Category Archives: Automation

by Sharon Florentine Sep 2, 2021

Robotic process automation (RPA) solutions company Automation Anywhere has announced channel executive Ben Yerushalmi has joined the company as its senior vice president of global alliances and channels.

Yerushalmi joins Automation Anywhere from Salesforce where he spent the last seven years building and leading alliance teams globally, according to a statement released by the company. His expertise includes driving partner and program strategy, go-to-market initiatives, demand generation and enablement programs designed to grow partners capability, capacity and competency in the channel with a focus on new product launches and strategic acquisitions, according to the statement.

At Salesforce, Ben most recently led the Global Digital 360 Cloud Alliances team focused on products and solutions driving digital transformation. He also served as the board observer for two of Salesforces strategic investment portfolio companies, led the global PwC alliance, managed Salesforces AMER Portfolio PAM team and built the companys Strategic Marketing Cloud Alliances Program, according to the statement.

Prior to Salesforce, Yerushalmi built and led a variety of teams at Oracle and Siebel, most recently in Oracles Global System Integrator (SI) Marketing Cloud Alliances business and Oracles CRM On-Demand Partner Program.

Ben Yerushalmi, SVP global alliances and channels, Automation Anywhere

Chris Riley, chief revenue officer, Automation Anywhere

Chris Riley, chief revenue officer at Automation Anywhere, commented on Yerushalmis appointment:

Bens leadership in the channel will help transform our global partner program to fuel worldwide expansion, build industry-focused automation solutions, while at the same time, enable our customers to accelerate their shift to cloud RPA to increase business resiliency. Bens veteran experience will also help supercharge our partner ecosystem, capacity, and solutions as we enter our next phase of growth.

Yerushalmi said:

The partner ecosystem is critical to enabling our customers to adopt and scale intelligent automation, freeing up knowledge workers to focus on innovation and ultimately improving and transforming the way we work. Automation Anywhere is leading this journey with the worlds only cloud-native, AI-powered RPA platform and I look forward to helping our partners and customers uncover the power of RPA to unleash human potential.

RPA software allows businesses to write code and bots that rapidly automate manual tasks across multiple departments from IT service desks to HR, finance, customer support and more.

The global RPA software market is expected to reach $13.74 billion by 2028, up from $1.57 billion in 2020. The markets compound annual growth rate from 2021 to 2028 is expected to be 32.8%, Grand View Research predicts.

Much of the RPA software industry focuses on enterprise customers. Still, RPA may even eventually catch on in the SMB market as MSPs seek to further automate internal and customer operations.

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Ben Yerushalmi Joins Automation Anywhere as SVP, Global Alliances and Channels - ChannelE2E

HOBOKEN, N.J.--(BUSINESS WIRE)--NICE (Nasdaq: NICE) today announced the latest edition of its robotic process automation (RPA) solution with advanced AI capabilities that fast-track achieving organizational and employee goals, The innovative features enable organizations to benefit even more from smarter process recommendations that are deeply customized to support their objectives. Real-time behavioral guidance enables NEVA (NICE Employee Virtual Attendant) to guide agents towards more rapidly increasing first contact resolution (FCR) and customer satisfaction.

NICE RPAs version 7.5 extends support across all phases of the automation lifecycle - from discovery to deployment and driving continuous improvements beyond. Expanded capabilities include:

Barry Cooper, President, NICE Workforce & Customer Experience Group, said, With the announcement of RPA 7.5, we bring to the CX industry smarter automation discovery made possible by our rapid AI capability advancements. Leveraging this unique AI, process automations are more tightly aligned with company needs allowing them to guide agents across each customers unique situation in the moment that matters and in a way that becomes personalized. This transforms interactions into extraordinary service experiences supporting critical business priorities, improving the bottom line.

About NICEWith NICE (Nasdaq: NICE), its never been easier for organizations of all sizes around the globe to create extraordinary customer experiences while meeting key business metrics. Featuring the worlds #1 cloud native customer experience platform, CXone, NICE is a worldwide leader in AI-powered contact center software. Over 25,000 organizations in more than 150 countries, including over 85 of the Fortune 100 companies, partner with NICE to transform - and elevate - every customer interaction. http://www.nice.com.

Trademark Note: NICE and the NICE logo are trademarks or registered trademarks of NICE Ltd. All other marks are trademarks of their respective owners. For a full list of NICEs marks, please see: http://www.nice.com/nice-trademarks.

Forward-Looking StatementsThis press release contains forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995. Such forward-looking statements, including the statements by Mr. Cooper, are based on the current beliefs, expectations and assumptions of the management of NICE Ltd. (the Company). In some cases, such forward-looking statements can be identified by terms such as believe, expect, seek, may, will, intend, should, project, anticipate, plan, estimate, or similar words. Forward-looking statements are subject to a number of risks and uncertainties that could cause the actual results or performance of the Company to differ materially from those described herein, including but not limited to the impact of changes in economic and business conditions, including as a result of the COVID-19 pandemic; competition; successful execution of the Companys growth strategy; success and growth of the Companys cloud Software-as-a-Service business; changes in technology and market requirements; decline in demand for the Company's products; inability to timely develop and introduce new technologies, products and applications; difficulties or delays in absorbing and integrating acquired operations, products, technologies and personnel; loss of market share; an inability to maintain certain marketing and distribution arrangements; the Companys dependency on third-party cloud computing platform providers, hosting facilities and service partners;, cyber security attacks or other security breaches against the Company; the effect of newly enacted or modified laws, regulation or standards on the Company and our products and various other factors and uncertainties discussed in our filings with the U.S. Securities and Exchange Commission (the SEC). For a more detailed description of the risk factors and uncertainties affecting the company, refer to the Company's reports filed from time to time with the SEC, including the Companys Annual Report on Form 20-F. The forward-looking statements contained in this press release are made as of the date of this press release, and the Company undertakes no obligation to update or revise them, except as required by law.

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NICE Robotic Process Automation Fuels Smarter Automation Discovery and Hyper-Personalized CX With Tailored AI Capabilities - Business Wire

DUBLIN--(BUSINESS WIRE)--The "Global Automation Market in Textile Industry 2021-2025" report has been added to ResearchAndMarkets.com's offering.

The publisher has been monitoring the automation market in textile industry and it is poised to grow by $405.99 million during 2021-2025, progressing at a CAGR of over 2% during the forecast period.

This report on the automation market in textile industry provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.

The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by the increasing demand for control devices and applications and favorable government policies.

The automation market in textile industry analysis includes the solution and component segments and geographic landscape. This study identifies technological developments as one of the prime reasons driving the automation market in textile industry growth during the next few years.

Companies Mentioned

The report on automation market in textile industry covers the following areas:

The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.

The publisher presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. The market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.

Key Topics Covered:

1. Executive Summary

2. Market Landscape

3. Market Sizing

4. Five Forces Analysis

5. Market Segmentation by Solution

6. Market Segmentation by Component

7. Customer landscape

8. Geographic Landscape

9. Vendor Landscape

10. Vendor Analysis

11. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/naev1j

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Global Automation Market in Textile Industry (2021 to 2025) - Featuring ABB, ATE Private and Danfoss Among Others - ResearchAndMarkets.com - Business...

SANTA CLARA, Calif., Sept. 02, 2021 (GLOBE NEWSWIRE) -- Ushur, the leader in AI-powered Customer Experience Automation (CXA), today announced the launch of Ushur AI Lab, a research group dedicated to tackling some of the most pressing issues in natural language processing (NLP), document processing and no-code conversational intelligence. The AI Lab aims to develop innovations that will make it even easier for enterprises to deliver automated, omni-channel, hyper-personalized customer experiences.

Over the last few years, Ushurs AI and machine learning (ML) team has developed key breakthroughs already integrated into Ushurs Customer Experience Automation platform. These include NLP and ML components for conversational tasks, document processing engines and a feature-rich, no-code MLOperations platform. Ushur has filed multiple patents in representation learning for documents, information retrieval from unstructured data, forms processing and tabular information extraction areas.

Customers benefit from these innovations every day when they use Ushur to train and deploy state-of-the-art NLP models. Ushurs no-code AI technology gives business users everything they need to build customer experience automation workflows: data anonymization and privacy protection tools, pre-trained language models, domain-specific datasets and more.

Mike Simmonds, COO at Unum, has said: The ability to rapidly prototype and automate customer journeys has provided Unum with a competitive edge, delighting our customers while allowing our employees to focus on higher-value work.

Now, with the launch of Ushur AI Lab, the company is formalizing its effort to pursue more challenging frontiers in knowledge processing. Positioned with real-world insights from its years of building accessible AI products for the customer experience, the group will aspire to solve industry problems that can only be approached with expert application of artificial intelligence. The AI Labs charter will include collaborative efforts with the research and academic community, including participation in top AI/ML conferences and tech talks and a dedicated blog for sharing insights. The research group will also incorporate an open-source program to share relevant models, tools and datasets with the larger ML ecosystem.

Ushur has appointed Vijayendra (Viju) Shamanna to lead the initiative. Previously Ushurs Senior Director of Engineering and Data Science, Shamanna has led all of the companys key ML innovations over the past three years.

Shamanna brings almost twenty-five years of experience in software and systems engineering, much of it spent in leadership positions spanning domains such as data center infrastructure, cloud-native applications and applied machine learning. Prior to Ushur, Shamanna bootstrapped the India engineering team for Vexata, a disruptive enterprise storage startup acquired by StorCentric. At Sandisk India, he led the emerging systems group that developed hyper-scale, disaggregated flash storage systems optimized for big data.

I believe that larger trends in ML research, such as self-supervised learning (SSL), multi-modal and representational learning, have the potential to deliver on the cornerstones of intelligent automation: Straight-through processing (STP) and hyper-personalization. Thats just the beginning of what Ushur AI Lab will explore, said Shamanna.

The AI Lab will serve as the connective tissue between fundamental and applied research to usher in the transformative power of AI for knowledge automation. AI-powered experiences are the way of the future, and I encourage ML scientists and data engineers who want to mobilize a category-defining company to join our efforts.

Ushur AI Lab represents our promise to continue creating scalable and accessible AI products as we grow from being a small startup to a customer experience solution provider with a global presence, said Simha Sadasiva, Ushur CEO and Co-founder. Weve seen real traction around innovations that have come out of Ushur AI Lab, such as our data intake and data linking technology. Our customers have learned that they can depend on us for solutions guided by the latest academic insights, but designed for the enterprise.

About Ushur

Ushur delivers the worlds first AI-powered Customer Experience Automation platform that has been purpose-built, from the ground up, to intelligently automate entire customer journeys, end to end. Designed to deliver delightful, hyper-personalized customer experiences through rapid issue resolution and unified, omnichannel engagement, Ushur is the first-of-its-kind system of intelligence. It combines Conversational Automation and Knowledge Work Automation in a No-Code, Cloud-native, SaaS platform to digitally transform every step of the complete enterprise customer experience from Micro-engagements to entire customer journeys. Backed by leading investors including Third Point Ventures, 8VC, Pentland Ventures, Aflac Ventures, and Iron Pillar, Ushurs Customer Experience Automation() solutions are currently in production at some of the leading insurance providers across the globe including Irish Life, Unum, Aetna, Cigna, and Tower Insurance.

Media contact

Theresa Carper

ushur@firebrand.marketing

415 848 9175

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Ushur Forms AI Research Group to Power the Future of Customer Experience Automation - GlobeNewswire

Foretellix, a platform provider automating the testing, verification and validation for automated driving systems (ADAS and AV), has secured $32 million in its latest series B funding round, bringing its total raised capital to more than $50 million. The round was led by MoreTech Ventures, with participation from several strategic investors, including Volvo Group Venture Capital, Nationwide, NI and Japan-Israel High Tech Ventures.

In addition, all series A investors, including 83North Ventures, Jump Capital, OurCrowd and NextGear participated in this new investment round.

Foretellix was founded in 2018 by a team of verification and validation pioneers with a mission to make automated driving systems safe and efficient. Foretellix uses a quantifiable approach to safety and hyper-automation to create and test all possible scenarios these systems may encounter, along with big data analytics to ensure the safety and completeness of the testing processes. This advanced platform is used throughout the development cycle, from the requirements stage through product development, verification and validation.

Foretellix said that it is experiencing a rapid increase in demand for its platform from leading OEMs and Tier1s developing ADAS and AV products, both on and off-road. Foretellix is now commercially engaged with dozens of the largest names in the automotive industry, including Volvo Group and DENSO Corporation.

We look for start-ups that are building the future with technological developments that will transform the transport industry. We believe that with Foretellixs advanced test automation tools and expertise, we can deploy current and future ADS. We have the same clear goal to infuse automation and metrics into the verification and validation process with open standards. The investment and ongoing partnership is a base for increased safety of our leading-edge automated driving systems. We are impressed by Foretellix and we believe that together we can add considerable value to the development of the business in the future.

Martin Witt, VP and Head of Volvo Group Venture Capital

In addition to the Volvo Group Venture Capital investment, Volvo Autonomous Solutions formed a closer partnership with Foretellix earlier this year with the aim of jointly creating a coverage-driven verification solution for autonomous driving that operates both on public roads and in restricted areas.

Volvo Autonomous Solutions and Foretellix entered into a partnership in March this year. We clearly understand the ongoing progress and the benefits of working with the Foretellix team and their verification platform. Based on our experience, Volvo decided to invest in Foretellix as well.

Nils Jaeger, President and head of Volvo Autonomous Solutions

The role of Volvo Group Venture Capital is to make investments that drive transformation by facilitating the creation of new services and solutions and to support collaborations between start-ups and the Volvo Group.

Against the background of the trends shaping the future of transportation and the strategic priorities of the Volvo Group, the key areas of investment for Volvo Group Venture Capital are logistics services, site solutions and electrical infrastructure. The organisation has a global scope and focuses on Europe and North America.

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Foretellix closes $32M Series B round; testing and verification of automated driving systems - Green Car Congress

Five trends complicating industrial automation cybersecurity and how a standards-based approach is the solution | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Five trends complicating industrial automation cybersecurity and how a standards-based approach is the solution - Security Magazine

The accelerating pace of global change is driving a biodiversity extinction crisis (1) and is outstripping our ability to track, monitor, and understand ecosystems, which is traditionally the job of ecologists. Ecological research is an intensive, field-based enterprise that relies on the skills of trained observers. This process is both time-consuming and expensive, thus limiting the resolution and extent of our knowledge of the natural world. Although technology will never replace the intuition and breadth of skills of the experienced naturalist (2), ecologists cannot ignore the potential to greatly expand the scale of our studies through automation. The capacity to automate biodiversity sampling is being driven by three ongoing technological developments: the commoditization of small, low-power computing devices; advances in wireless communications; and an explosion in automated data-recognition algorithms in the field of machine learning. Automated data collection and machine learning are set to revolutionize in situ studies of natural systems.

Automation has swept across all human endeavors over recent decades, and science is no exception. The extent of ecological observation has traditionally been limited by the costs of manual data collection. We envision a future in which data from field studies are augmented with continuous, fine-scale, remotely sensed data recording the presence, behavior, and other properties of individual organisms. As automation drives down costs of these networks, there will not be a simple expansion of the quantity of data. Rather, the potential high resolution and broad extent of these data will lead to qualitatively new findings and will result in new discoveries about the natural world that will enable ecologists to better predict and manage changing ecosystems (3). This will be especially true as different types of sensing networks, including mobile elements such as drones, are connected together to provide a rich, multidimensional view of nature. Given the role that biodiversity plays in lending resilience to the ecosystems on which humans depend (4), monitoring the distribution and abundance of species along with climate and other variables is a critical need in developing ecological hypotheses and for adapting to emerging global challenges.

Ecosystems are alive with sound and motion that can be captured with audio and video sensors. Rapid advances in audio and video classification algorithms can allow the recognition of species and labeling of complex traits and behaviors, which were traditionally the domain of manual species identification by experts. The major advance has been the discovery of deep convolutional neural networks (5). These algorithms extract fundamental aspects of contrast and shape in a manner analogous to how we and other animals recognize objects in our visual field. Applied to audio signals, these neural networks are highly effective at classifying natural and anthropogenic sounds (6). A canonical example is the classification of bird songs. Other acoustic examples include insects, amphibians, and disturbance indicators such as chainsaws. Naturally, these algorithms also lend themselves to species identification from images and videos. In cases of animals displaying complex color patterns, individuals may be distinguished, allowing minimally invasive mark recapture, an important tool in population studies and conservation (7). Beyond sight and sound, sensors can target a wide range of physical, chemical, and biological phenomena. Particularly intriguing is the possibility for widespread environmental sensing of biomolecular compounds that could, for example, allow quantification of DNA-scapes by means of laboratory-on-a-chiptype sensors (8).

Several technological trends are shaping the emergence of large-scale sensor networks. One is the ongoing miniaturization of technology, allowing deployment of extended arrays of low-power sensor devices across landscapes [for example, (9)]. In many cases, these can be solar-powered in remote locations. The widespread availability of computer-on-a-chip devices along with various attached sensors is enabling the construction of large distributed sensing networks at price points that were formerly unattainable. Similarly, the ubiquitous availability of cloud-based computing and storage for back-end processing is facilitating large-scale deployments.

Small, ruggedized sensors, such as this passive acoustic recorder, enable remote monitoring of biodiversity. New technologies are enabling such devices to process data and transmit information via wireless networks.

Another trend is advancements in wireless communications. For example, the emerging internet of things (10) enables low-power devices to establish ad hoc mesh networks that can pass information from node to node, eventually reaching points of aggregation and analysis. The same technology used to connect smart doorbells and lightbulbs can be leveraged to move data across sensor networks distributed across a landscape. These protocols are designed for low power consumption but may not have sufficient bandwidth for all applications. An alternative, although more power hungry, is cellular technology, which has increasing coverage globally. In remote locations, where commercial cellular data services may not be available, researchers can consider a private cellular network for on-site telemetry and satellite uplinks for internet streaming. However, in the near term, telecommunications costs and per-device power requirements may nonetheless prove prohibitive in certain high-bandwidth applications, such as video and audio streaming. An alternative for sites where communications bandwidth is limited by cost, isolation, or power constraints is edge computing (11). In this design, computation is moved to the sensing devices themselves, which then transmit filtered or classified results for analysis, greatly reducing transmission requirements.

One more trend is the advancement of machine-learning methods (12) that can classify and extract patterns from data streams. Much of this technology has been commoditized through intensive development efforts in the technology sector that have resulted in widely available software libraries usable by nonexperts. The aforementioned convolutional neural networks can be coded both to segment data into units and to label these units with appropriate classes. The major bottleneck is in training classifiers because initial training inputs must be labeled manually by experts. Although labeled training sets exist in some domainsmost notably, image recognitionfuture analysts may be able to skip much of the training step as large collections of pretrained networks become available. These pretrained networks can be combined and modified for specific tasks without the requirement of comprehensive training sets. Of particular interest from the standpoint of automation are new developments in continual learning (13), in which networks adjust in response to changing inputs. This holds the promise of automating model adaptation for detecting emerging phenomena, such as species shifting their ranges in response to climate change or other shifts in ecosystem properties.

Ecologists could leverage these developments to create automated sensing networks at scales previously unimaginable. As an example, consider the North American Breeding Bird Survey, a highly successful citizen-science initiative running since the late 1960s with continental-scale coverage. Expert observers conduct point counts of birds along routes, generating data that have proved invaluable in tracking trends in songbird populations (14). Although we hope to see such efforts continue, imagine what could be learned if, instead of sampling these communities once per year, a long-term, continental-scale songbird observatory could be constructed to record and classify bird vocalizations in nearreal time along with environmental covariates. Similar networks could use camera traps or video streams to reveal details of diurnal and seasonal variation across diverse floras and faunas. As with all sampling methods, sensing networks will not be without biases in sensitivity and discrimination, yet they hold the extraordinary promise of regional sampling of biodiversity at the organismal scale, something that has proven difficult, for example, by using traditional satellite-based remote sensing. These efforts would complement ongoing development of continental-scale observatories in ecology [for example, (15)] by increasing the spatial and temporal resolution of sampling.

Acknowledgments: Our perspective on autonomous sensing was developed with the support of the Stengl-Wyer Endowment and the Office of the Vice President for Research Bridging Barriers programs at the University of Texas at Austin, and the National Science Foundation (BCS-2009669). Comments from members of the Keitt laboratory, Planet Texas 2050, A. Wolf, and M. Abelson were invaluable in refining our ideas.

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Ecology in the age of automation - Science Magazine

INDUSTRY INSIGHT

As this fiscal year wraps up, many agencies are planning their response to compliance reporting requirements. Meeting these requirements -- particularly in advance of an audit -- can be incredibly time-consuming. While the Defense Department has made managing risk easier through Security Technical Implementation Guides (STIGs), its still dependent upon IT staff to help ensure their systems are continuously secure and compliant -- throughout the year, not just at a point in time.

Government IT systems are complex, budgets are limited and threats are constantly evolving. Ensuring that those systems have the right security controls, processes and documentation in place to demonstrate compliance with security standards can be challenging, but the effort is highly manageable, especially with automation. Lets consider how government IT professionals can use automation to take the time and guesswork out of compliance.

The problem with STIGs

A STIG is a set of security hardening standards and maintenance processes for networks, systems and platforms all DOD IT assets must comply with. There are hundreds of possible STIGs -- each with thousands of rules that must be followed -- and the number only continues to rise as new systems, versions and updates come online.

Monitoring server and network configurations against these compliance policies can be cumbersome. Even with the best change-control processes, it requires an army of people to manage and track all the configuration changes happening within the IT infrastructure. If a system has a particular STIG applied to it and happens to deviate from that control, how would system and network administrators know?

This is particularly problematic because these changes are happening all the time. A system or device can deviate from a STIGs expected baseline configuration for any number of reasons -- such as a system update or when a patch is applied to a vulnerability. Sometimes the deviation is deliberate. For example, an application may not run properly without introducing permission or authorization settings that deviate from the STIG. In each of these instances, administrators must create an exception to the STIG. They must also explain and document the exception in preparation for an audit -- a painstaking process.

These manual, time-consuming compliance tasks can take weeks and cost a significant amount of taxpayer money to implement across applications, servers and networks.

How automation can help ease compliance

Automation is critical to lessening the compliance burden on IT pros and allows them to focus on other priorities.

Applications, systems and devices are constantly in flux, and staying on top of any configuration drift is challenging. This isnt just a compliance issue. Any configuration changes in the IT infrastructure can lead to security breaches, outages and slowdowns.

However, with automation, administrators dont have to monitor each system in a cache of thousands of IT assets for potential configuration changes. Instead, the moment a configuration starts to drift from baseline security tools, monitoring tools detect the change and proactively notify administrators in near-real-time. IT teams also have visibility into who has changed the configurations, what changed and the related performance impact.

With this insight, they can troubleshoot faster, eliminate vulnerabilities, improve security, build in exceptions and demonstrate compliance far more effectively and efficiently than manual processes will allow.

Automation can also remediate the tedious task of compliance reporting. Administrators can quickly produce FISMA and STIG reports from their configuration templates and easily generate audit documentation and reports -- work that would otherwise take weeks to complete.

Compliance automation can help break down the barriers between security and operations teams. System and network administrators must know their systems are configured in accordance with security policy, but they often lack access to the right tools. However, with the ability to monitor server and device configurations against compliance requirements, they can quickly identify and fix issues without burdening their peers in the security operations center.

Stepping up to automated compliance

Mitigating security risks is one of the most important tasks IT and network administrators undertake. Its also one of the most complex, time consuming and costly -- particularly as it relates to compliance. This is where automation can really shine -- helping the entire federal IT team achieve compliance and deliver compliance reporting while lightening their load.

About the Author

Brandon Shopp is VP of product strategy with SolarWinds.

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How automation takes the time and guesswork out of security compliance - GCN.com

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

The number one reason knowledge workers regardless of role use automation is to save time, according to a new survey by Zapier. Marketers save the most time due to automation tools, an average of 25 hours saved each week, followed by IT professionals at 20 hours, customer service representatives at 16 hours and HR professionals at eight hours. The amount of time saved translates into each roles loyalty toward automation.

For example, marketers (86%), IT professionals (88%), and customer service representatives (79%) are also the most likely to say that theyll implement automation software in their next role. Most surprisingly, nearly 30% of knowledge workers say they received a raise or promotion as a result of using automation software. Additionally, knowledge workers say using automation tools improved their morale (35%), competence (34%), and confidence (32%) at work. Employees who dont have to stress about tedious tasks, inaccurate data, and miscommunication can help create a happier and more confident workforce.

How you use automation and how it benefits you depends a lot on your role. Accountants found that reduced errors (33%) was the primary benefit of automation. Given the detail-oriented nature of this role that makes sense. IT professionals said that receiving a promotion (36%) was the primary benefit. IT professionals are expected to help streamline other peoples work, so it makes sense that automation could help them get a promotion. Sales and customer service representatives cited increased confidence (35%) as one of the primary benefits. These high-pressure and often micro-managed roles are benefitting from the confidence automation can provide.

More than half of knowledge workers use automation tools every day. Marketers are leading in automation usage, which means there is considerable opportunity for other roles to catch up.

Read the full report by Zapier.

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Zapier: 60% of knowledge workers use automation to save time - VentureBeat

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didnt actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNows Dave Wright, where they will discuss the state of the automation market, why its so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks Ali Ghodsi, Salesforces Kathy Baxter and Puppets Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope youll join us. Its going to be a stimulating day.

Buy your pass now to save up to $100. We cant wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team byfilling out this form.

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UiPath CEO Daniel Dines is coming to TC Sessions: SaaS to talk RPA and automation - TechCrunch