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Tag: RPA

  • Red Hat Extends Scope of Ansible Automation Ambitions

    Red Hat Extends Scope of Ansible Automation Ambitions

    During the online Ansiblefest, Red Hat announced the availability of a preview edition of Red Hat Ansible Automation Platform 2 that is intended to make it easier to automate IT processes at scale.

    Richard Henshall, senior manager for Ansible product management at Red Hat, said previously most IT teams have been automating individual processes, but as organizations become more adept at creating playbooks to automate processes, they now need to find ways to manage multiple automations that are often dependent on one another.

    Ansible Automation Platform 2 also enables IT teams to automate processes that span both monolithic as well as cloud-native applications based on containers using an automation controller, formerly known as Ansible Tower, that is more tightly integrated with Red Hat OpenShift, an application development and deployment platform based on Kubernetes, using Red Hat Operator tools. There is also an automation mesh capability that connects disparate automation components together and provides status checks.

    Ansible Automation Platform 2 also introduces automation execution environments within Ansible Engine that make it possible to create self-contained automation spaces that can be easily replicated and repeated across an organization.

    Red Hat also includes an automation content navigator to helps teams validate that automation content is working as it should across even the largest environments, along with updates to the Red Hat Insights analytics service that enable issues that might occur with automation controllers to be surfaced and eliminated by automatically applying policies.

    Henshall said going forward, IT teams should expect to see Red Hat focus more on enabling events to be the catalyst for triggering automation. Today, most automated processes begin with an IT administrator triggering the process. However, as digital business transformation initiatives mature, it’s become apparent there will be a need to trigger automation when specific events occur.

    Henshall said while there are still many organizations that are just starting to automate IT processes, there are also many that are entering the next phase of maturity. As such, they now require an automation platform that makes it simpler to manage automation at scale.

    In general, Ansible is designed to make it possible for developers and IT operations teams alike to declaratively create automation without much programming expertise required. However, creating a playbook that works reliably still requires a deep understanding of the processes that are being automated. Most IT teams would be well advised to closely monitor processes when they are first automated to make sure they are performing tasks as anticipated.

    Ultimately, it’s now a question of when rather than if most IT processes will be automated. DevOps teams, of course, have always been at the forefront of automation, but now it’s clear that as IT environments become more complex—with the rise of both the cloud and edge computing platforms—enterprise IT teams need to embrace automation more aggressively. The biggest barrier to automation is simply finding someone who has the passion to automate a process to lead the charge, said Henshall.

    Naturally, most of those efforts will initially focus on repetitive, tedious and manual IT tasks, but the potential to automate IT processes at levels of unprecedented scale has never been greater.

  • Is AI Taking Over DevOps Functions?

    Is AI Taking Over DevOps Functions?

    Artificial intelligence has taken the tech world to a new level of automation. Today, almost every specialization demands the intervention of machine learning to develop AI technologies that help businesses do more with less time and resources. Still, some organizations question whether leading with AI is a good investment. 

    For DevOps, the answer is a resounding yes. AI can enhance DevOps practices to accelerate the pace of software releases, helping businesses achieve continuous delivery. This enables programmers to release software about 10 times faster and allows programs to be reviewed before they are released. 

    AI also has improved DevOps culture, from enabling more efficient decision-making processes to code quality enhancements and automated operations. So, let’s take a deeper dive into how AI impacts DevOps.

    Recent Changes in DevOps

    Monitoring and managing all the data generated in DevOps environments involves a high degree of complexity, making it difficult for teams to collect and use data effectively. In addition, the volume of data any given team may receive can reach well into exabytes, so AI tools provide much-needed assistance. After all, analyzing massive data sets manually would take too much time for a human and won’t meet the demands of modern businesses.  

    What’s more, surveys show that 57% of developers in the United States have less than five years of experience. For this reason, software testing must be an ongoing and in-depth process to check and double-check that no vulnerabilities impact the security of the code. AI can supplement manual review to increase speed and accuracy in finding bugs. 

    That being said, integrating AI into DevOps functions doesn’t mean developers aren’t necessary, even though their roles have evolved considerably. Backend developers are involved in the development and operations sides as they take on ops tasks and tests, especially around cloud-based infrastructure and cybersecurity. And frontend developers will always be needed for the technical support of creative design—which no one can do better than humans. 

    In today’s dynamic and fast-paced world, developers need to release code faster than they used to while the operations teams ensure that the existing systems function with minimal disruption. This partnership is easier when integrating AI, as it makes the collaboration between development and operations teams more efficient. AI-powered systems provide a unified view into systems and glitches in the complex chain of DevOps. 

    Is AI Taking Over DevOps?

    Short answer: No. AI is supplementing human activity in DevOps to make things more efficient and secure. 

    Due to the complexity of DevOps requirements, people have been looking for a more automated solution that could assist humans in performance and accelerate the processes. What’s more, you can expect to pay around $60 an hour for an experienced backend developer in the U.S. This means using AI to automate the grunt work can be a big cost saver as well.

    Here are a few ways AI transforms DevOps functions:

    Enhanced Security

    Distributed denial of service (DDoS) and related attacks have become a widespread threat to the security of websites and online services these days. AI-powered tools have the ability to identify and mitigate these threats. In addition, AI can help in the pivot to DevSecOps in teams still using traditional DevOps.

    AI security tools detect anomalies and threats based on real-time data and by analyzing past behavior. In this way, AI has played a vital role in protecting many organizations, including schools and colleges, from cyberattacks by supplementing human security experts.

    Improved Data Access

    The massive amount of data that needs to be collected, organized and analyzed in DevOps is beyond human capabilities. However, AI has resolved this issue by compiling data from multiple sources and organizing it for analysis. This drastically improves and accelerates the process of DevOps.

    Rapid Software Testing

    Software development and software testing has accelerated with the integration of AI. As a result, various testing types used by DevOps, such as user acceptance testing, regression testing, and functional testing, are more efficient and accurate than ever.

    These tests produce a large amount of data that would take humans ages to identify and collect manually. With the help of AI, identifying patterns and coding practices that lead to errors and vulnerabilities has become convenient and fast. This data can then be used later by the DevOps team to increase their efficiency and improve development practices. 

    Timely Alerts

    DevOps teams sometimes struggle to react and respond to the alerts during incident response. With no priority tags attached, it becomes challenging for the team to prioritize incidents effectively. A more efficient, comprehensive alert system should be capable of spotting flaws instantly and marking the more severe ones as higher priority. That way, the team can tackle the issues more systematically and resolve them without failure.

    AI can assist in prioritizing the responses to alerts based on certain factors, including the intensity of the alert, the source of the alerts and past behavior. This leads to efficient management in situations where systems get flooded with massive amounts of data and have to respond immediately. AI-powered alerts are particularly important in DevOps security, so this is a critical benefit of AI in the discipline. 

    Superior Implementation Efficiency

    DevOps teams are often burdened by the managerial tasks of a rule-based environment which decreases their time spent in more innovative and creative areas. When AI takes over, these tasks become self-governed, increasing efficiency and reducing human intervention. AI systems can work on their own, sparing humans from these tasks to focus more on innovation and creativity.

    This way, AI has turned the DevOps into self-governed systems that no longer require the rule-based, human management of analysis. This should resolve the complexity of research that DevOps teams were struggling to achieve and enable faster adaptation.

    Feedback Loops

    The primary function of DevOps is to use monitoring tools to gather feedback from every stage of operations and then use that to improve software development and delivery processes. Performance monitoring tools use machine learning to gather information such as performance matrices, log files, datasheets, etc. The feedback collected is then used to track potential issues in advance and make suggestions to resolve them accordingly. The application is then altered by applying these suggestions to make it more efficient.

    Artificial intelligence is driving increased efficiency in DevOps functions. However, it would be a stretch to say that it is taking over, as there are still many areas that require human intervention. The idea behind AI integration is not to change the players but to change the game.

    Even though AI is assisting humans in accelerating software development, developers still lead many aspects of the craft. But it also seems impossible to perform DevOps functions at the required pace without the integration of AI. So, if you wish to release software at a pace that keeps up with modern tech trends, you need to incorporate AI in your DevOps functions.

  • Federal IT Must Embrace Automation

    Federal IT Must Embrace Automation

    In the private sector, an automation explosion is underway.

    One major retailer, for example, is using a new automated system to increase speed and accuracy in how freight is handled at its regional distribution centers. Another retail giant is relying on new automated processes to replenish store shelves more efficiently, reduce excess inventory and free employees from mundane sorting tasks.

    They are among the retailers using automation to make the networks that interconnect all their locations easier to deploy and manage. But what about federal agencies, which are among the largest enterprises in the world?

    The story there is more of a mixed bag.

    At first glance, it may seem like apples and oranges to compare government agencies with some of the world’s largest retailers. But both are responsible for serving millions of people during a time of dramatic digital advancement, which requires significant reengineering of operations, systems and processes.

    In the case of federal agencies, digital pressures have intensified as citizens have come to expect service experiences similar to those they encounter every day in the commercial sector, and as the volumes and types of data that agencies must deal with have surged.

    The Benefits of Automation

    Agencies can leverage intelligent automation for a range of benefits, from relieving employees of rote, low-value work so they can contribute in more meaningful ways to making digital services faster and more responsive through the automation of manual, backend processes.

    Let’s say, for example, that a government website serving millions of citizens is prone to slowing down and even crashing. Automated software tools that analyze huge volumes of network data can proactively identify a potential issue and fix it before it becomes a problem.

    Sounds great, right? But uptake of automation technologies in federal agencies remains a work in progress.

    A recent survey found that 63% of federal agency executives thought they could benefit from intelligent automation and that 38% have already started implementing it. However, a large number–30%–remain on the fence and said they didn’t know if automation could help them.

    Interestingly, automation’s top benefits as perceived by the survey respondents were: Moving employees away from redundant or low-value work, reducing errors and offering a better experience to citizens and other users. Reducing costs was lower on the list, which is a good sign because it shows that more in the government sector are recognizing automation’s strategic value rather than fearing it as a job killer.

    Federal Adoption of RPA

    As a reflection of how automation is changing the nature of work and making employees’ jobs more interesting and productive, the federal government has stepped up a push to adopt robotic process automation (RPA) over the last few years. RPA is a technology that uses software robots to automate highly repetitive tasks that people would otherwise perform manually. This frees up human employees for higher-value work.

    These tasks can include extraction of data from websites and email text into a structured form for processing, data reconciliation and monitoring and responding to common IT and networking issues.

    In 2018, the White House Office of Management and Budget directed federal agencies and heads of executive departments to identify more opportunities to streamline operations via RPA.

    In 2020, the General Services Administration estimated that current automation programs “operating within agencies are achieving roughly five hours of workload elimination per employee. If the government deployed RPA at scale and achieved only 20 hours of workload elimination per employee, the net capacity gained would be worth $3 billion–and that is only scratching the surface.”

    The GSA leads the federal RPA Community of Practice to help agencies overcome the technical, management, and operational challenges in designing and deploying RPA programs.

    “At a government-wide level, RPA can represent a profound change, with the potential to empower non-IT professionals and process owners with the tools to automate a significant share of their workload,” the group’s report said. “RPA is considered transformative because it establishes the building blocks for artificial intelligence in terms of information technology infrastructure and task standardization. With effective RPA deployment, machine learning and intelligent automation are only a few, manageable steps away.”

    According to the group, “many agencies across the federal government have initiated RPA programs to automate tasks of varying complexity. Automations developed to date have focused on multiple functional areas including finance, acquisition, IT, human resources, mission organizations and security/mission assurance.”

    My own experience in working with federal customers is that many are at least dipping their toes into the automation waters, but not quickly enough and certainly not at the pace seen in some parts of the private sector.

    Why the continued reluctance? I think much of it comes down to trust. Worries remain that automation will take jobs when, in fact, machines should be thought of as non-human team members who take care of the drudgery while the humans contribute their knowledge and creativity to more important work. Federal enterprises should embrace the opportunity to test automation to prove it works as they expect, similar to interviewing a new employee. This is how to gain the trust needed to deploy automation to augment humans’ work.

    Automation also faces resistance from older employees who, on the verge of retirement, aren’t interested in learning new technology.

    The fact is, though, the federal government is on the cusp of an automation boom. More and more, it will become critical to agencies’ success. The time for fear and mistrust is over.

    Automation is here to stay, and agencies should embrace it.

  • Programmability is Key to Agile Transformation

    Programmability is Key to Agile Transformation

    Enterprise agility has rapidly become one of the most crucial variables for a business’s long-term resiliency. With the COVID-19 pandemic, never-ending disruptions to the global supply chain and nearly every industry’s typical processes flipped upside down, there has never been a more important time to prioritize agility than now.

    While the rewards of agility are high, organizations are struggling to implement key technologies and adhere to the methodologies that can get them there. In fact, the 14th Annual State of Agile Report notes that 59% of organizations are following agile principles, but only 4% of these organizations are getting the full benefit. But the ability to adapt quickly, seize new opportunities, and reduce costs is critical for survival in the ever-evolving and hypercompetitive digital age.

    Agility is about adapting quickly to changes and increasing productivity. Importantly, agile methodologies are not agile themselves and therefore will not magically transform enterprises into agile organizations. Instead, the focus needs to be on practicing the correct principles to become truly agile. With this in mind, let’s look at how two of these critical principles—programmability and observability—can increase productivity and allow for innovation across the business.

    Programmability

    Programmability describes the capability of hardware and software to change and accept a new set of instructions that alter its behavior. Let’s look at each of these two forms.

    One way programmability is used is to connect analog devices with software, for example, software-supported network routers. More recently, programmability has been extended to use networking to control the hardware involved and bridge with software systems. The rising popularity of infrastructure-as-a-service (IaaS) has increased the demand for programmability and brought concepts, such as infrastructure-as-code (IaC), into the realm of reality.

    The other use of programmability is to extend existing software capabilities. The two most widely known examples of this are application programming interfaces (APIs) and software development kits (SDKs). Using both approaches, a developer can extend the default capabilities of an existing program. The rebirth of APIs as business APIs has significantly extended the introduction of programmability into application software systems. The approach is particularly popular with application service providers since it enables end users to extend and customize the core capabilities provided by a product or service.

    Connecting Agility and Programmability

    So how are these two concepts connected? Agility requires a foundation and the right kind of environment to provide optimal results. Programmability is an enabler for creating an adequate path for agility. An organization with highly programmable components increases the ability to automate repeatable tasks. Let’s look at two scenarios: a business use case and a technical use case.

    Programmability in Business

    When a purchase order (PO) arrives, the customer care representative must verify the organization’s ability to fulfill the request and then create an order in the back-end customer relationship management (CRM) system. Traditionally, this process has required a good deal of manual effort: first, extract the PO data; then verify the details using other systems or spreadsheets and, finally, type the data into the CRM system.

    By contrast, consider a person working in a modern environment with high programmability. The PO can be received in an electronic format, such as JavaScript object notation (JSON), extensible markup language (XML) or electronic data interchange (EDI). Then it can be validated by connecting to other systems and reference data repositories, prompting the user to verify it and feed the data into the CRM system.

    Programmability in a Technical Environment

    In a typical technical use case, software developers will finish writing code; quality assurance (QA) engineers have to test the release; release engineers have to verify it and the DevSecOps team has to deploy the released artifacts. Similar to the business use case, this technical workflow has traditionally required extensive, tedious, manual work.

    However, the process becomes significantly more streamlined when the development organization makes the environment programmable. In this case, once developers commit the code, the automation flow will kick in, exert the committed code, run through the life cycle stages and deploy it into production.

    Through automation, which is enabled and extended by programmability, organizations can streamline their repeatable tasks and provide more time and space for users to work on essential or innovative tasks.

    Now, let’s look at the implementation details of the two use cases.

    Programmability in the Digital Supply Chain

    In the business use case, the automation of a workflow, such as PO processing, is treated as part of a modern digital supply chain initiative. The systems, messaging and data involved in such a flow are linked together using integration technologies.

    Integration approaches first involved point-to-point connections and then evolved to the use of centralized enterprise service buses (ESBs). More recent, modern approaches have centered around API-centric, integration microservices and integration platform-as-a-service (iPaaS) solutions that use decentralized models and cloud-native technologies.

    Now, organizations build or acquire digital platforms by combining APIs, integration and security technologies to create digital supply chain flows. Critical to the success of any digital supply chain is the ability to select systems with built-in API capabilities and enable existing systems to expose applications and services as APIs and make the entire enterprise programmable.

    Programmability in a Secured Software Supply Chain

    In the technical use case discussed earlier, DevSecOps automation provides the foundation for automating various aspects of the software life cycle. Here, development tools, testing tools and governance-related tools are all connected using a pipeline.

    IaC and automated build servers are two popular technologies used in automating such flow, which we call a secured software supply chain. Puppet, Chef and Ansible are widely adopted IaC scripting languages while Bamboo and Jenkins are popular build servers. However, as the DevOps space has evolved with cloud-native and decentralized technologies, GitHub and GitOps are also playing a vital role in building effective pipelines.

    Similar to the business use case, the components involved here need to connect to the build pipeline, and therefore the programmability of each element involved plays a significant role in automating the workflow. Furthermore, in order to build a comprehensive pipeline, the code quality, unit test, automated test, infrastructure provisioning, artifact building, dependency management and deployment tools involved have to connect using APIs and extend the required capabilities using IaC.

    Observability

    By analyzing both the business and technical use cases, we have a clear idea about the impact of automation and how it is enabled by programmability. However, we have missed one important element: observability. Let’s examine this here.

    The environment created by automation and programmability provides a good playground for teams to be practice being more agile, experiment with new ideas and take them to production rapidly. While agility increases, the responsibility of running healthy systems that provide a continuous digital experience is vital.

    Observability helps detect and predict issues using analytics, machine learning (ML) and artificial intelligence (AI). Modern cloud infrastructure provides a certain level of observability at the operational level. Meanwhile, some modern programming languages, such as BallerinaLang, have built-in support for application-level observability associated with the specific language. When working with languages that lack this functionality, developers will have to either use a third-party tracing library that is compatible with the programming model or directly insert observability instructions along with tracing and debugging instructions.

    Enterprises today are on a journey towards agility as they seek to optimize their digital strategies. However, organizational agility needs to catch up with project agility—and then, with each individual’s agility—to be more effective. This is the concept of “structured agility,” which is being fueled by “two-pizza teams” or self-organized teams.

    APIs are the foundation of programmability, which makes it possible to connect analog and digital systems, as well as extend software systems. Programmability increases the speed of delivery, enables an enterprise to become “composable” with high reusability and allows a flexible environment by opening it up for innovation—the core principles of agility.

  • 4 Ways to Manage Up for Automation

    4 Ways to Manage Up for Automation

    IT decision-makers understand how important a role process automation can play in a post-pandemic recovery. In a recent survey, 68% of these leaders said remote work caused key business processes in their organizations to break down. In the same survey, 97% described process automation as being vital to digital transformation and an important factor in achieving business outcomes.

    So, full speed ahead? Not exactly. While process automation’s benefits are well documented, many organizations remain hesitant to automate too many tasks because they’re afraid they’ll further complicate an already complex IT environment.

    This is where developers can help process automation initiatives get off the ground. They don’t control their organizations’ purse strings and they often don’t take the lead on planning new IT initiatives. But developers are the ones responsible for designing and building the software and tools that drive real business results. In a large sense, they occupy a key role at the heart of digital transformation.

    For organizations to gain the full benefit of process automation, developers need to be more intimately involved in automation projects. They need to have more say in how these initiatives are constructed, how they’re carried out and how they’re improved.

    Automation initiatives aren’t set-it-and-forget-it projects. They must tie in with end-to-end organizational processes, which will evolve over time. Organizations don’t want to bake in processes that won’t suit them two months or two years from now. With this in mind, process automation needs to be nimble, adaptable and smart–and it’s developers who have the skills and the insights needed to drive these initiatives forward, impacting broader organizational success. 

    To take on this larger role, though, developers must be given the right resources, tools and access to make their voices heard. Part of this is empowering them to manage up to senior leaders–on both the IT and business sides–to advocate for the types of automation projects that can drive true digital transformation.

    Here are four ways they can do it.

    Build Automation Demos

    The biggest enemy of a process automation project is time. The C-suite and senior IT leaders don’t want to spend months–if not years–debating the merits and feasibility of automating a series of tasks. They want to know quickly whether a project is viable and whether they should commit resources to it.

    Developers can set a project on the right course by creating a basic demo. It doesn’t need to integrate the complicated set of dependencies planned for the finished project; it just has to show people in power that the project will work. Demonstrate how a manual calculation project gets triggered by a quarterly deadline and estimate the time it will save a department on an ongoing basis. Show the leadership that the pain point workers have cited on feedback reports can be solved.

    Show Proven Examples

    While a demo visualizes how a process can work, a use case can illustrate how such a process has worked in another setting. Examples can be found in any number of places–in a formal case study, a tech article, a GitHub posting, a developer message board or materials supplied by a technology partner. Developers should tap their networks to get advice and run scenarios by peers. The goal is to find a situation where someone solved a similar problem with a manageable set of procedures–and it worked. Having a solid example takes the initiative out of the theoretical realm and gives the developer more credibility.  

    Get an Enterprise Architect on Board

    Once the demo and the use case are secured, the next step is getting buy-in on the vision for the automation initiative. This is the domain of the enterprise architect. It’s important for a developer to get this person on board as an ally. They should work with the enterprise architect to ensure that the set of tasks fits into the company’s architecture – today and in the future. If the company is pursuing a microservices strategy, the developer needs to show how automating a series of product quality checks can be accomplished inside this technology infrastructure.

    Keep Things Simple

    While organizations try hard to operate in an agile manner, sometimes that’s easier said than done. The people developers report to like to plan. Enterprise architects like to define perfect, future-proof architectures. The C-suite likes to envision scenarios and debate ramifications. Developers can break logjams by encouraging others to avoid the temptation to drown in detail. They should get comfortable with the concept, the demo, the use case and the vision–in that order–and then roll it out to the larger organization. 

    While process automation is widely seen as being vital to business transformation, building a project is often more challenging than it needs to be. Encouraging developers to take on larger roles can help organizations move automation initiatives past square one and on the path to digital transformation. 

  • How Low-Code is Anchoring the Digital-First Enterprise

    How Low-Code is Anchoring the Digital-First Enterprise

    When the pandemic hit, digital transformation was accelerated, and many companies turned to low-code development in the hopes of winning in the new agile world. Technology aficionados have heard the phrase, “Faster, better, cheaper — pick two.” Fortunately, low-code gives you all three and then some, adding “flexible” to the mix. It’s no surprise that low-code development interest is now surging, as it’s quickly becoming a critical component of a modern enterprise technology stack, with estimated low-code development market growth predicted to exceed 25%, growing from $13B in 2020 to $65B in 2027, according to research from Brandessence Market Research.

    With their roots in the rapid application development (RAD) tools of the 1990s, low-code platforms are an application development environment that use graphical user interfaces and configuration instead of traditional hand-coded computer programming. Formal software engineering skills are not required to create applications since a visual user interface in combination with model-driven logic is used. This opens the door to a wider range of people who can build apps for the business. With a little training, employees can rapidly create and deploy secure scalable software. One note to remember–low-code is not interchangeable with no-code, which is a subset of technologies aimed at business users primarily working on enhancing their individual productivity.

    A New Player in the Enterprise Technology Stack

    It’s impossible to argue against the need and demand for low-code in the enterprise. According to Gartner, more than 65% of application development in 2024 will be performed by low-code platforms. That’s a remarkable shift for a software category that did not exist a decade ago. The number of digital applications and services being built is exploding as well. Between 2018 and 2023, more than 500 million apps will be created according to IDC. To put that massive number into perspective, that’s more than the previous 40 years combined. With low-code, companies can rapidly produce applications within a shorter time span and at a fraction of the cost. Some skeptics may point out the lack of available IT resources, but business users can learn low-code development methodologies quickly, typically in less than one month. Clearly the old way of building apps cannot keep pace with today’s digital marketplace.

    Process Intelligence Jump Starts Low-Code Journey

    To help accelerate use of low-code and scale it across the enterprise, process intelligence is a key enabler. You cannot improve how you operate tomorrow if you don’t fully understand how you work today. And most companies truly don’t understand how they operate on a daily basis, especially at the granular user activity level required to automate a process or streamline a workflow. They have limited process understanding. They don’t know how their applications and data interact, and they don’t really understand what their customers expect.

    The impact of this gap in process data is well documented. The 70% failure rate of transformation programs is widely reported. McKinsey pegs the cost at nearly $1 trillion annually and noted that 14% of companies have seen a sustained and material improvement in their business. Another study from Gartner noted only 1% of companies have sufficient understanding of their processes to take full advantage of the technology solutions.

    Before embarking on a major initiative, a company must map its processes, its systems and its experiences. Today, that necessary level of operational intelligence just does not generally exist in most companies and on top of that it is very difficult to obtain without process intelligence.

    Low-Code and Process Intelligence – Better Together Than RPA

    Process intelligence was a similar catalyst for robotic process automation (RPA), but the opportunity with low-code is even greater. RPA programs enjoyed massive early uptake, but the challenge was how to scale the initiatives. Companies still struggle getting more than 50 bots deployed. Once any obvious low-hanging fruit is automated, it becomes difficult to identify what to tackle next and how to tackle it. Process intelligence answers those questions to help scale RPA.

    With low-code, process intelligence accomplishes that and more. The combination will overtake RPA as the gateway for AI and automation. Low-code is more efficient and scalable than traditional RPA development because it does not operate at the user interface (UI) layer, making the applications more resilient. Additionally, where RPA is traditionally limited to task-based activities, low-code is much more capable of handling sub-process and process level activities, making it much more valuable to the enterprise. Coupling process intelligence with low-code helps steer an enterprise toward a next-generation operation model that is faster, cheaper, better and flexible. It may also fully realize the promise of citizen developers that RPA struggles to achieve.

  • Appian Extends Low-Code Approach to Aggregating Data

    Appian Extends Low-Code Approach to Aggregating Data

    At an online Appian World conference, Appian today unveiled an update to its low-code platform that adds a set of visual tools that enables developers to aggregate data within an application with the help of a database administrator (DBA) or data engineering team.

    Malcolm Ross, vice president of product at Appian, said one of the things that holds up application development the most is the need to wait for data to be massaged in a way that makes sense within the context of an application. The latest edition of the Appian Low-code Automation Platform aggregates multiple data sources into a single data vector columnar data store that resides within the platform.

    In effect, Appian is applying a low-code approach to data engineering processes that, today, require dedicated expertise to set up and manage.

    Appian

    The latest version of Appian also adds an Intelligent Document Processing (IDP) tool enabled by optical character recognitions and AI models, application design guidance, developer collaboration tools and enhanced DevSecOps capabilities that enable software packages to move between development, test and production environments with a single click in a way that is governed by the Appian platform.

    Appian is also extending robotic processing automation (RPA) tools to centrally manage bots within an application to Windows platforms.

    Finally, Appian is making a formal, less-robust community edition of its platform available to a limited number of developers as part of an effort to expand the overall size of its developer community.

    Ross said as both professional and so-called citizen developers embrace low-code applications it’s critical to have guardrails in place that ensure applications are deployed in a way that adheres to DevSecOps best practices. The Appian Low-code Automation Platform includes the software development life cycle (SDLC) tools needed to set those guardrails regardless of the level of experience any developer has, noted Ross. In fact, Appian guarantees, for a flat services fee, that an organization’s first project will go live in just eight weeks.

    In general, Ross said the number of individuals capable of building applications continues to expand as more “business technologists” learn how to build applications. Previously, these individuals might have used 4GL tools or macros to build applications based on Microsoft Excel spreadsheets. The Appian low-code platform makes it possible for this class of power end users to build more robust enterprise applications using a set of tools that compare their efforts to known best practices and offer suggestions to make improvements.

    At the same time, professional developers are opting to build applications using low-code tools to reduce the size of application development backlogs. Not every application that needs to be developed requires developers to employ procedural code.

    The net effect is more applications than ever are being simultaneously built and delivered at a time when organizations are launching multiple digital business transformation initiatives.

    It may be a while before the effects of low-code platforms are fully realized within IT environments. However, the one thing that is certain is IT teams of all sizes should expect to be managing an exponentially higher number of applications.

  • Using RPA for DevOps Process Automation

    Using RPA for DevOps Process Automation

    Modern operations teams today are at the cutting edge of automation. DevOps and SRE practices are entirely based on an automation-first philosophy. CI/CD is fully automated and the next generation of automated testing is now being adopted.

    However, these same teams are still running their human processes based on hastily thrown-together wiki pages. The increase in remote work over the last year has amplified the problem. Engineering teams that were used to quickly getting the right people physically into a war room now struggle to find the people with the right knowledge to troubleshoot or resolve a problem.

    Teams with ample resources have built tools and bots for DevOps process automation and orchestration. These tools can help automate things like getting communications channels set up quickly, with the right people, the moment an incident is identified. Or, they may automate actions across the DevOps toolchain that would otherwise live as static documentation for an on-call engineer to try to decipher and execute. But only a handful of organizations have the resources to build this kind of automation technology in-house.

    These higher-level DevOps processes are ripe for innovation similar to robotic process automation (RPA). Companies like UiPath and Automation Anywhere have shown that there’s an enormous market for this in every business unit and industry. Process automation for DevOps needs to be another layer of automation available to any engineering team. Automated remediation of incidents and resolution of service desk tickets has to be the future of this space. However, there are important differences in how automation is optimized for DevOps versus the application of RPA for business functions.

    Where RPA Fits on the Automation Spectrum

    RPA is great for solving deterministic problems – those that can be resolved in the same way every time you run into them – which are fully automatable. An example of this type of end-to-end automation is any back-office task, which are consistently good candidates for the application of automation. It’s the reason why fields like HR and finance have made some of the greatest progress in automating business processes to date. Every time you close the books, for example, the steps are pretty similar.

    Process automation for DevOps and SRE, however, is on the other end of the spectrum. Although teams may benefit from RPA for simplistic, repeatable tasks, many DevOps processes are dynamic and unpredictable, making full end-to-end automation too often fallible. The complexity of modern technology stacks and infrastructure requires processes to be agile and adaptable to change, rather than deterministic. This is where it’s important to enable humans to step into the spectrum of automation. Human-in-the-loop automation – the idea that lets humans be in the cockpit while letting machines provide guidance and support – is the future of DevOps automation.

    Applying Process Automation to DevOps

    To use automation in a meaningful way, DevOps teams need a mix of what RPA provides with the added element of human judgment. Human-in-the-loop automation can be applied to numerous DevOps processes including incident management, service requests and continuous deployment and delivery.

    Rather than automating from end-to-end right from the start, DevOps teams should use incremental automation to strengthen and build trust in automation. This starts by codifying free-form processes so teams can more easily identify predictable steps that can be automated. Then teams can mature to more complex workflows, automating pieces within processes and incorporating human checkpoints for areas that will need judgment or approvals.

    As teams incrementally automate, they build up sustainability – in their systems and their roles – in high-stress environments and are able to orchestrate processes across the continuum. The goal is to find the right balance between leveraging machines to make lives easier while creating room for human judgment and adaptability.

    Level-Up DevOps Automation

    Looking ahead to the next few years, what the RPA movement did for back office tasks will also happen for DevOps and SRE. Technology leaders can take notes from the RPA movement and apply them to eliminating the toil of modern production operations. The issue is that, in DevOps, many problems are unique.

    The next evolution of DevOps automation will force us to look at how we can model the real world in a more holistic way, which includes incorporating human judgement and manual execution of non-repeatable tasks while automating repetitive steps.

  • ServiceNow Acquires Intellibot to Gain RPA Capabilities

    ServiceNow Acquires Intellibot to Gain RPA Capabilities

    ServiceNow’s acquisition of Intellibot, a provider of a robotic process automation (RPA) platform, this week is part of a larger effort to automate the development of applications that are driving digital business transformation within enterprise IT environments.

    Earlier this month, ServiceNow unfurled a set of revamped low-code tools that both professional and so-called citizen developers could employ to build applications to modernize various business processes. However, many existing business processes are either manual or paper-based. An RPA platform enables organizations to create bots that mimic, within an application environment, the way those processes are currently managed.

    Josh Kahn, senior vice president for Creator Workflow Products at ServiceNow, said ServiceNow envisions the Intelligent RPA platform as a mechanism to automate rote processes that, for one reason or another, don’t manifest themselves via a set of application programming interfaces (APIs) that application developers can easily invoke.

    Each IT team, of course, will need to decide when it makes more sense to employ bots to digitize an existing workflow versus reengineering it using a set of custom applications infused with machine learning algorithms and natural language processing (NLP) capabilities. As digital business transformation initiatives become more urgent, the application development backlog within many enterprise IT organizations that require those capabilities is steadily increasing.

    However, in some cases, an RPA platform can reduce that backlog by surfacing a set of visual tools to automate a process that doesn’t require a lot of developer expertise to effectively digitize.

    In general, Kahn said ServiceNow is making a case for building a wide range of applications on top of the same software-as-a-service (SaaS) platform many internal IT teams already employ for IT service management (ITSM). That approach enables IT teams to streamline processes as the number of custom applications that need to be both developed and, ultimately, managed, steadily increases over time, Kahn said.

    The developers of those applications might be professionals using a low-code tool instead of writing additional code to quickly build an application, or it might be a “power user” that has enough understanding of software development best practices to build an application. Regardless of who builds the application, the rate at which applications are built using low-code tools is clearly about to accelerate.

    Kahn said the next step for ServiceNow will be to embed the Intellibot RPA capabilities in the ServiceNow platform sometime after the deal closes in the second quarter. In fact, one of the reasons ServiceNow opted to acquire Intellibot is because, as a smaller startup vendor, it is easier to incorporate their RPA platform than it would be to reengineer a much larger RPA platform. However, ServiceNow remains committed to integrating with other third-party RPA platforms that are already being employed by its customers, Kahn noted.

    Regardless of how or at what rate applications are developed, the number of core technologies required to build them continues to expand. The issue IT organizations will need to come to terms with is the degree to which they want to stitch all those tools together themselves, or rely on a platform to do it for them.

  • Could No-Code Enable Everything Ops?

    Could No-Code Enable Everything Ops?

    It feels like DevOps principles are permeating every discipline, creating new buzzwords by the minute. DevSecOps. DatabaseOps. SalesOps. FinOps. MarketingOps. This “JargonOps” is clearly encouraged by marketing campaigns (and bloggers, wink, wink). Yet, the phrases do depict a real trend: all industries are getting an efficiency overhaul in the wake of increased automation.

    As I’ve covered before, low-code and no-code tools lower the barrier to entry to application development, enabling field experts to construct workflows as they see fit. For tech-savvy non-engineers, this could be a huge boon to transform copy-and-paste stopgaps into efficient workflow automations.

    I recently chatted with self-proclaimed “productivity geek” Sean Chou, CEO of Catalytic, to get his take on where this new era of automation is heading. From healthcare to logistics, advertising to supply chain management, every industry is likely to get an efficiency boost — and a lot of this digitization will be constructed using codeless environments.

    But first, how did we get here?

    From RPA to No-Code

    “The story of B2B software is largely about automation,” said Chou. If we consider classic examples of corporate applications, we see one common thread — B2B software has always sought to automate traditional office work.

    And so, it came to pass that computing and software devoured all these office tasks, from accounting to time-tracking, communications and many other areas. Though computer-based office work delivered huge efficiency boosts, these interfaces often introduced new hurdles; bad UI design, difficult navigation and a hundred open tabs, just to name a few.

    From there, robotic process automation (RPA) aimed to operationalize a growing number of tedious digital workflows across Excel spreadsheets, web apps and desktop apps. In Chou’s words, “RPA took the concepts of test automation software, and then pointed it toward production systems.”

    Though RPA’s process of recording screen interactions gathered much interest and attention (and funding), screen scraping is ultimately too fragile to be effective. It adds technical debt over legacy systems. It can be expensive to implement. Lastly, it’s not processes-centric and doesn’t implement reusable software-defined APIs.

    At that point, though, “Pandora’s box was open,” Chou said. Folks started to consider other ways to optimize, and reimagine how end users could automate workflows and stitch together interactions between disparate apps.

    Out of the ashes of RPA rose a new paradigm — low-code and no-code.

    No-Code Addresses Everything Ops

    With developer resources stretched thin and limited engineering talent available, it can be challenging to loop expert programmers into codifying traditionally non-IT processes.

    There’s not often enough developers to help with SalesOps, FinOps or any other Ops areas, explained Chou. “The only thing that can change the narrative is no-code technology that is effective.”

    Similar to how higher-level programming languages made low-level programming more accessible, Chou sees low-code and no-code unlocking accessibility for automation design by orders of magnitude.

    Four Examples of Everything Ops

    So, what are some examples of no-code use cases for automating operations? Chou outlined four specific categories that he believes will see much more citizen developer attention and action in the years to come:

    Customer Ops: “It’s really important to run customer operations as efficiently as possible,” Chou said. Especially in B2B interactions, people need to adopt various systems dictated by the customer. This can result in many ad-hoc implementations that beg for efficiency improvements.

    Financial Ops: Imagine the number of Excel spreadsheets that make up an account’s financial foundation. Consider the tedium of inputting data, generating reports or migrating calculations into other tools. There’s a ton to automate here. Providing no-code platforms to financial minds could have a “really transforming effect,” said Chou.

    People Ops: “The number of mini apps being developed for HR use cases is astounding,” Chou said. Think of all the digital tools HR must use: vacation requests, employee performance reviews, personnel databases – the list goes on. There’s a significant opportunity to clean up legacy applications within many HR departments to build a more coherent platform.

    SupplyChain Ops: Procurement and supply chain operations is another area where no-code could have an impact. Streamlining inter-company communications or engaging with potential customers are areas worthy of automation.

    No-Code? No Problem

    Across all these verticals, Chou notices three common patterns emerging.

    First, groups are using no-code to create standard operating procedures (SOP). SOPs could be used for onboarding or offboarding customers or ensuring compliance with a set of policies.

    Another everyday use is data pipelining — pulling information from many resources for analysis. A common use case is processing financial or customer data to generate a particular output. This is an area where embedded machine learning models could enrich the data.

    Lastly, a third common use case is creating workflow-powered surveys. These online forms could be used to measure employee engagement or to collect customer input. No-code workflows enable not only the collection of that data, but could allow for additional actions to be taken upon input.

    Implications and Governance

    Of course, giving non-developers the keys to the castle could have some downsides. Let’s face it — computer science is, well, a science. There’s a reason engineers are taught strict practices around version control, data governance and peer reviews. All of which must still be upheld when establishing a citizen development culture — especially at scale.

    When it comes to low-code and no-code, “governance is a huge thing,” said Chou. “Any company coming at this with no governance in mind can’t work beyond small teams.” Move beyond 10 or so citizen developers, and you’ll really need to establish some sort of shared framework or center of excellence.

    Making citizen development work in a large company requires IT visibility, data governance and a review process for new workflows, said Chou. IT will likely also want to verify that a new workflow doesn’t make permanent state changes. Otherwise, static apps that don’t touch core systems or affect persistent data stores are probably fine. “Having built-in governance is really key to making this successful,” Chou said.

    A Technology Leap

    No-code is giving more people the ability to produce unique workflows to increase their efficiency and do more with less. But with this movement comes worry. “With every technology leap, there’s always a wave of people concerned,” said Chou. Many naysayers are worried that automation will leave people out of work – “They’re coming for our office jobs!”

    Instead, Chou recommended we curb our expectations. Citizen developers are not going to build sophisticated iPhone apps any time soon. “If you think no-code is a panacea for all software, you’re coming at it all wrong,” he said. Instead, what they can actually can accomplish is far more practical.

    In reality, things are not as dire as some may make it seem. The new age of automation will likely open room for more jobs than it diminishes. “The net is always a plus,” said Chou.