Automation and hyperautomation - why it’s now priority #1

"Hyperautomation is not a luxury, it's a necessity. As businesses become more digital, they need to leverage automation technologies to remain competitive and agile."
Bill McDermott, CEO of ServiceNow

"Hyperautomation is not just about automating processes, it's about creating a new way of working. It's about empowering employees to focus on what they do best while leaving the routine tasks to the machines."
Daniel Dines, CEO of UiPath

Popularity

First off, these approaches to tech are now extremely popular in IT circles, making it increasingly difficult to find a meaningful role for human beings in competitive business sectors. Hyperautomation makes this problem even worse.

Hyperautomation itself leans on increasingly intelligent algorithms that are capable of analyzing more data in one millisecond than a human can process in an eight-hour work day. They are making decisions, self-monitoring, identifying failure nodes before they occur, performing root cause analysis, and in some cases are even self-healing. These systems proved they can absorb menial human tasks long ago. It seems that anything humans can do, these programs can do better.

“Automate, automate, automate” might be the mantra of business in the future, if it isn’t already. In China, Foxconn seeks to replace 80 percent of its workforce with robots in the next five to 10 years. JD.com, the Chinese e-commerce company, invested $4.5 billion in a  plan to make the company 100 percent operated by AI and robots.

But Automation isn’t just about robots, mechanization, and replacing factory workers. It is also about overseeing massive IT estates to keep them running optimally, or removing menial, labor-intensive work that is often better handled by bots than humans. AIOps and hyperautomation use AI + machine learning to analyze a company’s day-to-day IT operations, to keep it functioning properly, and to alert departments of any potential issues that arise.

AIOps

Gartner coined the term “AIOps” in 2017. It has been called “the next big thing in IT operations,” and AIOps might prove to be the rare case when hyperbole actually meets or exceeds reality.

There are at least 10 amazing use cases for AIOps in today’s marketplace:

• “What happened and how’d it get fixed so quickly?”
  Automated Incident Resolution
  AIOps can automate the resolution of common IT incidents, freeing up IT staff to focus on more complex issues.
• “Why did this happen?”
  Event Correlation and Root Cause Analysis
  AIOps can automate the process of identifying and resolving the root cause of IT incidents by analyzing data and correlating multiple events from various sources.
• “What’s happening right now in our systems?”
  Performance Monitoring
  AIOps can provide real-time performance monitoring and analysis of IT systems and applications, helping organizations quickly identify and resolve performance issues.
• “How much volume and data do we have today? How much do we need for the next 12 months?”
  Capacity Planning
  AIOps can provide real-time analysis of IT resource utilization and predict future demands, helping organizations purchase and allocate resources efficiently.
• “What can we prevent from happening?”
  Predictive Analytics
  AIOps can analyze large amounts of data from various sources to provide organizations with predictive insights and recommendations, providing a competitive advantage.
• “How many automated resolutions took place this month?”
  ChatOps
  AIOps can support ChatOps, enabling IT teams to collaborate and resolve incidents in real-time using chat tools such as Slack.
• “What’s the latest with our Azure and AWS apps?”
  Multi-Cloud Monitoring
  AIOps can provide multi-cloud monitoring and management capabilities, enabling organizations to take advantage of optimum applications across multiple cloud environments.
• “Did the deployment complete successfully”
  DevOps Integration
  AIOps can integrate with DevOps processes, enabling organizations to reduce time to value software delivery.

AIOps can also revolutionize the speed of the development teams as they pound through various projects. Developers can move a little faster if they know that the automation team is right behind them, picking up the pieces and empowering the final deployment into production with all the hooks and callbacks that are needed for the artificial intelligence engines to play their part. Obviously, there's a QA process required to ensure that nothing is missed and that everything functions before a developer hands code off to anybody, (typically the AI teams are isolated from the traditional development groups, so specialty leverage can be achieved).

And, it's important that we don't oversimplify here. IT systems have become incredibly complex, and IT departments have to ensure data is being utilized properly, that applications are running effectively, that alerts are being routed correctly, and that the system is, above all else, highly secure.

And the overall benefit? The promise is a high bar. AIOps promises us that it can identify and solve issues as they arise, or before they even materialize. AIOps tells us it can bring order to disorganized IT systems, helping to reduce operational noise. Once documented in workflows, it’s suppose to be able to reveal information instantly, and become a part of the ongoing intelligence rhythms and flows that keep problems proactively at bay. It supposedly can predict how one small change in one area of the business will affect processes throughout an entire IT system. AIOps also fosters collaboration within IT groups, which, ultimately, makes data more valuable.

So what's the catch? Obviously to create something this complex and far-reaching, there’d necessarily be an enormous amount of set up and configuration required. It would be ridiculous to claim that the bots can just become instantly aware, (with complete certainty), of a highly complex and customized information technology footprint. It would be equally ridiculous that a newly introduced software could come face-to-face with old software, (without reading all the source code), would possess knowledge of all of the activities and integrations of the old software.

Robotic Process Automation (RPA)

Welcome to the future. Instead of flying cars, however, let’s go with Excel macros and zipped batch file ftp uploads!

RPA is here and operations folks are thrilled. Pivot tables, tedious monthly calculations, and so many manual steps - all of this can be automated with an RPA bot. “Bots can be used to mimic or emulate selected tasks (transaction steps) within an overall business or IT process,” (Gartner).  Any form of data or files can be modeled and manipulated, ETLs can be devised and revised, and transactions can be triggered and completed. There’s little an RPA bot can’t do. RPA tools operate on the user interface of a system as if a human were pressing the keys,  virtually turning a computer into a functioning human who responds to a set of restrictive instructions. Humans are replaced in an “outside-in” rather than an “inside-out” method. RPAs perform simple, mindless, repetitive employee tasks so workers can work on more complicated and creative tasks that are considerably more valuable.

RPA capabilities are endless. It can help finance companies with contracting issues. For logistics companies, it simplifies order management and supply chain processes. RPA can automate entry forms and medical reports for healthcare companies or insurance providers. RPA can also be used in a sophisticated way to compare and contrast vendor RFP responses, thereby cutting countless hours out of truly tedious and dreary work. It also helps human resource departments manage payroll. Most customer service departments have rules-based processes that RPA can automate. RPA can automatically process invoices, perform sales functions, verify employee data, validate timesheets, and issue checks. The uses for RPA are endless and the savings quite substantial.

Hyperautomation

Where RPA stops, hyperautomation starts. It takes it up to a whole new level. Hyperautomation “deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.),” according to Gartner. It’s RPA on steroids.

According the German software giant SAP, hyperautomation can help retailers and e-commerce firms by enabling them to:

• Implement price matching faster and simpler
• Process returned goods with some or full automation
• Facilitate inter-store transfers
• Reconcile payments and coupons with little or no human involvement
• Distribute daily reports
• Manage credit applications, responses, and advertisements
• Execute store opening/closing checklists
• Assign individual products to stores

For banks, hyperautomation can enable them to:

• Complete application processing
• Facilitate inter-store transfers
• Reconcile payments and coupons
• Generate and distribute daily reports
• Assign individual product quantity to stores
• Manage credit applications, responses
• Work and rework marketing messaging iterations
• Perform store opening/closing checklist

For a sales departments, hyperautomation can help with payment reporting and reconciliation, omni-channel ordering, chatbot communication, and product returns, as well as complaints handling, says SAP. Other benefits include enabling them to:

• Streamline sales processes and workflows
• Enhance customer relationship management
• Improve lead generation and qualification
• Enhance sales forecasting accuracy
• Increase productivity and efficiency
• Improve data accuracy and reduce manual errors.

Taken to its limit, hyperautomation can result in the creation of a digital twin – a digital replica of a living or non-living physical entity – which allows organizations to visualize how their processes, functions, and KPIs interact to create organizational value. A digital twin provides real-time intelligence about an organization, offering substantial business intelligence as well as potentially driving significant business opportunities.

Hyperautomation often attempts to cut humans out of the process, thereby reducing the opportunity for human error. Processes can be made much more efficient as well. Because humans cause most delays, without humans involved, delays can also be cut out as a result. A machine can work 24/7 while a human cannot. And, because there are fewer human resources being needed to complete a business process, it reduces operating costs while simultaneously providing faster and higher quality service.

In the coming years, 20 to 25 percent of all products will be manufactured, packed, shipped, and delivered without a human touch, says Gartner. The person buying the product will be the first person to handle it.  However, hyperautomation will be needed to automate these processes.

The future of hyperautomation promises sophisticated decision-making capabilities, real-time data-driven insights, and increased automation enabled by AI, machine learning, and natural language processing. These advanced technologies will allow businesses to optimize their automated processes and quickly respond to changes in their operating environment. A hyper-efficient future awaits with complex orchestration in hyperautomation.

Leaders in the Space

Hyperautomation leaders are setting the standard in the industry by implementing innovative and advanced technologies to automate various business processes. They are leveraging the benefits of AI, machine learning, and low-code platforms to streamline operations and improve efficiency. By taking advantage of these cutting-edge technologies, hyperautomation leaders are able to improve decision-making processes, reduce costs, increase productivity, and enhance the customer experience. Additionally, they are paving the way in the industry by demonstrating the value of hyperautomation and inspiring others to follow suit. By embracing hyperautomation and continuously innovating, these leaders are positioning themselves at the forefront of the industry and shaping its future direction.

There are a handful of top CEOs and executives that are shining stars in the AIOps space.

1. Dr. Rik Tendulkar - CEO, AppDynamics (Cisco)
2. Dave West - CEO, CA Technologies
3. Sanjay Poonen - CEO, VMware
4. Alok Alström - CEO, Big Panda
5. Abhinav Asthana - CEO and Co-Founder, Postman
6. Rani Osnat - CEO, Sumo Logic
7. Shreyans Mehta - CEO, HCL Software
8. Umang Singh - CEO, ManageEngine (Zoho)
9. Brian White - CEO, Datadog
10. Bhaskar Ravipati - CEO, Dynatrace (Software AG)

Of them, Dr. Rik Tendulkar has provided leadership with his in-depth talks on AIOps. Tendulkar said, ”Digital transformation is driving the need for businesses to have real-time visibility and control of their applications, and the role of AIOps in this process is critical. AIOps helps companies quickly identify and resolve issues before they impact their customers and business operations.”

Interestingly, in their development of a taxonomy for and best processes applicable to the AI life cycle, de Silva and Alakahoon emphasize ethics, trust, and fairness... If nothing else, this certainly underscores the potential power of this process.

There is no reason to fear technology that surpasses human abilities. Technology has always been an extension of human ingenuity and has allowed us to accomplish tasks that were once impossible. The creation of autonomous technology that can operate efficiently and effectively without human intervention is a positive development. It frees up human time and resources, enabling us to focus on more important tasks.

Takeaways

For companies wishing to invest in AIOps Gartner provides seven recommendations:

1. Don’t wait to start
2. Choose initial test cases wisely
3. Develop and demonstrate your proficiency
4. Experiment freely
5. Look beyond IT
6. Standardize where possible, modernize where practical
7. Visualize full adoption

In many sectors, to remain competitive businesses will have to avail themselves of the many advantages of AIOps, RPA, and hyperautomation, which include:

• Increased productivity
• Reduced operating costs
• Cutback in labor hours
• Reduction in errors
• Increased collaboration
• Better use of company data
• Increased revenue
• Quick ROI