2020 was about implementing RPA bots and AI services. 2021 is about organizations scaling these technologies and realizing the full value of these investments. In connection with a culture of modern engineering (Agile, DevOps ) and a continuous innovation mentality, automation in software development and application management opens the door for companies to take the next step in digital transformation.
Companies should not lose sight of the following developments in 2021, because these 10 tech trends will have a decisive impact on the year 2021:
As-a-Service (aaS) is the prerequisite for transforming yourself into a truly digital company. The XaaS model is Everything-as-a-Setechnologies), in which the provided products, tools, technologies, and services remain entirely in the cloud instead of being provided locally or on-site within a company and the user has virtual access to almost all possible services. Technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) play a crucial role in setting up these services or in expanding existing services.
The digital transformation places special demands on the organization of business processes and the XaaS concept fully meets these. The business advantages of XaaS lie not only in the use of constantly new and current technologies, but also in cost and risk reduction, in-process and personnel optimization, regular support, and continuous updates by the provider.
Digital transformation – understood as the redefinition of business models that use technology to create a value chain that delivers innovative products and services that improve the user experience – can be achieved faster if the company doesn’t change the wheel with the introduction of new technologies must invent, but can immediately access innovative technologies as required.
Companies that choose XaaS can use the as-a-service model to reduce costs and simplify IT deployments. With each additional cloud service, a company can dismantle parts of its internal IT infrastructure, which leads to fewer servers, hard drives, network switches, software deployments, and more. And less local IT means less physical effort – such as B. Space for equipment, electricity, and cooling. This allows IT staff to focus on more important projects that add value to the business. In addition, using an outside service rather than local technology shifts a lot of capital costs into operating costs for the business.
There are innumerable examples of XaaS. The most common are Software as a Service (SaaS), Platform as a Service (PaaS,) or Infrastructure as a Service (IaaS). The success of the aaS models will also stimulate the introduction of automation solutions as a service.
RPA as a Service is the easiest and fastest way to use the advantages of Robotic Process Automation (RPA). RPA as a service means companies can take advantage of the many benefits of RPA without the upfront cost, inconvenience, and maintenance of buying their OPA technology or hiring in-house RPA developers. PaaS offers a selection of out-of-the-box RPA services and tools accessible through a monthly subscription.
RPAaaS solves the problem of a lack of in-house development resources not only by providing the development platform but also by offering bookable, qualified RPA experts, be they RPA business analysts, RPA developers, or RPA architects. Thanks to the model of resources that can be booked as required, load peaks can be flexibly balanced and the schedules for scaling bots can be adhered to. RPAaaS will reduce development and deployment costs and quickly and effectively meet the need for effective and reusable automation components.
The relocation of automation assets to the cloud and their use there opens up a way for financially constrained companies to tackle the planned business process automation or to scale existing automation approaches without having to hire qualified automation personnel.
With RPA as a service, a company does not need to purchase its servers, licenses or, professional services. RPAaaS is therefore also suitable for companies that only plan to use RPA for a limited period. Because in this case, there is no point in purchasing expensive RPA technology that will no longer be used after a certain project or time per period completed.
RPA as a Service has the potential to establish itself in 2021 with transaction prices tailored to requirements and seamless cloud provision.
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RPA only service providers will have to evolve into automation system integrators if they want to offer truly intelligent automation services that combine RPA with data science, machine learning, and other innovative technologies to create larger expert solutions. In recent years, the RPA offers have developed from a simple bot with low complexity to vertically and horizontally oriented special solutions. In the meantime, concepts of intelligent automation within automated processes also enable decisions to be made based on existing information or “collected” knowledge.
Big data, artificial intelligence, cloud services, and the Internet of Things (IoT) form a foundation for emerging digital technologies that companies need to achieve competitive advantages. It takes a lot more than just RPA to deliver resilient operational excellence. Critical operations, infrastructures, and data processes need to be automated with more robust orchestration and automation tools that offer programmatic integrations and deeper functionality.
Professional services providers who only offer RPA will experience a technology shift that will force them to join forces with other specialists to work on highly complex solutions. Those RPA vendors who can evolve and seamlessly integrate advanced technology features into their automation operating model will be more competitive in 2021 and beyond.
Voice-activated Artificial Intelligence is the technology that brings together speech recognition software, natural language processing (NLP), and machine learning to enable conversational interactions between people and voice-activated devices.
Voice-controlled AI, such as Amazon’s Alexa or Google’s Assistant, is already serving as a gateway to the Internet of Things and the networked home. They carry out their users’ commands, provide information, entertainment, benefit, and convenience, and enable consumers to bypass the advertisements they would normally see on a screen.
New scalable approaches for automated speech recognition, e.g. B. with the help of language models for neural networks, with techniques from linguistics and experimental psychology combined with exact data analysis, are the engine for the use of voice-controlled AI. The acceptance of NLP and automated speech recognition (ASR) functions will be given an additional boost by Covid-19 and the associated increase in remote working.
In the post-COVID world, which is likely to be more virtual and digital, the acceptance threshold for any AI solution will rise with the promise of convenience. And with the proliferation of voice assistants and voice-activated speakers, voice-activated AI will play an increasingly important role in customer interactions and search.
Voice-controlled AI will change the business world forever. Because of this development, it is important to integrate voice-controlled artificial intelligence into the digital business strategy at the earliest possible point in time so that the window of opportunity is not missed in which companies can be leaders in their industry or before it is too difficult to catch up with the technical lead of the competition.
Cloud computing has become increasingly popular in recent years. Manually deploying and operating features such as scaling, configuring resources, setting up virtual machines, and monitoring performance are repetitive, inefficient, and often error-prone, and can affect availability.
In the context of cloud automation, AI-based automation tools help optimize the optimal performance of the system and its resources by optimizing activities related to cloud computing. You can further improve efficiency by managing repetitive tasks or making real-time decisions about capacity or performance distribution. By using automation to run workloads in a cloud environment instead of on-premise, organizations can maximize their resources.
Cloud automation can include tasks such as automated storage and backup, managing security and compliance, changing configurations and settings, and deploying code. Cloud automation streamlines tasks or processes to improve efficiency and reduce manual workload.
The cloud is an effective way to drive business growth with scalability and flexibility. Cloud automation also offers companies the opportunity to further fuel their company’s innovation machine by releasing resources that need time for important, future-oriented strategic decisions.
Embedded intelligence is a term for a self-referential process in technology in which a certain system or program can analyze its processes. Embedded intelligence is often contained in business processes, automation programs, or task-based resources. With the help of embedded intelligence, companies can make the delivery of technology in corporate environments smarter. Embedded intelligence can take many forms.
Artificial intelligence (AI) deals with the automation of intelligent behavior. Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) enable the automation of work processes and the digitization of workflows.
Embedded intelligence as part of an automation solution can, for example, consist of dashboard and reporting tools that automatically summarize data about the functionality of a program and return it to human decision-makers. An analysis tool that focuses on a particular digital task is often classified as embedded intelligence because it is self-referential – it sees what the program is doing and reports what that particular program has done in the past for change and improvement purposes.
Embedded intelligence expands automation to intelligent automation and will become a growth engine for companies in the coming years. Those who do not tackle this issue run the risk of falling behind in the competition.
While application programming interfaces (APIs) used to be largely restricted to technical areas by allowing different program systems to communicate with one another, they are becoming an increasingly important engine for business growth. As the connective tissue between technology and organization, APIs enable companies to monetize data, build profitable partnerships, break new ground for innovation and growth, and achieve economies of scale. Above all, the realization of economies of scale creates significant competitive advantages for companies.
APIs make it possible, on the one hand, to obtain services quickly externally and, on the other hand, to open platforms for third parties through a uniform interface description and to integrate them as partners into one’s value chain. In this way, you can monetize your competencies by offering your services. In tomorrow’s digital world, more than ever before, all partnerships will be based on APIs.
In the last ten years, the digital landscape of our world has developed enormously and enables a limitless exchange of information and communication in real-time. Much of today’s data is confidential, be it intellectual property, financial data, personal information, or any other type of data that could be negatively affected by unauthorized access or disclosure. With the rise of machine learning and the constantly emerging new technologies such as the Internet of Things (IoT), the variety of attacks is increasing and at the same time, they are becoming more sophisticated, organized, and difficult to detect.
Since significantly fewer employees in the company will work in the same secure network due to Corona shortly, companies must constantly review their cybersecurity strategies and expand them to home networks and mobile work-from-home devices.
The automation of the setup and configuration of the infrastructure as well as the software delivery are the most important highlights of the DevOps practice in the future. Automation in DevOps increases speed and consistency provides greater accuracy and reliability and increases the number of deliveries. RPA tools help automate manual and error-prone tasks to increase productivity. Automation in DevOps encompasses the entire development cycle. In detail, it’s about automation
In addition, the expansion of digital apps as well as the automation of end-to-end user flows and tests will become a reality.
DevOps automation enables multiple real-time reports to be generated that provide a consolidated view of everything going on in a project. In an automated DevOps scenario, cross-tool integration data is automatically stored in a central repository. This allows users to generate different types of real-time reports.
Internet, connectivity, and software technologies have increased customer expectations for service, reliability, and quality. In the future, companies will have to react faster than ever before. The concept of automation in DevOps ensures the necessary flexibility and effective collaboration between all members of the DevOps organization.
Organizations are benefiting from the rapid rise in AI by using intelligent automation as part of an integrated solution. The times when you could only rely on generic process automation via RPA are over. Companies are looking for AI-driven automation solutions to solve their daily challenges. Because RPA bots reach their limits when solving more complex end-to-end back-office tasks, especially when it comes to tasks with unstructured data entry.
This is where the next level of process automation comes into play – so-called hyper-automation. Hyperautomation complements RPA with the full range of Artificial Intelligence (AI) to deal with unstructured information so that the number of possible use cases for automation increases exponentially. This is the approach to achieve end-to-end automation of everything that can be automated.
Hyperautomation orchestrates automation through multiple methods including APIs, command-line interfaces, databases, and of course RPA. Hyperautomation relies on AI – for example, to analyze and classify input data when the input data is unstructured. Instead of just automating simple tasks based on spreadsheet data, through the use of NLP and ML, hyper-automation can perform tasks based on, for example, real-time conversations with employees and customers, or analyzing voicemails to determine what has been requested by customers.
The advent of the hyper-automation mindset is an important development that enables companies to keep pace with current market developments, even in challenging times, and to use both past and current data to compete successfully. This is what makes hyper-automation so valuable for businesses in 2021.
Accelerated by the pandemic, more comprehensive and agile implementation of automation across all functions of a company is becoming a top priority. The ability for citizen developers to automate processes and tasks without programming knowledge enables companies to adapt their processes to changing business requirements much more flexibly and quickly.
AI and machine learning will support the work of employees to an even greater extent and help with the collaboration between humans and machines. Coupled with a culture of modern engineering (Agile, DevOps) and a continuous innovation mentality, automation in software development and application management in IT opens the door to becoming an innovation partner for the company and enables companies to use their resources even more effectively.
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