Experts from diselva are regularly asked to share our know-how with decision-makers in practical formats – be it in workshops, expert committees, associations or closed meetings of boards of directors or management. The focus is not on theoretical, academic constructs, but on sound experience from real projects.
Recently, we were invited by an industry association to give a presentation on the use of AI in companies . The target group: Decision-makers who feel that AI is relevant but are wondering: Where to start? What am I missing? What does it do for my business in concrete terms?
Our presentation started right here:
- Real examples instead of buzzwords: We don’t show what would be possible, but what works. From automated reporting to intelligent customer communication.
- Strategic embedding: How AI projects can be successfully integrated into the corporate strategy and what prerequisites need to be created.
- Practical experience: We report on specific projects that we have implemented together with companies from different industries.
An example …
AI is no longer an abstract topic of the future, but already a lived reality. It changes processes, products and business models on a daily basis. For managers, this means that the question is not whether, but how an organization faces up to this change and actively shapes it. However, this is not trivial, especially if there is no concrete use case yet or if business does not work hand in hand with IT (technology).
In compact and understandable presentations, we will show how AI already works in practice today and how a company can come up with concrete use cases. It is important to understand the basics and to recognize, create and use opportunities. After all, AI is not the same as classic software development.
What leaders should know about AI
What is AI, how does it work, and how can I use it in my context? AI is more than technology. It is a strategic instrument – with potential for increasing efficiency, optimising costs and promoting innovation. But the key to success lies in the right application, data, architecture and organization.
Comparison of AI (AI), Machine Learning & Deep Learning
Our approach to AI is …
- Explain comprehensibly instead of theoretically mechanize
- Tried and tested instead of hypothetical
- Relevant to the company instead of just visionary and aloof
Three application examples with effect
- Automated reporting: Time savings in controlling through intelligent data preparation.
- Intelligent customer communication: Chatbots and agents that simplify processes and increase quality – especially in service processes.
- Document automation: Offer and contract texts at the touch of a button – consistent, fast and compliant.
Unpleasant – but important: Legal and ethical foundations for the use of AI
Managers bear responsibility – also in the introduction of technology. When using AI, there are therefore a few key framework conditions to consider:
- Privacy: The Swiss FADP and the EU GDPR require transparency, consent and purpose limitation in data processing.
- Explainability: Decisions made by AI systems must be comprehensible or, in certain cases, even explainable (“explainable AI”).
- Fairness & Anti-Discrimination: AI must not reinforce social or ethical distortions (“AI ethics”).
- Copyright: Content created with generative AI raises new questions about intellectual property.
- Responsibility & Governance: Humans remain responsible – even if the machine cooperates.
diselva supports companies in using AI responsibly – technically, organizationally and legally supported.
The use of AI in Switzerland/EU must take intgo account data protection, ethics, regulation, social impact and security in order to ensure transparency, fairness and social benefit.
Employees must be empowered by the company.
Respect copyright.
How do we proceed?
The diselva AI Excellence Framework
For ideas to become real results, structure is needed. With our field-tested framework, we support companies holistically:
- Discover- Identify strategic goals, lay the groundwork, analyze enterprise architecture
- Validat – Prioritize use cases, check the data situation, analyze risks and business fit
- Prototype- Develop, test, evaluate PoCs – and allow learning
- Scale- Deploy scalable, robust solutions and integrate with systems
- Sustain- Long-term governance, monitoring, ethical control, continuous optimization
The most important thing – concrete results and prototypes
The examplary machine learning model can be integrated and used in applications. It shows the importance and challenges in the area of data.
For example, a web interface can be used to enter the parameters required for a forecast and the forecasts can be generated.
Models can run on local devices with local data and do not necessarily require a cloud.
This approach helps to balance opportunities and risks – and to realize the benefits of AI in the long term.
We offer more than impulses – we provide implementable solutions:
- Workshops and lectures for management committees
- Potential analyses and feasibility studies
- Concrete implementation projects – together with you
Conclusion: Think strategically, try it out and implement it.
Contact us – we look forward to exchanging ideas with you!