Workflows vs. Scripts for IT Automation

IT teams are consistently being bogged down with the increasing demands to maximize uptime, optimize service levels and minimize expenditure. Juggling multiple disparate systems while managing complex scripts only adds to the back-breaking pressure. Furthermore, with each system prone to its own vulnerabilities, others being program-specific and some scripts completely devoid of oversight, pinpointing problems can be an absolute nightmare.

No-code intelligent automation workflows, on the other hand, allow IT teams to simplify, unify and streamline. Let’s take a closer look at each scenario below.

Traditional Scripting

Many IT departments still cling to the old-school way of manual scripting. There are many reasons why this strategy is wasteful of time, money and organizational resources. For instance:

  • Developing scripts from scratch to connect disparate systems can be complex, and the more systems involved, the greater complexity
  • Maintenance of scripts is hard, even more-so when you lose the people who wrote them, or the skills needed to support scripts written in languages that have fallen out of favor
  • Script-based automation often has no oversight for the potential security vulnerabilities they introduce
  • Scheduling scripts with Windows Task Scheduler is not flexible, lacks auditing capabilities and makes it difficult to connect different scripts as well as pass data securely between them

This is a real-world problem, too. One of our team members recently spoke with a customer – a major player in the retail space – who lamented:

Turnover in employees caused a problem. We had script-based automation using Power Shell, and when the employee who wrote it left, we no longer understood what was going on.”

The solution to this is simple and straightforward: replacing scripts with automated workflows.

No-Code Workflows

In contrast to traditional manual scripting, “no code” visual workflow-based design, like Ayehu NG, offers a plethora of benefits, including:

  • Faster to develop
  • Easier to maintain
  • The system has built-in auditing, and is a single point of control , so less likely to expose you to security vulnerabilities
  • More flexible, easy to re-purpose building blocks etc.

To illustrate the difference between “no-code” and “code” based approaches to automation:

Visual design:

Code/Script based:

As you can see, there is a significant difference between traditional scripted automation (which technically isn’t really automation at all) and codeless workflows powered by artificial intelligence. To experience this game-changing difference firsthand, click here to try Ayehu NG completely free for 30 full days.

It’s the End of the IT Service Desk as We Know it (and We Feel Fine)

Ayehu and Rulai team up to turn chatbots into workbots, enabling the self-driving IT service desk

San Jose, CA and Campbell, CA — October 29, 2019 — Ayehu, a leader in intelligent automation, and Rulai today announced a partnership to deliver a self-driving IT issue resolution offering powered by AI. The Rulai integration goes beyond the limitations of traditional chatbot communication capabilities to deliver virtual service agents (VSAs) that complete IT service desk work.

“Let’s face it, people hate calling the service desk, experiencing long waits on IVR and sometimes waiting hours just to get a password reset,” said Gabby Nizri, CEO of Ayehu. “Ayehu and Rulai are disrupting the entire way the IT service desk works in enterprises today. Using the integrated solution, we eliminate the need for employees to call and wait for IT to solve their problems. Our chatbot VSA will perform these tasks, increasing productivity and employee satisfaction.”

IT service desk operation is extremely resource-intensive, with as much as 85% of the cost being labor according to some studies. Most of this work involves menial, repetitive tasks, that could easily be automated. This has led to the rise of chatbots, which can reduce the amount of human interaction considerably.

However, chatbots are often limited to basic interactions such as pointing the user to non-actionable static information pages, or at best, creating a well-formatted request that is then sent to a human operator for execution. Most chatbots use rule-based decision trees, have poor natural language understanding and limited ability to manage an ongoing dialog or execute a task. As a result, they are easily confused, and customers can become frustrated.

To address both limitations, Ayehu and Rulai have joined forces to create a solution that actually does the work.

Combining Rulai’s Level 3 Conversational Computing Platform with Ayehu’s intelligent automation engine, companies can turn chatbots into a Virtual Service Agent (VSA) – a workbot that has the ability to not only receive and communicate requests but complete and resolve them as well.  

Rulai’s platform allows companies to build AI-based conversational chatbots that can manage non-linear dialogue, and that require no code to develop or deploy. Ayehu provides an intelligent automation engine that receives user intent from Rulai, and acts on it. No code is required to build workflows that integrate with the widest range of applications, changing the IT service desk experience for the end-users.

“Improving employee experience has become one of the top goals for CIOs,” said Marc Vanlerberghe, CEO of Rulai. “We live in an instant gratification economy. Like never before, employees demand fast, frictionless engagement with companies. The combination of our Level 3 Conversational Computing Platform and Ayehu’s intelligent automation platform creates a delightful and frictionless IT experience, available 24/7, 365 days a year, vastly improving the employee experience.”

By running the service desk autonomously customers can achieve as much as a 35% cost reduction, while reducing MTTR by 98% which makes the end user satisfaction higher than ever.

Ayehu’s Rulai integration is available immediately. Please contact sdr@ayehu.com.

For more information, register for the upcoming automation webinar on November 13th: 

Rulai and Ayehu Present: How to Create a Self-Driving IT Service Desk. Register here:

https://info.ayehu.com/how-ai-can-reduce-service-desk-ticket-costs-from-20-to-4

About Ayehu

Ayehu’s AI-powered automation and orchestration platform 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. For more information, please visit www.ayehu.com and the company blog.  Follow Ayehu on Twitter and LinkedIn.

About Rulai

Rulai is a new Enterprise Conversational Computing Platform provider. Rooted in academia, the founding team has a combined 200 years of experience in AI research, published over 400 research papers and filed over 80 patents in advanced AI-based dialog management. It is the only SaaS platform in the market capable of supporting business teams in building Level 3 Virtual Assistants .

Enterprises in banking, insurance, retail, telco, and life sciences increasingly rely on human-centered automation to augment the work of customer service agents, as well as increase customer self service capabilities across sales and support. Rulai’s easy-to-use platform allows business users to create and evolve virtual assistants with support from IT. Rulai has been recognized by Gartner, Forrester, and Bloomberg, and was named to the Forbes 2019 AI 50 list.

Harnessing the Cognitive Capabilities of Intelligent Automation

In order for business leaders (and ultimately their teams) to meet the growing demands of maximum operational efficiency, organizations across the globe and in just about every industry have been turning to automation for decades. We have reached a time, however, in which basic automation is no longer sufficient. In response to this, enterprises are taking things a step further by utilizing the power of AI and cognitive technology through intelligent automation to address the increasingly complex challenges they’re facing.

The Next Generation of Automation

In its simplest form, machine learning is a technology through which machines (software robots, if you will) are capable of learning from data and applying what they’ve learned to either provide insight to key decision-makers or resolve problems independently. Rather than being programmed to follow a specified series of steps, intelligent bots can now complete tasks without the need for human intervention. The results have been impressive, particularly in terms of efficiency gains.

Logically speaking, automated tasks that are carried out by basic automation are fastest when they are repetitive. With non-intelligent automation bots, workflows that follow a set of predefined rules are most effective at producing effective results immediately. For instance, employees who waste hours a day manually copying and pasting data would realize instant value by moving that task to basic automation.

There are instances, however, when data is incomplete, requires multiple sources or needs to be enhanced in order to carry out a particular task. For example, resolving a single issue for a customer may require accessing half a dozen different systems and data sources. To bridge this gap, organizations need intelligent tools. AI-powered automation ties together siloed systems and provides independent resolution while reducing errors and ensuring compliance.

The key to this? Cognition. Leveraging automation tools that feature advanced cognitive abilities that are similar to humans, organizations can create a more unified infrastructure, improve business-related outcomes and dramatically accelerate and enhance customer service. In addition to basic automated tasks and workflows, companies can deploy virtual support agents to further automate processes, including those that require context and conversation.

What Should Be Automated?

To determine what to automate, decision-makers should begin by evaluating whether or not the tasks, workflows and processes could be improved or possibly even eliminated prior to introducing automation into the mix. Some things will be obvious – the mundane, repetitive and low-value busywork, such as data management or reporting. These things can (and should) be easily transitioned from human worker to automation robot.

Looking past these rote tasks, however, some work is currently considered to be more suited for humans. For instance, activities that require understanding of text, complex decision-making or matching of patterns. Additionally, tasks that rely on emotional intelligence and/or require interaction and collaboration with other human workers have previously been considered too difficult, if not impossible, to automate. That’s where intelligent cognitive automation technology has become a game-changer.

This type of advanced automation incorporates machine learning to facilitate decision-making, natural language processing for understanding and contextualizing written and verbal communications, and state-of-the-art predictive analytics and pattern matching to handle process exceptions.

Collaborative Process Optimization

Of course, even with all of these technological breakthroughs, humans are still required to choose, apply and manage automation – at least for the time being. One area where artificial intelligence and humans are already working in tandem is in the way of decision-making support. For instance, next generation tools like Ayehu combine cutting-edge automation and cognitive technologies to determine how processes are configured and/or currently operating. From there, automatic suggestions can be made to further improve workflows and optimize processes.

While many still worry about intelligent bots taking over human jobs, particularly due to the rapid evolution and development of artificial intelligence, a more accurate reality will be humans and intelligent bots working side by side to add value and collaboratively achieve optimal business outcomes.

Experience the power of intelligent automation for yourself by starting a free 30-day trial of Ayehu. Click here to get started!

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How to get a higher return on your Intelligent Automation investment

maximizing your intelligent automation ROI

Despite the fact that intelligent automation offers a multitude of benefits to organizations of all sizes and industries, there are still many key decision makers that fail to recognize the value this technology can have for their own enterprises. In order to demonstrate how important automation is for the future of business, IT managers must find a way to maximize ROI and demonstrate the quantifiable benefits to the powers that be. Here are five simple strategies for getting those numbers headed in a positive direction.

Define Needs, Benefits and Expectations

You can’t focus on improving anything – whether it’s efficiency through intelligent automation or the actual ROI it delivers – unless everyone understands what to expect. Time should be taken to identify and define the specific needs of the organization, and then specify how automation can solve those problems and meet those needs. Once this information is gathered, you can then more accurately measure all of the specific areas where automation is producing a solid return and how. A few key places to start include effort reduction, mean-time-to-resolution (MTTR), lowered rate of error, compliance and system up-time. Improving each of these areas will directly boost your return on investment.

Understand the Process and Where Automation Fits

The driving purpose behind intelligent automation is to use technology to replicate repetitive, manual tasks. To improve automation ROI, one must dig much deeper than this basic concept to understand the entire process at hand and identify exactly how automation can be integrated for optimal results. Important questions to ask in this analysis include:

  • What factors should trigger an automated process?
  • What must occur before and after the automated process?
  • What variables and inputs will be necessary to achieve the best outcome?

Most importantly, how does automation fit with the big picture – the larger business process as a whole? While individual tasks could certainly be automated, automating the entire process or workflow may actually produce a greater value for the business.

Recognize the Context and Customize Accordingly

Calculating accurate ROI involves understanding the specific context in which the automated process in question is running and customizing that process for optimal results. For instance, the automated response to a critical incident, such a systems outage, during peak business hours should be markedly different than the response to a similar outage that occurs in the middle of the night. These contextual considerations should be built into the automation process and they should also be considered whenever measuring results. By customizing the process, the intelligent automation platform can execute different actions based on each scenario, thereby producing a greater return overall.

Test Thoroughly Prior to Release

Testing an automated process manually or in a development system can certainly be time consuming, but it’s absolutely critical to achieving maximum ROI. Before an automated process is deployed in a live environment, it must be adequately measured to ensure that it is not only producing the desired results, but is doing so consistently. Once the automated process is released, ongoing testing is still strongly recommended, as this helps to ensure that the triggers, inputs, actions and outputs are all running as smoothly and efficiently as possible. Routine audits can also help to identify areas that could be improved upon for even greater benefit.

Ongoing Evaluation and Improvement

Intelligent automation may feel like a “set it and forget it” solution, and theoretically it is to a certain degree, but the organizations that reap the greatest rewards from this technology do so by taking a continuous process improvement approach. Regular evaluation of how automated process are working and analysis of where they may be expanded to produce even better results is a must if you are looking to maximize ROI. IT professionals should be asking whether additional tasks could be automated, or whether existing automated processes could be integrated with one another or built upon for greater efficiency.

Individually, each of these five tips can have an impact on your overall return. When combined, however, they can help to both improve short-term goals as well as drive long-term strategies to produce the desired results of reducing human effort, improving operational efficiency, boosting service levels, reducing errors and downtime, remaining compliant and much more. The end product is a consistently favorable return on investment, which can help to win over those who are not yet on the intelligent automation bandwagon.

Want to see some real-world numbers that can be generated by intelligent automation? Check out our latest case study below.

Ayehu

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

Author: Guy Nadivi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It’s that simple.

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

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

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

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

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

Enter AIOps.

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

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

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

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

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

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

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

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

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

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

Gartner concurs with this approach.

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

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

No ambiguity there.

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

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

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

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Ayehu Announces Availability of NG Intelligent IT Automation Platform Version 1.5

Latest AI-Powered Platform Enhances Productivity and Flexibility with New Activity Designer and GitHub Community Repository

San Jose, CA –- October 16, 2019 Ayehu, a leader in intelligent automation, has announced the availability of its Next Generation (NG) IT Automation and Orchestration Platform, Version 1.5. The new release provides IT and security operations teams with new features that give users more control and flexibility, driving increased productivity.

“As corporations are facing enormous digital challenges, CIOs are being required to do more with less,” said Yaron Levy, Co-Founder and Chief Technology Officer, Ayehu. “The NG platform automates the increasing influx of system alerts and incidents and can also potentially take care of all Level 1 help desk requests. Our latest version gives users more options to access, create and customize the workflows they need to automate. This accelerates results and maximizes the value of automation.”

Ayehu’s scalable NG platform delivers automated workflows that help enterprises save significant time on manual and repetitive tasks and maintain greater control over IT infrastructure.

By acting as a centralized hub that intelligently automates IT service management, cyber security, monitoring and messaging, and virtual support agent workflows, the AI-powered platform reduces mean-time-to-resolution by up to 90%. And as the backbone for intelligent virtual support agents and chatbots, Ayehu helps IT leaders embrace the future of work. The easily adoptable solution communicates and resolves tickets, issues and requests automatically. This greatly reduces, and in some cases even eliminates the L1 and L2 support demands.

The latest version includes the following productivity and flexibility enhancements:

  • Activity Designer – A new feature designed to give users the option to build their own activities as an extension of the library of more than 500 no-code, pre-built activities provided by Ayehu. Customers can now independently develop or modify existing activities in Python, C# or .net to extract further value through customization that meets specific needs.
  • GitHub Community Repository – A new community hub that contains more than 100 of Ayehu’s workflow templates, as well as source code for built-in activities. Customers can use this in conjunction with the Activity Designer to create custom activities based on existing pre-built workflows. The GitHub Community Repository also provides free access to useful peer-developed workflow templates and activities in Ayehu NG Workflow and Activities which have already been created and contributed to the community. 
  • Activity Designer Training – Two new Ayehu Automation Academy courses, Activity Designer Essentials and Advanced Activity Designer, train and certify developers in creating new activities using the Activity Designer. Certified Activity Designers can enhance their organization’s automation capabilities or develop new income opportunities for themselves by delivering high-quality activities for third parties. The Academy has already certified nearly 1,000 IT automation engineers since its inception earlier this year.

“Intelligent automation is a necessary force multiplier for CIOs and IT leaders who want to create successful self-driving organizations, achieve operational efficiency and improve employee experience with IT,” added Gabby Nizri, co-founder and CEO, Ayehu. “It is our mission to provide the technology, tools and resources that help them turn understaffed, strapped IT and security departments into happier, more efficient and productive teams.”

To learn more about the Ayehu Next Generation Automation and Orchestration Platform Version 1.5 visit: https://ayehu.com/ayehu-it-automation-orchestration-platform-powered-by-ai/

About Ayehu

Ayehu’s AI-powered automation and orchestration platform 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 more than 200 major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe. For more information, please visit www.ayehu.com and the company blog.  Follow Ayehu on Twitter and LinkedIn.

Episode #27: Why Enterprises Should Have A Chief Automation Officer – Dentsu Aegis Network’s Max Cheprasov

October 15 2019    Episodes

Episode #27:  Why Enterprises Should Have A Chief Automation Officer

In today’s episode of Ayehu’s podcast we interview Max Cheprasov – Chief Automation Officer at Dentsu Aegis Network. 

The ground-shifting digital transformations underway at many enterprises are beginning to transform the executive C-suite as well.  Traditional IT leadership roles like CIO & CTO are being joined by new executive titles like Chief Automation Officer (CAO).  The elevation of this function to the C-suite parallels the rise in strategic importance of automation, AI, and machine learning to an organization’s future competitiveness. 

To better understand what a Chief Automation Officer does and how they can positively impact business outcomes for global enterprises, we turn to Max Cheprasov of Dentsu Aegis Network.  As Chief Automation Officer of the world’s 5th largest digital marketing agency, Max focuses on leveraging automation & AI as a force multiplier for Dentsu’s 47,000 employees.  He shares with us results from some of the high profile use cases his team has implemented, the surprising way in which enterprises might reap the lion’s share of automation’s benefits, and who the most important strategic partner is for anyone leading an AI and automation practice. 



Guy Nadivi: Welcome everyone. My name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest today on Intelligent Automation Radio is Max Cheprasov, the Chief Automation Officer of Dentsu Aegis Network, a 47,000-person multinational media and digital marketing communications company headquartered in London. Max is responsible for leading the company’s digital transformation efforts using AI and intelligent automation solutions to improve operations, processes, collaboration, productivity, efficiencies, and profitability. And we’re eager to dig into his expertise on those topics.

  Guy Nadivi: Max, welcome to Intelligent Automation Radio. 

  Max Cheprasov: Yes, thank you. Thanks for hosting me. 

  Guy Nadivi:Max, you work at Dentsu Aegis network, which is a sizeable firm, but for those in the audience who may not be familiar with Dentsu, can you please give a brief overview of who the company is and what it does? 

  Max Cheprasov: Sure. So Dentsu is a global business that is focused on providing best in class expertise and capabilities in media, data-driven digital, and creative communication services. Our global portfolio of agencies includes powerful brands such as Carat, iProspect, Isobar, Merkle, MKTG, and McGarryBowen. And we have offices in 145 countries, servicing 11,000 clients, including 89 of the world’s top 100 advertisers. We’re the 5th largest agency group in the world. And a couple of things to keep in mind that makes Dentsu unique as it relates to our conversation today about AI and intelligence automation is that since 2013, Dentsu acquired over 150 businesses, and we have grown from 15,000 people to 47,000 people. We basically more than tripled in size in five years. And that kind of growth is both exciting and comes with certain challenges if you want to drive change and automation at scale. 

  Max Cheprasov: So each business that we acquired, of course, came with its own set of strengths and legacy solutions, systems, tools, processes, and best practices, which differentiated them in the marketplace and attracted us to acquire them in the first place. But that kind of process and system fragmentation is typically the main barrier to the adoption of intelligent automation. Of course, what made things easier for me and my team is Dentsu’s strength of integration and the diverse talent and highly entrepreneurial culture. Also given the size and significance of our business, we have access to innovative and cutting edge solutions powered by top technology companies like Google, Microsoft, Facebook, Amazon, and Tencent, and it’s always great to sit down with their product teams and get early access preview to their upcoming products and services. 

  Guy Nadivi: Your title is Chief Automation Officer, which is a title, I think, we’re likely to see at more and more companies as automation continues becoming more crucial to enterprise operations. Why should organizations have a Chief Automation Officer, and what are the most important benefits one provides? 

  Max Cheprasov: Yeah, I definitely hope to see more and more companies that employ Chief Automation Officers. But to me, it’s almost like asking 140 years ago when Thomas Edison began commercializing his incandescent light bulb whether you need to hire an electrician to wire your home and office or continue burning candles, right? It was a clear choice back then, and it should be a no brainer decision today. AI’s the new electricity, and think of the Chief Automation Officer as your master electrician. Every business needs to have an AI and intelligent automation strategy and plan to rewire the business and prepare it for the future. 

  Max Cheprasov: And I think the role of the CAO will continue to evolve as AI and intelligent automation industry continues to exponentially get more and more sophisticated. I think it’s unrealistic for anyone in the C-suite to just take all of that on as an additional responsibility. It really needs to be someone’s sole mission. And I often get asked about my role and who in the C-suite I report to and whether that makes a difference. And in my own experience, I have now reported to a Chief Executive Officer, a Chief Operating Officer, and Chief Technology Officer, sometimes with a dual reporting line. Well, the organization chart in a C-suite may change from time to time, my team’s focus and objectives have remained unchanged. Our ultimate goal is to bring together operational excellence, AI, and automation by closely working together with all of the business functions, regardless of the reporting lines. 

  Max Cheprasov: So appointing an expert who can navigate across the business and holistically weave AI and automation into every corner of the enterprise is necessary to remain competitive. So every business needs to think about either evolving the role of the Chief Operating Officer or Chief Digital Officer to expand their area of focus to include AI and automation, or if you’re a complex organization and don’t want to rock the C-suite boat too much, hire a Chief Automation Officer. 

  Max Cheprasov: I think we reached a point in the evolution of intelligent automation when you can no longer delay this decision. There was a new study released by Deloitte a couple of weeks ago saying that the number of companies applying AI has doubled this year, and it also states that 2020 will be a breakout year for scaling intelligent automation. So if you and I were having the same discussion a year ago, I would say that you still have a few months left for research, experimentation, and to tinker with different technologies. However, today you need to have a solid strategy and plan how you will begin to apply AI and intelligent automation technologies at scale, and you really need the Chief Automation Officer to lead the organization on that mission. 

  Guy Nadivi:  Max you implemented an automation center of excellence at Dentsu Aegis. Why should organizations deploying automation implement a COE, and what has been its impact on your automation efforts? 

  Max Cheprasov: So at Dentsu, our automation COE is a simple mission to unleash the full human potential and take the robot out of the human. It sounds simple, but really not easy. And what we’re trying to accomplish is we essentially want to make teams highly efficient and productive through the use of AI and intelligent automation, and we want to remove the necessary but highly manual repetitive routine low value activities from their day to day grunt work, and give the time back to them to handle more critical and strategic activities that require more creativity and thinking. 

  Max Cheprasov: And whenever I begin talking about intelligent automation at Dentsu to a new group of people, I start with a slide that has my favorite quote from Bill Gates. He once said, “I choose a lazy person to do a difficult job because a lazy person will find an easier way to do it.” If you think about it, and there is actually a scientific fact and you can research online about it, but humans are inherently lazy. We naturally look for ways to conserve energy and remove complexity from our lives, and we want to reduce the time that we spend on less important manual, repetitive, and tedious activities. So our brains are much better wired for collective social intelligence, innovation, complex problem solving, and creative thinking. 

  Max Cheprasov:  So it’s no surprise that we continuously see inventions that free us up from the routine like the fully automated assembly lines in factories. I mean how many people in the past do you think truly enjoyed the manual process of picking up a part, drilling a hole in it, giving it to the next person in line to inspect, and then repeating that same task a thousand times a day, five days a week, 40 years of their career? I bet not many people enjoy that kind of career.  Max Cheprasov: So the same type of need for automation exists in the office environment. And of course for the automation of the knowledge worker, we’re not talking about the mechanical robots, we’re talking about the software robots, the chatbots, the virtual assistants, cognitive machines, or we simply refer to them as the digital workforce. 

  Max Cheprasov: So the other thing I’d like to say is that it’s not really a competition, and it’s not about replacing people with robots. It’s how do we take advantage of the latest technology that’s available to us and have the robot and the human work together in a collaboration loop? The main question is how do we build an exoskeleton for the business that effectively combines humans and the digital workforce? 

  Max Cheprasov:  In general, I’ve seen companies take two paths here. You either hire consultants to begin your automation journey, or you try to do it in-house as a COE. And we can debate the pros and cons of each choice, but it’s important to remember that there is no silver bullet here and everyone’s situation is unique. I suggest that if you’re still in the early stages and unsure where to begin, invite some of the top RPA vendors to talk to you about their services and their observations on what has worked and hasn’t worked in companies like yours. Because they now have a ton of experience and can give you valuable and substantiated advice. And do the same with some of the top consultants in the space. You can also attend AI and intelligent automation conferences and try to interview some of the experts in this field. But it’s really not uncommon to spend the first three months, maybe longer over your intelligent automation journey, on research alone. I think it’s a very important first step. 

  Max Cheprasov: So whether you start to internally build up your own COE, or begin with consultants and then take it in house, don’t rush, and remember that intelligent automation includes a combination of multiple methodologies, and you’re not restricted to just one technology partner. Consider them all. 

  Guy Nadivi: To your point about lazy people. I can personally attest that some of the most efficient individuals I’ve known in my life have also been some of the laziest people I’ve ever met. So I think there’s quite a lot of merit to that statement. Can you tell us about some interesting intelligent automation use cases your team at Dentsu has deployed and what kinds of efficiencies they delivered?  Max Cheprasov:  Yeah, definitely. They are all interesting and exciting. And there is one use case that I’m most particularly proud of because it opened a lot of doors for us at all levels of the organization, and we now refer to this internally as the automation movement. 

  Max Cheprasov: Very early in our automation journey we intentionally picked a pain point that was sitting in the middle office in a process that tied together our back office functions in finance and our foreign office operations in media. So it was something that most executives in our business could easily understand and relate to. All we had to do was build a robot and illustrate the before and after. And the easiest way to tell a story, especially to the executives, in my opinion, is through visualization. So that’s exactly what we did. We recorded the side by side video of a human worker and a bot performing the same job function. Only the bot was able to achieve exactly the same output, 110 times faster, 100% error free, and it only cost us a fraction of the human labor cost. 

  Max Cheprasov: So that story not only validated our own COE’s approach and the capabilities of our technology partners, but also highlighted the major opportunity for the business, our employees, and our clients. I think we ended up eliminating close to 95% of human involvement in that process, and through that POC alone, we elevated 25,000 human hours to much higher value activities. And that was just at POC. We were just scratching the surface. 

  Max Cheprasov: Another use case that I think most listeners will be able to relate to is the RFP process. We get hundreds, if not more, RFPs every single month, and drafting an initial response takes some time and effort. So we built a robot that extracts information from incoming RFPs, applies natural language processing and fuzzy matching to review and classify the information in that RFP, and then extracts from the knowledge repository the best answers for each of the questions in the RFP. The bot then presents a couple of versions, a couple of options per answer to each question to the human expert to select from. And through supervised learning and feedback from the human expert, the bot updates its algorithm, updates its library, and gets better at answering similar questions over time. 

  Max Cheprasov: But that’s just the start. I think the best part about intelligent automation is trying to compress the time and steps that’s taken from step one to step n in the process, versus just focusing on one of the steps. So as the next evolution for this robot, imagine that the bot doesn’t find the right answer in the library. Who does it ask for for help? How does it find the right expert in the 47,000 person organization to help it answer the new question that it hasn’t encountered before? So our next step is to train that robot to access our HR IS system to find the right expert based on relevant skills and past experience, and collect that response from them. 

  Max Cheprasov: And there’s so much more that that can be automated after this as well. For example, once the RFP response is fully assembled, or an initial version of it, we can evolve the robot to engage with another AI assistant that schedules a meeting to review the response with all of the relevant stakeholders. And a third AI robot can join the conference call to listen in, transcribe the conversation in the form of meeting notes, upload that automatically to your CRM platform, and then schedule action items in your favorite project management solution. So this isn’t a hypothetical application, it’s what you can do today by orchestrating work and connecting different AI technologies together like building blocks. 

  Guy Nadivi: Those are intriguing use cases. Max, I’ve read you stated that strong business process management and master data management are the foundations for deploying artificial intelligence and machine learning solutions. Can you please elaborate a bit on what you mean by that exactly? 

  Max Cheprasov: Right. I believe that a successful AI and automation journey starts with lean business process management. And data is like fuel for the AI engine, right? It’s like oil, it needs to flow through a refine process first to remove any imperfections and waste from it. You need to have a solid plan for how you capture, refine, and manage your data, because every process in your business generates new data. So how you organize and validate that data is very important. You need to ensure that the processes are properly engineered and the data that’s used as inputs is accurate. Otherwise it’s garbage in and garbage out, in which case your machine learning models will be ineffective. If the training data set a solid, you can use machine learning to find anomalies in that data, identify new patterns, make inferences, and essentially make better predictions that will help accelerate the decision making parts of your automation. 

  Max Cheprasov: I have a hypothesis that one day in the near future, bots will be able to independently operate and make many of our day to day decisions with little to no human intervention in a single enterprise environment that has one rich data set behind it across all business functions without a need for independent and disconnected CRM, ERP, and HRS platforms. If you think about it, all those platforms, whether on premise or in the cloud, all they provide to humans is a user interface to perform certain actions with that data through clicks and mouse movements. And those actions result in either new data created or existing data transformed. 

  Max Cheprasov: If we’re building bots today that are replacing a need for humans to perform those same operations within those platforms, why would we still need to have access to that user interface? We’re in the early stages of this, but I see us transitioning to a kind of environment where humans work with bots and interact with them and the data using just voice. I mean, we speak three times faster than we type, and keyboards may become a thing of the past or will have very limited use in a couple of decades. Certainly AR and VR technologies will help us get there too. 

  Guy Nadivi: There’s certainly a lot of people today using conversational AI for a huge portion of their interactions. I see that even happening possibly sooner. Max, in a previous interview you did, I read you say that, “Automation is a catalyst for change, driver of creativity, and procurer of productivity.” That’s very interesting. What are some ways you’ve seen automation driving creativity in addition to productivity? 

  Max Cheprasov: Oh, of course, if you just automate your existing processes as is and unchanged from the way they’re designed today, you can expect at least two things, a reduction in cost and time that it takes to perform the same job, and an increase in human capacity to perform higher value activities. However, I think a better approach to solution design for AI and intelligent automation is to first ask the question whether the current way of work, that task or that process or workflow, that you are trying to automate is still going to matter and will remain relevant in two to five years from now in its current shape and form. I mean, will it look the same as it does today, or does it need to be rethought and re-engineered? And if it needs to be redesigned, where do you begin? Do you start with that original place where you identified the first use case, or somewhere upstream or downstream from that? 

  Max Cheprasov: I mean, quite often the pain point you’re trying to address using AI and intelligent automation may have a root cause that needs to be first addressed at the input or a data level somewhere downstream. So find that origin and start there, otherwise you risk ending up with a bunch of short term bandaid type of solutions. 

  Max Cheprasov:  I say that AI and automation is a catalyst for change management conversations because the hype and excitement around the topic, the opportunity, the expected benefits, all serve as an open invitation to have a round table conversation with the process experts, executive stakeholders, and end users. They all need to be engaged in a discussion of what you’re ultimately trying to achieve with AI, and how you will build a sustainable and durable solution based on the human need behind it. 

  Max Cheprasov:  Don’t be afraid to take a step back to reevaluate your current designs in the process before you automate something and analyze the entire assembly line. Take it apart, step by step, function by function, and engage everyone in that redesign conversation to reassemble it for the future of work with AI and intelligent automation ingrained. This is called design thinking, when you focus on your automation solution design on the people you’re creating it for. Understanding their core needs, brainstorming with them, and prototyping together with them. That leads to better product, services, and internal processes. 

  Guy Nadivi: Hmm. Given your expertise in this field, what do you think are the top two strategic reasons organizations should automate? 

  Max Cheprasov: Well, the first reason is simple. In the age of AI, the second place will no longer be good enough. Whoever in your competitive field gets to the winning AI and intelligent automation formula first will leave everyone else so far behind that it will be really expensive to try and catch up. I mean, the front runners will have impressive tools that will enable them to analyze and process more information, they will make better decisions, produce more output. They will do it much faster and much cheaper than those who don’t have that winning AI formula. 

  Max Cheprasov: And it’s not just corporations that are competing in this race, right? The governments around the world understand that AI is a foundational technology that can significantly boost competitiveness, increase productivity, protect national security, and even help solve societal challenges. 

  Max Cheprasov: So that’s the first reason. The second reason is that the next generation of employees and the younger workforce in your business have much higher expectations for their work life. I mean, Generation Z is the first fully digital generation, and they expect high tech solutions, not just in the palms of their hands at home, but also in the workplace. So if you’re still doing things on paper or if you try to assign a Gen Z to do repetitive routine, copy and paste type of activities, or any transactional activities that don’t require much creativity or thinking, you won’t be able to keep them around for more than six months at best. 

  Max Cheprasov: So you need to prepare your business for that, and I think the best way to accomplish that is by engaging millennials and Gen Z in the automation solution design process. If you invite them to participate very, very early in the discovery conversations and excite them about the opportunity to be involved, and just focus on redesigning the workplace around them, that benefits them and ultimately your customers. 

  Max Cheprasov: And beyond thinking about the incoming workforce, your AI and automation strategy and plan will also need to address how you will manage your current workforce that’s impacted by automation. You know, how will you be addressing the changes in their current job roles brought on by automation? Will you be reducing your human workforce in one place of the business but increasing it in another function? What will that transition plan look like? How will you be up-skilling or re-skilling those impacted by your automation efforts? So those are very important questions. In my opinion, the most important strategic partner for anyone leading AI and automation practice should be the leadership team of HR from day one. 

  Guy Nadivi: Interesting. When you evaluate a process to be automated, is there a minimum ROI you need to justify automating it? 

  Max Cheprasov: Yeah, that all depends on what you want to achieve through automation. Is your ultimate goal to reduce costs or improve employee satisfaction and provide a better work life balance? Or is it about reducing risks? Is it about eliminating human error or improving compliance? Most ROI models will be about cost cutting, of course. Some will be about improving work life balance, or quality of output, or customer satisfaction. But not all benefits will be easy to quantify or track and measure. 

  Max Cheprasov:  At Dentsu, we create the business case for every project, and it’s not because we need to justify the investment or predict the financial outcome every time. But it’s really because these projects can take weeks and months to deploy, in some cases longer, and the main purpose of the business case is to capture and to remember the reasons we decided that it’s important to automate in the first place. What is the ultimate destination? What is our why? Why did we decide that it’s important to automate? What alternatives have we considered? What are the risks associated with doing nothing? The answers, even within the same company, within our business, may be different for every project, every time. And our standard ROI model consists of about 20 factors, from the cost of investment perspective going into automation. 

  Max Cheprasov: We consider everything from internal staff costs, infrastructure costs, security training, third party technology license fees, just to name a few. And then from the benefits side, it’s the labor savings, cost avoidance due to 100% accuracy reduction, and time needed to conduct compliance audits, work life balance, employee satisfaction, customer satisfaction, the new business opportunities that you generate as a result. And I think the ROI model for every business will have many of the same factors, but in every case the weight or significance that you assign to these factors may be different from project to project. 

  Max Cheprasov: I would also say that, from our experience, if all you focus on is the labor savings and nothing else, you can expect to break even on average in six to 12 months depending on the complexity of the business case and the experience and maturity level of your automation team. However, the lion’s share of the benefits comes in a form that is hard to measure or quantify sometimes. For example, how do you measure the elevated potential of your human workforce, the creativity boost that you just gave them, or a moment in time when someone actually had the time to apply critical thinking to avoid a major crisis? 

  Guy Nadivi: You just spoke of work/life benefits, which makes me curious. What do you see as the future of work at Dentsu Aegis Network once AI, machine learning, bots, and other automation tools become more common? What’s a typical day in the life of a Dentsu employee going to be like five years from now? 

  Max Cheprasov: Oh, I see a beautiful future, and I have a difficult time every day trying to balance fantasy and reality. I think by end of next year, speaking realistically, we’ll start to realize benefits of AI enabled platforms and tools at scale. And we’ll see that intelligent automation will start to become a way of life as we played with the idea of introducing a citizen developer concept, where employees can create their own micro automation solutions in a low code to no code environment, while the COE focuses on top down opportunities and larger scale opportunities. 

  Max Cheprasov: I think over time, intelligent automation solutions will become more and more intelligent and adaptive to the processes being automated. So I see us becoming an environment with self-viewing, self-healing, and auto-optimization methods becoming widespread, and automation inherently becoming smart. And this is what I call an AI-driven enterprise, where AI is in the DNA of the business, and that is where we are aiming to be in five years. 

  Max Cheprasov: Like I said, I think it’s a beautiful future where every employee has a virtual assistant that is smart enough to pass the Turing test, and humans working together with cognitive bots becomes a new norm. And this is not a fantasy anymore, it’s already happening. It’s just happening in very few places and in very controlled environments. But as our AI and intelligent automation partners continue to evolve their products and solutions, we know that kind of future is realistic. 

  Guy Nadivi:  I think if you had drawn that vision 10 years ago, it would’ve still been considered more science fiction than fact. But of course as we’ve come to learn, there is no such thing as science fiction, there’s only science. So I think your vision is pretty realistic at this point.  Max Cheprasov:Yep, true. 

  Guy Nadivi: Max, for the CIOs, CTOs, and other IT executives 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 deploying automation and AI at their organization? 

  Max Cheprasov: Well, I will kind of repeat what I said in the beginning when I said that AI is the new electricity. Every business needs to have an AI and intelligent automation strategy and plan to rewire the business and prepare it for the future. Do you have one? If not, you need to have one soon if you want to remain competitive. And I think the biggest challenge for all of us today, of course, is the shortage of experienced professionals in this field. But very soon it’s going to be extremely hard to keep up with all of the emerging AI platforms as more and more VC money is being invested in artificial intelligence startups. 

  Max Cheprasov:  So no matter where you are in your journey, I believe that research and experimentation with these new technologies needs to remain a top priority and focus. And in turn, that should keep your AI and intelligence automation strategy informed and constantly evolving. It’s not going to be a static plan. So be prepared for a bumpy ride. An exciting one, too. 

  Guy Nadivi:  Good advice from somebody with deep expertise on the subject. All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Max, thank you so much for joining us today and being the first, but hopefully not the last, Chief Automation Officer we’ve interviewed. It’s been great having you on. 

  Max Cheprasov:  Guy, thanks for inviting me. It was a pleasure. 

  Guy Nadivi:  Max Cheprasov, Chief Automation Officer of Dentsu Aegis Network. 

  Guy Nadivi: Thank you for listening, everyone. And remember, don’t hesitate, automate.   



Max Cheprasov 

 Chief Automation Officer at Dentsu Aegis Network. 

Max Cheprasov is the Chief Automation Officer at Dentsu Aegis Network, a multinational media and digital marketing communications company with 47,000+ employees in 145 countries.  Max has 25+ years of experience within the Digital Economy, specializing in digital transformation, operational excellence, and AI-powered automation.  At Dentsu, Max founded the Automation Center of Excellence (COE) that develops and deploys highly effective machine learning systems, narrow AI and intelligent automation solutions (IPA / RPA), augmenting and enhancing employee experience with smart digital assistants, and improving efficiency and effectiveness across the business. 

Max can be found at:

Website:                             https://www.dentsuaegisnetwork.com/

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

Quotes

“AI's the new electricity, and think of the Chief Automation Officer as your master electrician. Every business needs to have an AI and intelligent automation strategy and plan to rewire the business and prepare it for the future.” 

"So every business needs to think about either evolving the role of the Chief Operating Officer or Chief Digital Officer to expand their area of focus to include AI and automation, or if you're a complex organization and don't want to rock the C-suite boat too much, hire a Chief Automation Officer." 

“…how do we take advantage of the latest technology that's available to us and have the robot and the human work together in a collaboration loop? The main question is how do we build an exoskeleton for the business that effectively combines humans and the digital workforce?” 

“So whether you start to internally build up your own COE, or begin with consultants and then take it in house, don't rush, and remember that intelligent automation includes a combination of multiple methodologies, and you're not restricted to just one technology partner. Consider them all.” 

“I think the best part about intelligent automation is trying to compress the time and steps that's taken from step one to step n in the process, versus just focusing on one of the steps.” 

“I have a hypothesis that one day in the near future, bots will be able to independently operate and make many of our day to day decisions with little to no human intervention in a single enterprise environment that has one rich data set behind it across all business functions without a need for independent and disconnected CRM, ERP, and HRS platforms.” 

“I say that AI and automation is a catalyst for change management conversations because the hype and excitement around the topic, the opportunity, the expected benefits, all serve as an open invitation to have a round table conversation with the process experts, executive stakeholders, and end users.” 

“In the age of AI, the second place will no longer be good enough. Whoever in your competitive field gets to the winning AI and intelligent automation formula first will leave everyone else so far behind that it will be really expensive to try and catch up.” 

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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Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation

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Disclaimer Note

Neither the Intelligent Automation Radio Podcast, Ayehu, nor the guest interviewed on the podcast are making any recommendations as to investing in this or any other automation technology. The information in this podcast is for informational and entertainment purposes only. Please do you own due diligence and consult with a professional adviser before making any investment

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

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

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

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

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

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

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

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

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

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

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3 Ways NOT Automating Could Cost Your Business (Big Time)

Industry experts frequently point out the many ways leveraging intelligent automation can benefit an organization. For instance, increased efficiency levels, lower expenditure, scalability and the ability to do more with less all top the list. What we don’t often mention, however, is the flipside. That is, the impact NOT automating could potentially have on a company’s ongoing success and future profitability. In fact, failing to automate can come at a substantial cost in three specific areas. Here’s how.

Human Error

As the IT realm continues to become more complex, the risk of errors by the individuals responsible for handling fundamental tasks and workflows will inevitably increase. These errors can be costly in a number of ways, including impact to both internal departments as well as clients, and potential loss of business. These costs can be further compounded by the amount of time it takes the IT department to correct said errors.

To gain a clearer picture of exactly how much human error can cost your organization, take some time to document all the recent mistakes your IT team has made and then assign a dollar value to each of those errors. You may be surprised at how much those losses can truly add up. What’s more, the larger your organization grows, the more complex your infrastructure will become, which means an even greater risk of IT errors. Intelligent automation dramatically reduces, and in most cases, entirely eliminates human error, ensuring a more efficient and effective operation overall.

Employee Turnover

Recent data estimates that replacing an employee can cost a business up to 33% of that person’s annual salary. To put this into perspective, let’s say you pay your average IT support technician a salary of $45,000 per year. If that employee quits, it could cost you up to $15k just to find someone to find their replacement. That’s an incredible waste of money.

In the IT field, however, turnover is a serious problem, the biggest reason for which is employee burnout. Being on call 24/7 may be par for the course, but those 2am phone calls get old real quick. Furthermore, having staff on-hand to monitor, maintain and update your infrastructure around the clock isn’t practical, nor is it typically feasible.

By introducing intelligent automation into the mix, you’ll alleviate much of the unnecessary burden from your IT team. They’ll be able to focus their efforts on more meaningful work, which will keep them engaged and happy. In turn, they’ll be more likely to recommend you as a good employer to their own networks, which could help you recruit additional talent when it comes time to scale.

Missed Opportunities

Tying in directly with the two points listed above is the third way lack of automation can cost your business: missed opportunities. When employees are bogged down with manual, repetitive and boring tasks, not only will they not have the time or energy to work on other, more meaningful projects, but if they’ve got one foot out the door, they probably won’t care much about your company’s success anyway.

Likewise, when human errors are causing issues with the current way your organization operates, it can stagnate your chances to scale and grow. In other words, your IT team will be so busy putting out fires and trying to recover from costly mistakes, they won’t have the time or energy to dedicate to other value-added and mission-driven activities.

If you want your company to be able to compete in the digital age, you need employees who are ready, willing – and most importantly – able to innovate. Intelligent automation complements human workers by doing much of the heavy lifting while enabling better decision-making and freeing up employees to fully utilize their cognitive abilities. This creates a “best of both worlds” scenario where everyone benefits.

So, can intelligent automation save your company money, make your operations more efficient and provide other valuable benefits? Absolutely. But it’s equally as important to consider what the real costs are of not automating. The question you should be asking isn’t should you automate, but rather can you really afford not to.

Get started with AI-powered intelligent automation today FREE for 30 days. Click here to claim your free trial.

Episode #26: How To Run A Successful Digital Transformation – Transformant’s Tony Saldanha

October 1 2019    Episodes

Episode #26:  How To Run A Successful Digital Transformation

In today’s episode of Ayehu’s podcast we interview Tony Saldanha – President of Transformant & former VP, Global Business Services for Procter & Gamble. 

The failure rate for digital transformations range anywhere from 70% to as high 84%, depending on which global management consulting firm you believe.  For an undertaking so vital to an enterprise’s long-term viability, and where failure shouldn’t even be an option, these figures are shockingly high.  Digital transformation is widely regarded as an exercise in technologically rewiring systems and processes to increase operational efficiencies.  Yet the primary reason organizations fail to successfully transition rarely has anything to do with technology. 

To better understand the dynamics of all this, we turn to the man who wrote the book on the subject, literally.  Tony Saldanha authored “Why Digital Transformations Fail” based on his experiences running a highly successful digital transformation at Procter & Gamble, the world’s biggest consumer goods company.  In doing so, he not only guided P&G’s reimagining of how its Global Business Services division should operate, but he also developed a 5-stage framework that any company can follow to successfully navigate its own digital transformation.  Tony shares many insights with us, including why the ultimate proof of an organization’s success at digital transformation is its cultural, not technical rewiring. 



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 Tony Saldanha, former Vice President of Global Business Services for Procter & Gamble, widely known as the biggest consumer goods company in the world. Currently he’s the President of Transformant, a consulting firm focused on helping organizations accelerate their digital transformations.

Tony is also the author of a recently published book called “Why Digital Transformations Fail”, which outlines a framework for guiding organizations undertaking the challenge of transforming themselves digitally. The book is largely based on his real world experiences with a hugely successful digital transformation that he led at Procter & Gamble. Tony, welcome to Intelligent Automation Radio. Tony Saldanha: Thank you, Guy. My pleasure. Thanks for having me.

Guy Nadivi: Tony, I found your book to be a surprisingly engrossing read, even for someone who may not even be involved with digital transformations. The reason I say that is because I particularly enjoyed the analogies you drew upon from the aviation, medical, and financial industries, which had historical perspectives and other aspects that were very relevant to an enterprise digital transformation. You also had some great case studies covering successes and failures ranging from organizational departments to entire companies, and even to a sovereign nation which underwent a famous digital transformation.

But I don’t want to steal your thunder. So, please tell our audience at a high level, first off, how you define a digital transformation, and second, why do digital transformations fail as much as 84%, according to your book?

Tony Saldanha: Yes, actually great questions, Guy. Firstly, how do you define digital transformation? This is actually one of the two reasons digital transformations fail. People really don’t know. Leaders don’t know how to define digital transformations. Just a little bit of background, when I was doing my final role at Procter & Gamble, which was to actually create an industrial level, disruptive ecosystem, including the top five IT services companies in the world, and hundreds of startups from the top 10 venture capitalists, I also did a fair amount of research, went around and talked to more than 100 different organizations and people from C-Suite executives to consultancies, to startups. I asked them, among other things, one common question. “How do you define digital transformation?” As you can imagine, I got 100 different answers, all the way from, “Oh, don’t worry about it, it’s all hype. We used to have digital watches in the 1970s,” all the way through to, “No, no, no. This is a real thing. This is the AI stuff that’s going to come for all of our jobs.” Now, I realize I’m paraphrasing, but only slightly. And that is the issue. When we talk about what digital transformation is, in my mind, there is only one definition that makes sense, right? That is the complete rewiring of an enterprise whether public or private sector, from whatever makes it successful in the 3rd industrial revolution, to what is needed to be successful in the 4th. Now, as background, we all know we’re in the midst of the fourth industrial revolution where technology is pretty much disrupting everything from the physical to the biological to even social, right? So, business models, the internal levels of operation, as well as products, whether smart or dumb, are changing as you go from the third to the fourth. So, real digital transformation isn’t a technology. It isn’t a project. It is basically the change in processes, technology, and organization skills, from whatever made people successful in the third, to whatever makes people successful in the fourth. That’s one reason why digital transformations fail. Leaders are stuck with, “What do I want to go after?” The second, interestingly enough, is that the processes, the approaches to executing digital transformation are still rooted in IT project management, and they completely miss two things. One is the creativity needed to completely rewire entire work processes. So for example, in a company, in the third industrial revolution you might say, “Let’s run expense reports as efficiently as possible.” In the fourth you might say, “Well, why do you actually need an expense report? Because the data needed to generate all of those is already there, so let’s get rid of it.” So you see what I’m saying about completely rewire processes, right? The methodology that’s needed to do that includes creativity, to ask those kind of questions, as well as the organization management to bring the entire organization along, and that’s one of the reasons why execution fails. Those are the two reasons people define digital transformation variously and incorrectly, and then secondly the execution methodology is very different.

Guy Nadivi: Now, you wrote in the book that, “Successful transformation during an industrial revolution is good, but sustainable market leaders need to go a step further. They need to sustain the business model. The transformation is incomplete if the new business model cannot be built with an eye toward perpetual evolution.” That lead me to an observation I wanted to ask you. It’s often the case that the founders of successful startups are not the best choices to lead the company to its next stage of growth once the business model is established. So, with that in mind, do you think the managers who successfully digitally transform a company are the ones best suited to sustain that new digital business model post-transition?

Tony Saldanha: That’s another very interesting question, Guy. I think that if we define digital transformation correctly, and in the book I lay out 5 stages with stage five essentially being perpetual disruption, or perpetual transformation. The best example there which you’re aware of, is Netflix, that’s arguably disrupted itself, maybe four times, or at least three times, from mail and DVDs to streaming media to original content, Game Of Thrones kind of stuff, to obviously international business models, right?

If we define successful digital transformations correctly, which is stage five, it becomes the living DNA of the organization. The leader that gets it is in a good position to continue to do that, right? But you make a really good point, because most leaders that are put in charge or actually sponsor digital transformations view digital transformation as a one-time redo of products and processes and sometimes even people. That’s insufficient as we know. I have a really interesting anecdote during my book launch in Cincinnati a month ago. One of the people in the audience said, “Hey, do you remember MapQuest?” I said, “Yes.” He said, “MapQuest disrupted obviously the entire book map industry in the early 2000s.” He said, “I went to the MapQuest website today and it’s horrible. It just doesn’t compare to pulling up your cellphone and saying, ‘Hey, get me directions online’ to Siri or something like that,” right? That’s a great example. One time is fine, ongoing is necessary. The people that actually get that obviously are able to do that. Your point around the leaders of startups are also not great people to actually run startups has been absolutely documented and is well known, because the people that are required to ideate and get things going are usually incapable of driving the kind of structure and scale. It actually goes against the grain, because they’re entrepreneurs. That’s one of the issues why digital transformations fail, because people view digital transformation as incubation, as starting up a project. But they are not seeing digital transformation for what it is. It is scale change. That’s the context in which I would say if you see digital transformation as almost like a startup, then you fail. Because real digital transformation is rewiring the entire company.

Guy Nadivi: You also talk about the resistance to change, characterized by, I love this term, a corporate immune system in place to keep the organization healthy. Specifically, you state that, “It is the middle management layer that’s on the critical path and has the potential to slow down or even block change.” You then talk about the effort that must be put into enroll middle managers in the digital transformation effort, including if need be, creating reward systems that incentivize them to get onboard. However, you point out that in some worst case scenarios, even that fails, and if so, it’s best to quickly kill the project. Tony, I’m curious, why not take it one step further before invoking that nuclear option, by having the CEO or some other C-Suite executive explicitly order the middle manager or managers to get onboard, or risk serious career-limiting alternatives? Especially when digital transformation is so vital to an organization’s future success these days.

Tony Saldanha: Oh, wow. Yeah, that’s a fantastic question. I think the point about having the overall leader of the company be very clear about, “Hey, this is transform or bust” is absolutely valid. You’re right, Guy. That is essential. In fact, what I have found is that if that kind of a serious tone is not set from the top, then the digital transformation has very little chance of being successful. You’re absolutely right in the sense that there is an interim step out there where the leader basically says, “Hey, this is either an existential threat, or an opportunity of historic proportions, so we need everybody to row in the right direction,” right? That’s absolutely essential. Now, what happens is very often even if the leader does say that, the fact of the matter is that the immune system in most companies exists because that is partly responsible for most companies continuing to run stable operations, right? Companies like Procter & Gamble for which I used to work have existed for more than 180 years because of that stability that drives the company, that let’s take some risks, but not crazy risks, right? Whereas with digital transformation, what you’re telling people is, “No, we’re going to have to take some crazy risks.” Some of that is actually not necessarily bad, right? Now, what happens with immune systems is that they’re driven by rewards and recognitions. So, the first thing you do, as you correctly point out, is you change the reward system. You have the leader basically say, “No, you absolutely have to do that.” But there are times when even that fails, right? When that fails, then I think you have to go into completely different options, including looking at other ways in which to drive the transformation, including by the way, things like acquisitions or other inorganic change. The Walmart acquisition of Jet.com is a great example, because they tried a few times to actually go online but couldn’t really drive the organization there, and then the next best option is to just go make an inorganic acquisition.

Guy Nadivi: In one of your chapters, Tony, you discuss the choices an organization has between hiring consultants and personnel to lead the digital transformation efforts, or reskilling the entire workforce, including the leadership at the top. And you justify this by stating that, “A stage five digital transformation involves embedding digital capabilities into the very fiber of the enterprise.” Now, as a lot of the listeners know, it can be very, very expensive to do all this retraining for so many people. How do you persuade an organization to invest in such an enormous commitment, and should the employees be expected to make a commitment themselves to stay at the organization for some minimum time period following the retraining in order for the company to realize a payback on that investment?

Tony Saldanha: That is something that actually is a very topical question. I happen to be part of MIT’s Future of Work, and this question of how much should you basically grow capability organically and should you then expect people to stay with the company is being debated as we speak. Now, here’s the way I look at this particular issue. I think that depending on the amount of change that you’re asking, and the time that is available to you, you might actually use different strategies. If time is running out and you really have very little appetite for change and capability for change, then you really have to bring the cavalry in from the outside, and try and drive a lot of the change, right? So the Walmart Jet.com example is a good one, right? But everything else remaining the same, it’s always better to drive the change internally, because of the topic that we talked earlier, which is real stage five transformation is an ongoing change of DNA. You really need your organization to become the drivers of change, not just one time, but again and again and again. The idea of bringing in somebody from the outside to constantly evolve yourself is just not such a hot idea. Now, once you basically commit to that, then what you have to do is figure out which of your people are going to be able to make it, because not everybody’s going to be able to make it, and how do you actually keep your organization going? Retrain again and again. This is where you start to see examples like Amazon which has actually recently announced that it’s going to spend $700 million to retrain their own organization. When I first read that, I was surprised because arguably Amazon is one of the most tech savvy companies around, and they’re investing $700 million for constant retraining. But that’s basically what’s necessary. Now, your other question which is having made that investment, is it right to expect people to stay on with the company? I think that basically goes to a little more of the values and principles of the company. I think most companies, and I endorse this, believe in an open market system which is, “Hey, we will train you. What we expect you to do is use that either to be successful inside the company or outside.” That’s part of the do I retain people or is it okay for people to leave? Because you may not want everybody that you train to stay with the company.

Guy Nadivi: Interesting point. By the way, the reason that I brought that up is because I remember that when I got out of college, I interviewed with a consulting firm that used to be well known called EDS that was started by Ross Perot, former presidential candidate. They didn’t pay a lot of money for people that started out with them, but one of the things that they touted was how much they were going to train you and that they were going to spend 18 months, or two years, I forget exactly, training you in all kinds of skills that you would need to be a successful consultant for them. I thought about that and thought, “Well, that’s kind of an interesting part of the offer.” Then they also added, “Oh, by the way, if you don’t stay with us for some period of time after the training is complete, you’ve got to pay us back $9,000” which was, I think believe it or not, half of the salary they were paying at the time. Because like I said, they didn’t pay a lot back then. It was the first time that I’d ever heard of a company requiring you to pay for your training if you didn’t stick around. At the time as a poor, broke college graduate, that wasn’t very appealing. In retrospect, now with the benefit of hindsight and experience, I see that it does kind of make sense. They’re investing in you and they want to get a return on investment, that’s just standard business practice. That’s why I was curious about that.

Tony Saldanha: Yeah. You make a really good point about this from a business standpoint, and I know there are companies there that try and enforce these bonds. I also know that some states basically consider these bonds to be invalid or illegal. It’s obviously a somewhat complicated topic there. As I said, one that’s starting to be talked a lot as companies have to face these decisions of making investments in their own people. So I suspect this is going to be a topical question that’s going to continue to be debated.

Guy Nadivi: Tony, one of the most intriguing recommendations you make in your book is about how you leveraged a small group of Procter & Gamble personnel to disrupt an entire industry by creating an ecosystem effect. Can you please talk a bit about what you meant by an ecosystem effect, and whether you think that approach is viable for companies smaller than giants like P&G.

Tony Saldanha: One of the most interesting findings that I had about building this ecosystem is how that idea is applicable to all companies, regardless of size. But to get to the first part of the question, what is an ecosystem? Look, it doesn’t matter if you’re Procter & Gamble and you have a lot of money and a lot of really, really good people. The fact of the matter is that 99.999% of all of the wealth and all of the brainpower in the world lies outside the company, so you’re better off trying to figure out how to tap into that as well. Procter & Gamble’s done that very successfully in the past, on product R&D, including a program called Connect and Develop that has been well published, written up in Harvard Business Review, and so on and so forth, where instead of having all research being done in the company, you basically write up a problem statement and throw it out to the entire world saying, “Hey, anybody out there that’s already solved this?” That’s basically almost like the predecessor to crowdsourcing. So, the ecosystem essentially is a way to use the energy and resources of the world, relevant resources of the world to actually get some of your work done. The way I actually built that at Procter & Gamble is I went to the top five companies, IT services companies, most of which already did business at P&G, and said, “Hey, what I’m going to do is take the mundane day-to-day business operations processes, payroll and accounting and so on and so forth, and I’m going to try and find 10x, 10 times the effectiveness of existing market practices. I’m going to use P&G as a playing ground to do that. Are you in? Would you like to pay to play?” They said, “Oh, absolutely, we’re in.” Because P&G’s global business, service and IT, is known to be best in class, and so the opportunity to come up with something 10 times better than that at Procter & Gamble is worth some money for them, right? And then I went to the venture capitalist community and said, “Hey, I don’t want your money, but I want access to your startups. In fact, what I’m going to do is give you guys problem statements and potentially business for your startups. Are you guys in?” They said, “Yes, of course.” The way this ecosystem worked was we took an idea, let’s say why should you do travel expenses in today’s world where the data around all expenses is there? We will tell the idea to the startups, and some of them would say, “Oh yeah, we’ve got a solution for this but it’s not scaled for enterprise.” Then, we would go to the big companies, IT companies and say, “Hey, can you scale it up? If you do, at no cost to Procter & Gamble, and if you’re successful, I will give you the intellectual property and the software to sell to anybody you want.” That’s worth billions of dollars to these companies, and so this is really how you make the whole ecosystem work. And then as you do that, obviously, you tap into a much, much bigger pool of resources that’s out there than anything that you could come up with.

Guy Nadivi: In your book, many of the case studies that you cited referred to large enterprises. But I’m curious about the mid-sized organizations, particularly the ones that have been in business for decades and have accumulated multiple strata of legacy systems, processes, and cultural norms. Given their lack of resources compared with larger companies like Procter & Gamble, what are their prospects for undertaking a successful digital transformation?

Tony Saldanha: Oh, I mentioned earlier that the surprising thing for me was how this concept is applicable, regardless of size, and that is absolutely true. Because here’s the way I think about it. In my consulting work, I do a lot of work with both medium and small sized companies in addition to the Fortune 100. Regardless of your size, you already have a supplier ecosystem that you work with, right? I mean, it doesn’t have to be an IBM or an HP-type IT organization. It may be a smaller set of people that you work with, but you do have suppliers that work with you, right? And then you have the open ecosystem, it doesn’t again, have to be the venture capitalists, although the VCs are also willing to work with medium-sized companies. Because all they’re looking for is one or two or three successful users of case studies for their startups before they can take the startups to the next level, right? But you also have the broader ecosystem of for example, analytics. You can go to Kaggle. Or, you can basically tap into companies that are actually offering the equivalent of X prize, which is, “Hey, you throw in a prize and we will throw your problem out to universities and different companies” and you maybe pay them $10,000 or some small amount for the organization that solves your problems, right? That’s basically what I find. The ecosystem’s always there. It is a little different depending on the size, but it’s your creativity on creating the business models that allow you to tap into it.

Guy Nadivi: I think it will be very encouraging for our listeners to know that when it comes to digital transformations, size doesn’t necessarily matter, and regardless of how big your organization is, there is a known path to success if you’re willing to take that journey.

Tony Saldanha: It is comforting, and actually, in many of the talks that I do for the SME, small and medium enterprises, I make one additional point, which is the small and medium enterprises are most likely going to be the disruptors, not the disrupted, right? Because they have, in addition to everything else, quick decision making that goes in their favor. They don’t have matrix structures and complex decision making and a lot of things to lose. So, if they are able to combine the agility of decision making, and the tapping into a broader ecosystem, there’s very little that stands in their way of being the disruptor.

Guy Nadivi: Alright. On that high note, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Tony, I know you’re a very busy man, but you were generous enough with your time today to fit our show into your busy schedule. I want to thank you for that, as well as writing this outstanding book which does a beautiful job of synthesizing a lot of interdisciplinary concepts into a roadmap for succeeding at digital transformation. We’ve really enjoyed having you on our show today.

Tony Saldanha: Thank you very much, Guy. It’s been a real pleasure chatting with you and again, thanks for having me and I wish you all the best.

Guy Nadivi: Tony Saldanha, former Vice President of Global Business Services for Procter & Gamble, current President of Transformant, and the author of “Why Digital Transformations Fail”, available from Amazon and other book retailers. Thank you for listening everyone, and remember, don’t hesitate, automate.



Tony Saldanha

President of Transformant & former VP, Global Business Services for Procter & Gamble.

Tony Saldanha is a globally recognized expert and thought-leader in Global Business Services (GBS) and Information Technology. He ran Procter & Gamble's famed multi-billion dollar GBS and IT operations in every region across the world during a 27 year career there.  Tony has over three decades of international business expertise in the US, Europe, and Asia. He was named on Computerworld’s Premier 100 IT Professionals list in 2013. Tony's experiences include GBS design and operations, CIO positions, acquisitions and divestitures, outsourcing, disruptive innovation, and creation of new business models.   

Tony is currently President of Transformant, a consulting organization that advises over 20 Fortune 100 companies around the world in digital transformation and global business services. He is also a founder of two blockchain and AI companies, and an adviser to venture capital companies. His book titled “Why Digital Transformations Fail” was released globally in July 2019 and ranked #1 on Amazon’s New Releases for Organizational Change. 

Tony can be found at:

Email:                                    tonys@transformant.io

Twitter:                                https://twitter.com/tony_saldanha

Website:                              http://transformant.io/

LinkedIn:                             https://www.linkedin.com/in/tony-saldanha-200591123/

Quotes

“Firstly, how do you define digital transformation? This is actually one of the two reasons digital transformations fail. People really don't know. Leaders don't know how to define digital transformations.”

"Companies like Procter & Gamble for which I used to work have existed for more than 180 years because of that stability that drives the company, that let's take some risks, but not crazy risks, right? Whereas with digital transformation, what you're telling people is, ‘No, we're going to have to take some crazy risks’."

“I think most companies, and I endorse this, believe in an open market system which is, ‘Hey, we will train you. What we expect you to do is use that either to be successful inside the company or outside.’ That's part of the do I retain people or is it okay for people to leave? Because you may not want everybody that you train to stay with the company.”

“Look, it doesn't matter if you're Procter & Gamble and you have a lot of money and a lot of really, really good people. The fact of the matter is that 99.999% of all of the wealth and all of the brainpower in the world lies outside the company, so you're better off trying to figure out how to tap into that as well.”

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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Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation

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Neither the Intelligent Automation Radio Podcast, Ayehu, nor the guest interviewed on the podcast are making any recommendations as to investing in this or any other automation technology. The information in this podcast is for informational and entertainment purposes only. Please do you own due diligence and consult with a professional adviser before making any investment