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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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Automation myths debunked: Why is Automation important for your business?

Hardly any company that strategically pursues their company growth can get around automation today. Automation enables tasks that were previously slow, manual, old-fashioned, and time-consuming to be supported with suitable software and thus run independently. As a human error can be unpredictable and happen when you least expect it, with the right technology companies’ processes are more accurate and faster. Use cases of automation are, for example, employee onboarding, analyzing reports on transactions, monitoring bookkeeping activities regularly, customer service, databases updates, sending personalized emails, perform inventory, etc.

Business processes automation can not only be used to gain efficiency. Availability of modern technology, as well as enhanced software applications, have made it easier to increase employee efficiency and you can get better results when you embrace automation. A win-win situation for companies and employees.

 

But despite these benefits, there are still myths surrounding automation that keep companies from getting started. Even though automated processes create positive changes, still, many companies fear high costs, difficult implementation, and staff changes – but these are just prejudices that we would like to address here and thus show that every company can benefit from automation.

 

Automation is a complicated and complex process

Hmmm, yeah. Not if it’s done right. As is often the case, good preparation is half the work. So, before starting with automation, make sure you understand what your company’s expectations are. Your decision to automate must depend on your needs, capacity to build it, and also your customers’ requirements. Specific goals can be developed using your personal business case. This step is essential so that automation succeeds and creates benefits for the company.

These requirements should also be discussed in-depth with different automation tool providers instead of falling for fancy advertising promises or the cheapest subscription. It is advised to meet with different process automation providers for not only choosing the right tool, but also to evaluate your own requirements.

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The future is automated

‍Automated processes bring a lot of advantages in operational processes, such as improved operational efficiency, long-term cost reduction, better customer service, visibility & transparency, and an increase in productivity. However, if a company only carries a small range of products, stores a manageable amount of goods, and generally only offers a small storage capacity, there is no need to implement a fully automated system. In such cases, manual or partially automated solutions that grow with you are the better options. Companies then have to weigh up whether small order quantities can be processed more efficiently in this way.

 

Automation is killing jobs

Nowadays it is constantly stated that automation is accompanied by a huge burden of unemployment. With the increased use of machines and automated processes, the fear of reducing or replacing staff increases. It’s true that automation is impacting various jobs in different sectors around the world. Due to automation, human intervention is certainly reduced in a business process. For instance, from production to planning, everything can be controlled by artificial intelligence or machine intelligence. It is easier to bring accuracy into the production process and increase overall productivity with machine intelligence. Every company wants to reduce the number of its employees as much as possible through technological improvements. But that does not mean that we are heading towards an unemployed society in years to come. As the machines are performing tasks previously done by humans, companies are busy transforming and redesigning jobs in a way that can make technological elements compatible with human capital development. The future workplace is where humans and machines will enhance each other’s strengths by working side by side.

 

Existing systems prevent the integration of new solutions

Automation doesn’t happen overnight – Companies are constantly faced with the challenge of proper integration of a set of services related to automation and ensuring that all expectations are aligned with business goals. Integrating your automation initiatives successfully is impossible without a flexible, scalable infrastructure. Therefore, on-premise infrastructure must be avoided/limited because of its limitation in terms of automation roll-out, scalability, and ease of use. For this purpose, cloud solutions are ideal as they let you get straight to work without wasting your valuable time on on-premise setup and maintenance.

 

So, now that you know that automation is here to stay, and can help you better run your business, it’s a safe bet that such automation can be trusted and utilized. By taking into account the myths discussed in this article, and learning the truth about each, you’ll be able to run your business more effectively.

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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COVID-19: Companies Journey toward Digital Expansion to become Faster, more Productive and more Responsive

Digital transformation progress

 

Our everyday life and way of doing things are completely changed since COVID19 started. It has accelerated the global digital transformation, according to the most recent F5 State of Application Strategy survey (SOAS). The seventh annual edition of this study is based on a survey of 1,500 participants from various industries, company sizes, and positions.

 

The need to adopt digital services across industries, geographies and communities is accelerated due to the dramatic shift in remote work and social distancing so that companies can improve their connectivity to interact with customers.

Business leaders have recognized digital technology as a key driver of revenue and raced towards digital transformation within their company. Here, below, are the key findings of the F5 survey:

 

  • AI-assisted business has tripled.
  • Applications continue to be modernized rapidly, with APIs a method of choice.
  • The importance of SaaS-delivered security is rising as organizations work to unify security across distributed applications while managing more architectures than ever.
  • Architectural complexity makes multi-cloud availability an imperative, and edge deployments are increasing, too.
  • Telemetry will take us to the future—but now, nearly everyone is missing the insights they need.

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Future-ready organizations are working to improve connectivity, reduce latency times, guarantee security and use data-driven insights. There is increasing interest in public cloud and SaaS, edge computing, and seeking application security and delivery technologies that are easy to deploy and provide data for decisions.

Modernization remains the top priority when it comes to operating on both modern and traditional application architectures for more than 87% of organizations that’s 11 percent more than in 2020. Additionally, almost half of all companies – 30 % more than last year – manage at least five different architectures.

 

Since last year’s SOAS report, the growth of AI and machine learning has more than tripled to 56%. This means that more and more companies are in the late phase of digital transformation. 57 % of those surveyed have begun digital expansion, an increase of 37 % compared to the previous year. This shows an increased focus on business process automation, orchestration, and digital workflows to integrate applications. 77% are already modernizing internal or customer-oriented apps, which is 133% more than in the previous year.

 

Additionally, two-thirds of respondents use at least two methods to create modern workloads, a mixture of traditional and modern application components. Of the companies with only one method, 44% say they use modern interfaces, either via APIs or components such as containers. More than half of the respondents already use infrastructure as code. Organizations using this approach are twice as likely to deploy applications even when using automation. They are also four times more likely to use fully automated application pipelines.

 

Companies are realizing the potential of edge computing. It enables new services and better performance by placing applications as close as possible to the sources and users of data, situation may vary for each industry and business function. Of course, COVID-19 is an accelerator due to the distribution of labor. No less than 76 % of those surveyed are using or planning edge implementations. The top reasons are to improve application deployment, performance and data available for analysis. In addition, 39% believe that edge computing will be strategically important in the years to come. 15% already host technology for app security and delivery at the edge. More than a third of companies (42%) will support a fully remote workforce for the foreseeable future. Only 15% plan to bring all employees back to the office.

 

Companies are creating and collecting more data than they have at any point in the past. All this data is coming from different sources. However, according to surveys, sufficient data does not necessarily deliver the insights companies really need. More than half of the respondents already have tools that assess the current state of applications. But an alarming 95 % say that they are missing important findings from the existing monitoring and analysis solutions. Accordingly, the collected data is primarily used for troubleshooting, followed by the early detection of performance problems. Almost two-thirds of respondents (62%) measure performance in terms of response times. Less than a quarter of companies use them to uncover degradation in performance. And only 12 percent forward the data to business areas.

 

More than 80% of respondents believe that data and telemetry are “very important” to their security, and over half are excited about the positive effects of AI. Participants also named platforms that combine big data and machine learning (also known as AIOps) as the second most important strategic trend in the next two to five years.

 

In many ways, the coronavirus pandemic has challenged businesses and governments around the world. In order to rise to the challenges caused by the pandemic, businesses have modernized and distributed applications in short term. Digital technologies have allowed many organizations to avoid a complete standstill, due to unexpected and urgent shifts in work. Companies must continue to discover and implement AI and other digital technologies for the continuity of their business.

 

The full report can be downloaded here: The State of Application Strategy in 2021

Business Automation & Multi-Cloud Management: Micro and Maxi trends for 2021 and Beyond

iot IoB covid gartner xorlogics

The year 2021 is all about transformation processes, primarily resulted by the exceptional situation we’ve witnessed in 2020. As 2020 caused a major shift in how business and IT teams operate, the development around COVID-19 was and still is a great challenge for all organizations. In addition to classic customer service, IT service, in particular, is confronted with more tasks and service requests. So that the workforce can work productively and quickly, the IT service needs intelligent tools for automation. Many changes have been on the agendas of IT departments for several years and vary from micro changes that affect the big picture to maxi changes that will affect future generations of employees.

 

In this continuously changing environment, organizations are exploring new ways to operate and drive growth. Each year, Gartner, Inc. releases a series of studies mentioning trends/predictions that will impact the business environment, IT, and technology in the coming years. Here below, we’ve gathered the most relevant trends to the IT automation market to help IT, professionals.

 

“Hyper automation is irreversible and inevitable. Everything that can and should be automated will be automated.” Brian Burke, Research Vice President, Gartner

 

  • By year-end 2025, over half of the world’s population will be subject to at least one internet of behaviors (IoB) program (private, commercial or governmental).
  • By 2025, 50% of enterprises will have devised artificial intelligence (AI) orchestration platforms to operationalize AI, up from fewer than 10% in 2020.
  • By 2025, 40% of physical experience-based businesses will improve financial results and outperform competitors by extending into paid virtual experiences.
  • By 2025, half of the large organizations will implement privacy-enhancing computation for processing data in untrusted environments and multiparty data analytics use cases. 
  • By 2024, organizations with IT teams that understand the needs of customers will outperform other organizations’ customer experience metrics by 20%.
  • 2023, 40% of all enterprise workloads will be deployed in cloud infrastructure and platform services, up from 20% in 2020.
  • By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
  • By 2025, most cloud service platforms will provide at least some distributed cloud services that execute at the point of need.
  • By 2025, customers will be the first humans to touch more than 20% of all products and produce.
  • By 2024, organizations will lower operational costs by 30% by combining hyper-automation technologies with redesigned operational processes.
  • By 2024, 80% of hyper-automation offerings will have limited industry-specific depth mandating additional investment for IP, curated data, architecture, integration, and development.
  • By 2024, more than 70% of the large global enterprises will have over 70 concurrent hyper-automation initiatives mandating governance or facing significant instability.

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2021: IPA- RPA & AI a Perfect Combination for your Organization

2021 IPA- RPA & AI a Perfect Combination for your Organization

 

Robotic Process Automation (RPA) is one of the most popular technologies for automating business processes. In recent years, many companies have decided and introduced RPA to drive process optimization and enabled fast and, above all, efficient automation of their standardized processes. In 2021 the trend towards RPA will not stop, because excellent results are possible with little effort. According to a Gartner forecast, the “global Robotic Process Automation (RPA) software revenue is projected to reach $1.89 billion in 2021, an increase of 19.5% from 2020.

 

A great advantage of RPA is that it does not require deep integration into different systems, but works via the existing user or desktop interfaces of the respective applications. Also known as the bridging technology, RPA supports the automation of numerous processes and thus lower costs without having to change or replace existing applications. RPA is used, among other things, for repeated data entry functions as well as for downloads and uploads in the Enterprise Resource Planning (ERP) area.

 

The key driver for RPA projects is their ability to improve and accelerate work process quality. Mimicking rule-based human actions, RPA automates all repetitive manual processes by lowering cost and time while improving quality. However, in this technology, the range of applications is limited by the need for structured data and programmable decision-making. Unstructured data is the main reason why technology is reaching its economic and technical limits. Thus, it becomes more difficult for many companies to find suitable processes for automation with (RPA) after a certain period of use. But this shortcoming can be overcome through the use of artificial intelligence as Intelligent Automation enables companies to take their existing automation strategies to a new level.

 

In the following, we will show you how artificial intelligence can help RPA bots to become smarter.

 

RPA and AI are two key technologies on the way to the intelligent automation of processes. Both technologies complement each other perfectly due to their different focus, so that from the user’s point of view they merge into an intelligent automation (IA).

As mentioned above, RPA need structured data as input, from different sources. It’s one of the biggest limitations of RPA. This means that the data must first be viewed, validated, and put into a structured form. If the input data is unstructured / semi-structured, artificial intelligence can be used to convert the data for the robots into a structured converted format.

 

Where RPA is weak, AI takes over. AI does all the initial work before data is transferred to the RPA. By using natural language processing, AI, extract the relevant data from the available text, even if the text is written in freeform language or if the information in a form looks or is distributed completely different each time. With semi-structured data, the AI ​​is able to extract the data from a document, even if this data is stored in different places on the form, in a different format or only appears occasionally. For example, on the invoices, the date could appear one time in the top right corner and another time in the top left. The invoice may or may not include a VAT rate, etc.

 

Once trained, the AI ​​is able to cope with this high variability with a high degree of confidence. If it doesn’t know how to process the file on its own, then the AI ​​can assign the task to a human who can answer the question, and the AI ​​, in turn, will learn from this interaction so that it can do its job better in the future.

 

The second limitation for RPA is that it cannot make complex decisions. RPA bots cannot make decisions based on their gut feeling. They need a clear set of rules according to which they operate. Some decisions are relatively straightforward and can certainly be handled by RPA, especially when it comes to applying rule-based scores to a small number of specific criteria. But if the required judgment is more complex, then another type of AI commonly referred to as “cognitive reasoning”, can be used to aid and improve the RPA process.

“Cognitive reasoning” programs work by mapping all knowledge, such as facts and experience that an expert has about a process in a model. This model, a kind of knowledge map, can then be queried by other people or by robots in order to make a decision or draw a conclusion.

 

As we’ve seen, RPA can provide some significant benefits on its own, but the real magic doesn’t come into play until the two work together. AI opens up many more processes for Robotic Process Automation and enables a much larger range of processes to be automated, even when complex and well-thought-out decisions have to be made. Everything is positive about this collaboration between RPA and AI: Investing in RPA is absolutely worthwhile. Existing system landscapes can be retained. AI intervenes flexibly and only where processes can be further improved in a targeted manner. A perfect combination of a leading company!

 

 

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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.

 

Sources:

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.

 

Source:

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.

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