3 Business Areas that are Ideal for Machine Learning

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

Internal/External Support

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

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

Cybersecurity

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

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

Human Resources

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

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

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

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Automation Engineers: 5 Essential Skills for Success

Everyone talks about the changes in the IT world – the increased complexity, the pressures to improve efficiency, and the need for tighter links between IT and business.

But how does this influence your IT personnel and their skill set requirements? Obviously, there’s a need for IT professionals with backgrounds in data center operations, systems integration, virtualization etc. Yet the demand for closer links between IT technologists and business operations implies new skills. Particularly with intelligent automation becoming an essential element, IT engineers need new skills beyond familiarity with technologies.

If in the past requirements focused solely on technical expertise and you were only looking for scripting wizards and troubleshooting superheroes – then today your IT group needs a much wider set of abilities. You need Automation Engineers who are able to understand the needs and processes of the business, translate those needs into IT activities, and prioritize and implement them in the most productive way.

So what are the additional skills an automation engineer needs? Here are 5 of the top ones in our opinion.

  1. Business perspective. A business/financial state of mind that enables the consideration and application of non-technical data inputs. For instance, figuring out key KPI’s affecting the IT project,  measuring return on investment (ROI), and optimizing an IT project implementation to successfully achieve financial goals.
  2. Process analysis. The ability to define and implement processes such as incident management, change management, operations, information security, business continuity & disaster recovery, and business service management.
  3. Project Management. Individuals who can not only oversee and monitor projects, but also identify business users’ needs and translate them into IT requirements. Automation engineers that can clearly justify how a business may increase its staff productivity and efficiency using different processes and tools.
  4. Process Implementation. Modern automation engineers must be capable of grasping end-to-end processes, have a deep understanding of workflows and the ability to create them in an automated environment.
  5. Interpersonal skills. The need for stronger communication with business managers requires solid interpersonal skills – i.e. the ability to communicate effectively with a wide range of people outside of the IT domain, understanding business peoples’ needs, concerns and different points of view, and that rare ability to negotiate and make compromises on both sides.

What skills do you think are required for a successful automation engineer? Tell us in the comments section. And don’t forget to download your FREE 30-day trial of Ayehu today!

Planning a Chatbot Strategy? Here’s What NOT to Do

When it comes to utilizing chatbots, there are plenty of resources out there to tell you what you should do, our own blog included. But as with anything in business, it’s just as important to know what not to do as it is to know best practices. By learning from the many common mistakes made by others, you can hopefully avoid going down the same wrong paths with your own chatbot initiative. That said, let’s dive into a few of those common mistakes below.

Not Gauging Need

Chatbots are great, but only if you’re using them the right way and for the right purpose. Adopting this technology just for the sake of it isn’t going to produce sustainable ROI, if any at all. To be successful with chatbots, you must first identify what you are trying to accomplish and what the desired end results should be.

For instance, are you trying to automate a simple process or are you looking for something more sophisticated, interactive and that will learn and improve over time? This will help you choose the right platform and strategize a plan for implementation.

Focusing on a Single Use Case

One of the trickier things about chatbots is that they are capable of far more than many business leaders realize. Unlike other packaged software and SaaS products, which are typically designed to meet a specific business need, the more a chatbot system learns, the more use cases it can take on.

For example, as a Q & A bot answers questions from customers and/or employees, its company knowledge and language understanding grow. As a result, the same core technology can be trained and used for a variety of different instances, thereby multiplying its value. If you limit your approach to just one or two use cases, you also limit the potential return you can achieve.

Overlooking the Human Element

With so much emphasis on training chatbots, it’s easy to forget that your human users also need to be brought up to speed. According to recent data, 43% of people who haven’t used chatbots yet are merely unfamiliar with the technology. And these aren’t tech illiterates, either. 65% routinely use SMS and 61% Facebook Messenger. They simply haven’t been exposed to chatbots nor given adequate guidance for their use.

Furthermore, even users who are familiar and comfortable with chatbot technology may need a reminder that it’s available. For instance, if a user only interacts with the IT helpdesk two or three times a year, they could easily forget that self-service bots are at their disposal. This is another powerful reason for leveraging bots for multiple use cases.

And on the other side of the coin, it’s also important not to overlook the value of the human connection. A shift to 100% chatbot support, for example, could result in frustration and backlash from end-users. Ideally, a bot-human relay should be established through which escalation from machine to human occurs when necessary.

In Conclusion…

With investment into chatbot development expected to top $1.25 billion by 2025, it’s clear that this technology is here to stay. Realizing savings and other benefits from chatbots, however, requires the right training and implementation. Knowing what mistakes to avoid, such as the three key areas above, can prevent your organization from having to deal with costly consequences.

The good news? You can now implement intelligent chatbot technology without the need to code or program. Resolve common IT actions, manage HR tasks, handle incoming customer support inquiries and more – and all via the interface of your choice. Click here to try Ayehu free for 30 days.  

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7 Time-Saving Tips for Busy IT Leaders

Without a doubt, the IT industry is one in which time is a precious commodity. It’s incredibly easy to become bogged down with the nitty gritty details and waste resources putting out fires to the point where other key areas of the organization begin to suffer. If optimizing your time is a priority for you (and/or your team), this article is for you. Read on to learn a few expert tips on how to find efficiencies, eliminate time-wasters and kick bad habits to the curb once and for all.

Tighten up your email practices.

Checking, sending and responding to emails is a huge time suck. But until it officially becomes a thing of the past, email is still something most IT folks will have to deal with. Optimizing your practices can make things more efficient. For instance, schedule specified time to manage email and use other communication methods, such as SMS, for urgent requests. Also, watch who you cc. If you’re including people on your messages who don’t really need to be included, you’re wasting your team’s time as well.

Ditch the waterfall.

Once a widely accepted project management methodology, waterfall has proven to be more of a hassle than what it’s worth, mainly because it can result in tremendous inefficiency. For instance, if developers discover something faulty with a previous step, the entire project must be scrapped and started afresh. And because testing doesn’t happen until later in the process, any existing bugs could have resulted in incorrect coding. If your team is still using waterfall practices, it may be time to consider making the switch to agile.

Expand your network.

It’s easy to feel as though the problems you, your team or your organization are experiencing are unique, but in reality most IT leaders are struggling with the same issues. Some of these other folks may already have figured out the best solution. Rather than wasting time, spinning your wheels and brainstorming on your own, why not tap into your network of peers. By leveraging the insight and advice of others, your decision-making will be faster and more on-point.

Automate.

To some, this one may seem like a no-brainer, but it’s surprising how many IT leaders are still dragging their feet on the automation front. Yet, when you look at the actual, quantifiable numbers, the benefits of automation and AI are staggering. According to a recent report by WorkMarket, 53% of employees say automation could save them up to 2 work hours per day (240 hours per year) and that number goes up to 3 work hours (360 hours per year) for 78% business leaders. At an average workweek of 40 hours, that equates to a time savings of 6 weeks for employees and 9 full weeks for leaders. What could you and your team do with that much time savings?

Scratch the standups.

Daily standup meetings may seem like a good idea on the surface, but when you gather your team on such a frequent basis, the results hardly make it worth the time. The real value of meetings lies in problem-solving, brainstorming and real-time collaboration. Daily scrum, on the other hand, tend to be more about status updates, which isn’t really the best use of anyone’s time. If daily huddles are currently your thing, you may want to consider spacing those meetings out and reserving them for specific needs rather than check-ins.

Fail fast and ditch what isn’t working.

Just because something’s always been done a certain way doesn’t mean it’s the best way. In fact, you or your team could very well be wasting precious time on practices and policies that are out-of-date and wildly inefficient. Agile isn’t just a methodology for project management. It’s also an important mindset – particularly for an IT leader. Make the coming year one in which you work to identify things that aren’t working and take the necessary steps to change them for the better.

Don’t be an island.

Just because you happen to be in a position of power at your organization doesn’t mean you have to solve problems entirely on your own. To the contrary, the most efficient and successful IT leaders not only value but actively seek the assistance of others. Think about it. You are already leading a team of educated problem-solvers. Your job should be to expose existing issues and then let the team determine the best resolution. Not only will this save you time and aggravation, but it’ll also enable you to develop a sense of trust and respect amongst your employees, which can go a long way toward retention.

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4 Hallmarks of a Successful AI Project

According to a recent Deloitte survey, 82% of early adopters of artificial intelligence are already realizing positive financial results from their investment, with a median ROI of 17%. What’s the biggest differentiator between a successful AI project and an unsuccessful one? The focus of the project itself. Organizations that get the most out of AI tend to focus on the specific business objectives that are to be achieved. They then use those results to prove value and scale up from there. If you’re looking for ways to translate AI into business value, here are a few things to keep in mind.

Target actual problems.

Business leaders may recognize the value of using artificial intelligence, but using AI well is where the true value lies. To do this, you must keep the focus on the concrete business problems that you are trying to solve. For instance, will implementing AI help you accomplish something in a way that is faster or cheaper? Will it help you generate more revenue? Can it be scaled? Adding business value should be at the heart of every AI project.

Understand and acknowledge limitations.

Ask an AI system that is trained on a particular set of data to make predictions based on an entirely different set of data, and chances are you’ll get a response that is completely incorrect. For someone who’s come to rely on those predictions, it can be easy to veer off in the wrong direction. To avoid misleading conclusions and misguided decision-making, make sure employees are trained enough to know which analytics model is the appropriate fit for the corresponding data set.

Listen to stakeholders.

Going back to the first point, successful AI projects keep the focus on actual business problems. Unfortunately, the individuals and teams spearheading these initiatives don’t always have the best insight into what those problems and needs happen to be. That’s why it’s so important to gather feedback and insight from key stakeholders – i.e. those who will be directly impacted by AI. Engage in detailed discussions with all interested parties right from the outset. This will save time and improve the outcome of the project in the long run.

Don’t underestimate the value of real-world testing.

The definitive proof of your AI project’s value will only become evident once it hits the real world. If you’re not prepared for this, your initiative is doomed to fail before it’s even begun. And while real-world testing is important for most new technologies, it’s especially beneficial for artificial intelligence. Why? Because early exposure allows more time for the tool to learn, adapt and improve. The sooner you can bring your project live, the sooner you can increase its ability and, ultimately, your ROI.

Artificial intelligence holds tremendous promise for businesses of every size and industry. Unfortunately, most organizations aren’t adequately positioned to take advantage. As an early adopter, the four factors above should enable you to transform your AI projects into quantifiable and sustainable business value.

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5 Surefire Ways to Derail Your Machine Learning Project

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

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

Lack of clear understanding.

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

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

Poor data quality.

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

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

No specific purpose.

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

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

Insufficient resources.

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

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

Poor planning and lack of governance.

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

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

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

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4 Cybersecurity Trends to Plan for in 2019

It’s that time of year again – a time to reflect on the past while also looking toward, and planning for, the future. As it has been in years past, the topic of cybersecurity will remain at the forefront for business and IT leaders in 2019 and beyond. As attackers continue to become savvier and their assaults more sophisticated, the methods used to defend against them must also continue to evolve. Let’s take a look at four trends that are likely to become the focus of the security industry over the new year.

AI will play a bigger role on both sides of the fence.

As the volume and range of security threats continue to increase, it’s become abundantly clear that the best and only suitable defense will be artificial intelligence. This is especially true since, historically, cyber criminals have access to the same or sometimes even better tools as the security folks. The only truly effective way to combat cyber-attacks in 2019 will be to leverage AI-based security solutions. In other words, organizations must be prepared to fight fire with fire if they are to keep sensitive data safe.

Biometrics will become more widely adopted.

The Face ID feature of Apple’s iPhone X has made facial recognition relatively mainstream. Given the fact that passwords continue to be one of the most vulnerable areas of a business, we can expect to see biometrics become more widely adopted as a safer, more secure alternative. One brand leading the pack is MasterCard who will begin requiring biometric identification of all of its users beginning in April 2019.

Spear phishing will become even more targeted.

Cyber criminals understand that the more information they have about a potential victim, the more effectively they can design spear phishing campaigns. Some attackers are already developing newer and more disturbing ways to enact their plans, including hacking into a victim’s email system, lurking and learning. They will then use what they learn to create incredibly realistic messages that appear to be from a trusted source. Security personnel must remain especially vigilant to protect against these sophisticated and costly attacks over the coming months.

Advanced cybersecurity training may become a requirement for the C-suite.

The training surrounding cybersecurity will continue to advance and mature. As such, certifications may no longer be sufficient for a security professional to progress in his or her career – at least not at the upper management or C-suite level. This is supported by the growing number of degree programs that are devoted to cybersecurity. The companies of tomorrow looking to hire CSOs and CISOs will likely require some type of higher education as it relates to infosec.

What about you? Do you have any bold predictions about what the future has in store for cybersecurity? Tell us your thoughts in the comments section below!

Watch the full recorded panel discussion

Could Self-Service Automation Be Your Saving Grace?

Self-service automation has become quite the buzzword amongst IT professionals, and for good reason. Simply put, this intelligent technology is revolutionizing the way organizations operate and dramatically improving the way employees perform their jobs. There are a great number of benefits to self-service automation, including helping the IT department save time and money while also empowering end-users to resolve their issues instantly, without the need to involve the helpdesk. As such, productivity and efficiency levels rise across the board.

But what, exactly, is self-service automation? Well, in the most basic of terms, this type of automation allows non-IT workers to proactively perform routine, repetitive and ad-hoc processes. These tasks could involve anything from new-user onboarding and generating reports to resetting passwords and performing system restarts. Previously, employees had to rely on IT each time one of these functions needed to be executed, which resulted in unnecessary delays, subsequent dips in productivity and, of course, frustration on the part of both parties.

With self-service automation in place, IT can establish a library of automated tasks, processes and workflows that can be easily implemented by the end-user community without the assistance of the tech team. By shifting these routine but necessary tasks to the end-user, IT personnel is then freed up to focus time, effort and resources on more critical business matters. Likewise, by eliminating the need for helpdesk involvement, employees are able to get their issues resolved faster, which reduces delays and promotes a greater degree of productivity.

Some folks in the IT realm are still on the fence about this technology, fearing it will ultimately make their jobs obsolete. In reality, while automation will indeed replace at least a portion of tasks and possibly eliminate some lower-tiered roles altogether, it will also create new opportunities for those in IT to further their skills and education, making them more valuable as an employee in the long run. Thus, self-service automation shouldn’t be viewed as a threat, but rather as a tool to make life better for everyone.

Self-service is also addressing the skills gap that currently exists in IT. Whether it’s a smaller to mid-sized company that can’t afford to keep a large IT department on staff due to budget restraints or a larger enterprise that simply cannot keep up with the increasing demand that is stretching even the most well-staffed IT department too thin. Taking those smaller, menial tasks off the plate of the tech team provides for a better allocation of resources.

Are you reaping the many benefits of self-service automation for your company? If not, the time to start doing so is now. Click here to launch your free trial of Ayehu’s automation platform today.

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5 Ways Machine Learning is Transforming the Business World

Today’s forward-thinking organizations are leveraging the power of artificial intelligence to automate the decision making process. In fact, corporate investment in AI is predicted to reach $100 billion by the year 2025. As a result of this rapid digital transformation, many changes are underway in the workplace. In particular, there are a number of ways that machine learning is already making an impact for companies in every industry. Here are a few to consider.

Personalizing the customer experience.

One of the most exciting benefits machine learning can have for businesses is the fact that it can help improve the customer experience while also lowering costs. Through things like deep data mining, natural language processing and continuous learning algorithms, customers can receive highly personalized support with little to no human intervention. And people are warming to the idea. In fact, 44% of US consumers say they actually prefer chatbots to human agents.

Improving loyalty and retention.

With machine learning, companies can do a deep dive into customer behavior to identify those who are at a higher risk of churning. This enables organizations to develop and implement next steps designed to target and retain those high-risk customers. The more proactive a company is in this area, the more profitable it will be over time.

Enhancing the hiring process.

When asked about the most difficult part of their job, corporate recruiters and hiring managers almost unanimously list the task of shortlisting qualified candidates for job openings. With dozens and sometimes even hundreds of applicants, sifting through and narrowing down the options can be a monumental task. Machine learning is fundamentally changing the way this process is handled by letting software do the dirty work, quickly identifying and shortlisting those candidates that are the best fit.

Detecting fraud.

Did you know that the average organization loses up to 5% of their total revenue each year due to fraud? Machine learning algorithms can be used to track data and apply pattern recognition to identify anomalies. This can help risk management detect fraudulent transactions in real-time so they can be prevented. This type of “algorithmic security” can also be applied to cybersecurity, leveraging AI to quickly and accurately pinpoint threats so they can be addressed before they are able to do damage.

Streamlining IT operations.

Another way AI and machine learning are revolutionizing how organizations operate is through intelligent automation. Powered by machine learning algorithms, agentless automation and orchestration platforms become force multipliers, driving efficiency and helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure.

Want to see machine learning in action? Schedule a free product demo today!

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The New Wave of Hybrid AI in the Automation Era – Insights from the Experts

Over the past few years, Gartner has made some pretty bold predictions surrounding artificial intelligence and automation technology. These include such projections as the generation of $2.9 trillion in business value, the recovery of 6.2 billion hours of worker productivity and the creation of some 2.3million new jobs. All of this is expected to take place over the next couple of years, if not sooner.

Whatever you believe about AI, one thing’s for sure – it’s not going away any time soon.In fact, all signs indicate that the not-so-distant future will look a lot different from what we are used to today, with machines performing not only the physical work of humans, but also the thinking, planning, strategizing and decision-making as well.

At Ayehu, we’re always trying to stay a step ahead of the curve in terms of technology and its capabilities. Such was the purpose of our recent panel discussion entitled The New Wave of Hybrid AI in the Automation Era, during which we sat down with several AI and automation thought leaders, including:

We asked these experts to take a look back at some of the emerging trends observed and experienced in 2018 and offer a glimpse into the future. We also asked each of our esteemed panelists to go out on a limb and make a bold prediction for 2019.

Here’s a little taste of what we uncovered.

Evolution in IT Operations

One of the first topics touched upon by our own Brian Boeggeman centered on the creation of automation centers of excellence (COE) and the automation engineers that are driving adoption and proliferation of automation within the enterprise.

“We saw a trend taking place this year where there’s a lot of focus put on the creation on these centers of excellence to really drive automation,” said Brian. “Automation is clearly seen as a business imperative and a clear necessity in order to orient the next phase of enterprise operations, where efficiencies can be uncovered and realized across the business.”

He continued, “Those automation engineers effectively become the change agents within the organizations to drive that cultural change within the enterprise. As we engage with many of our customers, we’re continuing to see that as a high focus for them actually making investments to build out these COEs to push across the entire organization and enterprise as a whole.”

Brian also talked about the acceleration and creation of new services through automation and AI, pointing out that automation has emerged as a key strategy to achieve the delicate balance between innovation and operational excellence.

Download the on-demand recording to learn how to unleash new capabilities of human capital as well as what trend Brian believes will forever change the service levels and operating capabilities of organizations.

How AI and ML are Impacting the Adoption of Automation in the Enterprise  

Next, we turned the floor over to Manish Kothari, who has been involved with artificial intelligence and machine learning since the 70s. Manish opened his statement by pointing how critical data is to extracting the full value of AI.

“I think we’re starting to see a very big transformation take place across all sectors from agriculture, to consumer, and especially in IT where the need is arguably one of the highest,” Manish commented.

He went on to say: “If you are at the IT infrastructure side, you are, actually for one of the first times, really in a position to become an enabler rather an impediment to innovation.”

Manish also shared his insight on what enterprise managers who have never dealt with AI should know before deploying it and how he envisions the roles of managers changing once AI begins to get a foothold.

Find out everything that Manish had to say here.

Top Reasons Organizations Should Automate IT Operations

The next topic on the agenda was turned over to Andrew Brill of Change Healthcare, who was asked to share his thoughts on the top reasons for automating ITOps.

He was quick to point out that while the initial reasons typically revolve around cost savings and efficiency, some really exciting things can begin to happen,particularly in terms of an organization’s ability to remain compliant and consistent in its operating practices. Automation essentially becomes a force multiplier, saving users time and skyrocketing productivity and facilitating innovation.

“When we transform our staff who were doing the more robotic functions and eliminate that from their daily work, it allows them to start thinking about how we advance the organization, how we build better engineered solutions, how we work with partners that add the most value,” Andrew points out.

“Their brains are more actively working on the things that solve business problems as opposed to the functional tasks and work that perhaps we’ve had them do over the past ten years.”

Check out the playback to hear more and learn what Andrew believes are the biggest benefits of automation.

The Impact of AI on Digital Transformation

The discussion then turned to Ross Tisnovsky of McKinsey who was asked what kind of impact he believed technologies such as intelligent automation and AI having on IT digital transformation efforts in the enterprise.

In his response, Ross shared how he approaches the adoption of new technology. In particular, he highlighted the main things that users want from IT which, in his opinion, include:

  • A workspace for collaboration and enhanced productivity
  • Improved decision-making
  • Support for business innovation
  • High efficiency at a minimal cost

According to Ross, the solution to these needs requires a hybrid of AI, automation and human decision making.

“Automation works on both sides of the picture and so does the artificial intelligence. In other words, it’s likely to become part of anything we do on the business side today.”

Watch the on-demand discussion to discover the Ross’ most intriguing thoughts on hybrid AI from his real-world experience.

Key Insights and Future Outlook

During the final portion of the panel discussion, the experts weighed in on such points as:

  • The role of humans in automation adoption
  • Biggest challenges to automating IT operations
  • Skills needed to automate effectively
  • Critical KPIs/success metrics to focus on at different stages of the IT digital transformation journey
  • What enterprise IT leadership should be doing right now to start preparing for the changes to come
  • Bold predictions for AI in 2019 (and beyond)

Don’t miss out on what was certainly one of the timeliest and most informative panel discussions we’ve ever had the pleasure of hosting. Tune in to the full recording today!

Watch the full recorded panel discussion