For years, Spirit in Projects has supported companies and public sector clients in designing, tendering, and implementing AI systems. One thing has been consistently confirmed: A structured, data-driven, and value-oriented process model is the key to success—regardless of whether the project follows V-Modell XT, Scrum, or a custom hybrid approach. Many AI projects fail not because of the technology, but due to a lack of methodological rigor in gathering and implementing requirements.
From Gut Feeling to Structured Requirements
In traditional projects, specifications with clear functionalities often suffice—but AI systems require a different mindset. Key questions include:
What is the AI’s actual objective?
What data is truly available?
How precise must the model be to deliver real value?
We use an internal methodological framework inspired by CRISP-DM, adapted for modern AI projects with needs like MLOps, retraining, and continuous improvement. This model structures our workshops and stakeholder interviews—from Business Understanding to Data Understanding to concrete evaluation criteria for AI models.
A Planned Approach—Even in Agile or Classical Contexts
Our clients work with different methodologies: some use Scrum, others the V-Modell XT. For AI projects, however, we recommend adding an AI-specific structure. We integrate our CRISP-DM-based logic into the existing framework:
In agile projects, it brings order to exploratory data work.
In classical environments (e.g., public tenders), it serves as a blueprint for AI-specific requirements.
The key is: We create a common language between business units, data scientists, and IT—ensuring requirements are not just documented but understood and verifiable later.
Requirements Need Iteration—and Data
Another guiding principle: AI requirements don’t emerge from a blank sheet but through iteration, discussion, and what the data reveals. An ideal process model accounts for this:
Data exploration often leads to new insights that reshape requirements.
Model testing shows whether target accuracy is realistic—or if adjustments are needed.
Feedback loops with stakeholders are not a nuisance but a valuable asset.
In our projects, we deliberately plan for these iterations—not as exceptions, but as the norm. This improves both result quality and user acceptance during deployment.
Conclusion: Structure Provides Security
An AI project without a structured approach is like a flight without a flight plan—you rarely end up where you intended. With a well-designed, AI-tailored process model, we help our clients gain early clarity, define realistic requirements, and create reliable tenders.
https://spiritinprojects.com/wp-content/uploads/2025/08/sip_ki_vorgehensmodell.jpg450845Tanja Meszaritshttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgTanja Meszarits2025-08-10 21:35:042025-10-22 11:39:14Requirements for AI Systems: How a Well-Designed Process Model Leads Projects to Success
At Spirit in Projects, we have spent years helping companies define requirements for complex IT systems and professionally prepare tenders. In recent years, a clear trend has emerged: Artificial intelligence (AI) is being integrated into more and more projects—and brings with it unique challenges.
When it comes to AI systems, defining the right requirements from the outset is crucial. Many companies still rely on the same methods used for classical software projects—and later struggle with poor quality, lack of explainability, or unrealistic expectations.
At Spirit in Projects, we specifically support our customers in formulating AI system requirements in a way that ensures they are robust for implementation, operation, and tendering.
Learning Instead of Programming
Classical software systems follow clearly defined rules: If input X, then output Y. Their behavior is deterministic and predictable.
AI systems work fundamentally differently: They learn from data and develop a model. The “rules” do not come from programming but from machine learning. This means: Data is the key to success. Even in the requirements definition phase, critical questions must be answered, such as:
What data is available?
What is its quality and volume?
How is the data currently maintained and made accessible?
Our experience shows: This aspect is often underestimated in practice—with dire consequences for the project.
Probabilities instead of Guarantees
Another fundamental difference: AI systems deliver probabilistic results. For example, an image recognition system might report, “This object is a cat with 95% probability.” However, it does not guarantee flawless detection of every cat.
Requirements for result quality (accuracy, precision, recall, confidence intervals) must therefore be explicitly defined. In our AI tendering projects, we ensure these quality parameters are clearly specified—to avoid later disappointment.
Dynamic Behavior
Classical software remains largely stable after deployment. AI models, however, often require regular retraining as data and environments change (concept drift).
This leads to specific requirements for:
Lifecycle management
Model monitoring and maintenance
Governance and responsibilities
Spirit in Projects helps customers integrate these aspects into their requirements definitions and tender documents—a critical point often neglected in many projects.
Explainability and Traceability
Especially in regulated sectors (e.g., financial services, public sector, healthcare), explainability is crucial. AI systems must not be black boxes.
Requirements must clearly define:
To what extent must explainability be ensured?
For whom must traceability be provided (e.g., internal audits, external regulators)?
In our practice, we ensure these aspects are structured in the requirements catalog—balancing technical feasibility with regulatory demands.
Conclusion
AI system requirements are not just an extension of classical software requirements. They must explicitly account for learning capabilities, probabilistic nature, and data dependency.
Spirit in Projects brings extensive experience from numerous AI projects and tenders. We help companies formulate sustainable and reliable requirements—laying the foundation for successful AI initiatives.
https://spiritinprojects.com/wp-content/uploads/2025/07/sip_blog_ki_anforderungen.jpg450845Tanja Meszaritshttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgTanja Meszarits2025-07-12 20:53:162025-10-22 12:08:43Requirements for AI Systems: Why They Differ from Classical Software Systems
Companies developing or deploying AI applications face a strategic dilemma: On one hand, AI promises efficiency gains; on the other, legal requirements demand caution and transparency. How can businesses responsibly navigate this tension?
The reality is that AI performance is no longer measured solely by response time or computational power. Instead, trustworthiness has become the decisive factor. But this is where a critical examination is needed: Ethical guidelines are only the first step—active implementation is what truly matters. The question we hear repeatedly from businesses: How can an abstract ethical commitment be transformed into genuine trustworthiness?
Trustworthiness means systematically embedding values like accountability, fairness, and transparency into every phase of an AI system’s lifecycle. What does this mean in practice?
Define objectives: What goals must be achieved?
Operationalize values: What does fairness mean for your company?
Take a holistic approach: How are these values integrated into AI development and operations?
Only when these questions are addressed and verified does trustworthiness become a measurable KPI—and data ethics a true quality standard.
Legal Framework as a Guide
Regulations provide orientation: The GDPR sets strict rules for processing personal data. The EU AI Act classifies AI applications by risk, distinguishing between prohibited uses (e.g., real-time biometric surveillance) and low-risk applications like chatbots.
Building Trust Through Ethical AI
Companies gain trust by firmly embedding: Encryption & “Privacy by Design”, Explainable AI (XAI) and Data Protection Impact Assessments (DPIAs)
Those who strategically integrate data ethics gain a double advantage: minimizing regulatory risks while standing out from competitors. Non-negotiable transparency, fair decision-making logic, and strong governance form the three pillars of trustworthy AI. If ethics are not considered from the start, they quickly become a strategic risk for businesses.
Turn Change into Opportunity
To ensure your AI initiative benefits from this transformation, Spirit in Projects offers targeted AI training programs.Contact us to learn how we can support you in achieving compliance and customer trust.
https://spiritinprojects.com/wp-content/uploads/2025/05/sip_blog_ki_ethik.jpg450845Karinhttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarin2025-05-07 16:20:582025-10-22 09:17:46Data Ethics as a Quality Standard for Artificial Intelligence (AI)
The speed of technical development in the field of AI is having profound repercussions on roles in the software development process. These are some of the current trends in how important roles in software development are currently changing.
Developers are being forced to deal with the ethical dimensions of their systems – issues related to fairness, data distortions and potential negative repercussions, which demand a high sense of responsibility, as well as the ability to think beyond the purely technical aspects of programming.
Going forward, not only will business analysts and requirements engineers need to gather functional and non-functional requirements, but will also have to examine the quality and availability of training data, all while working closely with experts in data sciences and machine learning. They’ll need to develop a feel of what problems are actually appropriate for AI solutions, and how to properly define requirements for learning systems.
Project managers are being faced with the challenge of managing development processes that are less deterministic, and significantly more iterative, and now need to plan on timetables and resources for data collection, model training and continuous improvement. And agile methods are becoming more important, since AI projects are often of an experimental nature and require more frequent adjustment.
Testers‘ roles are also undergoing a fundamental change – instead of carrying out pre-defined test cases using test data that was worked out in detail, testers now evaluate the performance and robustness of AI models under a variety of conditions. This includes checking for model accuracy, identifying distortions and making sure systems react appropriately in edge cases. As a result, testers need to become familiar with concepts like model overfitting, underfitting and generalization.
Finally, software architects are being faced with the task of designing systems that can seamlessly integrate AI components. They need to develop architectures that make it possible for models to handle large data sets, and also to enable the subsequent real-time processing of information. At the same time, architects must now take into consideration aspects such as scalability and maintainability as well as integration with existing systems, In addition, the architectures they design must be flexible enough to keep up with the rapid development of AI technologies.
An important foundation stone for you to benefit from this change process in your role is a solid basic knowledge of AI and how it works. The AI courses offered by Spirit in Projects provide you with tried-and-tested methodological knowledge.
https://spiritinprojects.com/wp-content/uploads/2025/03/ai_rollenrevolution.jpg450845Karl Schotthttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarl Schott2025-02-26 19:15:402025-03-16 19:53:00Are We Facing an Role Revolution?
Software Programming 2.0 – The AI trend is radically changing the ways in which we design, develop and implement software.
The term “Software Programming 2.0” is used for software development which focuses on the programming of artificial intelligence (AI). The paradigm shift from conventional software development (or “Software Programming 1.0”, as it’s now called) to AI-supported programming is profound. While the traditional method is based on explicit, rules-based algorithms for which developers specify, among other things, how a program should react to different inputs, Software Programming 1.0 leverages the power of machine learning and AI to create systems which have learned, from data, how they should function.
Instead of manually coding every rule and decision path in the system, AI models are trained on large sets of data to recognize pattens and make decisions. Complex neural networks, in particular today’s deep learning models, form the backbone of many AI systems. Such models have the ability to process high-dimensional data and to learn non-linear relationships, which makes them suitable for a wide variety of applications
Transfer learning
A key concept in this new paradigm is transfer learning, which starts with pre-trained models, then fine tunes them for specific tasks. This speeds up the development process significantly, and reduces the need to provide huge sets of data for every new project.
Still, most AI models in use today are developed specifically for their given tasks, and any new functions must be newly trained on the right data. In the future, AI systems will be designed so that they’ll continuously learn from new data in the desired context, and adapt themselves to changing tasks. Large language models already have the ability to use their extensive knowledge to solve a variety of text processing tasks for which they originally did not receive specific training.
New Challenges
The challenges involved in this new kind of programming are varied and complex. One factor crucial to the success of AI systems is the quality and quantity of the training data, while another is the interpretability and explainability of AI models. Many advanced systems, especially deep learning networks, are often treated as “black boxes”.
The development of interpretable AI models and also the implementation of methods to explain AI decisions are important fields of research which have not only technical implications, but also ethical ones. By this time, it appears as if the fields in which Software Programming 2.0 can be applied are virtually limitless. AI systems used in healthcare provide support for diagnoses and are also helping to advance personalized medicine. And AI systems are used in the financial sector for fraud detection and credit checks. The automotive industry uses AI for autonomous driving applications, while the e-commerce sector leverages the technology for personalized recommendations and to better analyze information on customer behavior. And for quite some time, AI-driven systems in the manufacturing industry have already been optimizing quality control and production processes.
“Software Programming 2.0 could mark a turning point in software development.”
– Karl Schott, CEO & Gründer
A Look into The Possible Future
The future of Software Programming 2.0 promises exciting developments. Automated machine learning (AutoML) continues to mature, and is making it possible for even non-experts to develop AI models. And moving AI computations onto edge devices is not only improving real-time processing, but also opening up new possibilities for privacy-aware applications, since personal data can be anonymized as soon as they’re collected. Software Programming 2.0 could mark a turning point in software development. It requires a fundamental rethinking in terms of problem-solving, data management and system design, and developers, requirements engineers, project managers, testers and architects need to expand their capabilities to adapt to the new reality. Not only do they need technical expertise, but also a deep understanding of statistics, data analysis and the ethical implications involved in their work.
The integration of AI into software systems offers enormous possibilities for solving complex problems and for coming up with adaptive, intelligent solutions. At the same time, the technology brings with it novel challenges with respect to data privacy, security and ethical responsibility. It’s vital that IT specialists and organizations become familiar with both fundamental and advanced AI concepts.
Software Programming 2.0 isn’t just a new approach to coding, but also a completely different way of considering problems and developing solutions. The boundaries between traditional software development and AI solutions are becoming increasingly blurred, and this requires us to continuously develop our capabilities as developers, designers and ethically responsible innovators. We at Spirit in Projects take these challenges into account in the courses we offer. Take a look at the AI training we offer, and start preparing for the future today.
https://spiritinprojects.com/wp-content/uploads/2024/12/software_ai_blog.jpg450845Karl Schotthttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarl Schott2024-12-07 15:59:512024-12-07 16:08:51Software Development in the Age of AI
In the first part of this article we gave an overview of AI tools and their potential impact in the field of software engineering. In this part we look at the effects on organizations and how people work together.
The key to good team collaboration will lie in flexibility, a willingness to learn and and a heavier focus on innovation and customer satisfaction. Those companies which best manage those changes will become more competitive and will be in a position to make optimal use of the opportunities afforded by the AI revolution.
Various models to describe the relationship between humans and AI.
Integration of AI in work processes
Teams will be working increasingly with AI systems so they can handle tasks more efficiently. This in turn will necessitate that the development process be adapted so it can integrate AI. Developers will use AI systems as tools for support, and learn how they can best profit from them. Man-machine collaboration is becoming the norm.
Development of agile skills
Employees will need to be more flexible and have the ability to quickly adapt to new technologies and tools, since AI systems and technologies continue to undergo rapid, dynamic development. And in order to use AI effectively, employees may need to acquire skills and know-how in new sectors, whether this be machine learning, data analysis or tools for AI development.
Changes in roles and tasks
Organizations are seeing the birth of new roles based on AI specializations, such as AI architects, data scientists and AI ethics officers. At the same time certain tasks, such as writing code and testing software, which up to now were performed by humans are becoming increasingly automated, and (as in previous digitalization waves) employees are gradually focusing on more complex tasks. As a result, another central task necessary to address the AI wave of change is the continued development of existing personnel to expand their skill sets.
Ethics and governance
An organization which makes use of artificial intelligence (AI) must develop clear guidelines on the related ethics and governance. These guidelines are crucial in ensuring that an organization’s use of AI reflects its values as well as the interests of all relevant interest groups – a few keywords which come to mind in this regard include transparency, data protection, fairness and security. In addition, compliance with ethical and legal standards should be monitored and employees trained accordingly. Finally, transparent communication with customers on the organization’s use of AI is also essential.
Knowledge building and knowledge transfer
Organizations must share and disseminate knowledge on how to use AI effectively in order to ensure that all employees can profit from the new technologies. The employees in an organization must develop at least a basic understanding of AI technologies, including familiarity with the different forms of AI, their areas of application and how they work. In addition, the use of AI within an organization requires specific domain knowledge on the company’s business processes and goals. This is essential for identifying relevant data, validating models and integrating results into the company’s business strategy.
Furthermore, organizations must invest in building up their data expertise. This includes the collection, cleansing and organization of data sources for the purpose of creating high-quality data set for AI applications. Finally, continuous training and adapting to changes are also extremely important since AI technologies are constantly undergoing further development, and both employees as well as organizations as a whole must keep up-to-date. This requires training, exchanges of best practices and a willingness to integrate new findings and technologies into company workflows.
Cultural change
Implementing AI often necessitates a cultural change to which employees must be open and also willing to accept new ideas and technologies to make the transformation a successful one. And in the face of technological innovation which is constantly accelerating, organizations must also open up and make better use of their existing potential.
Agile organizations respond to this change more quickly, and are better able to turn trends to their advantage. By contrast, hierarchical organizations are faced with the challenge that agile methods are accompanied by a change in mindset and culture, which radically changes existing approaches of collaboration and decision-making.
Customer orientation and customer relationships
Companies will use AI to better adapt their software to the needs of their customers and to interact with them more directly. These goals will necessitate stronger customer orientation as well as the ability to integrate customer feedback into product development.
TAKEAWAY: THE OPPORTUNITY TO USE AI
Integrating AI into the software development process offers countless advantages, from automating routine tasks to customizing advanced training programs for developers.
Nevertheless, it’s important to stress that AI should not be seen as a replacement for human specialist knowledge and intuition, but more as a tool which supports software developers in their work. By combining human creativity with the performance capabilities of AI, we can usher in a new era of software engineering which is more productive, efficient and innovative than ever before.
https://spiritinprojects.com/wp-content/uploads/2024/05/sip_blog_ki_kulturwandel.jpg450900Karl Schotthttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarl Schott2024-05-21 21:30:582024-05-21 21:44:22Technology as an engine of change: This is what AI in software engineering is changing in organization and collaboration
Technological progress often proves to be an engine which drives change in the ways we work and our organizational structures. In two consecutive articles we take a look at possible repercussions of AI on the software industry. In this first part we look at new tools and forms of work that result from innovations in the field of AI. The second part of the series then deals with effects on organization and collaboration.
Digital transformation is advancing at rapid tempo, bringing with it the need to optimize software development and make it more efficient. One key technology in this regard is artificial intelligence (AI), which is impacting not only the ways we work and our organizational structures, but even our corporate cultures.
Below we provide an overview of what we believe to be the most relevant AI-driven tools and their impact on the industry.
Automatic code generation
Imagine that instead of typing in code line by line, you just formulate your requirements in a natural language, then an AI application generates the corresponding code. Models like OpenAI’s GPT-4 already have the ability to carry out basic programming tasks on the basis of natural language queries. This can significantly reduce development time and also improve code quality.
Code review and quality assurance
All developers are aware of the challenges which come with code reviews. AI systems have the ability to continuously review code, identify potential errors and recommend best practices, thus enhancing human review processes and contributing to error reduction.
Optimization and test automation
Not only can AI help humans to write code which is more efficient, it can also automate tests. Instead of creating test scenarios manually, AI models which are based on existing code can automatically generate and execute test cases.
Effective project management
One of the greatest challenges in software engineering is resource allocation. How long will it take to develop a feature? What resources will it require? By analyzing historical data, AI can make more accurate predictions of development times and can optimize resource allocation.
Personalized training
AI can identify specific training needs and recommend customized learning paths for each developer on a team, making it possible to continuously improve the entire team’s quality and effectiveness.
The use of AI in software development is fundamentally changing the ways people work together and the kinds of roles they take on at companies.
Karl Schott, Gründer & CEO
In the second part of the article, we examine what impact these tools have on organization and collaboration in software engineering.
https://spiritinprojects.com/wp-content/uploads/2024/05/sip_blog_ki_technolgiewandel.jpg450900Karl Schotthttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarl Schott2024-05-06 21:17:532024-05-21 21:43:24Technology as an engine of change: AI tools in software engineering and their impact
Requirements engineering lies at the forefront of software development – and in the future, AI will play a transformational role in that field.
Requirements engineering (RE) defines what a system should do, even before any development begins, and thus is concerned with the systematic collection, documentation and management of requirements. Integrating AI into the RE process makes it possible for requirements to be collected more efficiently and accurately.
Natural Language Recognition
The communication surrounding software projects (emails, customer feedback, meetings, etc.) often contain hidden details with regard to requirements. AI models, especially those which use natural language processing (NLP), can filter out those details from that flood of information, and the recognition of entities, relations and context are key functions in that filtering process. A sentence like “The interface should be faster” is ambiguous to a human being, but by analyzing other known information, an AI system can bring it into perspective and translate it into specific requirements.
Automatic Classification
The development of software products often results in the generation of hundreds or even thousands of requirements. AI can help to automatically classify these requirements into pre-defined categories such as “functional”, “non-functional”, “user-centric” and “security-related”. Such classification is also useful to RE in that it makes it possible to recognize the completeness of requirements. This automatic classification and prioritization of requirements, supported by AI, makes it possible in turn for project members to concentrate on the more critical aspects of the project.
Continuous Learning
The dynamic nature of software projects necessitates systems which can adapt and learn. AI models are not static. They can, and should, be trained regularly on new data so that they can stay current. Through feedback loops and active approaches to learning, an AI system can improve its accuracy over time. Nevertheless, careful monitoring is also essential, to avoid so-called “model drifting” in which the predictions of a model become less accurate over time.
In sum, integrating AI into requirements engineering offers tremendous advantages which are reflected in RE processes which are more efficient, more precise, and more adaptive. Nevertheless, requirements engineers and software analysts should always keep an eye out and validate AI model results on a regular basis. The key to AI’s success lies in the balance between technology and the human ability to judge.
https://spiritinprojects.com/wp-content/uploads/2019/07/training-requirements-engineering.jpg6001200Karinhttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarin2024-09-11 14:21:182024-12-05 21:03:34Coaching for Specialist – Written Assignment
https://spiritinprojects.com/wp-content/uploads/2021/10/training_ux.jpg6001200Karinhttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarin2024-09-11 09:44:422025-01-30 15:46:18Introduction to usability and user experience
The future is right now! Dive into the the world of artificial intelligence (AI) and find out how it’s revolutionizing the IT landscape. In this article we explore how AI is relevant, and why expert knowledge in a variety of specializations is the key to success.
The meteoric growth of artificial intelligence (AI) has turned our world around and permanently changed the IT industry. In an era in which data has become our most valuable currency, AI is the very heart of our digital transformation. It’s pushing forward innovations at an unprecedented pace and making it possible for companies to increase efficiency, understand their customers better and develop groundbreaking solutions.
Development of AI
In recent decades, artificial intelligence (AI) has experienced significant advances in development which have fundamentally changed the way we see technology and business processes. AI’s early years (from the 1950s to 1970s) focused on symbolic AI where rules and symbols were used to simulate human intelligence. This led to the development of expert systems which had the ability to use specialized knowledge, specified in the form of rules.
The next phase, known as the “AI winter” (from the 1980s to the early 2000s) were marked by disappointments and financial setbacks, as the high expectations of AI were not being met and many projects were suspended.
The revival of AI began in the late 2000s, as advances in machine learning and the availability of huge volumes of data opened up new possibilities for the technology. Machine learning made possible algorithms which recognize patterns in data and develop models automatically. In particular, deep learning with neural networks led to groundbreaking advances in fields such as image recognition, natural language processing and autonomous driving.
Recent decades have seen the integration of AI technologies in a wide range of applications, ranging from personalized recommendation systems in social media all the way up to medical diagnosis and autonomous driving. In addition, ethics and governance in relation to AI has became increasingly important, since the technology has raised issues related to data protection, bias and responsibility. Finally, the development of autonomous systems such as self-driving cars and drones has fundamentally changed industries such as transportation and logistics.
All in all, AI has grown from a theoretical concept into a practical reality which has profoundly influenced our lifestyles as well as well as the way we do business. Focus has shifted from symbolic AI to data-driven approaches like machine learning, which in turn has led to significant advances in the performance capabilities and applicability of AI technologies.
The AI revolution: Where are we today?
Today, even mainstream users have access to a wide number of technologies such as large language models (LLM) and image generators, and AI is a hot topic in the media. AI’s areas of application have never been more diverse. For example, it’s used in healthcare to help physicians diagnose complex diseases. And in the automobile industry, it’s working to bring self-driving cars to the road.
The ethical and societal repercussions of AI are currently the subject of intense debate, and the way in which we address these issues will have a significant impact on our future.
AI is more than just a technological innovation – it’s changing the way we live and work, and presenting us with challenges that we must overcome together.
Karl Schott, Gründer & CEO
Importance of specialist knowledge
Multi-disciplinary teams which bring a wide variety of qualifications play a crucial role in determining whether or not a company uses AI successfully. Such diversity makes it possible for a company to consider AI applications from different perspectives and to develop interdisciplinary approaches to finding solutions. AI projects require expertise in a wide range of fields, including data analysis, machine learning, software development, ethics, design, project management and domain expertise. A multi-disciplinary team has the ability to better manage this diversity and to tackle a variety of challenges, from data collection and processing through model development and ethical assessment all the way up to implementation.
And since customer orientation is also of great importance, a diversified team has the ability to better empathize with the needs and expectations of the company’s customers and can develop AI applications which are truly of value. In addition, ethical and legal experts can see to it that the company’s AI applications meet ethical standards and legal requirements.
A team of this nature should be well-founded on solid technical qualifications such as data science and machine learning, software development, expert domain knowledge, ethics and governance, UX/UI design, project management, communication skills and last but not least a touch of creativity.
And for management, it’s essential that they’re aware not just of the opportunities afforded by AI but also how it works, so that the company uses the tools which are the right ones for its success and trains its employees accordingly. In addition, it’s always more relevant when making strategic, data-based decisions to be able to correctly interpret the corresponding data and decision processes.
The key: Up-to-date qualifications
The key to taking full advantage of this historic technological change is ongoing training which ensures that not only your company’s employees but you as well always have the right qualifications. That’s exactly where our current training program comes in.
We provide expert, proven, state-of-the-art knowledge in specialty areas of technical development which will reinforce your technical skill set. This is essential, since a solid technical foundation is a crucial starting point for managing innovation. At the same time, our newly designed innovation courses which focus on AI will ensure you apply your know-how in line with what’s happening right now.
Put together your own personal program of training to make optimal use of the possibilities provided by modern technologies. Now’s the perfect time to take charge – proactively shape your professional future by taking advantage of the opportunities afforded by AI!
Our trainings on artificial intelligence:
https://spiritinprojects.com/wp-content/uploads/2023/11/sip_blog_ki_title.jpg450900Karl Schotthttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgKarl Schott2023-11-11 11:39:022023-11-11 12:07:12The age of AI: the Future’s in your hands!
In today’s digital age, companies are under increasing pressure to digitalize their processes and data in order to stay competitive and meet customer demands. However, the digitalization process can be complex and full of challenges, and companies often find it difficult to embark on that journey effectively. Detailed analyses of business processes require a great deal of time as well as experience in order to effectively and efficiently achieve results in that area.
Spirit in Projects has developed a method here in which we examine the analysis from the standpoint of data touchpoints, since data is the basis for digitalization projects. A “data journey” is thus a trip made on the basis of the data processed in a company. In the process, it doesn’t matter whether the data concerns digital data twins of physical objects or a company’s processes which are entirely data-oriented.
Solution Approach: Data Journey
A data journey is a comprehensive approach for digitalization which uncovers where and how data are produced, collected, saved, analyzed, re-used and interpreted. This is thus implicitly reflected in a company’s value creation process and business processes.
To put it simply, a data journey analyzes the following aspects:
Data creation: Data are produced through various sources such as users, applications, sensors, social media, transactions, etc.
Data storage: Data are stored for later use using a variety of methods – in a file-based manner, in databases, data lakes or in the cloud.
Data processing: Data are produced then processed in order to cleanse and organize the information and convert it into a suitable format. This phase may also include a combination of data from different sources.
Data analysis: Data are analyzed using a variety of techniques such as statistical analysis, machine learning and visualization in order to uncover patterns, relationships and knowledge.
Data visualization and communication: The findings and results of the data analysis are shown in some visual format (e.g. diagrams and charts) in order to effectively communicate the information.
Data-supported decision-making: The findings and results of the data analysis are used as a basis for decisions and actions.
Data element value creation chain: Data are often enriched over time so that they can be used as data objects in a business context.
Nevertheless, the data journey doesn’t end with the analysis of what is produced and processed and where this happens. On the contrary, it involves finding out where and at what point data become redundant (possibly through different methods of creation, different applications, etc.), where in the process data objects are lacking, what organizational units require access to data objects and what kinds of information requirements they have and what benefits can be derived from the data.
Data Protection as a Core Issue
The legal aspect also plays a role in this process. Are the data being processed personal data? Does the company have rights of use and exploitation? This question often comes up when the data creation process occurs outside of the company (e.g. architectural plans, address lists, statistics, etc.)
Using an analysis and perhaps a visual depiction of the data journey, a company can get started on a targeted optimization of its business processes and digitalization projects and quickly reap benefits for the organization.
The Advantage for Your Company
All in all, a successful data journey can help a company to achieve its business goals faster and more efficiently. It can contribute to improving the quality of products and services, raising customer satisfaction and, when all is said and done, increasing sales.
A data journey also helps companies to set priorities for their digitalization efforts. By considering the various aspects of the journey, a company can determine which parts of the process are most important and can assign resources accordingly. This ensures that the organization makes use of its resources as effectively as possible and that the digitalization process is moved forward as efficiently as possible.
When all is said and done, a data journey helps a company to ensure that its digitalization efforts are in line with its general business strategy. A comprehensive approach to digitalization makes it possible for a company to ensure that the processes and systems it establishes are not only effective, but also correspond to the company’s general objectives.
In conclusion, a data journey can provide a company with a timetable for successful digitalization. A company which implements a data journey concept will be better equipped to cope with the complex road to digitalization and to achieve its goals. The experts at Spirit in Projects are always happy to help you develop a data journey for your company.
https://spiritinprojects.com/wp-content/uploads/2023/08/sip_blog_datajourney.jpg450900Wolfgang Hiermannhttps://spiritinprojects.com/wp-content/uploads/2020/04/sip_web_padding_10px_topbot.jpgWolfgang Hiermann2023-07-10 11:58:382023-08-04 13:19:52Data Journeys: A Path to Better Digitalization for Companies
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