Posts

3 New Roles AI Will Create for Humans

What if we were to tell you that robots are not coming to steal your job, but instead, they’re coming to give you a promotion? Would that change your perception of artificial intelligence? Probably. Thankfully, in most cases anyway, it’s the truth. While AI will, inevitably, make some roles obsolete, it will simultaneously be creating newer and even more lucrative opportunities for humans. In fact, we’ve pinpointed at least three distinct job categories intelligent automation will open the door for. Let’s explore each of these below.

Trainers

Artificial intelligence is just that – artificial. Yes, it is capable of improving autonomously, but someone still has to be there to tell the program what to do and ensure that everything is moving along as it should be.

For example, before you can tell your virtual assistant Amazon Alexa that you want her to call your spouse, you first have to “teach” her who your spouse is and what phone number should be used. Once AI tools and platforms have the basic understanding of what’s expected of them, they can then continue to self-learn, but just like any student, they need to be taught those basics first.

Evangelists

Like it or not, artificial intelligence isn’t going anywhere. In fact, according to Gartner, 70% of organizations will assist their employees’ productivity by integrating AI in the workplace by as early as next year.

Yet, despite this looming proliferation, not everyone is quite comfortable with the concept of AI. Fear of the unknown is still a very real problem for many organizations. That’s where AI evangelists come in. These are human experts whose role, at least in part, is to explain computer behavior to others.

Having champions of intelligent automation will enable the enterprise to overcome fear and resistance, clearing the pathway to successful digital transformation.

Managers

As smart as AI is, it’s not necessarily something you can simply set and forget. To the contrary, human oversight is still very much needed to ensure that systems are working properly, securely and responsibly.

The fact is, while chatbots have the capability of learning and communicating, they lack many of the characteristics that are innate to humans. One need only look back to the Microsoft Tweetbot debacle of a few years ago to understand this.

In March of 2016, the computer mega-giant introduced its interactive chatbot named Tay to the world of Twitter, with the goal of engaging 18-24 year olds with the concept of machine learning. The chatbot was taught to interact with other users like a real human through reading and processing actual tweets.

Unfortunately, within hours, savvy Twitter users had transformed the formerly innocent bot into something vile and offensive. Simply put, AI has the potential to run amok without adequate human oversight.

How Humans Can Prepare

Within these three categories there will be numerous positions and opportunities for human workers to future-proof their careers. The best place to start is through education. Learning new skills and gaining a fundamental understanding of intelligent automation technology can make you an invaluable asset and catapult your career possibilities.

Ayehu’s Automation Academy is designed to provide individuals of every level with the up-to-the-minute knowledge, experience and tools necessary to develop and expand their automation knowledge. Best of all, it’s available completely free of charge. Develop your skills today to become a trainer, evangelist or manager of tomorrow. Enroll for free today!

5 Biggest Blunders Organizations Make with AI

According to Gartner, implementation of artificial intelligence has skyrocketed by 270% over the last four years, with spending on AI software and hardware anticipated to soar from the present amount of $37.5 billion to a whopping $97.9 billion by the year 2023. There’s no argument. AI is here to stay, and it’s going to become a truly integral part of our everyday lives. As such, business leaders in every industry are wondering what AI means for their organizations and, more importantly, how they can capitalize on the tremendous opportunities this innovative technology presents.

In order to be successful with AI, however, it’s imperative that leaders not move forward too quickly without adequate awareness of the many obstacles that could delay, limit or even completely destroy their efforts. Specifically, here are five common traps many organizations have already fallen into that you can hopefully avoid with your own AI initiative.

Being misled.

Sorry to be the bearer of bad news, but not every AI product on the market is what it’s presented to be. In fact, many less-than-forthcoming and often downright dishonest service providers are selling what amounts to glorified automation tools. Automation is great, but the true value of AI lies in its ability to become smarter and more autonomous over time. Don’t be led astray. Do your homework and, if possible, take potential platforms for a test-drive before you invest. Trust us – you’ll know the difference.

Putting the cart before the horse.

Once you’ve got a true AI platform at your disposal, it’s easy to get so excited about the potential artificial intelligence presents for your company that you end up getting ahead of yourself. Keep in mind that AI isn’t an end, but a means to that end. Achieving big-picture goals often requires accomplishing smaller milestones along the way. Focus first on identifying specific business issues, pain points and areas of the business where AI can provide fast, measurable wins and then scale from there. Slow and steady wins the race.

Forgetting data.

The fact is, the outcome of an artificial intelligence engine is only as good as the quantity and quality of the data it is able to ingest. You can’t expect an AI platform to start churning out valuable insights or begin learning and improving contextually if you don’t adequate data to begin with. It’ll work to a degree, but it’ll produce substandard results, so what’s the point? Start by determining whether or not you have the right architecture in place, and if not, focus your efforts there first.

Leaving silos.

If you’re looking to store grain, silos are great. If you’re trying to implement an organization-wide AI initiative, not so much. Why? Because an effective, end-to-end AI strategy inherently depends on integration of data and collaboration of teams. You may have mapped out areas within your organization where AI might help, but if you don’t break down the roadblocks that exist within your infrastructure, don’t expect good results. Data gathered from one team that isn’t shared with the data scientists managing AI models is useless without seamless collaboration.

Failing to invest in skills.

Sure, the beauty of artificial intelligence is, well, that it’s artificial. Implemented properly, AI can run circles around human employees, performing work faster and eliminating error. That said, humans are still very much needed. In fact, some would argue even more-so now than ever before. Without the right team to ensure everything is running smoothly, you’ll quickly lose ground. Thankfully, fighting the war for external talent isn’t necessary. Oftentimes, reskilling in-house employees is not only sufficient, but a much better approach. In either case, an intentional investment in skills is equally as important as choosing the right AI platform.

Artificial intelligence presents tremendous opportunity for organizations of every size and industry. But in order to be successful with AI, leaders must apply their own human intelligence to the process. By knowing which common blunders to steer clear of, like the five listed above, successful implementation and the business value that follows will be well within your reach.  

Three Steps To Prepare The Enterprise For The Digital Workforce In 2020

Article originally published in Forbes Technology Council.

There’s no longer any uncertainty or ambiguity. Automation absolutely, positively will impact the way every one of us works. The degree to which that impact occurs will vary, but make no mistake: Humans in every industry and position, from warehouse workers to C-suite executives, will someday soon be working alongside digital workers (a.k.a. virtual agents).

Just what will this future digital enterprise look like? The answer to that lies in how organizations implement artificial intelligence.

The ‘How’ Vs. The ‘What’

For many workers, the way that automation and artificial intelligence technologies are adopted will be more effective than the technology itself. The same goes for organizations as a whole. To succeed in the digital age, business leaders must begin to shift their viewpoint from opportunistic to a more systematic approach. In years past, automating on an ad hoc basis was sufficient. Over time, however, that strategy led to silos that were not adequately governed, nor were they scalable.

The future of automation in tomorrow’s workplace must be rigorous and robust, policy- and data-driven, and, above all, enterprise-centric. In other words, it’s not so much about the “what” as it is about the “how.” This will be the main differentiator between organizations that succeed in achieving digital transformation and those that fall irreparably behind.

Three Steps To Success With Intelligent Automation

1. Build. New technologies like artificial intelligence and machine learning will inevitably affect some workers in adverse ways. This has always been the case, as people continue to be displaced from one economic sector to another. In fact, according to one estimation by McKinsey, up to 30% of the global workforce (and between 400 million and 800 million workers) could be displaced by automation by the year 2030.

But while some jobs will ultimately be eliminated, the current and ongoing technological innovation we are experiencing will simultaneously create new opportunities.

Perhaps it is more than fitting that a fictional Borg from the futuristic Star Trek series uttered the infamous words, “Resistance is futile.” Like it or not, AI and automation technologies are already having an impact on the workplace, and they’re not going away any time soon.

The future of work will ultimately belong to those individuals who are willing to embrace and leverage artificial intelligence to their advantage. This may come in the form of self-automation — that is, the foresight and desire to automate portions of one’s own job in the interest of productivity and efficiency. Organizational leaders can and should meet them in the middle by seeking out key employees who show promise, optimism and a willingness to adapt and reskill, if necessary.

Investing in human capital with the ultimate goal of developing an automation center of excellence will create a compromise between top-down mandated automation and bottom-up, enthusiastic support and participation. This is the ideal scenario and one that will drive ongoing innovation and success. Some key roles to focus on for the future include:

  • Automation architects.
  • Automation engineers.
  • Site reliability and DevOps engineers (SRE).
  • API product managers.
  • Data scientists.

2. Standardize. With the right people and teams in place, the next step toward leveraging intelligent automation for digital transformation should involve the standardization of processes and the creation of best practices.

To start, the focus should be on delivering continuous value rather than aiming for one major change. This is achieved via strategic increments.

Centralized governance will then help to ensure ongoing compliance and support future growth and expansion.

3. Invest. Many will find it surprising that technology is actually the final piece in the automation puzzle. This is due in large part to the old-school, opportunistic way of thinking. The new recommended approach is one that involves a strategic cultural change and focuses on people and processes first, and then tools and technology.

Once these first two factors have been determined, the search for the right automation platform can begin. Ideally, the criteria should include no-code or low-code solutions that are both robust and agile. This will enable the eventual proliferation of automation across the entire enterprise while also supporting the future growth and changing needs of the business and/or industry.

Closing Thoughts

What will the workplace of tomorrow look like? For human workers, it will be markedly different and require new skills and greater adaptability. For the enterprise, it will be a composite of real and artificial intelligence — humans and machines — working together toward a common goal of innovation and success.

Dare to take risks despite your fear. Organizations and their employees who approach these challenges with eagerness and optimism, a willingness to adapt and evolve, and the ability to strike the ideal balance between humans and machines will ultimately be the ones who rise to the top.

Is AI Killing Jobs….or Creating Them?

Ask a roomful of people how they feel about artificial intelligence in the context of jobs and you’ll undoubtedly received a mixed bag of responses. Some will undoubtedly express their concerns that AI is poised to destroy employment as we know it today. Others will take a more optimistic approach, viewing AI as a tool to help us work more efficiency and accomplish things we couldn’t do with human workers alone. Even the various analysts and economists range widely in their predictions of what role AI will ultimately have in the future of work.

The truth, as is quite often the case, lies somewhere in the middle. There’s no question that intelligent automation will eliminate some jobs, impacting just about every industry and sector across the board. At the same time, however, AI will create new jobs, both in categories we’re familiar with as well as many more that have yet to be developed.

AI = Job Killer?

Automation is nothing new. It’s a technology that’s been embraced and lauded by organizations for decades upon decades. The key differentiator here is the introduction of the word “intelligent.” It’s the cognitive abilities afforded by AI and machine learning that will ultimately enable businesses to optimize their use of human labor. And that’s where the shakeup will inevitably occur.

In fact, a shift has already begun – particularly in areas where the work is highly repetitive, regulatory intensive and prone to error. Why pay humans to perform work that could easily be carried out by an intelligent bot – especially when that bot is capable of understanding the meaning and context of the information at hand?

So, does this mean people are being eliminated from the workplace entirely? Not so fast. In fact, there are plenty of categories of employment that will remain relatively untouched by AI. For instance, many professional services categories will remain intact as they still require a level of responsiveness that cannot yet be replicated by artificial intelligence.

AI = Job Creator.

Studies indicate a surprisingly positive view of AI’s role and ultimate impact on tomorrow’s workplace. In fact, a recent survey revealed that 75% of U.S. workers do not view their jobs as being at risk of elimination – at least not within the next decade.

Additionally, the vast majority (87%) of workers say they wish their employers would automate more tasks and processes. Why? Because they recognize the promise that AI brings in terms of improved efficiency and increased productivity. In other words, most people understand that AI will make their work lives better, not worse.

But, what about those repetitive, error-prone tasks that are already being shifted from human to machine? Won’t those workers end up in the unemployment line? Not if they are willing to change gears. In fact, the rapid adoption of AI and intelligent automation is already creating exciting new roles and opportunities.

Part of the challenge here is that it’s difficult to fathom jobs and employment categories that may not yet exist. Thinking back 20 years, the concept of a role such as social media manager was completely foreign, yet today it’s something most organizations have. Likewise, looking forward 20 years into the future, there will undoubtedly be whole new sectors of the economy that do not exist today. And along with those emerging opportunities, human workers will also need to adapt and evolve.

Without question, the world is experiencing a revolutionary shift, thanks in large part to artificial intelligence technology. Thankfully, the magnitude of the impact that shift will have on the future of work is still largely within our control. Those who are at the greatest risk of redundancy can provide themselves with a safety net by proactively reskilling and reinventing themselves.

Not sure where to begin? Our Automation Academy is a great place to start. Enroll today!

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.

New Call-to-action

How to Predict and Remediate IT Incidents Before They Affect Business Outcomes [Webinar Recap]

Author: Guy Nadivi

The ability to proactively predict  and remediate IT incidents BEFORE they occur, rather than react to them after they’ve already happened, is one of the key value propositions of a new IT operations category called AIOps, which stands for Artificial Intelligence for IT Operations.

Leveraging the AI part of AIOps to mitigate problems before they become problems is a game changer for IT. So we’ve partnered with Loom Systems, who like ourselves are a Gartner Cool Vendor in their category, to demonstrate how two best-of-breed providers can integrate their respective platforms to create an enterprise-grade AIOps solution. In doing so, we believe the result is an early glimpse at the self-healing data center of tomorrow, and we think you’ll be intrigued to experience how you can peek over the horizon to see  and automatically remediate incidents before they impact end-users.

Let’s start with the obvious question many of you might have on your mind – what is AIOps? It is after all, a term that kind of snuck up on all of us.

The term AIOps, like a lot of buzzwords in our industry, was originated by Gartner. In this case, a Sr. Director Analyst named Colin Fletcher coined it in 2016, and its earliest published appearance (as best I can tell) was in early 2017.

Interestingly though, Colin told me he originally meant the term to refer to Algorithmic IT Operations.

Since then it’s evolved to refer to Artificial Intelligence for IT Operations.

Now we all know how it is in IT marketing. New buzzwords are used to refresh a category and create excitement. So is AIOps basically just a recycling of the term “IT monitoring”? Are IT monitoring and AIOps basically the same? Twins, so to speak, but with different names?

Here’s the definition for IT Monitoring, courtesy of an internet publication many of you are probably aware of called TechTarget:

  “IT monitoring is the process to gather metrics about the operations of an IT environment’s hardware and software to ensure everything functions as expected to support applications and services.   Basic monitoring is performed through device operation checks, while more advanced monitoring gives granular views on operational statuses, including average response times, number of application instances, error and request rates, CPU usage and application availability.”    

The operative words there are “gather metrics” – “through device operation checks”.

This reflects one of the primary characteristics of IT Monitoring – namely that it’s passive in nature.

And here’s Colin Fletcher’s original definition for AIOps:

“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

Unlike IT Monitoring, AIOps is proactive and far more sophisticated. So AIOps is a LOT MORE than just IT Monitoring.

At this point you may be asking yourself, “OK, but how can this benefit me?”

As we all know, in today’s Digital Era, most businesses are digital or undergoing a digital transformation, which means that IT systems are replacing many traditional physical business processes, and that in turn means more work for IT Operations.

In fact, IT Operations engineers have become responsible for the customers’ digital experience. When your organization’s systems are misbehaving, underperforming, or worse not working at all, your customers’ satisfaction is affected, which often leads to customer churn.

It’s that simple.

End users often use applications or websites and love how simple and intuitive they can be. In IT though, we all know that building something to look nice and simple, can actually be quite difficult. That’s because there are usually many technologies under the hood that need to work together seamlessly in order for these digital experiences to run smoothly.

As if that wasn’t enough, let’s add some more complexity:

With Cloud Computing on the one hand, and Microservices architectures on the other, things become even more complex, for the following reasons:

  1. Cloud computing means abstraction – that can lead to struggles understanding what the impact of a performance issue on a host will do to other components of your applications.
  2. These environments change dynamically, making it harder to stay on top of everything.
  3. Microservices often require disparate data sources, each generating its own logs and metrics, making tracing and correlation an inherent part of root cause analysis (RCA).

So, the increased complexity of digital businesses architectures, coupled with the explosion of different data types, and the elevated expectations consumers have these days for seamless end user experiences, makes the life of IT Operations teams quite challenging.

Enter AIOps.

AIOps is a set of tools that enable achievement of optimum availability and performance by leveraging machine learning technologies against massive data stores with wide variance. The big idea here is to use machines to deal with machines.

Here are some examples of the challenges customers often look to address by implementing AIOps:

  • Outage prevention – organizations in the process of cloud migration or architecture change, often look for modern technologies like AIOps to help them prevent outages before the business is affected. This is a marked difference from 2 years ago when the market was just focused on noise reduction. Artificial intelligence and machine learning have raised expectations of how much more is possible.
  • Capturing different data feeds – this means it’s not just about alerts anymore. There’s a huge need to consolidate logs, metrics, and events together, and to make sense out of them as a whole.
  • Consolidation of tools – this one is mainly about the workflow of the users. They’d like AIOps to make their daily lives easier and consolidate everything into one system.

A monitoring architecture for modern enterprises that can do all of the above would be a real-life example of a self-healing architecture.

Everything starts with observability. Many enterprises use one or more infrastructure monitoring tools. Application Performance Management (APM) monitors do a great job in monitoring performance, but are very limited for the application stack and log management, rendering them a bit unhelpful for triage and forensic investigations.

These monitoring tools are usually focused on specific data feeds or IT layers, and they emit alerts when things go wrong. However, these can lead to confusing alert storms.

This is another reason why organizations are beginning to leverage AIOps to work for them and make sense out of it all. Think of AIOps as a robot that turns monotonous data into information you cannot ignore. In our case, turning logs into predictions or early stage detection of an outage.

Now that you know something is about to break, can you prevent it from happening? That’s exactly the idea of self-healing. When working with an intelligent automation platform like Ayehu, you can build simple (or complex) remediation workflows, that can take the alert from Loom Systems and automatically remediate the incident BEFORE it becomes something more calamitous.

In your monitoring architecture, you want the Automation tool to seamlessly interact with both the AIOps solution and your ITSM platform, to open a ticket and update it as you’re taking remedial action.

When configured properly, this architecture can resolve issues before they affect the business, while also documenting what happened for future reference.

Gartner concurs with this approach.

In a paper published earlier this year (ID G00384249 – April 24, 2019), they wrote that:

  “AI technologies play an important role in I andO, providing benefits such as reduced mean time to response (MTTR), faster root cause analysis (RCA) and increased I andO productivity. AI technologies enable I andO teams to minimize low-value repetitive tasks and engage in higher-productivity/value-oriented actions.”    

No ambiguity there.

A little further down in the same paper, Gartner gave the following recommended actions, representing their most current advice to infrastructure and operations leaders regarding AIOps and automation:

  Embark on a journey toward driving intelligent automation. This involves managing and driving AI capabilities that are embedded by infrastructure vendors, in addition to reusing artificial intelligence for operations (AIOps) capabilities to drive end-to-end (from digital product to infrastructure) automation.”    

With AIOps + Automation, it’s possible to predict and prevent network outages or other major disruptions by proactively detecting the conditions leading up to them and automatically remediating them BEFORE disaster strikes. Given how costly a service interruption can be to an enterprise, avoiding issues before they happen will be a critical function in the self-healing data center of tomorrow.

New call-to-action

The changing role of CIO and intelligent automation’s impact.

With the ever-increasing volume and complexity of data coming in (thanks in large part to trends like the IoT, BYOD and, of course, Big Data), the role of the CIO has also begun to rapidly evolve over the past decade or so. These individuals are now facing pressures to keep infrastructure updated as well as analyze and leverage the data available to them for the benefit of the organization, and all while keeping costs down and internal networks, systems, applications and information secure. This is no easy feat, but thanks to intelligent automation, it is entirely achievable.

Due to the heavy volume of data being shared today, integrating automated workflows and processes has become increasingly necessary in order to analyze and derive value from that data, and in a way that is as cost-effective as possible. If IT departments are to remain relevant, drive efficiency and support a profitable operation, it is imperative that they employ the use of intelligent automation, and with the CIO as the key decision maker, it’s up to him or her to ensure that the right resources are in place.

As recently as just a few short years ago, the general public was becoming aware of the IoT, but today organizations of every size and industry are capturing insight and achieving real, sustainable ROI from this advanced (and ever-evolving) technology. Furthermore, intelligent automation is virtually revolutionizing everything from the SOC and NOC to the service desk and data center. Intuitive technology and artificial intelligence are being utilized to proactively monitor systems and devices, gather and evaluate complex data, remediate incidents and resolve issues – in many cases before any human worker is even made aware.

As a result of all of these changes, more basic requests, like password resets and system refreshes, which used to be handled almost exclusively by L1 support professionals are now being shifted to intelligent automation technology. Self-service chatbots are empowering the end-user like never before while simultaneously alleviating IT personnel of the heavy burden associated with these routine, repetitive (but necessary) tasks.

Of course, this hasn’t necessarily made life perfect for IT professionals. Increased consumerization of IT has resulted in the services of many IT departments being compared and contrasted against that of external service providers. Expectations of faster service and the demand to take on more while also minimizing costs as much as possible continue to rise, subsequently increasing the pressures on top IT personnel. Perhaps no one is feeling the pressures of these demands more than the CIO. Embracing intelligent automation is no longer an option, but a critical requirement.

At the same time, the IT world is witnessing a significant change in responsibilities for the CIO, shifting from the old way of the maintenance and provision of physical infrastructure and devices to more of a data management role with an emphasis on innovating and creating value. Digitalization is now the focus, with CIOs playing a lead role in developing and implementing it throughout the entire enterprise. Paradoxically, these high-level IT professionals are being forced to orient and align themselves more with value creation than the efficiency that once defined them.

Data analytics is now being hailed as one of the primary contributors to driving this value, particularly given the ever-increasing pool of available information. It’s important to point out, however, that CIOs and other top IT managers must take the time necessary to understand what data is available to them, what that data equates to and, most importantly, how they can best leverage that information to improve operations across all functions of the organization. Savvy CIOs will leverage intelligent automation to obtain key insights that will support current and future business goals as well as identify new insight and make data-driven decisions that will give the company competitive advantage.

Finally, the evolving role of the CIO will involve more engagement, inspiration and education of others than ever before. To fulfill these duties, it’s absolutely essential that the CIO develops into a strong visionary and consistent innovator for the organization. Through better data analysis and the more widespread use of intelligent automation, those in this important role will begin to morph into the position of strategic advisor, driving the business onward and upward toward increasing and sustainable success well into the future.

Are you a CIO that is struggling to adapt to your changing role? Intelligent automation, powered by AI and machine learning, could provide the foundation upon which you can continue to build your career and your legacy.

Experience the power of Next-Gen Intelligent Automation today!

Free eBook! Get Your Own Copy Today

Still holding out on IT automation? Here are 4 signs the time has come.

stop resisting IT automation

IT automation is certainly not a new concept. In fact, it’s been in use to some degree for over a century. Yet, there are still a great number of enterprise-level organizations that are on the fence about whether this advanced technology is really worth investing in. If you are one of these late bloomers and are still unsure of whether or not you should take the plunge and employ intelligent IT automation in your company, here are four signs that will let you know it’s time.

Your IT department is struggling to deliver services in a timely, efficient manner.

When a ticket gets opened to IT, how long does it take to achieve satisfactory resolution? In today’s fast-paced business environment, regardless of what industry you are in, agility and efficiency are absolutely critical to ongoing success and future growth. If the demands of your workforce are becoming too much for your skilled IT personnel to handle, the time to leverage technology has come. Not only will IT automation alleviate the burden of many of the day-to-day repetitive tasks, but it will also free up your talented technicians to apply their valuable skills in a more resourceful and profitable manner.

You have way too many staff members on hand just to handle those peak cycles.

Optimized resource allocation is the key to running a lean, profitable operation. If you have far too many IT employees on the payroll just so you can ensure smooth workflow during peak cycles, you are undoubtedly wasting money the rest of the year. Conversely, if your current IT department becomes completely overwhelmed during those peak cycles, your capacity is too low and you’re likely to see higher employee turnover rates. IT automation provides the ability to scale up or down as needed without having to make any changes to your human workforce.

Your employees are wasting an incredible amount of time and effort on repetitive tasks.

Even if you feel that your operation is being managed at the appropriate capacity and the turnaround time of your IT department is acceptable, if your IT team is spending the majority of their day completing manual tasks and processes, you’re wasting money and missing out on opportunity. You’re also facing a much higher risk of costly human error. Why not let artificial intelligence handle these simple, routine tasks? That way you’ll be paying an appropriate salary to workers who are able to better utilize their valuable skillset and the work will be completed faster and more accurately.

Your legacy systems and applications are operating independently.

Of course it doesn’t make sense to invest in an entire system overhaul, but what kind of operation are you running if every application you’ve got in place is functioning in its own silo. The problem many organizations face is the fact that legacy systems which offer useful benefits individually don’t have the capability of working together. This leads to tremendous inefficiency. The beauty of most modern IT automation and orchestration platforms is that they are designed to integrate existing systems, platforms and applications to create a more cohesive and streamlined infrastructure. This allows the organization to avail itself of all the benefits of each legacy system as they work in tandem, complementing and enhancing each other’s capabilities.

If you can relate to any of the four challenges listed above, the time to consider adopting intelligent IT automation is now. Get started today with your free 30 day trial and see for yourself what you’ve been missing out on.

eBook: 10 time consuming tasks you should automate

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.