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Hyper-automation: The Future of Digital Transformation

When it comes to digitization processes in companies, there is more and more talk of hyper-automation. But what is it actually? For some it is just a comprehensive form of process optimization; for others, hyper-automation is the future strategic technology that is not to be ignored.

“Hyperautomation has shifted from an option to a condition of survival,” said Fabrizio Biscotti, research vice president at Gartner. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world.”

 

Hyperautomation The Future of Digital Transformation

 

What actually is hyper-automation?

The term hyper-automation goes back to the market research company Gartner. This means a well-founded methodology for achieving tactical and strategic goals through the automation of business processes. Thus, Hyperautomation considers the automation of business processes on a large scale by combining a wide range of coordinated digital technologies.

According to Gartner, hyper-automation focuses on two aspects of business operations: The first is to automate whatever can be automated within an organization. The second is to combine different approaches, tools, and technologies to automate only individual tasks (RPA) but also complex processes with the help of artificial intelligence (AI), virtual assistants, and machine learning (ML).

 

How does hyper-automation work?

Hyperautomation is able to unlock maximum potential by combining a number of technologies that support each other and automate complex processes with unstructured data and a significant level of ambiguity. By using AI, ML, NLP, process mining, and intelligent technologies, the ability to discover processes independently is enhanced, and RPA bots are enabled to do much more than just perform the previous repetitive tasks.

Basically, hyper-automation takes on another level of human work. It’s not only a tool but a unified enterprise strategy or initiative with the ultimate goal of creating and optimizing end-to-end processes to achieve an even higher degree of automation that supports innovative new business propositions.

Hyperautomation only works if bots ultimately also perform the tasks that a machine learning (AI) has identified. Advanced artificial intelligence can better analyze unstructured data and implant it in an efficient workflow. With RPA being the fundamental part of hyper-automation, intelligent bots continue to process the data in hyper-automation and ultimately ensure that the work is done.

 

Benefits of hyper-automation

As already described, hyper-automation enables automation that goes beyond simple, repetitive process sections. With hyper-automation, comprehensive automation can cover even complex processes. Reducing costs and maximizing profits, but also conserving resources and designing a smart working environment are also considered as main benefits. When used correctly, hyper-automation leads to a higher degree of automation and higher productivity in the company. A significant increase in customer satisfaction can also be achieved by integrating the personalized customer service. The side-by-side collaboration of man and machine is the ultimate goal of hyper-automation.

 

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Smart companies: Tips for a smooth integration of AI

AI (Artificial Intelligence) has a long history of being considered science fiction but opens up enormous potential for companies in terms of productivity, the efficiency of business processes, gain sustainable competitive advantage and customer relationships. Covid-19 pandemic is the proof of accelerated use of AI across multiple industries around the globe.

Smart companies Tips for a smooth integration of AI

According to the latest title “Global Artificial Intelligence Market” published by Facts & Factors, the global Artificial Intelligence market size is expected to reach USD 299.64 Billion by 2026 from USD 29.86 Billion in 2020, at a compound annual growth rate (CAGR) of 35.6% during the forecast period 2021 to 2026.

Most companies believe that AI is certainly one of the foremost technologies of the future even though they still aren’t making the most out of their relationship with AI. Here below are few obstacles to AI adoption and how they can be avoided.

 

The Preparation Phase

For many people, there is still something mystical or threatening about AI. Although intelligent technologies act invisibly in our everyday life, the image of AI often emerges as futuristic, emotionless robots that look amazingly like Arnold Schwarzenegger are going to hunt us down and kill us. But AI is only aimed to develop machines/computers that are capable of doing things normally done by people. The lack of knowledge is one of the main obstacles to AI adoption. The implementation of new technologies should always be seen as a long-term project. As there really isn’t a textbook on how to adopt AI at the enterprise level, people with the right mindset need to be brought into an organization to help facilitate changes and capitalize on opportunities.

In many cases, high costs and a lack of resources are also decisive obstacles. But not every company directly needs its own computing resources or expensive, in-house developed platforms. In many cases, it’s worth taking a look at third-party AI platforms or in the public cloud. They enable the use of powerful and scalable AI solutions without the need for extensive investments of your own. The experience of the major platform providers also helps to implement projects as quickly as possible.

 

Communication is the key

The challenge of scaling AI and automation often does not lie in the technology itself. Rather, the corporate culture is often important in order to implement changes in the work environment. Thus, before the introduction of the AI, timely communication with employees is essential. The benefits of AI must be well elaborated and appropriate training must be planned for all employees. Artificial intelligence requires specialists who are well educated and have to be trained. This is the only way to develop, operate and maintain intelligent systems and to handle advanced troubleshooting and continuous improvement of these solutions. Tasks and responsibilities transformation must also be openly discussed to deal with the fear of losing jobs among employees, as, AI will complement rather than replace employees.

 

Introduction of a clear AI strategy

Small and medium-sized companies, in particular, are often reluctant to implement AI because they lack a clear strategy. In the first step, however, a fully developed strategy is not absolutely necessary: ​​rather it is more important for companies to understand the technology and recognize the possibilities it offers. At this point, experts should be consulted to elaborate on the benefits of AI and how can this actually benefit the company? What are the installation process and its duration? What type of data or tools are needed to work successfully?  What can be done to achieve results? Once all questions are clarified, and a strategy has been worked out the introduction can be prepared.

 

AI technical requirements

Like a human, an AI system also needs time to learn. That is why it takes time for the first successes to be measurable. In order to have a decisive influence on the development of companies, good implementation is requisite. The AI requires various available data that it can analyze. This is the only way to generate data models that can be used as a basis for future predictions or decisions. The implementation effort depends, among other things, on the flexibility of the software that a company uses. Another factor is the specific use cases that should be automated with the help of AI and that must be taken into account as early as the implementation phase. It is possible to start individually with each communication channel, regardless of whether it is email, chat or telephone. Preferably, however, the channels are placed one after the other. As a result, a company does not lose any time, because the advantages of an omnichannel system are that the training results of one channel flow into the learning process of the other channels. Depending on the use case, the AI ​​applies different algorithms and develops certain models. The learning is based on the trial-and-error method until it has developed the right model.

 

The AI ​​promises long-term optimization in terms of profitability and efficiency of in-house processes. With the ability of self-learning, algorithms can be used to improve existing processes and products as well as develop new business models. This means that AI has the potential to change entire industries and value chains over the long term. Artificial intelligence also opens up cross-sector value creation opportunities and growth potential for small and medium-sized companies. To gain all benefits related to AI, the first step is the will to deal with the topic of AI and ultimately its implementation. Therefore a well-developed strategy is required.

 

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4 Cloud Computing myths, debunked

Flexibility, scalability, and long-term business resilience are the huge boost to cloud adoption. The future of the cloud is bright. Over $ 287 billion growth is expected during 2021-2025 for the global cloud computing market. Yet there are many myths surrounding the use of cloud solutions that prevent companies from taking benefits of cloud services. Even after twenty years, the use of cloud applications such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) keeps coming up against it vague fears and rejection. It’s because, in addition to data security, access and control options also play a hugely important role. Cloud solutions certainly pose certain risks for companies and their data if not managed correctly. However, many myths are way much exaggerated. In addition, many risks not only affect the cloud, but also locally operated networks.

The following misconceptions about cloud computing prevent companies from taking advantage of the cloud and have made the acceptance of cloud services particularly difficult.

 

The company will lose control over its own data

Many CIOs and IT managers or administrators often feel that they will completely lose control of companies’ sensitive data and its management once it’s migrated to the cloud. Additionally, they also carry the fear of dependence on providers as the control over the server is also given away to the cloud provider. Overall companies are particularly concerned with the location of the data and a possible loss of control over their own data. But even though they have passed the operation and maintenance of servers into third-party hands, they are and will remain the sole owner at all times and can retain all rights and control over their data and can decide independently, depending on the services used, where the company-critical backup and archiving data is stored. Because administrators and those responsible for data no longer have to worry about small details such as updates or background processes, they can spend more time optimizing the infrastructure and suitable strategies for business growth.

 

The data is not safe in the cloud

For a long time, it was believed that cloud solutions are more susceptible to attacks than the company’s own IT. Cloud services, in themselves, are exceptionally secure. However, many companies are reluctant to cloud adoption and have huge concerns about cyber-attacks, data theft, and industrial espionage. Because there is no such thing as absolute security, more and more cloud providers are creating a secure cloud for their customers. Their business model hinges on preventing breaches and maintaining public and customer trust. Additionally, all cloud providers have to comply with stringent regulations and this requires them to put robust security measures in place, including the use of strict protocols and advanced security tools. Also, the latest data centers are equipped with various security measures and offer users a guaranteed high level of security for their data.

 

Migrating to the cloud is complicated

The companies’ IT departments are often considered to be busy maintaining ongoing day-to-day operations. They don’t have enough time or know-how to modernize IT operations through the cloud. They are persuaded that migration to the cloud will come along with additional requirements, will also increase the complexity of the IT infrastructure and administrative effort. BUT every cloud provider offers their support whether it’s before or during the migration and ensures that everything runs smoothly. The greatest advantages only become visible after the conversion and cloud automation of many tasks and processes, on the one hand, it relieves computer scientists in their everyday work as they no longer have to worry about updates, backups, archiving, or the complicated maintenance of IT systems.  On the other hand, it can meet the requirements of the specialist departments faster than conventional infrastructures.

A company that plans to move its applications from a data center to a large cloud platform, must check whether their applications are cloud-ready or need to be revised before the migration. Otherwise, they’ll end up paying a high price for a platform that they cannot take full advantage of.

 

Cloud is more expensive than the in-house computing

Cloud migrations are complex projects that quickly lead to unexpected costs. As with all operating costs, it is not just the monthly cost that needs to be considered, but also the total cost of ownership (TCO). The cost of going to the cloud depends on several factors such as license obligations, data center, and the company’s ability to control and optimize cloud consumption. The big advantage of the cloud is the flexible scaling and that you only pay for the capacities that you actually use. The up-front costs of cloud migration are often significant, but the longer-term savings usually dwarf that initial cost. Choosing the right provider and achieving more performance and lower costs requires know-how and experience with the multitude of services.

 

Almost every company knows how important it is to keep up with the times in the digital age in order to remain competitive. Cloud computing is playing a vital role in responding to the challenges of these unpredictable times. The cloud is seen as a tried and tested method to achieve the necessary flexibility and agility. It has proven to be an important driver of digital transformation.

 

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IT automation – Top Technology trends to look for Business Transformation

IT automation is one of the biggest IT trend terms in recent years. This involves a wide variety of solutions that give IT specialists more freedom in their day-to-day business. “IT automation is certainly a worthwhile goal because it saves a lot of money through lower personnel costs” – Tony Iams, Managing Vice President at Gartner. Yet IT teams in small and medium-sized companies often struggle with budget constraints and a shortage of skilled workers. When the demand for IT services increases, they are therefore heavily overloaded and look for ways to increase efficiency.

The 2020 IT Operations & Strategic Priorities for IT Executives report from Kaseya shows that IT automation is a critical need for SBMs, 60% of IT executives worldwide plan to invest in IT automation in 2021. IT automation is their primary strategy for doing more with less. With IT automation being a top priority for businesses today, IT teams are now applying it to a wider range of business and IT functions. Here below technologies are particularly efficient:

IT Automation Technologies for Business Transformation

 

Robotic Process Automation (RPA)

Robotic process automation (RPA) is changing the way companies operate around the world. It is well known that with Robotic Process Automation (RPA) you can optimize many processes and save time and money. An immense number of processes are still carried out manually in companies and authorities, although this is actually not necessary. RPA tools use software bots that simulate human-computer interaction to automate routine tasks. RPA is rapidly growing thanks to the benefits it has to offer such as lower labour costs and less human error. RPA bots usually do not require any software customization or deep system integration. According to Gartner, global RPA software revenue is projected to reach $ 1.89 billion in 2021, up 19.5% from 2020. It goes without a doubt that RPA is accelerating digital transformation.

 

Implementation of RPA

Despite the numerous advantages this technology has to offer, most businesses struggle with successful RPA implementation. The use of Robotic Process Automation (RPA) promises a high increase in process efficiency but also brings huge implementation challenges if it’s not seen as a central building block on the way to hyper-automation but just another technology. According to a study by Deloitte, many companies have found that scaling RPA is more difficult than expected. Larger RPA implementations, when the number of bots is more than 50, often take longer and are more complex and costly than companies originally expected. Particularly when introducing Robotic Process Automation, attention must be paid to a strong strategy, right expertise, and communication with employees for the realistic expectations of Robotic Process Automation, because the software robot should automate entire processes or at least make work easier through partial automation. A lack of acceptance makes the implementation of corresponding RPA projects more difficult and leads to distrust and displeasure among employees. Rather, if the employees actively participate in the development of the robot, particularities in the processing of the process can be emphasized. Positive communication also creates trust and motivates the workforce to contribute their own ideas.

 

Predictive analysis

As the name suggests predictive analysis techniques include machine learning, data mining, and predictive statistical modelling to use historical data from various operational sources to predict the likelihood of future events. It’s an emerging technology that is helping companies acquire and retain their most profitable customers and grow the customer base to improve operations. They also help companies predict future customer needs, business needs, human resource requirements, and process improvements that they should make to their operations. It’s being used by multiple companies to improve the efficiency of production and operations, reduce risk and fraud, and create better customer experiences by mapping the likely journey of customers and the expected touchpoints. It can also improve the accuracy of supply chain management, and help organizations create marketing plans with more precision, knowledge and confidence. This technology is also helping IT security experts to identify potential vulnerabilities, determine the likelihood of cyber-attacks and work on improving the company’s security structure.

 

Artificial Intelligence (AI)

AI refers to the ability of a machine to display human-like capabilities such as reasoning, problem solving, learning, planning and creativity. By using machines are programmed to think like humans and mimic their actions. The AI ​​system is fed by sensors in machines, ERP systems, customer relationship management systems and even Internet data. Alexa, Siri, Cortana – the chatbots from Amazon, Apple and Microsoft are examples of how artificial intelligence makes the interaction between humans and computers easier and more efficient. Business software manufacturers are increasingly using intelligent algorithms to make life easier for their employees and customers.

 

Hyper automation

Basically, the term hyper-automation covers the combination of different technologies. From a global perspective, the focus is still on the automation of simple and complex tasks, but the combination of different technologies such as PRA, ML, and IBPMS ensures more efficient processes. In addition, there is no need for human action. Hyper-automation contributes to higher productivity in the company. According to Gartner’s, List of Top Strategic Technology Trends for 2021 – Hyper automation is a key trend that digital strategy teams should consider.

 

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DMS: Intelligent Workflows Integration to Streamline your Business Processes and Increase Productivity

STREAMLINING BUSINESS PROCESS THROUGH DMS

Our everyday life is shaped by digitization. Nevertheless, numerous documents go through our hands every day in the office as at many workplaces there is still more paper in circulation than necessary. Appropriate management of company documents is extremely important to an organization. It’s a process that can be easily automated but in many cases is carried out manually. If, on the other hand, you are a Paper-less Office (mostly digital), you still have to manage documents and possibly scan, save and organize them beforehand.

 

However, in order to keep up with the rapid changes in the modern business world, companies nowadays have to rely on lean processes, transparency, and efficiency. A future-proof company cannot avoid digitization. Efficient management of documentation can allow employees to collaborate on tasks and save businesses significant amounts of money. A document management system (DMS) can make this process much easier for you so that you can concentrate fully on the business operations. Digital document management in connection with automated process management is one of the steps towards the digital transformation of your company. With a DMS you can automate your workflows, keep track of things and are no longer exposed to the risk of losing sight, stop shuffling paper and streamline your business processes.

 

Here below are some considerations that you should take into account when implementing a DMS.

 

Plan the DMS implementation:

Deciding to implement a DMS is one of the first great steps towards becoming a Paper-less Office. A well-thought strategy and planning is essential to ensure a successful implementation of DMS. Only when you know what you want to achieve with the DMS, you can pay attention to the corresponding functions and features. It is therefore essential that you get a clear picture of the company’s current situation, possible problems, new opportunities, goals, and future needs. All of these elements need to come together in order to find out the best DMS to satisfy your company’s needs.

 

Essential features package:

If your company is growing steadily, you should also consider future requirements when planning to embark on a DMS implementation project. Don’t just think about the present, but also keep an eye on tomorrow to achieve long-term gains. The following features are essential for a smooth working day:

  • Usability: Clear user interface and simple operation.
  • Digitization: Scan documents and save them in various formats such as PDF or Word.
  • Document input: Should allow inputting files through various sources such as scanner, Email, manual upload, mobile applications, web services, etc.
  • Versioning: Always use the latest version of a document and restore an older version in an emergency.
  • Full-text search: should be able to find documents quickly by allowing users to locate any word or phrase that appears in the document.
  • Automatic workflows: Self-definable forwarding and processing of documents.
  • Archiving: Safe storage of documents.

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Industry-leading document management system providers, always go beyond the basic features and provide to their customer’s innovative features and solutions that will improve their overall business productivity. So, all other features and functions are also welcome, but if they are associated with considerable additional costs and are not absolutely necessary for your business, then you shouldn’t choose them in your package. This saves you a lot of unnecessary costs and allows you to concentrate on the essentials.

 

On-site vs Off-site DMS:

When looking for a suitable DMS, you can opt for on-premise or off-premise depending on the amount of data your organizations handle. With an on-site DMS, then you can transfer gigabytes of data in a matter of seconds. On the other hand, if you are running a company that has a large number of users to manage and your investment in IT is not convincing, then you need to go for the cloud or off-site DMS. It’s also suitable if you have a work from home of off-premise working culture within your company.

You should also pay attention to integration options whether new DMS can be integrated into the company’s existing systems such as BPM, CRM, or ERP, so that there is no need for a major changeover.

 

Service of the provider:

There are a lot of DMS providers on the market, they differ with their individual user interfaces as well as with their range of functions. That’s the reason why, in addition to extensive and solid document management software, you should choose the provider that best fulfills your requirements and most importantly when it comes to DMS implementation.

A serious DMS provider should be able to provide a transparent presentation of the costs incurred, such as for setting up and maintaining the software, through to any other services that may arise. These include, for example, training courses such as:

 

  • Introduction to document management software
  • Correct handling and use of the DMS
  • Establishment of automatic workflows
  • Assistance in any undesirable emergency situation. A permanent contact person in such a case can be helpful to resolve any issue that occurred. A DMS usually works perfectly, but in an emergency, it can never hurt to have a good connection to the manufacturer.

 

If you have carefully studied the use of a DMS within your company and know what you want to implement with it, you can specifically pay attention to urgently needed functions. A right DMS accrues significant benefits to the organization. Hence it is imperative to choose the correct DMS to makes your day-to-day work easier.

If you are interested in a DMS, we would be happy to work with you on your company’s digitalization project and accompany you step by step with the introduction of a DMS in your company. We look forward to hearing from you and would be happy to answer any questions you may have.

 

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2021: Ensure Your Business Growth by Becoming Data-Driven Company

ensure Your Business Growth by Becoming Data-Driven Company

 

In 2021, government agencies and businesses will need to be able to make decisions based on current/real-time data faster and more accurately than before. Because: due to COVID-19, markets, supply chains and customer behavior have changed in recent months, only data-driven businesses are able to respond quickly and effectively in a rapidly changing world. In order to transform into a data-driven business, it’s not only important to understand the importance of data quality and governance. But it’s also key to drive a data strategy that is aligned with your business strategy. By integrating analytics into business strategies, businesses can transform data into decisions that improve lives and results.

 

A study of more than 3,500 business executives and senior IT decision-makers across the UK, France, Germany, and the Netherlands found a gap between companies using data to inform decisions during the pandemic and those who are not. The YouGov survey, commissioned by Tableau, asked executives of small, medium, and large businesses about their use of data during the pandemic, lessons learned, and confidence in implementing long-term business change. For executives in data-driven companies, a majority (80%) believe they had a key advantage during the pandemic.

 

These leaders are also deeply committed to the important role data plays in the future of their business. A large majority of 76% plan to increase investments in data literacy; especially after the long bumpy ride we have all been on since the start of 2020. Additionally, 79% are confident that they will ensure business decisions are supported by data. The results show that non-data-driven companies are slower to grasp the meaning of data in these uncertain times. Only 29% see this as a key benefit and 56% say they will reduce or stop investing in data skills. Additionally, only 36% are confident that the data will support business decisions.

 

“This year has accelerated change for businesses and ushered in a fully digital world faster than anyone could ever have imagined. Data is at the heart of this digital world,” said Tony Hammond, Vice President Strategy and Growth EMEA. at Tableau. “In this age of data, our research shows that data-driven companies see clear benefits and are more confident about the future of their business. As a result, they really rely on the power of their data. Companies that haven’t woken up to it run the risk of falling behind. But businesses big and small can rest assured that it’s not too late to harness the power of data – the time is now.”

 

When asked how it helps to be data-driven during the pandemic, company leaders recognized several benefits. At the top of the list are: more effective communication with employees and customers (42%), the ability to make strategic business decisions faster (40%) and improved collaboration between teams for decision making and problem-solving (36%).

 
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“We started building data skills in our company in 2013, and due to the pandemic, we have definitely benefited from these functions,” explains Dr. Dirk Holbach, Senior Vice President and CSCO Laundry & Home Care at Henkel, one of the world’s leading consumer goods and industrial companies. “For example, within a few days, we were able to record all of our personal protective equipment controls so that each facility can see how we are equipped in this regard so that our business can continue to operate. I am confident that we will take some good lessons with us in the future, especially when it comes to working together. “

 

For all respondents, the key takeaways from the pandemic are: the need to be more agile (30%), prioritize and implement projects faster (26%), and access to more accurate, up-to-date, and cleaner data (25%). Jay Kotecha, the data scientist at full grocery brand Huel, said of his data strategy: “Our data-driven strategy helps the company respond to consumer behavior and enables us to pivot and react faster and more clearly. It’s about empowering the entire organization through data. Employees examine data from across the company and turn it into insights we can act on, whether it’s sales projections, sales effectiveness, or marketing spend. “

 

Across Europe, the results show that just over half (56%) of business leaders consider their companies to be data-driven, while one in three (38%) think they do not. These results indicate a clear way for organizations to leverage data to support business resilience and decision making during this time. German companies are taking the lead with 62% as their business is data-driven, while the UK lags behind with just 46%.

 

The promise of digital change is based on the ability to harness the power of technology to grow your business, open up new markets, and acquire new customers. It also means that you need to understand all of the data (the digital exhaust – the trail of data left behind by browsing the Internet) that new customer experiences create. A data governance strategy as well as information and data quality management as an integral component of management systems significantly supports the achievement of the organization’s goals, ensures compliance conformity, increases throughput, and supports organizations in the transformation to a data-oriented culture. Organizations thus secure their competitiveness and can expand this further through increased data intelligence.

 

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Data-driven companies are more resilient and confident.

Data-driven businesses vastly more optimistic – research

How can Digital Marketing benefit from Artificial Intelligence?

AI in Digital Marketing xorlogics

 

AI is now more accessible than ever with it’s potential to change business forever. With its positive impact, and being a lot more affordable than before, both big and small companies are able to benefit from the insights and automation options it provides. With the integration of AI brands can not only leverage customer’s data but also anticipate their customer’s next move, understand sales cycles better, correlate their strategies for converting prospects into paying customers and improve the overall customer’s journey with machine learning efforts.

 

As a subset of AI, machine learning involves the analysis of historical data from various business interactions with customers / prospects (for example: when and what was the last time the customer ordered?). As ML has the ability to analyse extremely large sets of data, its being used in the digital marketing departments around the globe to identify sales patterns and increase success factors. Algorithms for ML generate insights via predictive analytics, based on these insights, marketers can either take actions individually or automate AI to do the job. For example, they can automate emails that are aimed at re-targeting your audience, giving you a better chance of a higher ROI.

 

ML is also playing a major role in SEO. Even tough SEO algorithms change across major search platforms, with AL and ML tools, the insights from searchable content may become more relevant than specific keywords in the search process. In order to maintain a high-ranking place on search engine result pages, consider the quality of your content rather than simply the keywords included. By doing so, you’ll be ahead of the game when it comes to future-forward content creation and SEO.

Marketers can also benefit from ML tools to analyse what type of content, keywords, and phrases are most relevant to your desired audience. Once they have the key insight, they can optimize their dialogue and develop engagement across multiple online platforms, to drive brand awareness and create meaningful relationships with leads, prospects, and customers alike

 

The key difference between modern AI-based and traditional outbound marketing strategies is the integration of contextual data, i.e. information that results from the interaction with the customer is not stored in labour-intensive and time-consuming data warehouses but is available in real time and can be used for decision-making. With these customer profiles and interests recorded in parallel to the interaction, the previously defined decision strategies are fed in order to achieve high planning security for high, profitable conversion rates.

 

Artificial intelligence opens up possibilities in marketing domain far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive. In addition to that, AI, robotics and other forms of smart automation will bring great economic benefits and contribute up to $15 trillion to global GDP by 2030.

 

With AI integrated marketing, business can forecast customer behaviour and run data-based campaigns to have remarkable results. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers. Business are creating value through transforming customer journeys by providing immediate response to consumer’s queries or issues. According to McKinney’s study, 75% of customer demand NOW service within 5 minutes of online contact. If business can beat this time, they can convert a ‘visitor’ into a ‘paying’ customer.

Here below are some interesting statistics of AI in marketing:

 

  • According to research from Callcredit, 5 hours and 36 minutes is the amount of time that the average marketing professional spends collating data and getting it ready for presentation. An AI integrated marketing dashboard can do the collating in minutes and give you more time for reporting process. Statwolf
  • 97% of leaders believe that the future of marketing lies in the ways that digital marketers work alongside machine-learning based tools. QuanticMind
  • By 2020, 30% of companies worldwide will be using AI in at least one of their sales processes. Gartner
  • By 2020, 85% of customer interactions will be handled without a human. Gartner
  • For 61% of marketers AI is the most important aspect of their data strategy. MeMSQL
  • 80% of business and tech leaders say AI already boosts productivity. Narrative Science
  • When AI is present, 49% of consumers are willing to shop more frequently while 34% will spend more money. PointSource
  • Large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one. MIT Sloan Management Review
  • Netflix is saving $1 billion per year by using machine learning to make personalised recommendations- Artelliq
  • 44% of consumers don’t even realise they’re already using AI-powered technology platforms. Pega
  • 45% of end users prefer chatbots as the primary mode of communication for customer service inquiries. Grand View Research

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The ways that ML is being used in digital marketing practices helps organizations to expand their understanding of their target consumers and how they can optimize their interactions with them.  So, when developing appropriate AI applications for marketing purpose, the collaboration between software developers and domain expert’s must not be neglected. The ultimate decision-making competence when it comes to the all-important question of which next best actions are to be implemented in order to achieve the greatest possible marketing success usually belongs to the domain experts. You should have the experience and expertise to incorporate the right NBAs into the self-learning models for decision-making strategies.

 

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RPA – Robotics Process Automation Trends and Statistics for 2020

RPA Robotic Process Automation XORLOGICS

 

This year, many companies found their business growth through the smart interaction between human resources and supporting software robots as the direction is set towards digitalization and automation. The combination of RPA with artificial intelligence (AI) and machine learning (ML) is playing a huge role in the global economic environment.

According to a new report by Grand View Research, Inc. the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027. In addition to that, in Gartner Top 10 Strategic Technology Trends for 2020, hyper-automation, autonomous things and AI security were at the top 3.

 

RPA is a type of IT solution that allows organizations to automate many of their tasks through the use of specialized software programs. Many business executives believe that RPA enables their companies to automate structured tasks that take just a few work steps and repeat themselves frequently – like transferring data from one IT system to another. RPA can relieve employees of tasks that consume a lot of time but do little to add value and are prone to errors – keyword typing errors. It also helps to automate routine jobs on the computer and processes to increase efficiency, improve service and save costs.

 

One should not think that RPA is similar to BPM. RPA only simulates human data entry and helps with simple and recurring tasks, to save time because the software does things in parallel that an employee can only work through one after the other. Such an application needs simple rules with few exceptions – and the biggest advantage is that, unlike humans, it runs around the clock. Unlike BPM which stands for describing, contolling, modeling, and optimizing the processes that are present in an organization.

 

Here below are the statists and trends of RPA that shows it’s worth:

 

    • RPA as an industry is growing exponentially– the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027 – Grand View Research

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    • The RPA industry will grow from $250 million in 2016 to $2.9 billion in 2021. This is one industry that is growing at a lightning speed. It was already worth $1.7 billion in 2018 – Forrester

 

    • RPA will achieve “near universal adoption” in the next 5 years – Deloitte

 

    • By 2025, the market for collaborative robotics is expected to reach $12 billion – MarketsAndMarkets

 

    • By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes. Gartner

 

    • The RPA fast adoption is helping business to reduce the operational costs and enhance overall customer satisfaction, improve transparency and visibility for service functions and reduce of manual efforts – Reportlinker

 

    • 11,214 results This is the number of open positions produced by a recent search for “robotic process automation”on LinkedIn’s jobs site. Titles vary within this growing IT jobs category, but “RPA developer” (and variations of the same) is an increasingly common one – reflecting the need for IT pros who can build the bots that enable organizations to offload repetitive, time-consuming tasks – LinkedIn

 

    • RPA deals with the application of advanced technologies including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans – Gartner

 

    • RPA is offering a lot of benefits to the business by giving access to collaborative intelligence where humans and technology works side by side so that they can perform their roles optimally. As employees don’t need to perform repetitive tedious tasks, they can be educated to work with automation tools and learn the latest business and marketplace information through machine learning – Xorlogics

 

    • The market for RPA in Healthcare is driven by the increasing demand to automate claims and process management. RPA vendors are focusing on developing best-in-class intelligent process automation bots – Research and Markets

 

Demand Forecast Powered by Machine Learning

Demand Forecast Powered by Machine Learning

 

The business landscape is rapidly becoming more global. Largely due to improvements in communicationsand increasing globalization which are dramatically impacting the way business is managed. No area of a business is more affected by the trend of a global business environment than the supply chain. Supply chain logistic, known as the backbone of global trade, is a network of many partners involved such as customer, dealers, manufactures,transportation, external warehouse,suppliersand inventory. Sometimes a delivery comes along with delay, sometimes there is something wrong in a package, delivered article is different to ordered article and sometimes a shipment is lost. This is annoying for all sides. It costs time, energy, money and sometimes even the customer. Challenges for decision-makers in supply chain management are growing due to the widely networked supply chains and the constant change in the environment of companies.

 

In fact, many companies are facing hurdles in their existing business processes and technologies that aren’t flexible enough to deal with “large and global” business environments. Therefore, areas such as manufacturing, distribution, sourcing of materials, invoicing and returns are impacted by the increased integration of a global customer and supplier base.

Supply Chain specialist must deals with long-term planning in terms of location, make-or-buy decisions, supply relationships, capacity dimensioning, logistics strategy and general tasks along with cost optimization in structuring of the logistics and production processes. Hence, in order to initiate the demand forecasting, it’s highly recommended to understand the workflow of machine learning modeling. This offers a data-driven roadmap on how to optimize the development process.

 

Operational inefficencies in SCM often lead to potential revenue losses, increasing costs, and poor customer service, ultimately diminishing profits. With the help of AI, machine learning techniques are able to forecast the right number of products or services to be purchased during a defined time period. In this case, a software system can learn from data for improved analysis. Only good data produces good results!

Data interpretation is a vital part of supply chain management and demand forecast as it’s used to improve your ability to estimate future sales, reduce shortages and overstock. Once the data is interpreted correctly, both in national and international trade results in having the right products at the right time in the right number at the right place.
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So, for demand forecasts that are generated by self-learning algorithms require data that is closely related to sales. However, in order for machine learning to achieve a high quality of forecasting, a certain amount of quality data is required. The result of ML process depends solely on the quality and quantity of data provided.

To ensure that the data is up to date, the input data should not be older than 5 years. Data selection can be a special hurdle before using machine learning methods, because it can be very time-consuming. In connection with the data quality, it must be ensured that there are only a few missing values of the data records in the input data, otherwise the machine learning model may generate incorrect results. Data preparation is necessary for successful implementation and definitely pays off later. If the data record does not have sufficient data quality, it must be prepared through an intensive process and carry sufficient information for qualitative algorithms and for a good forecasting performance.

 

The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Therefore, ML fed with qualitative data can generate precise forecasts and thus ensure a secure basis for planning. The resulting benefits, such as reducing inventory levels and simultaneously optimizing the ability to deliver, also improve the operating result. ML uses learning algorithms to recognize patterns and regularities in data and is able to adapt automatically and independently through feedback and thus react to changes.

 

Compared to traditional demand forecasting methods, machine learning not only accelerates data processing speed but provides a more accurate forecast, automates forecast updates based on the recent data in order to create a robust system.

Machine Learning and Data Protection in the Cloud

Machine Learning and Data Protection in the Cloud

 

As more organizations are shifting infrastructure and services to the cloud, more are adopting a multi-cloud strategy. In fact theworldwide public cloud service market is expected to reach $623.3 billion by 2023. 83% of enterprise workloads will be in the cloud by 2020 and 94% of enterprises already use a cloud service.

 

These numbers show that most companies are already using cloud solutions in one form or another. In addition to the many advantages that the cloud can offer, it also increases the attack surface. Cloud growth creates new security threats, and organizations are struggling.

To guarantee a high level of security in the cloud, modern security solutions with machine learning, analytics and automation can help. Machine learning has already taken a massive increase in adoption over recent years and many businesses have already started to plan or have already developed machine learning models for security purpose.

 

Most IT departments are well equipped and informed with ensuring local network security. But how this really works in detail in the cloud is still relatively unfamiliar in many organizations. In addition, many business are already busy with securing their existing infrastructure, thus securing cloud infrastructures can be ignored or forgotten. What many businesses underestimate is that even complex hybrid infrastructures can be secured with advanced technologies that use machine learning, with a manageable cybersecurity budget.

 

In almost all industries, the cloud is already being used as an effective extension or replacement for some of the data center resources. And if you are not yet in the cloud, you are probably planning this step at the moment. But before moving to the cloud, make sure that critical security and regulatory questions are answered. As companies who adopt a multi-cloud strategy need visibility across it all to prevent downtime and to stay ahead of hackers.

 

Cloud-related threats are at an all-time high

 

It is not surprising that cloud-related threats keeps increasing, and there is no reason to believe that this will change in the near future. According to the recent study by the Ponemon Institute, the average cost of a data breach worldwide now equals $3.86 million – $148 per compromised record. However, the numbers vary greatly from country to country. In addition to that, a recent Skyhigh Networks study found that every organization operating in the cloud had almost two security incidents each month. These include: inside threats (both accidental and malicious), privileged user threats, compromised accounts, or data exfiltration. This shows that data is fundamentally endangered regardless of the storage location. Contrary to the often erroneous assumption, the provider of the cloud is never responsible for securing corporate data. For example, AWS states that they are responsible for the security of the cloud, but their customers for security within the cloud. Responsibility thus falls into the IT security of the companies using it, which often does not have the right personnel or know-how to guarantee security in the cloud.

Along with other challenges, IT security teams have a dual mandate to secure data in cloud services procured and managed by their organizations, as well as corporate data that employees upload to cloud services that they find and use on their own.

 

What specific steps can you take in this case to ensure security in the cloud as well?

 

A robust cloud security program includes technologies to protect, detect, investigate and respond to security incidents. The most effective cloud security technologies are data encryption, network encryption and SIEM (Security Information and Events Management). With the exception of SIEM, most technologies focus primarily on protecting and building a defense. However, next generation SIEMs also include proactive monitoring, analysis to improve detection and Reporting and forensics about security incidents, investigation and response to security incidents along with alerts based on analytics that match a certain rule set, indicating a security issue.

This makes the latest generation of SIEMs the ideal solution to secure hybrid environments. SIEM tools work by collecting and aggregating logs, reports and alerts from all of your security tools and solutions. These tools then present this information in a centralized location, by improving visibility and speeding incident analysis and response.

 

Machine learning key to securing data in the cloud

 

Fact that hackers are growing in sophistication and their malware is evolving quickly and continuously, enterprises must adapt a robust data protection strategy. Terms such as “data science” or “machine learning” started long ago as buzzwords in the IT industry. For some time now, however, the clever algorithms have been helping to recognize patterns in many areas and are already doing so in IT security. Many companies in numerous industries are currently facing the challenge of securing existing or future infrastructures in the cloud. Already used in speech recognition software and ERP systems for data management, the clever algorithms of machine learning are now being leveraged in cloud security solutions in order to enable enhanced threat detection and real-time security.

 

Next-generation machine learning algorithms are ideal for today’s cloud security because large data volumes are the most important condition for their reliability. These ‘intelligent’ solutions are able to take in high volumes of data from high-traffic environments andperform an extensive property and behaviour analysis to detect threats and take the appropriate actions in a variety of scenarios.

Therefore, the use of machine learning is the logical response to the growing amount of data and the ever-evolving threats found in the cloud. Machine learning solutions are helping organizations to secure data and enabling them to securely adopt any cloud application, despite advanced malware.

 

Sources:

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