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AI: The next step in Software Development

AI has been revolutionizing businesses worldwide, from healthcare to banking, from automobiles to logistics. It’s innovations are developing very quickly and growing significantly on a global scale. AI refers to technologies that make it possible to equip computer systems based on algorithms with human abilities such as thinking, learning, problem-solving, etc., to make them intelligent and thereby help people to carry out different tasks.

 

With advances in machine learning, natural language processing, and data analysis, also in the world of software development, technology is changing rapidly and AI is leading the way. The global AI market reached USD 93.5 billion in 2021 and will expand at a growth rate of 38.1% annually by 2030. Innovations such as Edge AI, computer vision, decision intelligence (DI) and machine learning (ML) are shaping the market in the years to come. Additionally, robots are increasingly penetrating our everyday lives. And the current research suggests that this trend will continue in the coming years when robots and drones can take on more and more tasks in a meaningful way. These advances are related to the popularity and widespread use of AI & its promises of impressive growth opportunities.

 

The aim of AI is to create machines that can work and react like humans. However, AI is not just about creating human-like machines; it is also about making machines that can work better than humans. For example, a machine is able to process data much faster than a human can & also remember more information than a human can.

 

Since AI offers great potential for different areas, it is no wonder that its use cases are becoming more diverse with each passing year. AI solutions are already helping with:

  • Business process automation
  • Automated document creation
  • Management of production processes
  • Predictive Maintenance
  • Customer Analytics
  • Risk management
  • Supply chain management
  • Personalized service delivery
  • Software development

 

Since its inception, AI has made significant progress in software development. Early successes included creating programs that could play checkers and chess, as well as solve simple mathematical problems. In recent years, AI has been used to develop more complex applications such as autonomous vehicles, facial recognition systems, and machine translation. Looking to the future, AI will continue to play an important role in software development. With the rapid advancements being made in machine learning and natural language processing, there is no limit to what AI can achieve. As we move forward into the next era of computing, it is exciting to think about all the new possibilities that AI will enable us to realize. Let’s explore how AI is impacting software development and how it will continue to revolutionize the industry in the years ahead.

AI Software Development

Software development aka application development consists of winding together instructions for one or more programs that carry out required tasks or actions. The development team carries the task of translating problem-solving processes & algorithms into program code. Basically, we’ve known the classic methods of software development such as the agile and waterfall methodology. However, AI development works differently than classic software development. In AI development, data plays a central role – it is the center. In AI software development the behavior of AI depends on the self-training with the data. In the classic approach, the programmer had to set the rules himself, something that isn’t possible with AI development.

 

With AI, developers can create smarter and faster algorithms that can more accurately comprehend our intentions and behaviors within their applications. AI platforms promise faster development, more accurate prediction of user needs and behaviors, and continuously improving algorithms for data processing. This helps developers automate various tasks, from code quality analysis to bug fixing & save time on repetitive tasks. For example, if a developer needs to fix a bug that occurs often, they can train an AI system to automatically detect and fix that bug. This frees up the developer’s time so they can focus on more important tasks.

 

AI can also improve the quality of code. By using AI-powered static code analysis tools, developers can identify potential errors and bugs before they even write any code. This not only saves time and money by preventing buggy code from being deployed, but it also helps improve the overall quality of the software.

 

AI can help developers create more user-friendly applications. By using machine learning algorithms, developers can automatically generate user interface (UI) designs that are optimized for conversion and usability. This means that users are more likely to have a positive experience with the application, which could lead to increased customer retention and loyalty.

 

The work & results in AI development are characterized by the acquisition, analysis, and preparation of data and by training the models. We can say that an AI solution is gradually approached through smaller experiments and experience is gained in the process. For this reason, exact, conscientious, and transparent documentation of every single step & every attempt are essential.

 

To achieve quicker results, however, several work streams can run in parallel, all of which are dedicated to solving the same topic. This requires a high degree of flexibility.  It is evident that the rise of AI will revolutionize software development and open up a world of new possibilities. With its ability to process information faster than ever before, AI technology can help streamline projects and shorten production times. As more companies continue to invest in this form of technology, we can expect even greater advancements from artificial intelligence in the future. There’s no doubt that there are both positive and negative implications associated with embracing this kind of technology but for now, we must use what advantages it offers us to move forward into the digital age.

Digitization: Why should companies invest in Artificial Intelligence training

Technology is a key helper on the way to the digital future. Artificial intelligence is considered a crucial future technology in the worldwide economy and more and more companies see an opportunity for their own business in artificial intelligence (AI). Whether predictive maintenance, process optimization, system control, or individualized products – everyone is talking about the fact that everything will be AI-supported in the future if not even function autonomously. AI can also improve processes in companies from production to sales or serve as the basis for new products and services.

 

Artificial intelligence also gives enormous competitive advantages. A survey conducted by McKinsey highlighted that a majority of survey respondents say their organizations have adopted AI capabilities, as AI’s impact on both the bottom line and cost-saving. Regarding the employees, however, there is an urgent need for action as they are poorly prepared for the use of artificial intelligence in this suddenly changing environment. Employees must perform skilled jobs that require more education and training compared to their normal routine jobs.

 

Another survey conducted by the market research company Statista on behalf of the TÜV Association among 1,000 people aged sixteen and over, including 568 employed people has revealed that 78 percent of employers agree that companies need to invest more in training their employees when it comes to AI. Many companies must invest significantly more in further training in artificial intelligence to make their workforce fit for the digital world. This involves both in-depth knowledge for the use of the technology, but also user knowledge since many tools already work with AI today. According to the results of the survey, a start has been made, 28 percent of the employees surveyed have taken part in further training on AI content in the past two years. And 34 percent of those in employment planned to do so within a year.

 

With basic AI knowledge, TÜV association expert Fliege observes considerable deficits in the companies. “Many employees only have a vague idea of ​​what AI is and where they encounter it.” AI is already in use in many cases, sometimes even unnoticed. “Algorithms work quietly in numerous systems – for example in IT security, where they recognize and resist cyber-attacks,” says Fliehe. AI is perceived more strongly in factories, for example, where it supports production control. The use of AI promises more efficiency and greater process automation in production. That doesn’t have to have a negative impact on employment, says Fliege: “Interesting new fields of work can arise for employees because they are relieved of routine activities.” The development is still in its infancy, and a lot is in flux. “A whole new door is opening for companies and employees.” According to Fliehe, the use of AI for small and medium-sized enterprises (SMEs) is particularly promising. “They usually have to make do with scarcer resources and are committed to high efficiency.”

 

“Knowledge about artificial intelligence is improving as the technology spreads,” said Stenkamp. At the same time, the attitude of the citizens is also improving. Fifty-one percent of respondents feel something positive when they think of AI, compared to the previous study by the TÜV Association in 2019, this is an increase of five percentage points. On the other hand, only 14 percent feel something negative, two years ago this value was twice as high at 28 percent. Thirty-five percent are neutral (up 14 points).

 

However, one thing is certain: the responsibility of the employees will increase, because they will remain the final decision-making authority. “Users in companies must know that AI decisions are not optimal in every situation,” says Fliehe. It may therefore be necessary to check whether an algorithm has captured all the valuable information. “Human expertise and experience will not become less important through the use of AI, but even more important,” emphasizes Fliehe. Employees would have to be able to guide the algorithm and classify the results. “Employees must become designers and also recognize the limits of AI.” In this way, the employees also contributed to the security of AI systems. “AI applications must not endanger or disadvantage people,” says Fliege. Corresponding legal regulations for the use of AI in security-critical areas are currently being developed in the EU as part of the planned “AI Acts”. The “TÜV AI Lab”, founded last year, supports politicians in developing standardized testing tools for artificial intelligence.

 

To prepare workers for more automated workplaces, professional training must be considered as an individual right. The transition to modern technologies and onwards will be a continuous process. Thus, the training and re-training of employees must not be ignored.

Data: A key resource for Digital Business Models

Businesses are generating and collecting large amounts of data, due to the rapid digitalization in all sectors and industries. Digitization has come to stay so not only the volume but also the variety of data – structured and unstructured – is growing exponentially in companies around the globe. New sources of income and growth opportunities are what companies strive for today. The evaluation and analysis of data help significantly and are becoming more and more important for companies – because the results of such analysis are considered as a strategically valuable asset.

Data A key resource for Digital Business Models

Companies don’t just want to collect the data, but their goal is to get real benefit from it. The analysis of data, which arises from individual digital interactions, generates significant added value for companies. This also includes the processing of omnichannel data, regardless of whether they are structured or unstructured, to get better insights into the entire customer journey. In order to unlock this potential in companies, a data strategy is required, i.e. a plan for data value creation in the company. Because only those who use data in a targeted and intelligent way can make better and faster decisions, optimize processes, improve planning or develop new approaches, define new customer-specific offers, or even innovate the entire business model & define short- and long-term objectives.

If implemented correctly, a data strategy offers companies from all industries and, in all areas of the company, sustainable benefits. In the transformation process, management must take into account the data infrastructure, data management, the general data strategy approach, and data analysis and its use.

 

Data infrastructure

The development of future-proof and efficient data infrastructure is based on a high degree of flexibility in data generation, virtualization, and processing. For high-quality data analysis, companies should therefore increasingly focus on independent software components (microservices) that have interfaces with common communication standards and thus enable quick and flexible integration into new applications without detours. Particular attention should be paid to fast network architecture, the prevention of data silos (e.g. by introducing a data lake), and the inclusion of secure cloud solutions.

 

Data strategy

The development and implementation of a data strategy often present companies with challenges. A suitable data strategy must be adapted to an organization and its objectives. It defines how data is handled and which goals are pursued. Since data value creation is not just a technology issue, but rather affects almost all areas of the company as the core of digital transformation, uncoordinated and decentralized approaches often do not lead to success.

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Therefore, a correspondingly fast and agile strategy is required to achieve results quickly. If the decision is made on a static approach, the motto is usually “defense instead of attack”. The focus is clearly on strict regulations in the handling of data and data quality. With a strategic approach, companies choose the middle ground between strict guidelines and targeted data usage. Companies that take a dynamic approach to data handling use a large amount of data in order to be able to react quickly to market changes and to improve their own analytical skills. Fast data access and quick results are important here. In the dynamic approach, cost savings and profitability through the use of data come to the fore. Flexibility in analysis and quick availability of results, for example through user-friendly data visualization, is essential.

 

The data warehouse

No matter what you need your data for and how you ultimately create your data strategy, every modern organization with a lot of data needs modern data warehousing. The data warehouse is the universal place where data from many different sources is collected. If you are planning a suitable data strategy, the data warehouse is one of the most important core elements to be reviewed. In many companies, IT teams are looking for a suitable data strategy that simplifies administration and helps to draw value from all the existing data. A modern data warehouse must therefore be part of a successful data strategy. Companies working on a data strategy cannot avoid modernizing their data warehouse. And one of the most important tasks in this modernization is the automation of the data warehouse.

 

The goal of a strong data strategy should always be to build an organization where agility is an integral part of its DNA. Such an organization works with an operating model that focuses on the employee and the customer. The model equips the company with the ability to quickly recognize changing business requirements and to continuously develop. We support you in designing, implementing, and integrating data correctly into your existing system landscapes and thus into your processes. Feel free to reach out to our consultants that offer a wide range of business and technical know-how as well as the necessary instinct to solve the individual challenges of your organization. We can’t wait to help you find success.

 

 

Sources:

SAP White Paper: Digital Transformation – Digital Business Modeling A Structural Approach Toward Digital Transformation

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

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