient as possible while simultaneously limiting the amount of resources being used to do so. As a result, IT leaders have to find a way to maximize output while also minimizing the time and costs associated with producing that output. With humans, this simply isn’t possible without either extending the hours worked or hiring additional staff, neither of which will achieve the goal of keeping expenditures down.

Another area of pressure IT leaders experience is that of user satisfaction. Internal service levels and external customer expectations are increasing at a rapid rate, and if you can’t meet those demands effectively, you will lose your competitive advantage. In other words, your bottom line will suffer. Yet again, in order for human agents to achieve these goals is to either work more or hire additional team members.

Enter intelligent automation, or a team of artificially intelligent robots who can basically take on all the time consuming day to day tasks that the help desk currently handles. Whether it’s password resets, system monitoring, incident management or some other complex workflow, intelligent automation can be leveraged for it all. As a result, the work will be completed faster, with fewer mistakes, driving productivity and efficiency up while also bringing costs down.

Many IT professionals erroneously view these robots as a threat to their very livelihood. After all, if a machine can be used to do all these tasks, what’s the point of keeping humans on staff? Why not automate the entire operation? Well, for starters, many automated processes still require some type of human input.

Secondly, while intelligent automation may take some of the work away from humans, it will inevitably free those humans up to apply their skills to more complex and important business matters. So, it’s not a replacement, but rather a shift in responsibility. Furthermore, experts predict that AI will actually create 58 million new jobs by 2020. As such, it should be embraced rather than feared.

So, when the big boss stands up and hails automation as the company’s busiest employee, that doesn’t necessarily mean bad news for the people who work there. As long as the technology is leveraged properly and viewed as the powerful and innovative tool it truly is, its role as employee of the year is something that will drive the ongoing success of the organization and make the lives of human workers easier and/or more meaningful.

Give the employee of the year a test drive in your own company and experience the power and impact of intelligent automation for yourself by starting your free 30-day trial of Ayehu.