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Artificial Intelligence: Everyone's Talking About AI - But Where's the Use Case?
The use of artificial intelligence (AI) is expected to radically change the way companies operate in a relatively short period of time. AI applications include the automation of business processes or entire process chains, increased reliance on data-driven decisions in corporate management, and the development of innovative business models based on generative AI.
Introducing and integrating AI poses significant challenges for any company. Both the value-creation processes and internal business processes, data infrastructure, and IT systems need to be analyzed and adapted.
It must be ensured that the IT infrastructure and, in particular, IT security are suitable for AI applications.
Additionally, corporate, IT, and data strategies must be aligned to incorporate AI into value creation processes. Employee qualifications, ethical guidelines, and legal frameworks must be reviewed and adapted to ensure a responsible and successful implementation.
Our Contribution
4C GROUP supports companies in identifying, evaluating, and prioritizing use cases as well as developing and utilizing them through:
- Creating a general understanding of AI and its forms
- Identifying and evaluating use cases
- Assessing the existing infrastructure in terms of data, applications, and know-how
- Designing a suitable target architecture, infrastructure, data, and applications
- Designing the right target operating model with a vision, goals, processes, organization, governance, and control mechanisms for the sustainable and professional operation of AI solutions
- Implementation including risk management, provider selection, execution control, and transformation
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What is Artificial Intelligence (AI)? Artificial"Artificial intelligence (AI) refers to the ability of machines to autonomously perform human-like cognitive functions such as learning, problem-solving, decision-making, and pattern recognition using algorithms and data." AI is currently a key driver of transformation in companies. Several methods and approaches fall under the umbrella of AI, each used depending on the specific application:
The key is not to see AI as an end in itself but as a tool that can be strategically integrated into your business model-while preserving the philosophy, values, and DNA of your organization.
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AI enables the automation of manual and repetitive tasks, the extraction of valuable information from massive data sets, and the generation of new perspectives or insights from structured and unstructured data. To succeed in this transformation, employees need to be empowered for new tasks.
This requires companies and their employees to acquire completely new skills, ranging from describing and evaluating requirements for AI use cases to developing and operating the infrastructure and effectively leveraging AI's capabilities.
A crucial aspect is also the proper handling of data, as its value is becoming more central. Companies must recognize this "data treasure," protect it, and use it strategically. AI can be a tool to mitigate demographic challenges, helping organizations retain the knowledge of experienced professionals who will soon leave the workforce.
The key to success lies in integrating AI strategically into the corporate strategy and existing operating models. Only with clear goals and a well-thought-out implementation can AI contribute to long-term growth and sustainability.
Implementing AI systems involves complex challenges that go beyond purely technological concerns. Companies must take a strategic and holistic approach to unlock AI's full potential sustainably. Four key challenges in leveraging AI as a genuine competitive factor are:
- Engaging and Empowering People:
The success of AI adoption depends heavily on how well employees are prepared for the new technologies. Simply providing AI systems is not enough-employees must learn to critically evaluate results and use the tools effectively. A passive or naive attitude toward AI should be avoided: AI solutions are not panaceas and require competent and reflective application. Properly conveyed, however, AI can generate genuine enthusiasm, as employees experience benefits like increased efficiency, creative problem-solving, and new opportunities for personal development. - Ensuring Data Quality and Management:
The success of AI depends on the quality of the underlying data. Companies must identify suitable data sources and ensure they are current, relevant, objective, and unbiased. It is equally important to avoid training AI with faulty data, as this could lead to distorted results in the long term. - Integration into Existing System Architectures:I
Technical integration of AI solutions into existing systems and processes is often a challenge. AI applications must be seamlessly embedded within the existing infrastructure to fully harness their benefits. This requires stable interfaces that provide AI with current and relevant data and allow for smooth integration with surrounding systems. The right architecture is essential for ensuring long-term usability and making future adjustments easier. - Scaling into Operational Use:
Many companies start by testing AI applications in pilot projects or isolated environments. Transitioning these projects into the regular operating model is a complex task. Costs, efficiency, and risk management play a central role here. AI applications must be scaled in a way that ensures sustainable value creation without financially or organizationally overburdening operations. Clear metrics should be developed to measure the effectiveness and economic impact of AI applications.
Those who hesitate to embrace AI today will lose the competition tomorrow – we ensure that you shape the future, rather than chase after it.
Focke Meyer | Partner 4C GROUP
Use Cases
The Business Case for AI
AI becomes a positive business case when large volumes of relevant data are transformed into real information and used as a competitive advantage. AI is increasingly becoming an integral part of corporate and functional strategies, helping companies redefine their goals and serve as an effective, future-proof tool for achieving them.
Digitalization, automation, and artificial intelligence offer concrete solutions for companies to ensure their long-term competitiveness and sustainability.
What Can AI Do Well?
- Rapid access to vast amounts of information
- Extracting relevant information through successive processes
- Summarizing numerous sources (with or without references)
- Converting information from speech to text or between languages
- Recognizing patterns and likely outcomes
Which Levers Can AI Activate?
- Processes requiring access to vast information (e.g., service instructions, legal texts, internet searches)
- Translation of spoken language (chatbots, commercials, customer inquiries, transcripts)
- Text translations (e.g., letters, articles)
- Generating content (e.g., marketing texts, presentations)
- Application of logical languages (e.g., coding, debugging)
- Rule-based automation (e.g., invoice analysis for accounting)
- Structuring text for decision-making (e.g., email and inquiry analysis)
- Statistical models for pattern recognition (e.g., fraud detection)
- Predictive models for future scenarios (e.g., logistics, buyer behavior)
Companies have numerous opportunities to implement artificial intelligence (AI) profitably. Here are some specific applications:
Success Factors
Success Factors for AI Implementation
To successfully implement AI within a company, several factors must be considered:
- Vision: The long-term vision defines the company's desired future state in the context of AI implementation. It outlines how AI will be strategically used to gain competitive advantages, drive innovation, and achieve business objectives.
- AI Strategy: Derived from the vision, the AI strategy details the specific path to achieving these goals. It includes clear milestones, investment decisions, technology roadmaps, as well as organizational and structural adjustments.
- Target Operating Model: A target operating model specifically designed for AI ensures seamless integration into existing business processes and optimizes the use of technology.
- Risk Management: Comprehensive risk management covers all aspects of data, methods, know-how, and outcomes to ensure the integrity and reliability of AI systems.
- Data Management: High-quality, up-to-date, and secure data are the foundation for effective AI applications.
- Scalable Technology: Companies need scalable IT infrastructure to efficiently operate AI solutions.
- Governance and Transparency: Building trust in AI systems requires transparent processes, strict process discipline, and clear ethical guidelines.
- Employee Qualification: Training and continuous development of employees in handling AI technologies are crucial success factors.
Regulation & Governance
The growing adoption of AI technologies necessitates clear guidelines to ensure their use is safe, transparent, and responsible. Such frameworks provide the necessary guardrails to align technological innovations with legal requirements, ethical standards, and societal expectations. With the EU AI Act, the European Union has developed a comprehensive regulatory framework aimed at ensuring the transparency, safety, and reliability of AI applications while simultaneously fostering innovation.
EU AI Act
When implementing an AI strategy, it is essential to ensure that it meets the requirements of the EU AI Act. The EU AI Act provides the legal framework for the use of AI in Europe and clearly differentiates between various risk categories (see diagram). Exploratory applications, professional applications, and those involving sensitive data are subject to different regulatory requirements. Companies are required to demonstrate that their AI systems either pose no risks or are thoroughly protected. This is particularly relevant for applications involving personal data or technologies that could potentially be misused, such as in weapons technology.
The EU AI Act presents companies with new challenges but also offers significant opportunities. It creates a clear framework for the responsible use of AI and strengthens trust in AI applications, especially in sensitive sectors such as healthcare and public administration. By complying with these requirements, companies can fully reap the benefits of AI transformation and secure their competitiveness in the global market.
Why 4C?
We Guide Your AI Transformation
4C offers a holistic approach to AI projects, operating at the intersection of strategy, management, and transformation. Instead of standardized solutions, the focus is on developing tailored, flexible AI solutions that are scalable and adaptable in the long term.
Analysis and Understanding
- Evaluation and prioritization of use cases through a structured approach
- Understanding digital business models and the resulting importance of data
- Understanding decision-making processes and the role of data as a basis for decisions
Process Optimization and Digitalization
- Process preparation based on focused analysis and best practices
- Identification of digitalization potential, including evaluation and implementation of selected options
Transformation and Change Management
- Experience in managing transformation processes for automation and reorganization using AI
- Assessing the impact of software implementation through expertise in governance, risk management, and compliance
Entrepreneurial Action and Strong Partnerships
- Entrepreneurial perspective: Understanding the impact of decisions on the business
- Strong technology partners: Collaborating with reliable partners for successful implementation
With our extensive, field-tested expertise, we help you fully leverage the opportunities of artificial intelligence and future-proof your organization. Trust our practical approach and in-depth knowledge-together, we will shape the future of your company.
Insights
4C Whitepaper: Artificial Intelligence (AI) for HR
How Artificial Intelligence is shaping the future of human resources and is already being implemented in leading companies today - explore exciting use cases in action.
4C Whitepaper: "Digital Finance"
Download our Whitepaper to learn how digitalization is driving the transformation of financial processes and roles, making strategic realignment and data-driven decision-making essential.
4C Study 2023: Collaboration of Innovation Departments
Collaboration with Münster University of Applied Sciences on the following research question: How can a balance be achieved between the freedom and creativity of innovation departments and successful collaboration with specialist departments from the core business?
Thank you for understanding that we can currently only offer our studies in german. For more information feel free to contact us directly.
Your Temporary Co-drivers
Do not hesitate to contact us to learn more about the sustainable use of AI to drive efficiency, create added informational value, and enhance decision-making confidence as a true competitive advantage. Together, we can successfully shape the future of your business.
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