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.

How is AIOps Really Used in IT?

How is AIOps Really Used in IT?

Digital transformation has simultaneously simplified and added a layer of complexity to the modern world of IT operations. Managing multiple environments across a number of locations invoked the need to introduce several disparate tools and platforms, leaving IT siloed and, oftentimes, overwhelmed. This has perpetuated the need for artificial intelligence for IT operations, or AIOps for short. For those not yet leveraging AIOps, or who are still in the beginning stages, here are three real-world, value-added use cases to consider.

Threat Detection – AIOps is the perfect complement to a security management strategy because its machine learning algorithms are capable of mining massive amounts of data for scripts, botnets and other threats or anomalies that could potentially harm a network. This is especially true for threats that are complex and sophisticated, which is why it’s such a valuable addition.

Intelligent Alerting – Today’s ITOps teams are being inundated with alerts of which only a small portion are actually critical. AIOps can manage these alerts autonomously, evaluating, identifying core issues, prioritizing and either escalating or remediating them without the need for human intervention. Imagine trimming that overflowing inbox of alerts down to just one or two that truly matter.

Capacity Optimization – Through the use of AI-based statistical analysis, IT operations teams can optimize application workloads and availability across the entire infrastructure. This technology is capable of proactively monitoring bandwidth, utilization, CPU, memory and much more, with the goal of maximizing application uptime. AIOps can also be used for predictive capacity planning.

Of course, this is really just the beginning. As environments become increasingly complex and technology options continue to grow, IT operations teams will find themselves under even more pressure to deliver maximum business value with minimal downtime. AIOps emerges as the ideal solution, facilitating infrastructure monitoring and management that is much faster and far more efficient. It’s no surprise, that IT leaders and other key decision-makers are starting to take notice.

Today, AIOps is all about threat management, streamlined alerting and maximizing uptime. Tomorrow, IT automation powered by artificial intelligence, machine learning and natural language processing technology is positioned to forge entirely new pathways for innovation and growth. In other words, the journey has just begun and the future is beaming with possibility.

Want to get in on the ground floor? Grab your free 30-day trial of Ayehu NG and put the power of AIOps to work for your organization.

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Bridging the NOC and SOC for an Integrated IT Powerhouse

The similarities between the role of the Network Operation Center (NOC) and Security Operation Center (SOC) often lead to the mistaken idea that one can easily handle the other’s duties. Furthermore, once a company’s security information and event management system is in place, it can seem pointless to spend money on a SOC. So why can’t the NOC just handle both functions? Why should each work separately but in conjunction with one another? Let’s take a look a few reasons below.

First, their roles are subtly but fundamentally different. While it’s certainly true that both groups are responsible for identifying, investigating, prioritizing and escalating/resolving issues, the types of issues and the impact they have are considerably different. Specifically, the NOC is responsible for handling incidents that affect performance or availability while the SOC handles those incidents that affect the security of information assets. The goal of each is to manage risk, however, the way they accomplish this goal is markedly different.

The NOC’s job is to meet service level agreements (SLAs) and manage incidents in a way that reduces downtime – in other words, a focus on availability and performance. The SOC is measured on their ability to protect intellectual property and sensitive customer data – a focus on security. While both of these things are critically important to the success of an organization, having one handle the other’s duties can spell disaster, mainly because their approaches are so different.

Another reason the NOC and SOC should not be combined is because the skillset required for members of each group is vastly different. A NOC analyst must be proficient in network, application and systems engineering, while SOC analysts require security engineering skills. Furthermore, the very nature of the adversaries that each group battles differs, with the SOC focusing on “intelligent adversaries” and the NOC dealing with naturally occurring system events. These completely different directions result in contrasting solutions which can be extremely difficult for each group to adapt to.

A new set of problems arise, however, when the two teams become siloed, with each group focused on only half of the equation. The resulting gap, particularly in terms of data that is not being shared, perpetuates an even broader gap in the necessary knowledge to maximize the effectiveness of each team. Efforts by the SOC that fail to take into account operational requirements or efficiencies cause bottlenecks that can result in a disruption in network performance. Likewise, fingers can be pointed at the NOC for implementing network designs that leave critical resources exposed and vulnerable.

The best solution is to respect the subtle yet fundamental differences between these two groups and leverage a quality automation product to link the two, allowing them to collaborate for optimum results. The ideal system is one where the NOC has access to the SIEM, so they can work in close collaboration with the SOC and each can complement – rather than impede – the other’s duties. The SOC identifies and analyzes issues, then recommends fixes to the NOC, who analyzes the impact those fixes will have on the organization and then modifies and implements accordingly.

So, what’s the best way to achieve this cross-functional collaboration and optimization? The most important goal is to eliminate operational and/or technical silos. By leveraging a cross-silo intelligent automation platform, security incidents can be detected and resolved while events simultaneously trigger automatic changes both to security as well as network device configurations. This essentially closes the loop on cyberattack mitigation while effectively bridging the distance between security and ops teams.

As the IT environment introduces increasingly complex applications and workflows across a spectrum of systems and devices, and oftentimes in a variety of different locations, the demand for a more streamlined, holistic approach also continues to grow. The time has come to rethink the way the NOC and SOC work together. With an orchestrated approach, powered by intelligent automation, organizations will be able to close the gap between the two departments to more effectively address today’s multifaceted threats, regardless of where they happen to occur within the network.

Ayehu NG is an intelligent IT Automation and Orchestration platform built for the digital era. As an agentless platform, Ayehu is easily deployed, allowing organizations to rapidly automate tasks and processes, including interoperability across disparate solutions and systems, all in one, unified platform.

If you’re ready to bridge the gap between your NOC and SOC to create an integrated IT powerhouse, click here to start your free trial.

Solving your “what if” scenarios with intelligent automation

When it comes to convincing businesses that intelligent automation is the way of the future, the biggest objection to overcome is the age-old question, “what if….” Many IT professionals and other key decision makers within an organization carry the fear that automated tasks which are put in place to solve a problem may actually end up causing more harm than good.

What could go wrong? What if the whole thing blows up in our faces and we end up with an even bigger mess on our hands? The answer is simple: when automation is designed and tested properly, everything should work out just as it is planned, and the results will be well worth the effort.

Creating and Designing Your Workflow

The first step in setting up intelligent automation so that it works properly is creating and designing your workflows. You have to have your end result in mind, and then figure out the steps necessary to achieve that end result. Various criteria will need to be identified, so that you know whenever a certain function or task occurs, the next step in the workflow will automatically be triggered. So, to summarize, establish your desired end result, and then develop a list of steps to help you achieve that result. List each criterion in the process and determine what next step each criterion would trigger.

Testing 123…

The next important step, once you’ve created your workflow, is to try it out in a controlled environment. Test each step in the process to verify that the desired result for each is achieved. If something isn’t working properly, re-evaluate to determine why and then work to fix that piece until the entire process is functioning correctly. We recommend starting small and testing a variety of situations and scenarios to really be sure everything is working properly. Continue this process until you are confident that your automated workflow is working precisely as it should.

Implement

Once you’ve tested your automated workflow enough to be confident that it’s functioning as it is meant to function, it’s time to put it into action. It can be a bit nerve wracking to implement a workflow for the first time live, but once you see it in action, you’ll become that much more confident that it will be there to meet your needs whenever necessary.

Call on the Experts

If, at any time during the above outlined process, you feel as though you’re not getting the results you’re looking for, or you need some guidance and support, don’t be afraid to reach out to the experts. Remember, part of choosing the right intelligent automation product is choosing a company that offers plenty of training and support to its customers. Any company will be there for you when you’re in the process of making a purchasing decision, but you want to make sure that you choose someone that will also be there for you after the fact. If you’re feeling overwhelmed or just have a few questions, don’t be afraid to reach out to your software partner for assistance.

The hands-off nature of intelligent automation can make some professionals feel uneasy. They may wonder if the very system that’s being put in place to solve a particular problem within the organization will actually end up causing more harm than good. The truth is when you know the steps to take, and you’re careful to work through each step just as you should, the result will be exactly what you’re hoping for. When automation is created, developed, tested and supported properly, there is no longer the need to ask “what if”, but rather “why did I wait so long to do this?”

What are YOU waiting for? Contact us or better yet – download your free trial today to start leveraging intelligent automation for your organization.

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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!

How to Leverage Intelligent Automation to Better Manage Alert Storms [Webinar Recap]

Author: Guy Nadivi

As most of you already know, there’s a digital transformation underway at many enterprise organizations, and it’s revolutionizing how they do business. That transformation though is also leading to increasingly more complex and sophisticated infrastructure environments. The more complicated these environments get, the more frequently performance monitoring alerts get generated. Sometimes these alerts can come in so fast and furious, and in such high volume, that they can lead to alert storms, which overwhelm staff and lead to unnecessary downtime.

Since the environments these alerts are being generated from can be so intricate, this presents a multi-dimensional problem that requires more than just a single-point solution. Ayehu has partnered with LogicMonitor to demonstrate how end-to-end intelligent automation can help organizations better manage alert storms from incident all the way to remediation.

The need for that sort of best-of-breed solution is being driven by some consistent trends across IT reflecting a shift in how IT teams are running their environments, and how costly it becomes when there is an outage. Gartner estimates that:

Further exacerbating the situation is the complexity of multi-vendor point solutions, distributed workloads across on-premise data centers, off-premise facilities, and the public cloud, and relentless end-user demands for high availability, secure, “always-on” services.

From a monitoring standpoint, enterprise organizations need a solution that can monitor any infrastructure that uses any vendor on any cloud with any method required, e.g. SNMP, WMI, JDBC, JMX, SD-WAN, etc. In short, if there’s a metric behind an IP address, IT needs to keep an eye on it, and if IT wants to set a threshold for that metric, then alerts need to be enabled for it.

The monitoring solution must also provide an intuitive analytical view of the metrics generated from these alerts to anyone needing visibility into infrastructure performance. This is critical for proactive IT management in order to prevent “degraded states” where services go beyond the point of outage prevention.

This is where automating remediation of the underlying incident that generated the alert becomes vital.

The average MTTR (Mean Time To Resolution) for remediating incidents is 8.40 business hours, according to MetricNet, a provider of benchmarks, performance metrics, scorecards and business data to Information Technology and Call Center Professionals.

When dealing with mission critical applications that are relied upon by huge user communities, MTTRs of that duration are simply unacceptable.

But it gets worse.

What happens when the complexities of today’s hybrid infrastructures lead to an overwhelming number of alerts, many of them flooding in close together?

You know exactly what happens.

You get something known as an alert storm. And when alert storms occur, MTTRs degrade even further because they overwhelm people in the data center who are already working at a furious pace just to keep the lights on.

If data center personnel are overwhelmed by alert storms, it’s going to affect their ability to do other things.

That inability to do other things due to alert storms is very important, especially if customer satisfaction is one of your IT department’s major KPI’s, as it is for many IT departments these days.

Take a look at the results of a survey Gartner conducted less than a year ago, asking respondents what they considered the most important characteristic of an excellent internal IT department.

If an IT department performed dependably and accurately, 40% of respondents considered them to be excellent.

If an IT department offered prompt help and service, 25% of respondents considered them to be excellent.

So if your IT department can deliver on those 2 characteristics, about 2/3 of your users will be very happy with you.

But here’s the rub. When your IT department is flooded with alert storms generated by incidents that have to be remediated manually, then that’s taking you away from providing your users with dependability and accuracy in a prompt manner. However, if you can provide that level of service regardless of alert storms, then nearly 2/3 of your users will consider you to be an excellent IT department.

One proven way to achieve that level of excellence is by automating manual incident remediation processes, which in some cases can reduce MTTRs from hours down to seconds.

Here’s how that would work. It involves using the Ayehu platform as an integration hub in your environment. Ayehu would then connect to every system that needs to be interacted with when remediating an incident.

So for example, if your environment has a monitoring system like LogicMonitor, that’s where an incident will be detected first. And LogicMonitor, now integrated with Ayehu, will generate an alert which Ayehu will instantaneously intercept.

Ayehu will then parse that alert to determine what the underlying incident is, and launch an automated workflow to remediate that specific underlying incident.

As a first step in our workflow we’re going to automatically create a ticket in ServiceNow, BMC Remedy, JIRA, or any ITSM platform you prefer. Here again is where automation really shines over taking the manual approach, because letting the workflow handle the documentation will ensure that it gets done in a timely manner, in fact in real-time. Automation also ensures that documentation gets done thoroughly. Service Desk staff often don’t have the time or the patience to document every aspect of a resolution properly because they’re under such a heavy workload.

The next step, and actually this can be at any step within that workflow, is pausing its execution to notify and seek human approval for continuation. Just to illustrate why you might do this, let’s say that a workflow got triggered because LogicMonitor generated an alert that a server dropped below 10% free disk space. The workflow could then go and delete a bunch of temp files to free up space, it could compress a bunch of log files and move them somewhere else, and do all sorts of other things to free up space, but before it does any of that, the workflow can be configured to require human approval for any of those steps.

The human can either grant or deny approval so the workflow can continue on, and that decision can be delivered by laptop, smartphone, email, Instant Messenger, or even via a regular telephone. However, note that this notification/approval phase is entirely optional. You can also choose to put the workflow on autopilot and proceed without any human intervention. It’s all up to you, and either option is easy to implement.

Then the workflow can begin remediating the incident which triggered the alert.

As the remediation is taking place, Ayehu can update the service desk ticket in real-time by documenting every step of the incident remediation process.

Once the incident remediation is completed, Ayehu can automatically close the ticket.

And finally, it can go back into LogicMonitor and automatically dismiss the alert that triggered this entire process. This is how you can leverage intelligent automation to better manage alert storms, as well as simultaneously eliminating the potential for human error that can lead to outages in your environment.

Gartner concurs with this approach.

In a recently refreshed paper they published (ID G00336149 – April 11, 2019) one of their Vice-Presidents wrote that “The intricacy of access layer network decisions and the aggravation of end-user downtime are more than IT organizations can handle. Infrastructure and operations leaders must implement automation and artificial intelligence solutions to reduce mundane tasks and lost productivity.”

No ambiguity there.

Ayehu

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.

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Hybrid AI in the Future of Work

 Hybrid AI in the Future of Work - ITOps Guest Post
This article was originally posted on ITOps Times

Due to ongoing improvements in artificial intelligence and machine learning technologies, we are on the cusp of an entirely new era in automation. Not only are software robots adept at performing routine, repetitive tasks on behalf of humans, but they are now capable of carrying out activities that rely on cognitive abilities, such as those requiring the use of judgment and emotion. One only needs to look at the cars we drive to recognize just how far automation technology has come.

Does this mean that there will be no place for humans in the future? The answer – at least for the foreseeable future – is a resounding no. That’s because, despite the growing list of benefits, there are also a number of drawbacks to having a system that is entirely autonomous. That’s where hybrid AI comes into play.

The concept behind hybrid AI is remarkably simple, even if the actual technologies and strategies driving it are incredibly complex. In basic terms, a hybrid model integrates humans throughout the automation process, but uses advanced technologies like deep learning and natural language processing to make automation systems even smarter.

AI needs humans
Beyond the hype, the truth is that artificial intelligence technology is simply not yet ready to replace humans – particularly when it comes to mission-critical applications. Take, for example, Tesla’s autopilot feature. While the vehicle itself is equipped with the capability to drive on its own, the driver behind the wheel is still required to remain alert and attentive to ensure his or her safety. In other words, AI is capable of running unassisted, but when it comes to mission-critical functions, it still needs humans, not only to train it, but to make sure everything stays on track.

The truth is, when artificial intelligence gets things right, everything is peachy. But when it doesn’t, the outcome can be disastrous – especially for larger organizations. And while modern AI may have some impressive cognitive capabilities, at the end of the day, it’s still just as its name indicates: artificial. Keeping humans in the mix ensures that the nuances of communication are present and that the output is accurate and relevant.

Humans need AI
On the other side of the coin, humans can benefit tremendously from artificial intelligence technology. And with 37% of organizations having already implemented AI to some degree, it’s clear that people and machines working side by side is becoming the norm rather than the exception. The reason being, artificial intelligence is like a force multiplier for human workers.

For example, data mining can be handled far faster and in much more massive volumes than any human being is capable of. Using AI, organizations can more effectively turn data into insights that can then be used to assist in human decision-making. This thereby drives innovation and competitive advantage.

Bringing it all together
As we progress toward a more automated future, a hybrid approach to integrating AI can help organizations figure out how to get from point A to point B with as little business disruption as possible. One way executives are handling the shift is to create automation centers of excellence (COE) that are dedicated to proliferating automation throughout the organization. Taking a structured approach like this helps to reduce confusion and limit friction.

Members of the COE are responsible for planning, ongoing testing and continuous oversight of the enterprise automation strategy. Typically, this group is made up of individuals who possess a mix of critical IT and business skills, such as developers, operations specialists and business analysts. Additionally, an entirely new role of automation engineer is being created to support the COE.

CIOs may choose to create their COEs with existing employees who are reskilled or newly hired team members. Regardless, COEs represent a strategic approach that is designed to drive adoption across the enterprise while delivering key results in support of company goals.

Ultimately, choosing a hybrid approach that includes a combination of humans and artificial intelligence, is simply the logical evolution of any disruptive technology. It safeguards against the risks of early-stage gaps and helps organizations get the most out of new solutions every step of the way. Done right, technology enables humans to focus on mission-critical applications while using AI to streamline operations and identify the best opportunities and strategies for ongoing organizational success.

AI is not an either/or proposition. It’s up to each organization to determine the right mix of humans and technology that makes sense. As new capabilities and options emerge, that mix will inevitably evolve. And the IT leaders that fully embrace their increasingly strategic value will know how to create the balance that will continually optimize and elevate staff, technology and the entire future of work.

This article was originally posted as a guest piece on ITOps Times. Click here to redirect to the official publication.

4 Ways Digital Transformation will Impact IT Support

The IT help desk, as it once existed, has changed. Driving that evolution has been the changing demands and expectations of digital customers. Simply put, digital is revolutionizing the world of IT support and service. These newer and more complex requirements of digital customers (which include employees) are causing IT teams to re-evaluate what they have to offer in terms of support and capabilities. If your organization or team is at a similar crossroads, here are four key areas on which to focus. 

IT Support Strategies

Regardless of whether your IT service desk happens to support a company of ten or a multi-location, enterprise level organization, the time to start thinking digital is now. A great place to begin is by uncovering how employees have evolved into so-called “digital consumers” and, more significantly, how this evolution has changed the expectations they have of IT support.

To do this, evaluate the gap between your current situation and those changing expectations. In particular, look at your current channels of support. Poll employees to determine whether they feel the current channels they are using to contact IT support are sufficient and effective. Figure out what channels they might prefer. Also, examine common customer use cases and needs. Then, use this information to develop a strategy that incorporates newer, more innovative support channels (like self-service chatbots and virtual support agents).

Operating Models

How does your IT service desk engage with customers? The focus here should be more on this approach as opposed to best practices and ITSM processes. To bring your operating models in line with digital transformation, ask yourself and your team the following questions:

  • Is your approach to IT support adequately in line with your realistic business needs and expectations? For example, what is the overall goal? Cutting IT support costs? Minimizing lost time and revenue at a business level? Understand your objectives and align your strategy accordingly.
  • Do your IT support agents understand “personas” of its customers (i.e. the common characteristics and behaviors they share)? Do your operational practices accurately reflect these personas?
  • How does your IT support desk measure success? Is it primarily related to how the IT service desk has helped and/or improved customer and business operations?

Once you’ve answered these questions, use the data you’ve gathered to identify any and all disconnects between IT support status quo and the actual needs and desires of both the customer as well as the business as a whole. These gaps are where changes must be made.

IT Support Technologies

Without question, the future of IT support will rely heavily on automation. In fact, newer technologies have already made it possible for organizations to augment their human workforce by leveraging ever-improving artificial intelligence capabilities. With these advanced technologies deployed in the right areas, IT support teams are able to more effectively deliver on the increasing demands of digital customers.

Whether your service desk is already leveraging virtual support agents or is planning to in the near future, it’s important to ask the right questions. In particular:

  • Are your virtual agents being used to their fullest potential?
  • Are your virtual support agents being employed at the right points during the customer journey?
  • Do end-users feel that the VSAs improve their support experience?
  • Have you established a robust and accurate knowledge-base from which the VSAs can draw?

This last point is key, as virtual IT support will only be as good as the data behind it. That being said, creating an environment that blends high-tech automation with the human touch of IT support agents will position your organization for greater success.

IT Support Staff

The question of whether human service desk agents will be assisted, augmented and possibly even replaced by virtual support agents is no longer an “if,” but rather a “when.” Getting employees onboard with the concept of artificial intelligence isn’t always easy, especially those L1 agents who view automation as a threat to their livelihood. But it’s essential for an organization of today to remain competitive tomorrow.

Educate your IT support team on the value and benefits that AI has to offer. Make it about them – how AI will make their lives easier, enable them to perform more meaningful work, provide an opportunity to learn new skills and make themselves more marketable, etc. – not just about the company. And start investing in your current workforce. Identify champions of the cause and reskill them so they’ll be ready to face the digital future with confidence. Get them excited about the possibilities that lie ahead!

There is no longer any doubt. IT support as we know it today is changing. Only those organizations that are willing to adapt and evolve their strategies, models, technologies and people alongside those changes will make it through unscathed.

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