The prompt – the language of AI

Technical basics, advantages, and possible use cases 

I have already written a few things on the topic of prompts. Nevertheless, here is another article. Why? Quite simply because AI, and with it the topic of prompts, is developing very fast.

The rapid evolution of artificial intelligence (AI) has given rise to numerous new professional fields and skills in recent years. One of these key skills is prompt engineering, which involves the targeted formulation of inputs (prompts) for AI systems in order to achieve optimal results. 

However, despite all this momentum, enthusiasm has declined somewhat and the mood is somewhat uncertain in some cases. One reason for this is that the results are not quite as simple and perfect as the providers suggest...and this is where the topic of prompts comes into play.

What is a prompt?

A prompt is an input or instruction given to an AI model in order to obtain a specific response or action. In generative AI systems such as large language models (LLMs) – for example, GPT-4 or similar models – prompts are usually text commands, questions, or tasks. The quality and precision of the prompt significantly influence the relevance, accuracy, and usefulness of the AI output.

Prompt Engineering: The Art of Correct Input

Please stop calling it Prompt Engineering, unless you really mean it! – This is how Wictor Wilén titled an article on this topic. 

Technically, the term has become established. In my opinion, he is absolutely right. Read more here: https://

Prompt engineering refers to the systematic and strategic creation of prompts to maximize the performance of AI models. Various methods are used, such as iterating formulations, testing different scenarios, or combining contextual information. Technically skilled users, for example, use placeholders, variables, and complex instructions to control the AI in a targeted manner and obtain consistent results.

Google now delivers a number of results on the topic of “prompt” in  seconds. So anyone who wants to find out more should have no problem finding sources. I can personally recommend this course:  5-day intensive course "Masterful Prompting with AI https://www.facebook.com/photo?fbid=122140401698939897& set=gm.1502673077451596&idorvanity=1417848559267382 Found on Facebook. Can be accessed and used even without an Facebook account.

Technical advantages for expert

• Efficient use of AI resources: Those familiar with prompt engineering can use AI systems more efficiently and avoid unnecessary computing power, time, or erroneous outputs.
• Workflow optimization: Targeted prompts can be used to automate routine tasks, perform complex analyses, or generate creative content—all with minimal effort.
• Better control over results: Technically skilled users can control the AI to meet specific requirements, e.g., certain text formats, data structures, or logical processes.
• Expanded application possibilities: With advanced prompt techniques, AI models can be adapted for a wide range of tasks, such as in data science, software development, research, or content creation.

Practical examples

In technical environments, prompts are often used to generate code, analyze data, or solve complex tasks. An experienced prompt engineer can, for example, get an AI model to create a complete program code according to specific specifications, find errors, or provide optimization suggestions. Prompt engineering is also playing an increasingly important role in the automation of business processes and the creation of technical documentation.

Conclusion

The ability to formulate effective prompts has become a key technical skill in the age of artificial intelligence. Those who are familiar with prompt engineering can exploit the full potential of modern AI systems, optimize processes, and develop innovative solutions. Technical understanding and experience in this field open up a wide range of opportunities – from automation and creative content creation to problem solving in complex IT systems.

The reasons why AI initiatives often fail – A company-wide perspective

Artificial intelligence (AI) offers enormous opportunities for companies when understood and used as a strategic tool for overall success. However, many AI projects fail because they are launched in isolation, without clear objectives or without the involvement of the entire organization. To ensure that AI initiatives are fully effective, they must contribute to company-wide goals and be supported across all departments.

Individual interests based on AI can also make sense

Individual interests based on AI can also make sense

Although successful AI initiatives should focus on company-wide added value, targeted individual interests or department-specific projects should not be underestimated. Innovations often arise exactly where individual teams or departments address specific challenges with AI and find creative solutions.

Such initiatives can act as a catalyst for the entire company: they make it possible to test the new technology on a small scale, gain experience, and develop best practices. Individual use cases provide valuable insights that can later be scaled and transferred to other areas.

It is important that these individual interests do not remain in silos, but are actively shared with the organization. This way, everyone benefits – and the company can make targeted decisions on which initiatives are rolled out company-wide. Individual initiatives are therefore not at odds with overall success, but rather an important building block for sustainable innovation and continuous improvement.

Typical pitfalls and how companies avoid them

• Unclear objectives: AI should not be an end in itself. Successful initiatives arise when the company works together to identify relevant business problems and develop solutions that promote overall success.
• Lack of trust: AI can only gain acceptance if its introduction is communicated transparently and all areas are involved at an early stage. Trust in the technology and processes is crucial for company-wide success.
• Excessive trust in automation: Human supervision remains essential. AI should support employees, not replace them. Only through the interaction of humans and machines robust, company-wide solutions can be created.
• Poor data quality: Data is the foundation of all AI. Only when the company creates a consistent, high-quality pool of data and breaks down silos can AI deliver sustainable added value.

Breaking down silos – challenges and alternatives with Microsoft Fabric

Breaking down data silos is one of the biggest challenges facing data-driven companies. Structures that have grown over many years, different systems, and proprietary data formats mean that valuable information is “trapped” in individual departments or applications. These silos not only hinder the company-wide use of AI, but also make it difficult to develop a holistic data strategy. The result: decision-making processes slow down, opportunities go untapped, and innovations don't reach the whole company.

Breaking down data silos is one of the biggest challenges facing data-driven companies. Structures that have grown over many years, different systems, and proprietary data formats mean that valuable information is “trapped” in individual departments or applications. These silos not only hinder the company-wide use of AI, but also make it difficult to develop a holistic data strategy. The result: decision-making processes slow down, opportunities go untapped, and innovations don't reach the whole company.

Why is breaking down silos so hard?

Data does not like walls – Why we should bridge silos rather than tear them down

In today's corporate reality, we encounter them everywhere: historically grown IT landscapes that act as silent witnesses to past projects and strategies. Different systems and databases have been introduced over the years – often with the best of intentions, but rarely with an eye to the big picture. The result? A patchwork of technologies that separates more than it connects. This technical diversity is also reflected in the organization. Departments operate in their own cosmos, pursue individual goals, and establish processes that rarely extend beyond their own doorsteps. This is how data silos are created.

The alternative? Don't tear everything down – connect it instead.

It is neither sensible nor realistic to radically remove every silo structure. Especially in large companies with complex requirements and established processes, it can be more effective to respect existing structures – while still exploring new avenues. The key here is a consolidating layer that brings data together without disrupting existing systems. Middleware solutions such as Microsoft Fabric offer exactly this functionality. They enable data from different sources to be brought together without replacing the underlying systems. 

Ultimately, it's not about technology. It's about people, trust, and the willingness to achieve more together. Silos are not the enemy – they are part of our history. But it's up to us how we shape the future: with bridges instead of walls. It is not always sensible or realistic to completely eliminate existing silos. In large companies in particular, it can be more effective to respect existing structures and instead introduce a consolidating layer. 

The costs of failed AI projects for the company

• Financial losses and damage to reputation affect the entire company and weaken its overall business performance.
• Missed opportunities mean that the company falls behind in the market while others move forward.
• Team burnout occurs when projects are implemented without a clear strategy and without support from the organization. 

Success factors for company-wide AI initiatives

• Early, company-wide risk management: Risks are best identified when all relevant departments and management levels are involved.
• Continuous review and learning: AI projects are an ongoing process over a longer period of time. Regular evaluation and feedback from all areas of the company improve results and identify errors at an early stage.
• Data quality through company-wide standards: A shared database needs standards in order to serve as the basis for AI solutions.
• Linking to company goals: AI projects must always make a measurable contribution to the company's strategic goals – whether through increased sales, reduced costs, or increased customer satisfaction.

Checklist for your next AI project – with a view to the entire company

• Are all relevant areas and stakeholders involved?
• Is there an open dialogue about risks and opportunities at the company level?
• Is data used responsibly and consistently across the company?
• Is the contribution to the company's success clearly defined?
• Are there transparent success criteria that apply to the entire company?
• Is knowledge shared and developed across departments?
• Is there a contingency plan in place for undesirable effects?

Conclusion:

AI only succeeds when implemented company-wide

The success of AI initiatives is not measured by local optimizations or individual interests, but by how much they promote the overall success of the company. Those who focus on cross-departmental collaboration, transparency, and shared responsibility create real added value—for the entire company and its sustainability. Solutions for individual interests can also make sense in this context.

Using AI without IT

By chance, I came across an article on social media with the headline “Using AI without IT”:

(For those who don't understand German: the screenshot shows an picture that says: Using AI – without an IT team. The technical report shows how 500+ companies are doing it).

Source: https://business.simpleclub.com/

AI – here to stay

Unfortunately, this is a scenario I have heard about at several workshops and customer meetings. IT departments still struggle with the topic of AI in some cases, but this doesn't have to be the case! See, for example: https://m365techtalk.blogspot.com/2024/05/dont-make-me-think-challenges-with.html 

AI is not just a nice gimmick! It can help employees do their jobs better, use their time more wisely, and unlock their potential. Microsoft AI / Copilot is a tool that promises exactly that – but how can you really leverage these benefits?

Here are five practical points that not only increase ROI but also put the employee at the center:

1. Taking decisions

Copilot helps to structure information and reveal connections. This means better decisions, less uncertainty, and more confidence. And not just for managers, but for everyone who makes decisions on a daily basis—in projects, in customer contact, in teams.

2. Time is money – and Copilot can save you both

Copilot can take care of repetitive tasks: drafting emails, summarizing meetings, structuring content. This saves time – but the real benefit is that employees can focus on what their actual job is. Technology as a liberation, not a burden.

Imagine having 30 minutes more every day. Not because you're working less, but because Copilot is helping you with routine tasks.

3. Empowerment

Even the AI Regulation obliges companies to train their employees in AI.

Article 4 of the European Regulation on Artificial Intelligence deals with “AI competence” and says:

"Providers and operators of AI systems shall take measures to ensure, to the best of their ability, that their personnel and other persons involved in the operation and use of AI systems on their behalf have a sufficient level of AI competence, taking into account their technical knowledge, experience, education, and training, and the context in which the AI systems are to be used, as well as the persons or groups of persons for whom the AI systems are intended to be used."

Source: Regulation (EU) 2024/1689

Based on our experience with our customers, I can say that the success of AI and Copilot depends on how well employees understand the technology. Training is important – but even more important is a culture that promotes learning, allows for mistakes, and rewards curiosity. Copilot is a tool that you have to get to know.

4. Clean up your data – otherwise nothing will work

Copilot needs good data to work effectively. This is not a task for IT alone, but a shared responsibility. Transparency, governance, and clear structures help. See, for example: https://m365techtalk.blogspot.com/2024/05/dont-make-me-think-challenges-with.html 

5. Measure impact – have the courage to change

Of course, ROI is important. But it shouldn't be the only metric. Anyone introducing Copilot should also consider the following: How is collaboration changing? How do employees experience working with AI? How much time is left for training?

Copilot is a beginning. Not an end. Anyone who introduces it should also be prepared to go further. Try new things. Collect feedback. Measure KPIs, for example with the Copilot Dashboard. See also: Connect to the Microsoft Copilot Dashboard

And above all: let people do their thing. Because innovation doesn't happen in an imaginary world, but in everyday life.

Conclusion: Copilot is not a miracle cure—but it is a damn good tool!

Microsoft AI/Copilot can do a lot. But only if you use it correctly. With planning, training, data maintenance, and a dose of courage. Then you will also see a return on investment. At the beginning of an AI project, it is always important to identify the needs of employees. See also: https://learn.microsoft.com/en-us/viva/insights/org-team-insights/copilot-dashboard