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Self-service automation is becoming more of the norm rather than the exception. In fact, a recent survey by SDI found that 61% of businesses were focusing on some type of self-service initiative (up from 47% in 2015). And it’s not only for making your customers’ lives easier. Many organizations are realizing the benefits of providing self-service options to employees to eliminate the need for many of the common issues plaguing the help desk, such as password resets and system refreshes. If you’re thinking about jumping on the bandwagon, here are a few common mistakes you should actively avoid.

Inadequate Communication – If you want your employees to adopt and embrace self-service technology, you have to ensure that they understand its many benefits. This is particularly important for your IT team, some of whom may feel uneasy or even threatened by the thought of automated technology handling some of their tasks. Gain acceptance and buy-in by communicating how self-service options will actually make the lives and jobs of everyone easier and more efficient.

Lack of Knowledge – What types of activities can you – and more importantly – should you be transitioning over to self-service? Many otherwise savvy IT decision makers rush into self-service implementation before they truly have a good understanding of what tasks are most beneficial to automate. Take time to learn about what your IT team is bogged down by and also what areas the end-user might not only benefit from, but actually appreciate the ability to handle things on their own.

Not Choosing a Tool Carefully – Not all self-service automation platforms are created equal and if you don’t carefully and thoroughly do your homework, you could end up with a less-than-ideal result. Not only does implementing a faulty tool mean more headaches for your IT department, but the frustration of everyone who has to use it will ultimately lead to disengagement, resistance and/or complete lack of adoption. Make sure the platform you choose is robust, user-friendly and versatile enough to handle both full and semi-automation needs.

Setting and Forgetting It – Like anything else in technology, self-service automation isn’t something that you can simply put in place and never think about again. Not only is it important to keep up to date from a tech standpoint, but it’s equally important to ensure that the system you have in place remains as effective as possible. Conducting regular audits of both the IT department and the end-users can help you determine whether new tasks could be automated or if existing ones could use some tweaking.

Forgetting the Intangibles – Last but not least, maintaining an environment in which self-service automation is embraced and celebrated involves regular assessment and selling of the many benefits this technology provides. When calculating ROI, don’t forget to also consider the intangible ways self-service is good for your organization, particularly how it allows IT to improve its meaningful contribution to the organization. That is a value that can and should be recognized across the board.

What could self-service automation do for your company? Why not find out today by starting your free 30 day trial of Ayehu. No obligation, just enhanced efficiency and better overall operations. Get your free trial now by clicking here!

The post 5 Mistakes to Avoid with Self-Service Automation appeared first on Ayehu.

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Every organization reaches a point (or several points) at which auditing and updating applications and business systems becomes necessary. Whether your company is currently at that junction or it will be coming down the road, it’s imperative that you use this opportunity to explore the power of intelligent process automation. From streamlining repetitive tasks to deploying AI-powered virtual support agents to perform complex end-to-end IT workflows, automation can skyrocket productivity and efficiency.

All this being said, there are a significant number of automation solutions available on the market today, all of which are not created equal. Decision-makers must educate themselves on the various levels and capabilities of automation. Let’s take a closer look at these levels below.

Simple Automated Tasks

The most basic level of process automation that exists presently involves simple tasks which are triggered by basic “if, then” actions. This type of entry-level automation has been around for decades and can (and should) be applied to all business functions.

IT teams specifically can use basic automation to create simple triggers, such as activating network changes whenever traffic thresholds are met or exceeded. Simple process automation is also capable of being integrated with other tools to create a more robust system. For instance, a monitoring system like Solarwinds can be integrated with an ITPA platform so that alerts trigger end-to-end automated workflows.

Chatbots and Self-Service Automation

The next phase in the process automation lifecycle is that of self-service automation, most of which is handled by chatbots. With this type of automation, an end-user can initiate a support ticket for basic needs, such as password resets. The automated bot is pre-programmed to respond to certain triggers in the interface and perform the requested action without the need for intervention from a human agent.

Self-service automation is valuable because it saves time, both for the end-user as well as the IT support team, thus facilitating a much higher degree of productivity across the board. It does, however, lack in the way of context. In other words, chatbots are only capable of understanding basic commands and following pre-determined decision pathways. They cannot interpret meaning or context.

Cognitive Intelligent Process Automation

This phase of automation takes the concept of chatbots to the next level by introducing advanced technologies, like artificial intelligence, machine learning and natural language processing into the mix. Unlike basic self-service automation, virtual support agents (VSAs) are capable of understanding context in communication with an end-user and delivering intelligent responses based on automated research. They are also capable of learning, making their own decisions and executing complex tasks.

It’s important to note that in order to be effective, intelligent process automation must be built upon the foundation of a strong and accurate knowledge-base, as it is this data from which the tool will pull its answers.

The Next Wave of Intelligent Process Automation

What does the future hold for automation? Tomorrow’s (and to some degree already, today’s) business environment will feature on-demand, shared services that facilitate end-to-end operations and enable automation of even the most complex business processes. Thanks to ever-improving artificial intelligence technology, platforms will not only be able to learn and evolve from user interactions and human input, but eventually identify and create newer and better processes for even more streamlined operations.

The good news is, you don’t have to settle for basic automation, nor do you have to wait for the future to witness the power of cognitive intelligent process automation. In fact, you can experience it firsthand today by downloading your free 30-day trial of Ayehu NG.

The post Understanding the Stages of Automation appeared first on Ayehu.

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July 15 2019    Episodes

Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & AI

In today’s episode of Ayehu’s podcast we interview Philippe Vié – Group Leader Energy, Utilities and Chemicals at Capgemini.

Philippe Vié – Group Leader Energy, Utilities and Chemicals at Capgemini - SoundCloud
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The Energy, Utilities and Chemicals industries are vital to our everyday lives today, & the digital world of tomorrow. Futurists envision amazing new technological capabilities that will rely heavily on these sectors. Yet these industries currently face so many challenges, there’s growing concern about their ability to keep pace with expectations. Enter Philippe Vié, Capgemini’s Group Leader for Energy, Utilities and Chemicals.

As an industry thought leader, Philippe advises many of the biggest Energy, Utilities and Chemicals players on how intelligent automation can accelerate their digital transformation. Recently, his Capgemini team published a report which found that the energy and utilities sector could realize $237 to $813 billion of cost savings if it were to implement intelligent automation in its target processes at scale. Philippe shares with us a number of insights from this report, along with the revelation that intelligent automation can not only cut costs for organizations, but also generate new revenue streams.

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Guy Nadivi: Welcome everyone. My name is Guy Nadivi, and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Philippe Vié, Group Leader Energy, Utilities and Chemicals at Capgemini, the world’s 2nd largest consulting firm by revenue based out of Paris, France. We’ve never had an expert on energy, utilities, and chemicals on our show before, but there’s a lot of interesting things going on in this space with regards to automation, AI, and machine learning. So we reached out to Philippe and asked him to join us, and he graciously accepted.

Philippe, welcome to Intelligent Automation Radio!

Philippe Vié: Thank you, thank you so much for interviewing me, Guy. This is the appropriate moment, since we have collected answers from 500 energy & utilities executives, and we have published May 20th point of view on intelligent automation for our industry. Thank you.

Guy Nadivi: So let’s talk about some of those findings, Philippe. What are some of the biggest ways automation, AI, and machine learning are impacting the energy, utilities, and chemical industries today?

Philippe Vié: First of all, energy & utilities are considering a lot of use cases for core business processes and super-functions too. It seems thanks to our studies in this sector but also in other industries, that 38% of energy & utilities players report at least one use case which has been deployed at scale, and 15% [report] multiple use cases at scale. But these figures show also that for the moment only a minority of players have been able to scale up their intelligent automation initiatives. For which benefits?

In average they answer that, 30-35% of executives report an operations boost compared to 15-30% in other sectors. 35-50%, there is a range because we have multiple KPI’s around these benefits, 35-50% of executives report top-line growth. And 70-80% increase in customer satisfaction, which is also ahead of other industries. And our calculations, and you will find it on the report we have published today, is that there is a put-on-shoulders savings range from $200 Billion to $800 Billion, depending on the way you consider automation & intelligent automations.

More than 30 use cases were reported in core functions & 50 for super-functions. Some examples – forecasting…..weather forecasting, load forecasting, the typical example in core functions. Create behavior interface. Energy storage. Energy trading, in which you have a lot of possible automations. Vegetation management, meaning intelligent ticketing for transmission & distribution operators. Complaints management on the retail side of the business. Customer chatbots on top of costs of the classical and well-known predictive maintenance that is coded in any energy & utilities player.

All should be starting with quick wins, low complexity but tangible results. This is the landscape of potential benefits of AI & automation.

Guy Nadivi: So let’s talk a little bit about that greater landscape. Between June 2014 & January 2016, oil suffered a drastic decline in price, dropping by about 2/3, which led to many layoffs in the energy business. And in addition to that, it was estimated in 2015 that over the next 5 – 7 years, 50% of the workforce would be retiring, leaving behind a huge talent shortage. How has this massive personnel turnover affected adoption of automation, AI, & machine learning in the utilities, energy, and chemicals businesses?

Philippe Vié: 2 questions in fact, Guy, here. The first one on oil price’s drop, which was artificial. It was artificial because OPEC & Russia wanted to kill US shale oil producers 3 years ago.

It obliged US shale oil producers to make efficiency progresses, and they used automation for that. But this war is today over. Oil prices are more comfortable for all the players, floating from $60-$80 per barrel, and should remain at that level, if we trust the analysts. With demand growth, it depends on the economic health of the planet. We are not {unintelligible}. And international political tensions, President Trump against Iran, and US-Iran waivers.

On the last part of your questions, in our views, the adoption triggers for intelligent automation, for automation, resides more in technology adoption and performance improvement targets than in dealing with aging workforce & retiring personnel consequences.

Energy & utilities players are focusing on quick wins, as I already mentioned, rather than automating to replace [an] aging workforce, and the answer, and this is a good question, that the talent-related challenge remains, even with a high level of automation. Of course, not the same skills shortage, but skills shortage again.

Guy Nadivi: Philippe, You are one of the co-authors of a Capgemini report called “The Digital Utility Plant” which found that only 8% of utility companies have operations which could be described as digitally mature. Perhaps this is at least partially because so many utilities have natural monopolies structurally shielding them from competition, & insulating them from the need to automate or to innovate. What do you tell utilities executives to persuade them that now is the time to become what Capgemini calls Digital Masters, or innovation leaders by implementing AI, automation, and machine learning?

Philippe Vié: The publication of this “Digital Utility Plant” you mentioned was a long time ago, in 2017. 2 years is a very long time for digital transformation. And we have observed since that, growing appetite for digital operations to save costs up to 20-30% savings {unintelligible}. Digital operations for centralized generation assets, decentralized generations assets – renewables, and also transmission and distribution networks.

So this potential for digital operations is today’s top priorities of the players when they go digital, and they are all going digital. They have all started by the customer experience.

When you see for example EDF, the French leading utility is the 2nd-largest utility in the world, they are going full blast in nuclear engineering digital transformation, and they are trying to create a digital twin for each reactor, new reactor, or to-be-retrofitted reactor, to expand their lifetime. This is a huge project and a huge investment, but very profitable. When you see smart grids deployment at scale after years of experimentation in several world-leading distributors, in EDF for example in Europe and many in the US also. This is a clear signal that energy & utilities are really moving forward on the digital transformation route.

So our arguments are to push concrete histories related to techno-leverage, not only AI & RPA, but also IoT, Cloud, and other digital nuggets, to help decision-makers to move forward with “of use, use cases”, low complexity, or big profit potential, easy to develop & deploy on the 3 pillars of the customer experience, digital operations, and new business models around digital transformation, but also on worker enablement in any section. The selection of key use cases based on their value is very, very useful to select the appropriate initiatives with which you should start.

Guy Nadivi: Now Philippe you mentioned costs there, and automation, AI, and machine learning are often applied as ways of optimizing resources and cutting costs. In the utilities industry though, I understand these technologies are also being touted as the basis for entirely new services. So can you tell us a bit about some of the more interesting use cases Capgemini is involved with where Automation, AI, and machine learning are creating new revenue streams for utilities?

Philippe Vié: Yes, let’s start with some use cases and with the profit that can come out of their implementation and their deployment. Just a long list of possible use cases, I will bring 6-8: • Online self-services & self-sales. • Smart charging / smart discharging for electric vehicles • Energy management solutions software for building microgrades • Automated demand response for getting access to flexibility to better manage load & the demand • Smart lighting • Transactive energy solutions offers personalizations and far more What kind of KPIs the energy & utilities are considering when they get through these new business models is new revenue streams.

First of all, the answer is that 47% of them, the executives answer that they can get quicker access to customer data and more reliable customer data.

41% of them insist on the faster time to market.

45% of them an increase in inbound customer leads.

And 40% of them in quicker break even for these new business models.

Very tangible results in which the utilities are really engaged and they have demonstrated this value.

Guy Nadivi: According to Capgemini’s March 2018 Automation Advantage report, 46% of firms are refraining from innovation due to concerns about cybersecurity. Philippe, what impact are concerns about cybersecurity having on utilities executives’ decision to move forward with the kinds of digital transformations that automation, AI, and machine learning can produce?

Philippe Vié: First of all, electricity, gas, water, [and] oil are critical assets in any country, and it’s not because of digital transformation. They are critical assets, meaning that energy & utilities players are used to deal[ing] with cyber security threats. With threats in general, and cyber security threats, which is particularly true for exploration prediction, generation, transmission, and distribution. This is less real for retail and energy services.

So they start by selecting cyber-proofed or certified platforms with a national agency certifying cyber security of some products, and they work also mainly with serious players, well-known for managing cyber security threats. System integrators, for example, and really taking into account these cyber security threats. It seems that these threats don’t prevent them to move forward, which is good, but can make the automation project longer and more expensive. But that’s it. They have no choices as they want to move forward. They have to move forward to get the value out of the intelligent automation projects, and they have to manage on the other hand cyber security.

Guy Nadivi: You were quoted in La Tribune as stating that of the forty plus energy suppliers in France, ultimately, only 3 to 4 major players will survive. You then encouraged them to accelerate their digital transformation, in particular by making better use of AI. What are the top 3 ways Philippe, that you would recommend that AI, machine learning, and automation be used by utility companies to survive into the future?

Philippe Vié: In European markets, which are open to competition since more than 20 years now, all European markets. This is {unintelligible} and you see 40 competitors in France, 70 in UK, far more in Germany & in Austria, and only 3 to 5 will get significant market share, and some of them are dying every day in the smaller countries.

Considering AI, machine learning, automation – our recommendations are to move forward with quick wins first, then evaluate & choose carefully pragmatically intelligent automation use cases which can be the more profitable or the more interesting in terms of competitiveness in the market. To integrate & optimize the right processes for deployment, and deploy at scale as soon as they have demonstrated the value of their use cases. Quick wins are the most profitable ones, but more complex. And finally, to involve their workforce to invest in their capabilities, to put their money on the table to be successful, and to drive dedicated change management program around these new processes which change the life of their workforce, and also the way they interact and sell to their clients and the new value they can bring to the market.

Guy Nadivi: Aside from doing those things because you need to, to survive, is there a single metric other than ROI that best captures the effectiveness of automating IT operations in the utilities, energy, and chemicals businesses?

Philippe Vié: In fact, as already mentioned, we have 3 dimensions – customer satisfaction, operations boost, and top-line growth. And for each of them in our paper, you will find about 10 KPIs and probably more in some energy & utilities players, on which you can make real measurement of your successes. Depending on the chosen use cases which can be divided in this circle of KPIs, let me give you an example for each pillar – customer satisfaction, operations boost, and top-line growth.

Customer satisfaction, you can reduce the number of steps in customer interactions. You can improve your customer experience through faster response. You can be more customized to your customer needs and bring the appropriate answer.

On boosting operations, you can definitely improve your workforce efficiency & agility, and you can measure that with related KPIs.

On top-line growth, I have just mentioned before the typical KPIs, quicker access to customer data, faster time to market, increase in inbound customer leads, quicker break even, and so on and so forth.

Guy Nadivi: Chatbots or Virtual Support Agents are playing an increasingly important role in the automation of IT operations by enabling end user self-service. What do you envision Philippe, will be the role of Virtual Support Agents for companies in the utilities, energy, and chemical sectors?

Philippe Vié: Virtual Support Agents can bring various advantages to a company in IT, but also in many other domains of operations & core business support functions. Let me choose an example – applications diagnostics, customer credit check, real-time conversation with your customer analysis, payroll management, employee data management, finally a lot of use cases you will find and a lot of Virtual Support Agents you will find in our paper too.

Guy Nadivi: Philippe, for the CIO’s, CTO’s, & other IT executives from utilities, energy, and chemical companies listening in, what is the one big must-have piece of advice you’d like them to take away from our discussion with regards to implementing automation & AI for their operations?

Philippe Vié: Difficult for me Guy, to answer with only one. Generally these profiles, meaning CIOs, CTOs, IT Executives, they don’t need explanations or particular focus on intelligent automation potential advantages, because automation started very, very early in the 80’s, 90’s to automate their information systems.

They all, when you interview them, they all have one or two compelling stories to tell about the gains or savings they’ve recorded through these technologies. On data quality, on application diagnostics, on monitoring protocol compliance. If I were just to mention one or two benefits, I would say first – reducing complexity, the number of applications.

We have seen a lot of energy & utilities players moving from thousands of applications to hundreds of applications, and this has realized the ability to simplify the portfolio of their applications.

And the second one I would mention would be cost saving[s] on the run side, which is very important today. Servers, infrastructures, the run [side] is very important & shrinks their ability to make more developments. So this is cost saving on the run to enable more developments.

Guy Nadivi: One last thing Philippe, please tell our audience once more about the report Capgemini just issued & how they can get a hold of it for themselves.

Philippe Vié: So you go to Capgemini.com, you have industry-specific reports, and you will find on energy & utilities/chemical this report, published May 28th. You can download it. You can download also an infographic of this report with key figures of the report. Again we have interviewed 540 executives from energy & utilities, specifically on this topic – intelligent automation. And you will find a ton of figures in this report, 40 pages report. And we will be very pleased to engage a conversation in any country with you around this topic and around our great experiences and also our fails on bringing intelligent automation to life in many utilities and what are the advantages against that we can report to you. Guy Nadivi: We’ll be sure to include a link to that report also from our website, along with this episode so people can download directly from there.

Alright! That’s all the time we have for on this episode of Intelligent Automation Radio.

Philippe, merci beaucoup for coming on to the show and giving us great new insights on the state of automation & AI in the energy, utilities, and chemicals sectors. We’ve really enjoyed having you.

Philippe Vié: Thank you, Guy. Thank you, and enjoy reading this report and going through intelligent automation for the benefit of your companies.

Guy Nadivi: Philippe Vié, Group Leader Energy, Utilities and Chemicals at Capgemini. Thank you for listening everyone, and remember – Don’t Hesitate, Automate!



Philippe Vié

Group Leader Energy, Utilities and Chemicals at Capgemini

Philippe joined Capgemini in 1997, after a career in software, where he founded an early 80’s startup. He is now Vice President, Capgemini Group and Energy Utilities and Chemicals sector leader, based in Paris.

Philippe has over 25 years of Energy and Utilities industry experience and dedication, with a strong focus on Utilities Transformation projects, digital or not.  His tenure has also notably covered the deregulation and market opening period.  His many roles within the Energy, Utilities and Chemicals sector include:

  • • Thought leader, managing strategic studies and shaping Capgemini group EUC offers portfolio
  • • Leading the annual World Energy Markets Observatory by Capgemini (www.capgemini.com/wemo)
  • • Performance benchmarks (DNO – Distribution Network Operator – and Retail)
  • • Writing multiple POVs and press articles
  • • Creating Capgemini offers: Digital Utilities Transformation, Utilities to Energy Services, Utility in a Box, Digital edge, and training Capgemini representatives to sell and deliver the related services
  • • Delivering keynotes on industry trends

Philippe Vié can be found at:

Office:                      +33 (0)1 57 99 19 83

Mobile:                    +33 (0)6 12 72 82 67

Email:                       philippe.vie@capgemini.com

LinkedIn:                  https://www.linkedin.com/in/philippevie/

Quotes

“…in our views, the adoption triggers for intelligent automation, for automation, resides more in technology adoption and performance improvement targets than in dealing with aging workforce & retiring personnel consequences.”

"When you see smart grids deployment at scale after years of experimentation in several world-leading distributors, in EDF for example in Europe and many in the US also. This is a clear signal that energy & utilities are really moving forward on the digital transformation route.”

“Virtual Support Agents can bring various advantages to a company in IT, but also in many other domains of operations & core business support functions.”

About Ayehu

Ayehu’s IT automation and orchestration platform powered by AI is a force multiplier for IT and security operations, helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure. Trusted by hundreds of major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe.

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Episode #1: Automation and the Future of Work
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The subject of IT outsourcing has long been one of staunch debate over the past few decades or so. It seemed that with the increasing use of cloud computing, having IT operations managed elsewhere would become the norm rather than the exception. Yet, there’s one thing that is now giving outsourcing a run for its money and making the need for it nearly obsolete. That thing is intelligent automation, and here’s why it’s tipped the world of outsourcing on its side.

While the concept of outsourcing may not be disappearing completely, the need for it – particularly in the IT industry – seems to be shrinking. The reason is simple, really. Intelligent automation has provided a solution to each of the main reasons organizations turned to outsourcing in the first place.

Cost – For small to medium sized businesses, the costs associated with managing IT operations in-house were simply too high to justify. Instead, they turned to outside sources to provide these services, reducing expenditure. Automation makes managing IT operations internally much more affordable, so there’s really no need to rely on a third party provider.

Scalability – Another common reason businesses choose to outsource is the ability of these services to evolve along with the changing needs and demands of the business – something managing IT in-house couldn’t easily accomplish in years past. With intelligent automation, however, businesses can scale up or down at the click of a button – far faster than any third-party outsourced solution to could ever do.

Resources – Finally, there is the topic of logistics. Housing IT internally once meant the need for bulky and complex equipment, something that many smaller to mid-sized organizations simply could not accommodate. Intelligent automation, combined with cloud technology, eliminates this need, streamlining IT and making it simple to manage in an office of any size.

Essentially, intelligent automation is enabling enterprises of any size to do more with less. No longer is it necessary to employ a huge group of professionals to manage IT operations. Rather, the day to day IT tasks needed to keep the business running smoothly can be handled by just a handful of people. Now, what once made sense both financially and logistically – outsourcing –  is becoming more of a hassle than it’s worth.

As we’ve pointed out in previous articles, there are many benefits to keeping IT operations in-house. These advantages include, but are not limited to:

  • Increased control over procedures and processes
  • Enhanced security due to limiting access to only internal employees
  • Improved flexibility and customization (unique to each organization’s specific needs)
  • Time savings – no more relying on a third party
  • More cost-effective option than outsourcing to an MSP

For those businesses that choose to continue outsourcing their IT needs, automation will likely still play a significant role, as IT service providers leverage automation to deliver timely, precise and efficient results to their clients. So, either way automation will impact your business in some way, even if that impact is indirect.

In any event, as technology continues to evolve, it’s becoming more and more evident that the decision to outsource IT operations will be an increasingly complicated one. Overall, the trends indicate that automation will ultimately win the battle and provide businesses of all shapes, sizes and industries the ability to run efficient, effective internal IT departments.

Want to start harnessing the power of IT process automation for your own organization? Begin your free 30 day trial today by clicking here.

The post 3 Ways Intelligent Automation is Outpacing Outsourcing appeared first on Ayehu.

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These days more and more businesses are adopting intelligent automation to help streamline operations, improve efficiency, boost service levels, cut costs and more. And while the overall goals and objectives of each organization may differ, sometimes to a great degree, there are a number of universal reasons that key decision makers cite for why they ultimately opted to automate their IT processes. If you’re still on the fence, here are 6 key advantages to consider.

Automation of Repetitive Maintenance Procedures – Every IT department has its own fair share of routine processes and procedures that must be performed to ensure that operations continue to run as smoothly as possible for everyone within the organization. Unfortunately, many of these IT processes are highly redundant, such as checking disk space, system restarts, monitoring log files, resetting passwords and managing user profiles. All of these things can and should be shifted to automation.

Enhanced Incident Management – Incident management is one of the most frequently automated and subsequently optimized IT processes. Businesses are under constant threat and it’s become increasingly clear that the human workforce simply cannot keep up. By automating the incident monitoring, response and remediation process, the entire operation maintains a greater degree of accuracy and security.

Reduction of Errors and False Positives – IT personnel are constantly being inundated with incoming requests and, as a result, are often bogged down putting out fires and chasing their tails. This heavy volume of work coupled with the increasing demands can dramatically increase the amount of costly errors committed. Incorporating automation as a central part of critical IT processes can dramatically reduce errors and also eliminate time-consuming false positives.

Empower Skilled Employees – Automating basic, routine and repetitive IT processes is something everyone in the department can benefit from. IT leaders can focus their valuable skills and experience on more complex, mission-critical business initiatives and front-line IT workers are empowered to resolve issues without the need to escalate to management.

Integrate Disparate Systems, Programs and Applications – Maintaining a plethora of different systems, apps and programs is a very inefficient and ineffective way to do business. In many cases, these silos actually work against, rather than with, each other, further hindering operational efficiency. The right automation tool can effectively integrate with these legacy platforms to create a more connected, cohesive and collaborative interdepartmental environment.

Establishment of Documented Best Practices – The very nature of IT automation is that it creates and maintains a series of consistent, repeatable (and therefore often predictable) patterns and processes. It also provides visibility, insight and the ability to identify, establish, document and hone best practices for improved operations moving forward.

Could your organization benefit from any of these basic advantages of automation? Find out today by starting your free 30 day trial of Ayehu NG.

The post Still need a reason to automate your IT processes? Here are six. appeared first on Ayehu.

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

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

Is a business case necessary for AI?

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

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

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

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

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

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

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

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

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

Practical advice for organizations just getting started with AI:

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

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

A few more considerations:

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

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

The post Your Top Artificial Intelligence Adoption Questions, Answered appeared first on Ayehu.

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July 1 2019    Episodes

Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies

In today’s episode of Ayehu’s podcast we interview Mark Campbell – Chief Innovation Officer of Trace3

Over two millennia ago, the famous ancient Greek historian Thucydides wrote about how best to dispense with Spartans. He could not have known that 2,400 years later his writings would enter the pantheon of organizational thinking about innovation.  Yet as unlikely as it sounds, that’s exactly what Mark Campbell believes has happened.  As Chief Innovation Officer of Trace3, Mark uses the age-old reflections of Thucydides to help advise IT executives today.  Navigating the labyrinth of emerging technologies is a Herculean task, and Mark has lots of sage advice on what innovations to take advantage of, as well as which ones to avoid.

In this episode, we chat with Mark about a number of emerging technologies from automation, AI, and machine learning to quantum computing.  Along the way we’ll learn what questions a vendor should answer to ascertain if their product’s AI capabilities are based on engineering or marketing hype, the potential pitfalls awaiting any enterprise that decides to handle the “people problem” later, and the one biggest fear customers have about emerging technologies.

Read Full Transcript



Guy Nadivi: Welcome, everyone. My name is Guy Nadivi, and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Mark Campbell, Chief Innovation Officer of Trace3, an emerging technology consulting firm based out of Irvine, California. As Mark’s LinkedIn profile states, he specializes in, “Squeezing the hype out of emerging tech.” Thanks to his work in the venture and startup ecosystems to identify emerging enterprise IT technologies and innovation trends, and introduce these to customers, partners and the industry. Mark and his team review over a thousand tech startups each year, and he’s also a frequent speaker and presenter on innovation, so we’ve invited him to come on our show to help us squeeze the hype out of such technologies as automation, artificial intelligence, and machine learning. Mark, welcome to Intelligent Automation Radio.

Mark Campbell: Well, thanks for having me, Guy. Greatly appreciate it.

Guy Nadivi: Mark, as a Chief Innovation Officer, you have an interesting definition of innovation, involving something called “positive deviance.” Can you elaborate on that a bit and how you incorporate it into your definition of innovation?

Mark Campbell: Sure. We get that question quite a bit from customers, especially customers starting up their own innovation group, or tackling an innovation project, or trying to inject emerging technology into an existing business model. The term positive deviance, I’d love to claim credit for it. It is so smart, but it was actually invented by a guy by the name of Dr. Jeff Degraff out of the University of Michigan, and then all of the very wordy and academic definitions that I’ve encountered over the years, I do think that Dr. Degraff has distilled this down into two words, the real essence of what it means to innovate – positive deviance, both sides of that. Kind of the idea there being that for every innovation, regardless of industry, technology, outcome, or pitfalls you’re trying to avoid, it means that you have to deviate from the status quo.

Mark Campbell: You have to kind of overcome your own internal business inertia, your own internal processes, your own internal way of doing business, your own internal technical skills. That deviation, of course, can take many different forms. Not all of them are beneficial. Sometimes deviation for the sake of deviation has gotten companies into trouble. Certainly, New Coke is a terrific example of that, but positive deviation where you’re kind of taking a look at this perfect future that you’re aiming for, and what sort of deviations are going to be the ones that give you the greatest probability of gain and the greatest probability of avoiding the pitfalls that comes from deviating from your current business models.

Mark Campbell: I think smart automation is certainly one of those examples that we are seeing in the market today, where automation is a terrific and wonderful thing that is helping us, if you will, streamline the status quo. When we talk about smart automation, we are really talking about deviating from that, and so I think that’s a very good example of how positive deviance, an example of it anyway, how we’re seeing it applied in our customer base.

Guy Nadivi: You’ve talked about there being three key drivers of innovation – fear, honor and interest. How should IT professionals factor those key drivers into their decision-making when considering moving forward with a potential enterprise innovation?

Mark Campbell: Well, I think before even jumping into an innovation project, or looking for innovation targets, or forming an innovation team, I do think the executive sponsor of the innovative initiative needs to take a look at these three core values, this fear, honor, and interest. If you prefer a little bit more modern terms, you can say fear, pride, and greed. This was a pattern discovered by a Greek researcher, oh, about 2,400 years ago, by the name of Thucydides. Now, at the time, of course, he was trying to figure out a more effective way of killing Spartans, but nonetheless, it actually applies very apropos today. When we take a look at this deviance, we’re going to deviate.

Mark Campbell: It does require us to evaluate the fear, the fear of staying where we are and having our competition beat us, versus the fear of changing, or potentially the pride and honor of damaging our brand with a failed innovation initiative, or increasing our leadership, our industry prowess by creating a new, innovative and disruptive product that changes our whole marketplace. I think like when we get looking at the interest or the greed, or what’s in it for us side, certainly there is a danger in releasing new innovation projects by displacing existing revenue streams, or existing products, or existing customer bases, and that has to be balanced against the potential upside of a new line of business, a new market, a new expansion, a new threat to bring against your competitors. I think innovation leaders, right at the very get-go, when they’re starting to contemplate using innovation as a weapon need to balance that, right? “What do we fear more? Where’s the greater honor, and what’s in our best interest? Doing this or not doing this?”

Guy Nadivi: Mark, let’s talk about AI and machine learning. Please tell our listeners where you would squeeze the hype out of these emerging technologies.

Mark Campbell: Well, certainly AI has had a long and roller coaster-ed history going back to the 40’s and 50’s, and in that period, we’ve seen kind of this ebb and flow of various techniques and technologies that are enabling AI to tackle harder and harder problems. However, when we take a look at a lot of products hitting the market, we trifurcate these into three buckets. We call them the simple, the savvy and the smart. Simple being just regular, procedural type algorithms, whether that’s a Excel spreadsheet or an autopilot on an airplane. Then, there are savvy products.

Mark Campbell: These are those that have embedded knowledge bases or access to expert systems. Of course, these were very popular in the late 80’s and 90’s, but really, they embody the knowledge of whoever created the product. Then, we get into the last of the three buckets, the smart bucket, and this is where solutions really learn. They take and ingest data, discover patterns, discover behaviors, maybe they discover baselines and report on anomalies from that baseline. Nonetheless, they are smart, they do learn and they do adapt.

Mark Campbell: When we look at this AI space, we are seeing hype being introduced by simple and savvy products out there, that are having their marketing department inject terms like deep learning, or machine learning, or AI, or convolutional networks, or reinforcement learning, or what have you, and to where their AI actually exists in their marketing department, not in their engineering department. That, that disconnect there can be very confusing for our customers who see a great story, they hear a great speech, they may even see a good canned demo, but looking under the hood a little bit, there really isn’t AI there. This is just AI-washing, a savvy or even worse, simple product. There are some techniques that we discuss with our customers on how to make that differentiation. One of the core truths about AI as we mentioned is that it learns.

Mark Campbell: It’s smart, and that learning is based upon a lot of data. A technique that we talk to our customers about is dig into that. When you’re evaluating a product and you want to really make sure that it is a smart product, not just the savvy product, talk about the learning. “How was this trained? How does it learn? Is it delivered in a pre-trained fashion, or does it continue to learn after I install it in my environment?”

Mark Campbell: “What data is being used for the learning? How much data is required? Is it canned data, is it publicly available data, or is it my proprietary data?” Digging into that layer of it when you’re confronting a potential smart solution. If the product out there does not truly incorporate any learning or any AI, you’re going to get very evasive answers, “Well, that’s a trade secret,” or, “Well, that’s a lot of smarts that our guys in the back room have injected into the product,” or, “Well, I’m not really able to go into that. I’d have to shoot you.”

Mark Campbell: If you keep pressing on that and get these evasive answers, you should kind of flag that as something really to be concerned about. On the flip side, if you talk to a true AI company, and you ask them, “Well, how does your product learn? What kind of data? Can I use my data? What happens after I install it? How do I re-baseline?”, you’re going to see them light up.

Mark Campbell: The analogy I use is like sitting next to a grandmother on the airplane, and you ask, “Do you have any grandkids?” If they don’t, they’re going to tell you, “Shut up and don’t be that guy,” and you’re going to get a silent plane trip for the rest of the way. If in fact they are a grandmother, you’re soon going to see Josh’s kindergarten play, you’re going to see a photo album, you’re going to see the birthday card that they got them last year, and you’re going to have a very, very conversation-filled journey. The very same is true with an AI product. If it truly has AI digging into that, it’s going to just open up a whole world, to the point that you almost don’t care what the answer is, but that enthusiasm and that passion that you see coming from the vendor, you can make a safe bet that you’re on the right path.

Guy Nadivi: What about automation? Where does the hype need to be squeezed out there?

Mark Campbell: Well, right now, we’re seeing a ton of automation products come to market. Certainly, what we talked about before about separating the savvy from the smart, equally true on automation products, especially those touting to be smart automation, so those all hold true. The other point of hype that we do see quite a bit in automation is the promise that this is going to be a single click and your problems are solved. Certainly, from a technology point of view, there are some great advancements out there. Certainly, there are a lot of techniques and products that truly will, in a smart way automate your business processes or your internal development life cycle or what have you.

Mark Campbell: The one thing however that is very often glossed over is the technology part’s the easy part. The cultural part is where things get a bit difficult, and these kind of happen on three levels. On a personal level, you do have people that maybe fear that automation is going to displace them, or at least displace some of the skills they’ve garnered over the years. At an organizational level, when you start talking about automating techniques within your group, there also is going to be a little bit of dissonance. Typically, organizations have well-worn processes.

Mark Campbell: They have rules of thumb, and certainly, if the automation is truly smart, it may suggest ways of doing things that are not part of the playbook so to speak, and that causes some organizational tension. The other thing that tends to happen at a corporate level is sometimes automation isn’t isolated into one particular team. Typically, when you’re automating, especially business processes, these start to leak over into other business units, other parts of the organization, and the cultural, political and personal ramifications of that, unless they’re addressed right upfront in a project. Even if the technology is perfect and flawless out of the box, these are some potential pitfalls that await to any enterprise that decides to handle the people problem later.

Guy Nadivi: What are you seeing as some of the most interesting innovations right now around automation, AI, and machine learning?

Mark Campbell: Well, we have a distinct advantage in that we’re partnered up with a few dozen of the world’s top-tier venture capital firms, so we do get an opportunity to see a lot of products when they are at the proverbial “two guys & a PowerPoint stage”, and watch them mature. Now, by the way, there’s a ton of infant mortality, and a lot of these companies don’t ever see the light of day.

Nonetheless, when we start watching these, as you mentioned earlier, we do have the opportunity to take a look at thousands of startups a year, you do start to see patterns forming, and certainly, when we look at areas that the venture community right now is spending a lot of attention on, certainly the AIOps, applying AI to IT operations. Smart SecOps, this is applying AI into security operations. Those two are huge right now.

Mark Campbell: There is such a large market out there, and there is such a dearth of products to satisfy that market that there are some very good products coming to market right now that solve a myriad of problems, but one other area is robotic process automation. We are seeing … As you’ve probably noticed, there are several products on the market that have IPO’d and their IPOs are enjoying a terrific ride right now, but that’s echoed in our customer base. When we go and talk about robotic process automation, whether it’s actually workflow automation, whether it’s screen automation, smart chatbots, call center interactions, across the board, we are seeing a big interest right now from our customers to bring those processes under automation and if you’re going to go through all of that smart automation. It’s not just about business process management or business process automation anymore, we are kind of seeing this, let’s say the maturation of the use cases that are being solved with AI, now allowing all three of those AIOps, smart SecOps, and robotic process automation to baseline and report on anomalies.

Mark Campbell: Sometimes this is called behavior analytics, to actually correlate, especially in the security space where you have thousands of alarms to correlate those down into clumps, and then for each clump, determine a root cause. We’re also seeing smart automation being used to not just react or control existing situations, but to actually make predictive alerting on things that could be going wrong, or bottlenecks that may be appearing, or issues that may manifest themselves further on down the line. At the very hairy edge in the automation space, and this is a little bit controversial right now, certainly in the security space, is automated remediation. If we are being attacked or we do have a storage array that goes offline, or we do have a workflow that all of a sudden halts, do we want automation to jump in and automatically remediate that? Of course, the answer is it depends.

Mark Campbell: Certainly, if it’s a low-level, we have someone from accounting that can’t get in because their password is jammed up, certainly stepping in automatically and remediating that, resetting their password, probably not that big of a deal, but taking an auto manufacturer’s assembly line offline, that’s a fairly financially onerous decision to make. I think over time, that’ll move, but that’s certainly the areas we’re seeing investment being made in today.

Guy Nadivi: I understand you’re doing a lot of exploratory research on quantum computing right now. How do you think quantum computing will disrupt automation, AI, machine learning for IT in the future?

Mark Campbell: Well, it’s still a little bit nascent, but we do have customers that are spending time and money evaluating quantum. Right now, the two hot areas are quantum computing, which includes quantum computing as a service, so instead of buying a quantum computer, just renting time on an existing one. That’s one big area. The other area is quantum encryption, and so that certainly leaks over into the security side of the house, but these are still in development. There are some great systems out there.

Mark Campbell: There are real products that you can buy today. There are open source projects that can be implemented today, and the main targets that these are approaching, one is optimization problems. These are your typical traveling salesman, flow dynamic, scheduling and network optimization. Not necessarily physical networks, but even human and social network optimization. Quantum is quite effective at solving optimization problems, even the primitive machines we have available to us today.

Mark Campbell: Certainly, when we take a look at automation and we’re talking about automating a workflow, today, what we’re doing is we’re actually automating heuristics. In a general sense on large scale processes and flows, it isn’t mathematically possible to come up on a digital computer with all of the combinations and select the best. However, with a quantum computer, that does appear to be a very solvable problem. As I mentioned, we do have small and primitive systems today, but even on medium-sized problems, that is becoming a little bit more of a reality today. This idea that quantum computers in the optimization space, at least, will be able to replace the heuristics being used in automation.

Mark Campbell: That definitely is a fairly likely outcome. The other area is AI training. You certainly can look at the training of an AI system as a non-deterministic and even probabilistic activity that once an AI system is trained, you’re not truly guaranteed that that was the optimal training. It just works with the training data that we’ve presented it with so far. There are…the term being bandied around right now is quantum intelligence, to where you can actually use a quantum system to take, again today relatively small AI networks and come up with the optimal training that is out there with a fairly high confidence. As these quantum computing systems mature and incorporate more and more cubits, the sample space of data’s going to increase. The amount of solution space that you’re able to address is also going to increase. I think that’s going to have a direct impact on smart automation both on the automation side and the smart side of them.

Guy Nadivi: Is there a single metric other than ROI perhaps that will cause you to recommend a particular innovation to your customers and partners over others?

Mark Campbell: Well, I think when we take a look at our customers, the one thing was, especially in the emerging technology space that’s a big fear is, “Is this going to be around tomorrow? If we implement this really cutting edge solution from a bunch of smart folks that have their own little startup, what’s the story going to be in six months? Are they able to keep up that trajectory? Are they still going to be around?” It really breaks down into this, “What is that product sale’s pipeline?”

Mark Campbell: Sometimes that’s a bit hard to measure, and, “How innovative is that solution? Is it the right type of innovation for the right time for the right problem, and how is the market responding to it?” Now, I know that I cheated a little bit and gave you three answers to that, but if you roll all of those up, it’s what we call momentum. When we take a look at a startup, certainly there’s a ton of other ancillary attributes that a startup has to have, like smart and experienced leadership, a great product suite, some good early results from their Alphas and Betas, but if you want to boil down one thing, it’s very easy to go look if a top-tier VC has already funded them. Now, if they’re onto their B or C round funding, that typically means that they’ve convinced at least two or three top VCs to do their funding, and one of the key attributes VCs look at before they write the big checks is exactly this momentum area.

Mark Campbell: If you don’t have access to funding data from VCs, there are a handful of emerging tech research companies out there that attempt at least to combine these. One example would be CB Insights. They put together something called the Mosaic Score, which is composed of market, the market strength that they’re targeting, the momentum they’re seeing in that, and how much money they’ve garnered, and how far have they burnt through it. There are metrics out there, but it all hinges around this momentum idea.

Guy Nadivi: Mark, what can CIOs, CTOs and other IT executives start doing right now to prepare for the innovations you think will be the biggest disruptors to IT in the next three to five years?

Mark Campbell: Well, I think that’s a very good question, and certainly one that we get brought in to deal with, and I think every customer realizes their market, their business, their culture, their skills, their budget are all very unique and shape that, but if I was to condense those down, I would actually put things into two buckets. The first bucket is what I would call defensive IT. This is using emerging technology to shore up your IT assets, set another way from a business point of view. This is to do cost reductions, efficiencies, to where the business isn’t worried necessarily about the money they’re pumping into their IT’s infrastructure, and the return that they’re getting from this. Typically, defensive IT helps buoy up those “ility’s”, availability, scalability, agility, portability, maintainability, a lot of those non-functional type requirements.

Mark Campbell: These are what we kind of call defensive IT. We’ve seen a ton of great innovations come on the defensive side, certainly things like containers, or cloud, AI, where it’s allowing us to do more with the..

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

Instant Access

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

Consistency

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

Cost Savings

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

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

Scenario 1

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

Scenario 2

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

Scenario 3

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

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

The post Need Scalability? Intelligent Automation is the Answer appeared first on Ayehu.

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

Focusing on tools and tasks instead of people.

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

Failing to adequately calculate and communicate ROI.

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

Not setting appropriate expectations.

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

Automating broken processes.

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

Not using the right platform.

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

Thinking about trying automation but not sure where to begin? Check out these common tasks and processes that can and should be automated and then download your free 30 day trial of Ayehu NG to experience it for yourself.

The post 5 Things to Avoid for a Successful Intelligent Automation Rollout appeared first on Ayehu.

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Author: Guy Nadivi, Sr. Marketing Director, Ayehu

Leading analyst firms are forecasting a lot of turmoil ahead for MSPs, so I thought it would be well worth exploring not only the ramifications of this expected market upheaval, but also how smart MSPs can actually take advantage of it.

Are any of you fans of old westerns?  I’m personally a big fan of movies about the old west, an interesting time in American history.  Western cinema, as many of you already know, often depicts harsh wilderness landscapes where people end up in a shootout, fighting over something that’s important to them.

One of the best examples of Western cinema is a classic movie called “The Good, The Bad, & The Ugly”.  Maybe some of you have seen it.  Back when I still purchased DVDs, this was actually the first DVD I ever bought. 

This movie title, “The Good, The Bad, & The Ugly” is an apt metaphor I’m going to use to help me describe the current situation in the MSP market, where much like the old west, many MSPs find themselves in a shootout over something very important – market share.

Let’s start with ‘The Good.’  According to MarketsAndMarkets, a research and advisory firm focused on B2B markets, “The managed services market is expected to grow from over $180 billion in 2018 to $282.0 billion by 2023, a Compound Annual Growth Rate of 9.3%.”  That’s a pretty good market to be in.

Here’s the ‘The Bad,’ though. According to Gartner, “…as competition heightens, service providers will be forced to aggressively roll automation out across their client base and service lines because, if an existing provider is slow in implementing automation, this will leave that account quite vulnerable to competition, proposing a strong artificial intelligence proposition with the possibility of a vendor switch.”  In other words, Gartner’s saying that MSPs who don’t start introducing automation & AI to their customers, are now at risk of being left by that customer for another MSP that does.

And here’s ‘The Ugly.’ Again this is from Gartner and please note, this is advice they’re giving to sourcing executives at enterprises that hire MSPs.  “Understand the provider’s service capabilities, product development plans and AI roadmap to be able to negotiate effective reductions associated with new technology. Otherwise, consider moving away from the service provider if investments are lacking, lagging, or the service provider is not actively sharing the benefits with the client.” 

That last part might require a bit of explanation. Gartner is telling customers that they should EXPECT their MSP vendor to start automating their service offerings, and begin sharing the benefits of automation back to the client in the form of reduced charges. If an MSP doesn’t do that, they’re explicitly telling sourcing executives to go find another MSP that does!

Some of you might now be realizing seeing why I’m using the old west as a metaphor for the MSP market.  There’s a big shootout coming among MSPs that don’t start rolling out effective automation for their clients.

Here’s another one. The Good – according to Gartner, “Many of the large players in the Gartner Magic Quadrants that address IT infrastructure have rolled out intelligent automation that provides for effective management of the data center, end user, service desk and applications. The rollouts have been in the operations area and have reported improvements of 30% cost savings with 30% gain in service quality.”  So the organizations that have started automating are seeing significant benefits, meaning that automation is working really well.

But, also according to Gartner, here’s the The Bad – “Reconcile with the fact that revenue cannibalization is bound to happen in the near term because of automation. Instead, prepare to divert cost savings into fueling new projects. This is the best way to protect your turf.”  Interpretation: Gartner is telling MSPs that automation will cause unavoidable revenue losses in the immediate future, but your best bet for safeguarding market share is to invest in new automation projects now.

Finally, here’s The Ugly, and this too is from Gartner, “Use forward pricing to reap the benefits of artificial intelligence in your infrastructure outsourcing deal.” BTW – this is another Gartner recommendation specifically addressed to sourcing executives.  They’re advising them to incorporate the expected savings of artificial intelligence, and by inference automation, into their outsourcing deals REGARDLESS of whether or not their infrastructure provider offers it.  Meaning that whether or not automation and AI are part of an MSP’s strategy, the market will be expecting it to be, and that will put further downward pricing pressure on a business with already thinning margins.

Let’s do just one more of these.

The Good.  Gartner says, “Those providers that invest early will see their business flourish for a few years, and then will land in a position where the business around those services is underpinned by a positive and sustainable margin performance. Yes, it will be transactional and maybe per quantity in nature, but it will be nevertheless sustainable.”  So if you make the investment in automation now, you will reap the profitable benefits down the road in a SUSTAINABLE way.

Here’s The Bad.  “Those that fail to invest will see quick revenue erosion, followed by margin erosion, because they will be forced to lower prices without being able to enjoy the reduction in delivery costs that automation can offer.”  So again, they’re saying the market is expecting MSPs to provide automation, and will also expect lower pricing going forward, regardless of whether or not the MSP even offers automation.

And here’s the last Ugly.  “It will not be a question of getting to ‘smaller but sustainable,’ but a case of exiting with a strong focus on damage limitation.”  This advice from Gartner basically boils down to a warning that if you’re not going to start using and offering automation soon, you should consider getting out now while you can still get some value for your business.  Pretty dire warning!

Now that you’ve heard what the experts think, if you’re an MSP is it time to hit the panic button? 

NO!  Don’t panic.  Not yet anyways.

Let’s return again to our old west theme.  Back in those days when people went to the local saloon to enjoy some recreational fun, everybody played the same card game just like they always did. 

In modern times, up until recently, the game was always the same for MSPs too.  Now though, the MSP game is changing.

In fact, thanks to automation and other technologies, the MSP game is changing dramatically and everybody is being dealt a new hand. If you’re an MSP, your new hand in this new game means a new opportunity to increase market share!

Just to be absolutely clear about the changing game for MSPs, let me illustrate what exactly is changing.

Traditionally, the MSP game was about filling up massive cubicle farms to provide services with inexpensive labor.  Unfortunately, that’s just not sustainable anymore. Even if you’ve got a supply of the absolute cheapest labor and you can double up people in each cubicle, there’s one inconvenient fact that can’t be escaped – people don’t scale very well.

That includes even your very best data center workers, who can only handle so much. Today, analysts and thought leaders are telling companies to walk away from these kinds of outsourced deals, no matter how cheap your labor is.

And why are they recommending that?

Because the new reality is that digital labor is MUCH cheaper.  Not only that, but digital labor takes no vacations, or coffee breaks, or sick time, and it never has mood swings.  It’s always available, 24/7/365 and unlike people, it is extremely scalable.

The new game for MSPs is Automation-as-a-Service.  Leveraging digital labor to provide a much better offering, and doing it for even less than before.

Remember, in this new game MSPs are expected to play, Gartner and others are telling your customers that what they should demand from you is more quality, increased speed, and better results, all at a lower cost.  The only practical way an MSP can do that is with automation.

Back once more to the old west.  One of the really great stories in the history of that time period was the gold rush.  The gold rush of the 1800’s made a lot of MSPs very rich.  Yes, you read that right – MSPs.  Except back then, M.S.P. stood for Mineral Searching Prospectors.  (Alright, maybe I’m the only one who thought that was kind of funny.)

Today’s gold rush doesn’t involve any shovels or pick axes or specialized pans for sifting gold nuggets out of rivers.  That’s because today’s gold rush is in automation powered by AI.  Ayehu predicts that between now and about the middle of this century, a lot of MSP’s are going to get very rich by using an enterprise automation platform to provide Automation-as-a-Service for their customers.

Before diving into that though, I’d like to talk just a little bit about open source automation.

If there’s one character from the old west that best epitomizes the idea of working with open source software, it’s undoubtedly the blacksmith.  Everybody knows what blacksmiths did back then, right?  They took a piece of metal, and forged it into something like a horseshoe.  And by forging I mean they did a lot of hammering and a lot of sweating.

That’s exactly what you’re going to do with open source software.  Except instead of hammering, you’re going to be doing a lot of coding, but you’ll still probably do a lot of sweating too, just like the blacksmith. 

Maybe that’s appealing on some level.  Build it yourself and take full pride in forging an automation tool that does exactly what you want.  Except the problem there is that while you’re hammering away on your keyboard just to build the automation tool itself, your competitors are using commercial-grade automation software like Ayehu that works right out-of-the-box and is fully supported by the publisher.  That means your competitors are orchestrating actual workflows for their customers that are up & running quickly and in production to start earning those customers an ROI.  The best way to stay competitive as an MSP is to go with the tool that’s already proven itself and can earn a fast ROI for your customers.

Remember – generally speaking, your customers aren’t worrying about the plumbing.  They just want you to give them hot water.

Let’s discuss a couple of use case examples.

The first use case is a major international financial services firm, with what can only be described as a colossal environment:

  • They have over 60,000 servers
  • They also have over 10,000 database instances
  • And they have nearly 500 supported applications!

Their challenge was the high cost of monitoring and maintaining this massive infrastructure.

Using Ayehu’s automation platform, they realized:

  • A 40% improvement in MTTR
  • A 90% improvement in response times
  • And together, those two yielded a 15% cost savings in year one!

Not a bad return on investment, and a huge win for our MSP partner that delivered these results to this customer.

The second use case involves one of the largest department stores in America.  Not quite as big as the previous company, but pretty big nonetheless.  Their infrastructure included:

  • About 20,000 servers
  • Nearly 6,500 database instances
  • And all this was spread out between 2 different datacenters!

Their staff was spending a lot of time and effort on manual, repetitive tasks that were impacting their resolution times.

After Ayehu was deployed, they experienced:

  • a 95% improvement in MTTR
  • a 1,500 man-hour reduction of effort in Year 1
  • and a savings of nearly half-a-million dollars!

Quite an impact.

The final case study I want to share with you shows the power of automation in reducing the cost of operations for the MSP.  This case study comes from a global MSP partner of ours who’s among the largest $ multi-billion MSPs.

They were looking to reduce operations costs and improve their margins at one particular client where they had a multi-year contract with a project value of $11.6 Million per year.

After implementing Ayehu at that customer to automate numerous repetitive manual processes, their operational costs steadily dropped each year until by the 3rd year of their engagement, they were saving 35% in costs using Ayehu automation, all of which dropped straight to their bottom line.

And thanks to Ayehu, they were able to deliver a 30% FTE optimization while increasing their SLA performance by 98%.

As you can imagine, now that this MSP has mastered our automation platform with such success, they’re going to be aggressively competitive in the market place.

Speaking of SLAs, I should also point out that incorporating automation into your managed service practice will allow you to say goodbye to SLA penalties and missed targets. As previously mentioned, automation never takes a break, and it also remediates incidents much faster.  That more than anything will give your MSP practice its best shot at hitting its KPI goals. Typically with automation, you can reduce ticket-handling time for incidents down to seconds.

BTW – Since offering automation will alter your cost structure as it did for the MSP above, it will open up many more opportunities for you that were not previously economically profitable.  Automation will also enable you to generate more business opportunities from your existing customer base.  On average, our partners tell us Ayehu has increased their MSP wins by about 10x.

Q:          What’s your onboarding program like & how long does it take?

A:           Onboarding generally takes 6 weeks.  During that time we’ll put your team through training, help you get your own Ayehu environment up & running, and hold your hands helping you build your first workflows.  We’ll also help you build POC’s with your clients, and enable your success however we can.

Q:          What’s the difference between your solution and a freebie Open Source Software download?

A:           It depends on what open source software you’re referring to.  In general though, open source software means you’re doing all the heavy lifting of building out your own tool.  So be prepared to do a LOT of coding.  We’ve invested over a decade of man hours building out the Ayehu automation platform and it’s ready to go out-of-the-box right now without any coding.  The first question you should ask yourself then is, would you rather invest your time & effort reinventing the wheel, or using the wheel that’s been on the market for over 10 years to start adding value to your clients from day one?

Q:          How should an MSP determine when to use Ayehu versus some other automation tool?

A:           That depends on what it is you want to automate.  There’s a lot of different automation tools out there with a lot of different specialties.  Ayehu has a very specific focus on automating IT & Security operations.  We’ve been doing it a long time, we’re very good at it, and we’d be a great choice for any MSP looking for that kind of solution.

Q:          What is the minimum time to learn Ayehu?

A:           Very minimal.  Usually hours, but no more than a couple days.  We like to tell people all the time – take your lowest-level SysAdmin (even an intern), preferably someone who’s never written a single line of code in their lives, and let us train them for just one day.  Afterwards, they’ll probably end up being the most productive person on your IT staff.  Ayehu is very easy to learn.  If you’ve ever used a tool like Visio to build something like an org chart, then you’re already pretty well qualified to build automated workflows with Ayehu.

Q:          What makes Ayehu a platform that MSPs should use, compared to other automation tools?

A:           The Ayehu automation platform is actually designed with MSPs in mind.  So that means features like:

  • Being SaaS-ready which allows an MSP to create their own automation cloud, and since it’s also multi-tenant that means you can partition the same automation cloud out to different customers while managing it all centrally from one instance.  We deliberately made Ayehu an enabler for MSPs that makes it easy for them to offer Automation-as-a-Service.
  • Providing white labeling, so you can rebrand Ayehu as your own tool, which is a great way to reinforce brand loyalty with your customers.

•            Offering a strong partner enablement service that gets you up & running quickly so you can start delivering value to your clients ASAP & begin conquering more market share with automation.

Q:          You mentioned that Ayehu includes AI, but you didn’t give much detail.  Can you please elaborate on what Ayehu’s AI capabilities are?

A:           Ayehu is partnered with SRI International, formerly known as the Stanford Research Institute.  SRI holds something like 4,000 patents worldwide including for things like the original mouse & SIRI, Apple’s conversational AI.  SRI is Ayehu’s design partner, and they’ve designed a lot of the really cool stuff like Machine Learning-driven Dynamic Activity Suggestions.  That means that based on the workflow you’re building, our system provides a real-time recommendation on the next best activity to incorporate into your workflow, based on what we know has worked best for other customers building similar workflows.  That’s been available since last year.

              Another cool AI/ML feature is Dynamic Rule Suggestions, to augment the current static rules we have for triggering workflows. What that means is that when an incident comes into Ayehu, if we have a static rule that matches its profile, then that rule will kick off a workflow to remediate that incident.  Dynamic Rule Suggestions will allow us to suggest rules for incidents that don’t match any rules so they don’t just fall through the..

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