Monthly Archives: February 2022

Intelligent automation is the fastest growing area of enterprise tech investment. The potential to boost performance in the typical company with these tools is both broad and deep. But as companies look for places to apply these tools, they often fall into two common traps: chasing quick, easy wins that wont have much impact, or big, ambitious projects that will offer major strategic advantages. What they should focus on, however, is a third option: projects that build their capacity.

Whenever a new wave of technology splashes onto the scene, managers face the same questions: Where do we start applying it first? Do we go after the low-hanging fruit that will produce quick wins and build the case for more ambitious projects? Or should we strategically focus, with no delay, on the applications that will give us a decisive edge over competitors?

Right now, with the arrival of a revolutionary set of technologies for automating knowledge work artificial intelligence in particular we see teams grappling with these questions at high levels in organizations. Intelligent automation (the term commonly used for robotic process automation, machine learning, and artificial intelligence in organizations) brings unprecedented speed, accuracy, and pattern-recognition power to business processes that routinely call for deciphering information, from fielding customers questions to complying with government regulations to detecting fraud and cyberattacks. Because that describes so much of the activity of modern workplaces, the deliberations about where to start and how to proceed are different than with other technologies. The same old answers dont apply.

The potential to boost performance in the typical company with these tools is both broad and deep. In one company we know, a team was assembled to survey all of its operations, find areas where peoples time was being consumed by repetitive information-processing work, and come back with candidate tasks for automation. The list stretched to hundreds of things a smart machine could do to leverage workers creativity, increase speed to decision, improve accuracy, or enhance service to customers.

There are also strong competitive incentives: Because of this potential, companies are investing in these tools at blistering rates according to Gartner, intelligent automation is the fastest growing area of enterprise tech investment. The pandemic gave the toolkit a giant shove forward as companies suddenly had to find new ways to perform mission-critical processes.

Whether driven by the opportunities or competitive pressure, your organization will likely soon be using intelligent automation in many, many corners of your operations. So, where should you start?

Instead of framing your goals in terms of quick victories (which wont really move the needle) or major strategic applications (which require skills and foundations you dont yet have in place), focus on how your first steps will advance capability-building in your organization. You should sequence the projects you take on knowing you will ultimately take on hundreds so that the early ones build the AI talents and put in place the AI tech infrastructure for the projects you will take on next, and next, and next.

Capability-building developing the strength of an organization to solve a class of problems it will keep facing in the future is a challenge you might have tackled in other realms. In areas from strategy formulation to project management, teams recognize that they can and must get better by learning from experience. And because there are fundamentals that must be mastered before they can advance to higher-order capabilities they have to walk before they can run teams often take their guidance from so-called maturity models, outlined by experts who have watched others travel the same path before. Given that your people will need to rise again and again to the challenge of implementing intelligent automation solutions, this is the approach that makes sense, but more of the thinking about the best sequence of steps will be up to you.

Planning this journey requires mapping out how your team or organization will deliberately move from a state of being a novice to being an expert.

The first step is usually an assessment of existing capabilities: the challenges your people already know how to tackle and the sophistication of the tools they have to solve them. Perhaps you already have strong data analytics skills on staff, for example, or people who have been involved in RPA installations elsewhere.

Your next step is a gap analysis. This details the difference between your current capabilities and the demands of the most challenging solution you can envision taking on. This might reveal that your current IT infrastructure is simply not equal to a coming wave of applications that will need to interact with disparate data sources. Or that much more effective collaboration will be needed between software developers and business process owners than has been seen in the past.

Finally, with the beginning and end states clearly articulated, you can then specify a step-by-step journey, with projects sequenced according to which ones can do the most in early days to lay essential foundations for later initiatives.

Heres an example to illustrate how this approach can lead to better choices. At a construction equipment manufacturer, there are three tempting areas to automate. One is the solution a vendor is offering: a chatbot tool that can be fairly simply implemented in the internal IT help desk with immediate impact on wait times and headcount. A second possibility is in finance, where sales forecasting could be enhanced by predictive modeling boosted by AI pattern recognition. The third idea is a big one: if the company could use intelligent automation to create a connected equipment environment on customer job sites, its business model could shift to new revenue streams from digital services such as monitoring and controlling machinery remotely.

If youre going for a relatively easy implementation and fast ROI, the first option is a no-brainer. If instead youre looking for big publicity for your organizations bold new vision, the third ones the ticket. You can set up a tiger team or separate organization and give it full license to disrupt the existing business. But note that neither of those approaches really prepares the ground for intelligent automation to spread to other applications by the existing organization; they dont make the people of your organization generally more interested, receptive, or able to apply intelligent technology elsewhere. In other words, as an organization, taking these routes doesnt take you far up the learning curve, toward greater maturity with the technology.

This is what option two would do in large part because it would demand that the company get its act together on data. Without a good enterprise data strategy, people in different parts of the organization lack common standards regarding what data needs to be gathered and how it should be organized, cleaned, and prepped for analysis. This is a foundational capability that the company will need to have in place to make headway in using machine learning at scale. From the standpoint of capability building, it is easy to see how progress on enterprise data would unlock, say, 10 other projects which in turn can be prioritized by the further capabilities they could add. Our manufacturing company could lay out a roadmap showing how, five years later, it will not only be reaping the returns of the specific projects, but also be generally and profoundly more ready to take on truly transformative initiatives.

Fifty years ago, when the legendary Peter Drucker coined the term knowledge workers, he also recognized how their rise in the global economy would challenge organizations. The most important contribution management needs to make in the twenty-first century, he wrote, is to increase the productivity of knowledge work. Finally, in intelligent automation, a powerful toolkit exists for doing that and the race is on. Avoid the mad dash that has your organization chasing possibilities but with no collective progress. Choose your spots wisely, and your investment in intelligent automation can be a capability-building journey.

Continue reading here:

How to Pick the Right Automation Project - Harvard Business Review

February 9, 2022 Sam Langrock

Few topics spark conversation like security automation. Automation is the entire premise around programming; routines and repetitive patterns are tasked to computers while humans work only on higher priorities. For security practitioners, this is essential because even a small network can have thousands of endpoints that need protecting while the security staff is miniscule. Yet the challenge facing organizations in 2022 is how to automate, not just the collation and data collection tasks where machines excel, but to automate the repetitive human decisions made daily to defend an enterprise. Join us for a three part blog series on automation and for a webinar on February 22nd titled, Fight Ransomware Robots With Automation Intelligence.

Ransomware gangs and security practitioners battle each other similar to how a baseball pitcher and hitter would duke it out. In this game dark web criminal actors focus on causing incidents, while security automation focuses on incident response. To increase their velocity and volume of attacks ransomware gangs are leveraging automation throughout their attack cycle. To keep up, security practitioners have turned to intelligence-led automation, which enables businesses to defend at scale with the speed necessary to make contact on every pitch. Much like baseball, in the cyber world there can be no ties. Intelligence provides the upper hand.

To help security practitioners gain an advantage, Recorded Futures Insikt Group reported on automation in the criminal underground. In their report, Insikt Group identified 10 key strategies ransomware criminals use automation to enable their attacks.

Hacked and compiled databases are sold on underground forums. These databases, often consisting of user credentials, give threat actors access to accounts and credentials of clients and employees. Once threat actors have access to these user-level accounts, actors can use leverage techniques, such as local privilege escalation vulnerabilities, which can be used to gain further access to internal systems or to commit fraud.

Stolen credentials from automated marketplaces need validation in order to ensure they will function as the criminals expect. Tools such as checkers can help threat actors to quickly and efficiently validate or access passwords for thousands of accounts. Brute-forcers are tools which automatically cycle through thousands of passwords a second in order to defeat systems with unlimited login attempts.

Loaders and crypters are tools which allow threat actors to obfuscate and deliver malicious payloads, bypassing antivirus solutions.

Stealers and keyloggers enable threat actors to gather sensitive information from victim systems, including credentials, personally identifiable information (PII), payment card information, and other data.

Threat actors use banking injects as fake overlays over legitimate sites to financial institutions and similar sites where they can collect sensitive information from victims trying to visit the legitimate site.

Exploit kits allow threat actors to use multiple exploits simultaneously to target various vulnerabilities across different targets.

Threat actors gain access to hundreds of thousands of potential victims for their lures with spam and phishing services.

Bulletproof hosting services (BPHS) provide secure hosting for malicious content and activity, and assure anonymity to threat actors.

Sniffers infiltrate legitimate online shopping sites and collect sensitive information such as payment cards and the PII of customers from trusted online stores.

Automated marketplaces and logs vendors allow threat actors to sell stolen credentials and digital fingerprints to other threat actors, who use them for fraud or to facilitate further breaches, frequently circumventing anti-fraud measures.

Bad actors are well acquainted with subverting defensive automated technology. For example, they might craft malicious code to appear normal to automated scans, such as antivirus applications. Security teams with careful monitoring and logging established can create rules to detect these seemingly-normal patterns and behaviors for the malicious files they are. However, threat actors can quickly take action, such as rotating their infrastructure, to get around being blocked. This means rules must be manually generated for each new iteration of malware, leading to a security treadmill where efficiencies are lost to an endless cycle of detection/patching new malware.

Step off the treadmill with Intelligence. Intelligence gives your team a cheat code, enabling them to pull rules already tested to identify and mitigate ransomware attacks from doing damage.

Join us for a webinar on February 22nd titled, Fight Ransomware Robots With Automation Intelligence to learn more about how automation can assist your organization.

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How Ransomware Gangs Use Automation, and How You Can Beat It - Recorded Future

The selection and development of test scenarios is one of the most critical components of any software testing strategy. Test scenarios describe what components, functions, interfaces or other pieces of the application a team will test. In addition, they will generate the foundation for the documentation that surrounds those testing procedures.

In this article, we'll examine the technical details behind test scenarios, including their direct connection with test cases, how to develop effective test scenarios that accelerate QA processes and -- perhaps most importantly -- the enormous role they play in determining which tests are sensible targets for automation.

We'll also review which team members should be involved in these types of decisions, as well as a technique known as the test automation pyramid.

While test scenarios and test cases are often discussed in conjunction with one another, and sometimes thought of as synonymous, it's important to remember that they each play a distinct role in both manual and automated testing efforts.

While the test cases form the coding structure QA teams will use to create automated test scripts, the test scenarios will help those teams determine which specific tests to automate.

Test scenarios are the high-level documentation of what QA professionals will test. They are developed according to business and systems requirements, and describe the functionality and how the user will execute the function.

Test cases, meanwhile, are specifications that provide a detailed, step-by-step documentation of application testing procedures, including a list of expected test results. QA professionals can execute test cases manually or develop them into automated scripts.

The selection and development of test scenarios is a critical component of any software testing strategy, and even more important in automated testing. While the test cases form the coding structure QA teams will use to create automated test scripts, the test scenarios will help those teams determine which specific tests to automate.

Typically, teams will assemble test scenarios using a combination of user stories and user acceptance criteria. However, test scenarios can also stem from personas, which act as an overarching profile of expected user behaviors and anticipated needs. Well-developed personas can help teams create test scenarios that focus on the features they think will be most important to the vast majority of users.

Behavior-driven development (BDD) can be another productive approach. BDD loops the business side into the development process, but still provides focus on anticipated user behavior. For instance, the Gherkin syntax implements the Given-When-Then formula using a plain, easy-to-read language that both software-side and business-side personnel can understand. With the support of the Cucumber BDD testing framework, Gherkin then facilitates the development of automated test scripts directly from the test scenarios it contains.

Once a team identifies their test scenarios, it's time to create an actual testing strategy. This involves determining the specific scenarios the QA team will test manually versus ones they will slate for automated testing. To make effective decisions, this work needs to be done early in the development process.

During this step, it's critical to identify the domain under which the testing will take place. For example, certain regulatory requirements can determine the types of test scenarios the team selects and executes, including the level of detail those testing procedures and corresponding documentation encapsulate. Another relevant factor might be the methodologies currently in use, as well as the general maturity of those methodologies.

While automation has increasingly become a critical part of today's test strategies, not every test scenario will be an appropriate target for automation. Even when merging an automated continuous testing process into a CI/CD pipeline, QA teams should still execute things such as UX-focused GUI tests manually.

However, those teams should choose manual test scenarios carefully. To help clarify this, here are some examples of test scenarios that are appropriate for manual testing:

Alternatively, test scenarios merit automation when critical features require repetitive testing and are simply too hard for teams to handle manually. For instance, tests that involve large volumes of data or complex data entry procedures often fit the bill for automation. And while test cases that run over long periods of time can be complex to automate, it's a labor investment that will significantly accelerate QA processes down the line.

Choosing test scenarios for automation should be a team effort that includes test leads, automation specialists, SDETs (software development engineers in test), developers and product owners. The product owners are especially important to loop in here, as they are uniquely qualified to point out scenarios that most accurately simulate the real-world user workflow. Developers also carry a lot of weight here, as they can highlight the scenarios that require testing on complex collections of code.

One technique that can be extremely helpful in the manual vs. automated testing decision is the test automation pyramid.

To start, the test automation pyramid dictates that automation should primarily reside at the unit and component test level, as these tests are typically the least complex and easiest to automate.

In addition, a high level of unit test coverage can help teams detect defects and implement remedial fixes in the early stages of the development process.

Meanwhile, GUI-level tests reside at the top of the test automation pyramid. Tests performed on functional components like a GUI are usually the most difficult to maintain; therefore, it's best to keep automated GUI tests to a minimum, provided that those targets for automation aren't particularly complex components.

Read more:

The crucial role of test scenarios, especially in automation - TechTarget

Years Later: Research Shows Employee Opinion on Automation

Researchers were able to determine how warehouse employees really feel about their automated coworkers.

There are over 1.5 million employees working in warehouses across the country and some have coworkers of a robotic nature. Recently, a team of researchers set out to seek the opinions of those who have worked closely with automation technology to gain a better understanding of how it has begun to settle into everyday work.

According to a Harvard Business Review article, Joe Lui, Raghav Narsalay, Rushda Afzal, Ida Nair Sharma and Dave Light wrote about interviews conducted with over 65 employees who worked in a warehouse facility. The video interviews were a peek behind the curtain into the $15 billion AI technology market, which is currently set to double within the next four years.

The researchers asked questions based on the duties of the employee. For warehouse workers the following questions were asked:

For those employees who were front-line supervisors, the researchers asked the following:

Researchers found, though data analysis that extracted key themes from responses, that the response was about 40 percent negative and 60 percent positive when it came to automation in the workplace.

Those who felt negatively were afraid to lose the jobs, worried about having inadequate access to training resources, and dealing with downtime or errors caused by technology malfunctions. Of those that responded positively, worker were optimistic that the automation was actually helping them to complete their duties in a more safe manner, was increasing productivity and improving the quality of their work.

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Years Later: Research Shows Employee Opinion on Automation - Occupational Health and Safety

These intelligent automation jobs are specially for professionals and aspiring tech enthusiasts

Automation has enhanced how enterprises work nowadays with artificial intelligence to compete and survive in the years ahead. The integration of intelligent automation in business enterprises can help business managers automate the most mundane tasks with minimum errors, incorporate advanced AI services to understand customer demands and avoid complexities that can become long-term hazards to these businesses. Most companies are trying to hire skilled professionals who can leverage the advancements in automation technology, incorporate it in between the systems and prevent the duplication of data in the systems. In this article, we have listed such top intelligent automation jobs available right now in February 2022, for tech professionals who are trying to enhance their career growth and for those aspirants who wish to try their luck in the intelligent automation domain.

Offered by: Mutual of Omaha

Location: Remote

Responsibilities

Skills Required

Offered by: Deloitte

Location: Arlington, TX

Responsibilities

Skills Required

Offered by: Price WaterHouse Coopers

Location: Remote

Responsibilities

Skills Required

Offered by: Ameriprise Financial

Location: Boston, MA

Responsibilities:

Skills Required:

Offered by: Significance, Inc.

Location: Washington, USA

Responsibilities:

Skills Required:

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Top Intelligent Automation Jobs to Apply For in February 2022 - Analytics Insight

The surging adoption of business process management, rising demand for automation in enterprises, and low-cost of digital process automation platform will offer immense growth opportunities.The low-code integration is another factor supporting the digital process automation market share growth.Organizations in many verticals are constantly looking for new sectors or verticals through which they might lower their total operational costs.Low-code development is a general term that includes process development solutions, low-code application development, and software development tools. Low-code development platforms enable IT to quickly assemble new processes and build applications without having to research, write and test new scripts.

However, the data security and privacy issues will be a major challenge for the digital process automation market during the forecast period.Companies are increasingly concerned about sharing sensitive business data, and this issue is growing in the market. Malware, hacker attacks, and data theft are all important security dangers to the reliable execution of business processes.Although automation can be a huge assistance to businesses, it can also be dangerous if it is misused, neglected, or overused.

View Report Outlookfor more factors influencing the market growth.

Digital Process Automation Market2022-2026: Segmentation Analysis

Learn more about the other contributing segments and country-wise market share,Download FREE sample Report

Digital Process Automation Market 2022-2026: Vendor Analysis and Scope

The digital process automation market is fragmented and the vendors are deploying organic and inorganic growth strategies to compete in the market. To leverage the current opportunities, market vendors must strengthen their foothold in the fast-growing segments while maintaining their positions in the slow-growing segments.

Some of the vendors and their key offeringscovered in the report are:

Some other dominant players covered in this report are:

The report also covers the following areas:

Digital Process Automation Market 2022-2026: Key Highlights

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Digital Process Automation Market Scope

Report Coverage

Details

Page number

120

Base year

2021

Forecast period

2022-2026

Growth momentum & CAGR

Accelerate at a CAGR of 15.46%

Market growth 2022-2026

$ 8.08 billion

Market structure

Fragmented

YoY growth (%)

14.87

Regional analysis

North America, Europe, APAC, MEA, and South America

Performing market contribution

North America at 51%

Key consumer countries

US, UK, Canada, Japan, and Germany

Competitive landscape

Leading companies, competitive strategies, consumer engagement scope

Companies profiled

Appian Corp., Bizagi Group Ltd., BP Logix Inc., Infosys Ltd., Innov8tif Solutions Sdn Bhd, International Business Machines Corp., Newgen Software Technologies Ltd., Open Text Corp., Oracle Corp., and Pegasystems Inc.

Market Dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and future consumer dynamics, market condition analysis for the forecast period,

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Key Topics Covered

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by Deployment

Customer landscape

Geographic Landscape

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contact

Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: [emailprotected]Website: http://www.technavio.com/

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USD 8.08 billion growth in Digital Process Automation Market| Evolving Opportunities with Appian Corp. and Bizagi Group Ltd. |17,000+ Technavio...