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.

Microsoft Ignite 2025 summarized

Microsoft is focusing heavily on AI agents that no longer just provide support, but independently take on tasks, prepare decisions, and control workflows. At its core is Agent 365, a new platform that companies can use to develop, manage, secure, and monitor agents. This is complemented by new identities for agents (Entra Agent ID) as well as security solutions and comprehensive governance tools.

Microsoft 365 Copilot gets specialized agents for Word, Excel, and PowerPoint that create high-quality content, analyze data, and take over entire work processes. In Teams, agents communicate with third-party apps such as GitHub or Jira via the Model Context Protocol.

Three new layers—Work IQ, Fabric IQ, and Foundry IQ—form the semantic basis for enterprise AI. They link data from M365, business processes, locations, and internal knowledge sources and make it usable for agents.

In the security area, there are twelve new security agents that analyze threats, verify identities, control permissions, and support compliance – embedded in Defender, Entra, Intune, and Purview.

For developers, there are new tools for running AI locally or in the cloud: Windows offers new on-device AI APIs, and cloud PCs can run agents in controlled environments (Windows 365 for Agents). The entire AI lifecycle – from design to deployment – is being improved.

According to a new study, 68% of companies are already using AI; companies that adopt agents early on achieve significantly higher returns. Microsoft is thus positioning AI agents as the standard for the next generation of enterprises.

Announcements

Here are the points that are particularly relevant for companies – operational, organizational, and regulatory:

Agent 365 & Entra Agent ID → important for regulated industries

Germany has strict requirements for governance, data protection, traceability, and identity systems (especially Bafin & ISO environments).

Agent 365 offers:

•    Complete monitoring of agents

•    Audit logs for every action

•    Policies & roles that can be configured to comply with GDPR

•    Identity verification for agents via Entra Agent ID (important for the financial sector, industry, public administration)

=> This provides a controllable framework for AI, as is often required in Germany.

Security agents

The twelve new security agents can:

•    automatically analyze incidents

•    monitor identity risks

•    check compliance rules

•    clean up permissions

=> This is particularly relevant given that, according to Bitkom, Germany has a lack of thousands of security experts every year.

Work IQ & Fabric IQ → big impact for small and medium-sized businesses

German SMEs often have:

•    fragmented data silos

•    little internal AI expertise

•    heterogeneous IT landscapes

Fabric IQ and Foundry IQ create a uniform, semantic database – without huge data warehouse projects.

=> This makes AI “SME-compatible.”

New M365 agents => Productivity boosters for office-intensive industries

Particularly relevant in Germany for:

•    Mechanical engineering (documentation, quotations)

•    Consulting (analyses, presentations)

•    Public service (forms, letters, evaluations)

•    Insurance (reports, damage analyses)

The agents in Word/Excel/PowerPoint are designed to:

•    create complete documents

•    prepare tables

•    automatically build presentations

=> Significant time savings for highly regulated or documentation-heavy processes.

Local AI in Windows → important for data protection & industry

Many companies do not want to send data to the cloud.

The new on-device APIs offer:

•    AI without the internet

•    Stable image and speech models locally

•    Lower latency

•    Possibility for edge computing in Industry 4.0

Windows 365 for Agents => secure cloud operation

Cloud PCs are attractive for companies because:

•    Data remains in the data center

•    Centralized management is easier

•    Hybrid work has become the norm

Agents can now run on these cloud PCs – securely, in a controlled manner, and scalably.

=> A good solution for using AI, but without the risk of uncontrolled growth.

MCP connections (Teams ↔ Jira, GitHub, SAP)

MCP = Model Context Protocol, a protocol for connecting AI models with external data and tools.

Many companies work extensively with:

•    Jira (IT & project management)

•    SAP (ERP & logistics)

•    GitHub Enterprise

•    Atlassian stacks

Agents can interact directly with these via MCP.

=> This makes AI immediately usable for existing core systems.

==> All announcements can be found in the Book of News: https://news.microsoft.com/ignite-2025-book-of-news/ 

How Copilot Works – some further aspects

The article describes how genAI and, above all, Microsoft Copilot AI worked. The aim is to take possible options into account when designing the solution architecture and approach in order to achieve the desired result later on. This is because Copilot uses some functions in M365 to generate its answers—and that brings some special challenges with it.

How does Copilot work in Microsoft 365? Data flow of a prompt

Microsoft 365 Copilot is not only a powerful tool for increased productivity, but also a secure and compliant solution. With its advanced data protection and governance features, Copilot ensures that data remains within the boundaries of the Microsoft 365 service and is protected in accordance with existing security, compliance, and privacy policies. The same applies to the semantic index.

The semantic index for Copilot is a feature that helps AI understand context and deliver more accurate results. It builds on the keyword matching, personalization, and social matching features in Microsoft 365 by creating vectorized indexes to enable conceptual understanding. This means that, unlike traditional methods for queries based on exact matches or predefined criteria, the semantic index for Copilot finds the most similar or relevant data based on semantic or contextual meaning, rather than just keywords.

Source and further details: Semantic indexing for Microsoft 365 Copilot and YouTube video from Microsoft Mechanics: How Microsoft 365 Copilot works | Timestamp 139 seconds. Also, check out Michael Bargury's blog post titled: Copilot Vulnerable to RCE. To explain how the RCE hack works, he explains how Copilot works under the hood.

How exactly does the data flow work?

Key points about how Copilot for Microsoft 365 works

• Starting point: Entering the prompt
• The user enters a prompt in a Microsoft 365 app (e.g., Teams, Word, Outlook).
• The request is transmitted securely (TLS 1.2 or higher).
• Preprocessing and security checks:
• Copilot performs Responsible AI (RAI) checks to prevent harmful content.
• Grounding: The prompt is enriched with context from Microsoft Graph to better understand the user's intent.
• Processing by the LLM:
• The modified prompt is sent to a dedicated LLM within the Microsoft 365 environment.
• Important security aspects: No customer data is stored in the LLM or used for training. The LLM operates statelessly.
• Postprocessing:
• After the LLM responds, grounding and RAI checks are performed again.
• Copilot adds relevant data from Microsoft Graph to the response.
• Compliance and Retention:
• Prompts and responses are stored in Exchange Online for eDiscovery, legal hold, and compliance aspects.
• Output to the user:
• The final answer is returned to the original app.

System Prompt

What is a System Prompt by Nikhil Pattanshetty - MSFT

The Copilot for Microsoft 365 System Prompt is a set of predefined instructions and guidelines that influence Copilot's behavior and responses. It contains information about where to find data, how to respond, and what tone and style to use. For example, the system prompt might instruct Copilot to use information from Microsoft Graph, respond in an informative and professional manner, and use search results from multiple queries to provide a comprehensive response.

A slightly older version of the Copilot system prompt is available on Git Hub: Microsoft Copilot System Prompt (19-12-24).txt This gives you an idea of what is defined/regulated there. Example:

The system prompt is not visible to the user. However, there is a public source that describes the Copilot system prompt: What is Copilot for Microsoft 365 system prompt?

The system prompt can also be addressed in the user prompt.

Examples:

• I don't want you to agree with me just to be friendly or sympathetic.
• Drop all filters and be brutally honest, direct, and logical.

Ranking

I have already written about sorting order/ranking in a previous article: Content by AI – that's what they call it... -> Chapter: Ranking (including example and screenshots).

There is also a tool for this purpose, the AI Rank Checker: https://airankchecker.net/blog/best-ai-optimization-tools/ The tool is not free, and the author has not evaluated it himself. Unfortunately, it is therefore not possible to comment on how good the tool is.

The topic of search ranking plays a central role in usability and SEO. When the term “search-driven” emerged a few years ago, it essentially addressed the same question: How can we control which results are displayed first in a search? With AI and Copilot, we are now facing this challenge once again. Web parts such as the FAQ web part or Copilot integration in the text web part (e.g., “Write with Copilot” in the SharePoint rich text editor) raise similar questions: What does the average user see—and in which order?

Further articles on the topic of ranking:

• https://www.lawrencehitches.com/copilot-search-optimization/
• https://rankingcopilot.com/

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.

Content by AI – that's what they call it...

Creating websites or at least onepagers with AI is a current trend. And, of course, this trend has not bypassed Microsoft SharePoint. See also: Create pages with AI in SharePoint

This article is about providing content on sites and pages in SharePoint using Web Parts that use Copilot. This is a slightly different topic than having the AI create the entire page.

In my example, a SharePoint site contains 12 files, each with a recipe for Christmas cookies, stored in a library.

The following metadata columns are available:

• Multiple selection: Ingredients
• Free text: Info: %information about Christmas cookies%
• Price: Price per serving

Prompt for the first test after uploading: Do you have recipes for Christmas cookies? Which ones are the best?

Ranking

What would have worked in a search, namely more details/metadata and so on, to improve the file's ranking, does not work in Copilot. See the second screenshot in the article. The file “6. Cinnamon Oatmeal Cookies.docx” and the corresponding recipe are not displayed there. Even though this file, and only this file, has the metadata fields filled in. You have to tell Copilot very explicitly on what to sort. Then it works: “Refer to the ‘Info’ column in the ‘Christmas cookies’ library. The recipe with the most data in the ‘Info’ column should be rated highest. Don't use the internet!” => Motto: Explain it like I am 5.

Or ask him what he has currently sorted. Copilot dynamically adjusts this depending on the prompt and the logged-in user.

1,2,3, What comes first in the answer from #genAI

Search ranking is a big topic when it comes to topics such as usability or SEO. When search-driven became a topic a few years ago, it was the same game. How can you control what the search outputs first? And now we have exactly the same thing again with AI/Copilot. Web parts such as the FAQ web part or the integration of Copilot into the text web part (details: Writing with Copilot in the SharePoint rich text editor) raise these questions. What is displayed there for the normal user and in what order?

The search ranking is not relevant here. Instead, Copilot decides based on the context, i.e., depending on the prompt and the user. You can also ask Copilot: What did you sort by?

To include sorting or filtering, the prompt must be adjusted. In my example: Sort the result by the “Price” column.

Related topics: Create an AI-generated webpage using Copilot

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 

Fake it till you make it - how good are AI detectors really?

The fact is, you have to make it clear when something was created by AI. But who wants to control that and how?

It's the same age-old game as counterfeiting banknotes, counterfeiting products and so on. As soon as one side gains a new level, the other side has to follow suit or at least pretend to have caught up.

In the case of AI, we have the EU AI Act or, in Germany, the KI-Verordnung, which set out the legal framework. This states in Article 50, quote: ...shall disclose that the content has been artificially generated or manipulated: https://www.euaiact.com/article/50 

ATTENTION: This is in no way a legal advice!

If the person publishing the content does not do this, however, numerous app providers are now advertising that they can do this for you:

How do these checking apps work?

AI checkers identify various characteristics, including recurring phrases, consistent sentence structures and the absence of personal aspects in the texts. By examining these patterns, an AI detector can recognize whether content was created by humans or by an AI.

Research currently classifies these three approaches:

• Machine learning: classifiers learn from sample texts, but need to be trained for many text types, which is expensive.
• Digital watermarks: Invisible watermarks in text that can be recognized by algorithms. Providers of AI tools would have to insert these watermarks, which is very unlikely.
• Statistical parameters: Requires access to probability values of the texts, which is difficult without API access.

IT journalist Melissa Heikkilä says: “The enormous speed of development in this sector means that any methods of recognizing AI-generated texts will look very old very quickly.” Source: https://www.heise.de/hintergrund/Wie-man-KI-generierte-Texte-erkennen-kann-7434812.html

OpenAI released the AI Text Classifier in early 2023 to recognize AI-generated texts. However, the recognition rate was very low at just 26%, and 9% of human texts were incorrectly classified as AI texts. Due to this insufficient accuracy, OpenAI removed the tool from the market in mid-2023. A new version of the tool is not yet available. This example shows that we should not have too high expectations of AI recognition tools.

In general, anyone can use the following aspects to decide for themselves whether a text was created by a human or by an AI:

• AI-generated texts often are not very original or varied and contain many repetitions, while human authors vary more when writing.
• A style with many keywords strung together could also indicate an AI as the author.
• AI tools often make mistakes with acronyms, technical terms and conjunctions.

How well do the AI detectors work?

There are now quite a few of these apps. From free / commercial financed or to be paid, there is everything. Here are two overviews:

• Best AI Detector | Free & Premium Tools Compared: https://www.scribbr.com/ai-tools/best-ai-detector/
• The best AI content detectors in 2024: https://zapier.com/blog/ai-content-detector/ 

I did my tests with noplagiat. The tool is rated as good to very good and recognized texts that I had created for this article by Microsoft Copilot in Edge.

Test 1:

My prompt: “List the planets in our solar system. Tell which is the largest and which is the smallest. Name the largest moons. How old is our solar system? Explain how our solar system was formed. Formulate the answer as a scientific essay.”

Result and rating:

Test 2:

BUT a simple and small adjustment to the prompt caused noplagiat to stumble:

PROMPT: "List the planets in our solar system. Tell which is the largest and which is the smallest. Name the largest moons. How old is our solar system? Explain how our solar system was formed. Formulate the answer as a scientific essay. Use the writing style of Stephen King."

Result and evaluation:

Only this small addition in the prompt reduced the rate from 47% to 6%. Well, you might not want to write an essay about our solar system in the style of Stephen King. However, the example clearly shows where the weaknesses of the current solutions are.

Test 3:

It also recognized texts that were 100% not created by an AI correctly. This is the abstract of my new book, which is just about to be finalized:

The next version of AI apps

Enriching the AI-generated text with content, reviewing the text and adding text passages/facts from other sources is what separates the next version of AI apps. Here it is the case that you do not receive the answer to your prompt immediately, but that it can sometimes take a few hours. With Studytexter.de, for example, up to 4 hours.

Here are three examples of such solutions:

• https://studytexter.de/: Quote from the homepage - Your entire term paper at the touch of a button in under 4 hours. Innovative AI text synthesis - especially for German academic papers. 1000x better than ChatGPT.
• https://neuroflash.com/de/: Quote from the homepage - Strengthen your marketing with personalized AI content. The all-in-one solution for brand-compliant content with AI, from ideation to content creation and optimization. neuroflash helps marketing teams save time, ensure a consistent message and improve creative processes.
• https://thezimmwriter.com/: ZimmWriter is the world's first AI content writing software for Microsoft Windows. It allows you to use the AI provided by OpenAI directly on your desktop! -> Note: The app advertises 10 features that are supposed to be entered and many details that are supposed to make generated text unique.

Conclusion

Considering that humans do themselves well to check the output and adapt / reformulate it if necessary, it is currently not possible to verify reliably whether a text was created by an AI or not. 

AI and the productivity and quality of consultants work

The study Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality examines the impact of artificial intelligence on the productivity and quality of life of knowledge workers. This is a field study conducted by the Harvard Business School in collaboration with the Boston Consulting Group.

Key statements:

• Experimental conditions: The study included 758 counselors who were divided into three groups: without AI access, with GPT-4 AI access, and with GPT-4 AI access plus an introduction to prompt engineering.
• Increase in productivity: Consultants who used AI were significantly more productive. On average, they completed 12.2% more tasks and required 25.1% less time.
• Quality improvement: The quality of the tasks supported by AI was more than 40% higher compared to the control group.
• Different effects: Consultants with below-average performance benefited more from AI support (43% increase in performance) than those with above-average performance (17% increase in performance).
• Limitations of AI: For tasks that were outside the capabilities of AI, consultants with AI support were 19 percentage points less successful in providing correct answers.
• Use of AI: Two main patterns of successful AI use were identified: “Centaurs”, who divide tasks between humans and AI, and “Cyborgs”, who fully integrate their workflows with AI.

The study highlights that AI can offer significant benefits for the productivity and quality of life of knowledge workers, but also names challenges and risks, especially for tasks that are outside the current capabilities of AI.

A detailed summary can be downloaded here:

Zusammenfassung über KI und die Produktivität und Qualität von Wissensarbeitern.pdf

Recap on AI and Knowledge Worker Productivity and Quality.pdf

The linke to the complete study is this one: https://www.hbs.edu/faculty/Pages/item.aspx?num=64700

#genAI - has pushed business to the limit even further!

We simply took a system that was already at its limit and added another layer to it!

The Great Acceleration (Die Große Beschleunigung)

This is the title of a book by Christian Stöcker (Die Große Beschleunigung: Climate change, digitalization, economic growth - how we can hold our own in an exponentially changing world | https://amzn.eu/d/a77rREs)

A key topic of the book is the exponential growth and rapid change that we are currently seeing and have seen in recent years. For people, but even more so for entire cultures or companies, it is a challenge to understand and manage such changes. The rapid pace of change in today's world has far-reaching consequences for the economy and for companies. Christian Stöcker mentions the following in the context of artificial intelligence, for example:

• Technological disruption: Progress in technologies such as generative AI is driving change. Companies must continuously integrate new technologies in order to remain competitive.
• Lack of skilled workers: The demand for qualified employees is increasing, particularly in areas such as IT and data analysis. 
• New business models and processes: Digitalization often requires a complete realignment of business models and internal processes. This can lead to an increased workload and the need for constant reorientation.
• Regulatory requirements: New laws and regulations, such as the AI Regulation, the EU AI Act, pose challenges. Companies must ensure that they meet these requirements.
• Cultural change: The changes brought about by artificial intelligence / GenAI also require an adaptation of the corporate culture. Flexibility, a willingness to innovate and a new form of employee management are becoming increasingly important.

These facts show that in a rapidly changing world, companies not only need to work faster, but also smarter.

The article Capitalism in the mistrust trap by Michael Hüther follows the same line. The high rate of change and the constant emergence of new pseudo-innovations creates enormous pressure on companies. In the medium and long term, this can lead to an exhaustion of resources and the workforce.

Gartner, where do we stand?

The Gartner Hyper Cicle is a good indicator / orientation for innovations.

The Hyper Cycle is a graphical representation of innovative topics in five phases. It shows the maturity and acceptance of new technologies.

• Innovation trigger: A technological breakthrough arouses interest and generates media attention. Often there are no usable products yet and the commercial feasibility is unproven.
• Peak of Inflated Expectations: Early successes and exaggerated expectations lead to hype. Many companies show interest, but there are also many disappointments.
• Trough of Disillusionment: Interest declines as the technology fails to meet high expectations. Some providers fail or withdraw.
• Slope of Enlightenment: The benefits of the technology become clearer and better understood. Second and third generations of products appear and more companies begin to fund pilot projects.
• Plateau of Productivity: The technology is widely adopted and its market relevance becomes clear. It is now widely used and brings measurable benefits.

Hyper Cycle help to separate the hype from the actual drivers of a technology and make well-founded decisions about technology investments. As far as artificial intelligence / GenAI is concerned, it looks like this.

2023 - 2024 shows we are entering the field of disillusionment / the valley of disappointment:

The article Almost every third GenAI project is discontinued also fits in with this. The article lists the following reasons, among others:

• Poor data quality: Many projects fail due to inadequate and incorrect data.
• Escalating costs: The development and implementation of GenAI models is expensive.
• Unclear business value: Companies have challenges proving the business value of GenAI projects.

GenAI solutions, such as Microsoft Copilot or Google Gemini, are currently in several Hyper Cicle phases simultaneously. It is still an innovation trigger that causes amazement, especially among people who are less tech-savvy. This still leads to Peak of Inflated Expectations. Employees in this phase name use cases that GenAI solutions will not be able to achieve in the foreseeable future. Example:

• Suppliers should be regularly evaluated by an AI with regard to their quality. The AI should analyze relevant complaints and take them into account in a summarized evaluation. In doing so, the AI should be guided by the supplier's previous assessments and automatically inform the supplier of any changes to its status.
• The AI should evaluate and analyze commodity index data available online in order to make forecasts about the current and future price and supply situation.
• An AI application that comprehensively checks applications for guidelines and evaluation criteria and decides whether the application should be approved. The AI also creates a detailed and legally binding justification.

If it becomes clear that such scenarios cannot be implemented by GenAI, at least not at present, the next step is Trough of Disillusionment.

GenAI as an enabling technology

The article GenAI as an Enabling Technology: Empowering Yourself and Gaining Your Employee by Dr. Jim Walsh describes how GenAI can help people increase their skills and productivity. It can enable users to perform their tasks more efficiently and successfully.

Examples:

• GenAI supports the automation of routine tasks, the creation of content and the analysis of large amounts of data.
• Content creation: Automated generation of texts and graphics for marketing campaigns, for example.
• Personalization: Optimization of advertisements and customer segmentation for targeted marketing measures.
• Chatbots and digital assistants: Support in customer service and internal processes.
• Program code generation: Automated creation and improvement of software code.

Reality check

Unfortunately, some of the marketing promises made by the major brands are still a long way from reality. This is shown, for example, by the comparison between Microsoft Copilot and Google Gemini 

Here are two further examples that clearly show that we are still at the beginning with some of the new solutions:

• A guide to the length of the documents you provide to Copilot
• Approximately 80,000 words is the limit for Microsoft Copilot to summarize a document.
• Rewriting with Copilot works best with a document of less than 18,000 words.
• Using your organization's branding with Copilot in PowerPoint - minimum requirements for templates.
• Follow the instructions below to get the best results with Copilot...
• It is recommended to use a single slide master and rename it. If you need to use a template that contains multiple slide masters, you should ensure that each slide master complies with the guidelines in this article.

Neither of these examples are general showstoppers. However, the expectation of users and companies is that new solutions will bring benefits and simplifications rather than having to fulfill conditions. This is the kind of thing that puts people off in the first place.

But - and there is almost always a “but”

There is a silver lining on the horizon. The topic of GenAI is becoming more and more mainstream. Together with LinkedIn, Microsoft has published the 2024 Work Trend Index Annual Report.

The most important findings as to why GenAI will become established were summarized as follows:

• Employees expect AI in the workplace because they already know and use the apps from their private lives.
• AI raises the bar for employees and breaks down career barriers.
• A type of AI power user is emerging that will play a special role in the future.

For details see also: GPT - here to stay

The key point is...

... that GenAI is not a no-brainer in companies either. When Facebook came along and social intranets became a trend in companies at the same time, people often said: “Nobody needed training for Facebook either. So why should that be necessary for our social intranet?”

As many will remember, training and implementation concepts were necessary for social intranet projects to be successful. It's exactly the same with GenAI.

Ok, there are exceptions - here's one:

• AI can help to minimize less demanding tasks so that employees can focus on more important and essential activities.

This is derived from the 𝙀𝙢𝙥𝙡𝙤𝙮𝙚𝙚 𝙎𝙞𝙜𝙣𝙖𝙡𝙨 survey, which is conducted every six months to gain insights into employee wellbeing and productivity. The survey results show that access to AI can increase employee productivity and engagement. Source and further details: The Key to a Thriving Workforce? A Smart Approach to AI.

Mind you, “can” and not “must”. And without an adoption plan, this will only apply to a few committed employees.

Companies and IT departments are lagging behind the trend

IT departments, innovation drivers and strategy departments in companies are often still struggling with the switch to cloud solutions with their evergreen approach. In addition, there are the aspects that were explained at the beginning of the article. And now there is also the new topic of GenAI.

Another point is that the self-awareness of employees has changed. Until a few years ago, users used the IT solutions that were made available to them. Today, the motto is increasingly: Isn't there an app for this that we can download and use? In the context of GenAI: ChatGPT <-> Microsoft Copilot or Google Gemini etc.

When users take IT into their own hands and how to deal with it

IT departments can no longer reduce themselves to technology alone. The “strategic consulting” factor within the company is the key to success and acceptance among employees. Multi-speed IT approaches such as “Information Competence Centers” have proven their worth. However, such approaches also require IT or an “Information Competence Center” to take on a new / different role in the organizational chart.

References:

• IT and administration – a dying breed, or future-proof even in the cloud era?
• The cover term 'digital transformation' – or: When the department takes IT into its own hands

Retrieval-Augmented Generation - The metasearch engine in the age of AI

What is Retrieval Augmented Generation / RAG?

A nice analogy that also makes it clear what RAG is, is the concept of a metasearch engine. Here, the search query is forwarded to several other search engines. The results of all the requested services are then collected, processed and made available to the user. RAG is a technique in which an AI model is combined with other data sources in addition to the data in the LLM (Large Language Model) in order to generate more precise and contextually relevant answers. This is therefore a very similar approach to the metasearch engine. Even the two schematic diagrams of the technologys are similar:

RAG is used in this way in Microsoft 365 Copilot. To extend the capabilities of the AI, information is retrieved from various data sources and integrated into the response generation. This enables Copilot not only to access pre-trained data, but also to use current and specific information from other sources, including the data in the M365 Tenant. Access is via the Microsoft Graph. This also ensures that the underlying permission concept is always respected by the AI.

Source and further details: How Copilot for Microsoft 365 works: A deep dive

Copilot in Microsoft 365 uses RAG - this cannot be customized

In Microsoft 365 Copilot, RAG is used to improve responses to user queries. Copilot can access various data sources, such as documents, emails, Teams chats, etc., to provide well-grounded and accurate answers.

This also determines which functions / roles Copilot provides in the respective apps.

Examples:

• Word: Generate text with and without formatting in new or existing documents.
• Excel: Suggestions for formulas, chart types and insights for data in Excel sheets.
• PowerPoint: Create a presentation from a prompt or a Word file.

Complete overview:

Source: Copilot integration with Graph and Microsoft 365 Apps

Now we have GraphRAG - that can be customized

The article Unlocking LLM discovery on narrative private data describes GraphRAG, a new method from Microsoft Research that extends the capabilities of large language models (LLMs) to access and analyze your data.

GraphRAG combines LLM-generated knowledge graphs with machine learning to improve document analysis performance, for example. This method shows significant improvements in answering complex questions compared to standard approaches.

A key benefit of GraphRAG is its ability to identify and understand topics and concepts in large data sets, even if the data was not previously known to the LLM. Here are some practical use cases for this technology:

• Information extraction: GraphRAG can be used to extract specific information from large document collections or databases.
• Content generation: GraphRAG helps to create content that requires in-depth contextual knowledge.
• Customer support: GraphRAG can improve customer support by accessing a knowledge base and providing accurate answers to customer queries.
• Knowledge management: In large organizations, GraphRAG can help to make efficient use of existing knowledge by retrieving and consolidating relevant information from different departments and documents.

Quickstart

To get started with the GraphRAG system (https://github.com/microsoft/graphrag), it is recommended to use the Solution Accelerator package (https://github.com/Azure-Samples/graphrag-accelerator). This offers a user-friendly end-to-end solution based on Azure resources, quote: One-click deploy of a Knowledge Graph powered RAG (GraphRAG) in Azure

The graphic shows, for example, the following sources for own solutions and GraphRAG:

• Azure Blob Storage
• Cosmos DB
• Azure OpenAI
• Azure AI Search / Vectorstore
• Container Registry
• Application Insights

As described on GraphRAG's GitHub page, Prompt Tuning options can also be used to customize the solution to your needs and use cases:

Source: GraphRAG Prompt Tuning

Google Gemini compared with Microsoft Copilot

The release of ChatGPT by OpenAI at the end of 2022 has re-shuffled the cards on the AI market. Microsoft is the largest investor at OpenAI. OpenAI's technology is therefore also the foundation of Copilot products.

Google is taking a slightly different path. Its own company, Google DeepMind Technologies Limited, has developed the Gemini solution. Google Gemini was originally called Google Bard and is the follow-up to the LLMs LaMDA and PaLM 2.

What is being compared in this test?

Microsoft Copilot	Google Gemini
Copilot in Edge https://www.bing.com/chat	Google Gemini https://gemini.google.com/app
Copilot in Microsoft 365 / Microsoft Word https://www.office.com/chat	Gemini for Google Workspace add-on https://workspace.google.com/solutions/ai/

Overview

Google Gemini:

Google Gemini is not based on a single model, but on a series of different LLMs. Each of these LLMs has different dimensions and a different mix between efficiency and the ability to find answers.

Details are described here, for example: https://cameronrwolfe.substack.com/p/google-gemini-fact-or-fiction

The official homepage of Gogole Gemini is this one: https://blog.google/technology/ai/google-gemini-ai

Feature availability:

• Gemini is available as part of the Google Early Access Test Program.
• The solution is also available via a Gemini for Google Workspace add-on and for users with private accounts via Google One AI Premium.

The Gemini for Google Workspace add-on was used for this comparison.

Microsoft Copilot:

The Copilot solution from Microsoft has a little different architecture. Copilot in Edge, formerly Bing Chat Enterprise, is very similar to Google Gemini. Copilot in Microsoft 365, on the other hand, is integrated into the Microsoft 365 cloud solution and is therefore always part of an M365 subscription. Copilot in Microsoft 365 has access to the data in the tenant via the Graph interface. The permissions model, i.e. who has access to which data within Microsoft 365, is an aspect that is always respected.

In addition, Copilot in Microsoft 365 uses orchestration. Copilot knows from which app the prompt was sent, and this has an impact on the output. For example, Copilot in Word focuses on being a writing assistant, while Copilot in Excel has its benefits in formulas and diagrams. There is no such deep integration in Google Workspace with Gemini.

Comparison

Copilot in Edge (formerly Bing Chat Enterprise) & Google Gemini App

One of the major points of generative AI solutions is that there is only limited transparency about the data used to train the models. For GPT 3 there is this list from OpenAI:

• Common Crawl -> 60%
• WebText2 -> 22%
• Books1 -> 8%
• Books2 -> 8%
• Wikipedia -> 3%

Even this is only very high level and for many other models / versions there is not even that. Also for Gemini only this statement could be found: “According to Google's Terms of Service and Privacy Policy, the sources of training data for Google's Gemini AI include publicly available sources and information from Gemini apps. These are used to improve and develop Google's products, services and machine learning technologies.”

The sources on which the LLMs were trained can therefore only be determined to a very limited level and lead to curious / incorrect results over and over again.

Test 1

Prompt: “Who scored the most goals in a soccer match?”

The answer focuses purely on men's soccer. It is remarkable that the two apps provide different answers. The very simple prompt is surely also partially the reason for this.

If you ask in the dialog with the prompt: “Which woman scored the most goals?”, the apps provide the following answers:

Findings:

Both apps show similar behavior. The AIs only respond to women's soccer when asked.

Test 2

Prompt: “Can I log in to ChatGPT via Azure authentication?”

Findings:

The answers from both apps are not good / misleading. The answer from Gemini is also wrong. In general, you can log in to OpenAI and therefore also to ChatGPT with an Azure account / Entra ID.

However, that was not a good prompt either. (PS: Prompt Engineering: https://platform.openai.com/docs/guides/prompt-engineering 😊 )

A prompt that would work better would be, for example: “Can I use an account from Azure AD or Entra ID to log in to OpenAI / https://chatgpt.com/auth/login?”

Copilot in Word & Google Docs + Gemini for Google Workspace Add-On

Both solutions offer the feature to analyze and summarize texts as well as to create texts.

The “Gemini for Google Workspace Add-On” was used in Google Docs: https://workspace.google.com/u/0/marketplace/app/ai_assist_for_gemini_in_sheets_docs_and/985356259375

“Copilot in Microsoft 365” was used in Microsoft Word: https://www.microsoft.com/de-de/microsoft-365/microsoft-copilot

Test 3

Context: Ask me anything about this document

For this comparison, the same Word document (docx) was opened in Microsoft Word and in Google Docs. The document “A quick guide to secure Office 365.docx” describes the possibilities of securing Office 365 and monitoring and controlling access with features such as Defender for Cloud Apps etc.

Copilot in Word welcomes the user with the message “Ask me anything about this document”. The predefined prompt: “Summarize this document” generates a correct result:

Questions to the document such as “What does the document say about multifactor authentication? Should this be used?” are also answered correctly. Copilot generates in addition jump labels to the respective place in the document.

Gemini for Google Workspace Add-On welcomes the user with “Enter prompt here”. The Refine -> Select the text -> Summarize function is available to summarize the document. The result is also correct.

The feature to “chat” with the document and ask questions was only available in the early access test program for Google Workspace Labs at the time of testing (June 2024). Unfortunately, this function could not be tested with the add-on used. Here is an example from Google on how it would look like:

Findings:

The integration and therefore the usability of Copilot in Word is better than the Gemini solution with Google Docs. Example: If you use a Word version that is set to German, for example, Copilot also delivers its summary in German. Gemini does not do this when using exactly the same settings (document in English and Google Docs in German).

Test 4

Context: Describe what you would like to write

When it comes to using the apps as a writing assistant, you are greeted by Copilot in Word with the text “Describe what you would like to write”. Both solutions offer this feature. The following prompt was used for the comparison in both apps: “Write an essay about Dietrich Bonhoeffer. The text should be an overview of his life and work as well as his role in the resistance. Also include what happened after his death.”

Findings:

Both solutions provide a comparably good result.

Azure OpenAI Studio & Google AI Studio

Even before Copilot, the Azure OpenAI feature was available from Microsoft. Google AI Studio is the counterpart to this solution.

When comparing the two products, it is noticeable that Google AI Studio is an interesting prospect, especially in terms of price and the number of tokens. The Azure solution scores points with its strategic partnership with OpenAI and the ability to use all the extensive Azure features, including security and compliance, in the context of AI solutions.

Google Gemini

• Models: Gemini 1.0 Pro, Gemini 1.0 Ultra, Gemini 1.0 Ultra Vision, Gemini 1.5 Pro, Gemini 1.5 Flash
• Features: Text generation, translation, Q&A, code completion, complex tasks, multimodal interactions, visual data processing
• Tokens: Maximum number of tokens of 1 million (for Gemini 1.5 Pro and Gemini 1.5 Flash)
• Price: Gemini 1.5 Pro is 30% cheaper than GPT-4o for input and output tokens

Azure OpenAI

• Models: GPT-4o and older GPT models such as GPT-4, GPT 3.5 etc.
• Features: Text generation, translation, Q&A, code completion, complex tasks
• Tokens: No specific maximum number of tokens specified
• Price: GPT-4o is more expensive than Gemini 1.0 Pro and Gemini 1.5 Pro
• Other aspects:
• Partnership: Azure offers OpenAI models via API, Python SDK or web interface.
• Integration into the Azure Suite

Summary

Microsoft Copilot and Google Gemini look very similar at first glance. The user interface is similar and the functionality is also similar. The price of the two solutions is also roughly the same. However, if you take a closer look, it quickly becomes clear that Copilot and Azure OpenAI are currently ahead of Google Gemini.

I have done a number of tests and these are my findings:

• Microsoft Copilot is ahead of Gemini in the quality of AI generated answers. The results are more accurate and consistent. Gemini still makes mistakes too often. As an example, see the result of Test 2
• Gemini's user interface is clean and straightforward. At first glance, Microsoft Copilot in Edge is more feature-rich but a bit more game-like than Gemini. 
• Gemini integrates with Google Workspace apps, but this integration is not on the same level as Copilot in Microsoft 365. As described in the Overview chapter, Copilot in Microsoft 365 has its own architecture and is not just an add-on. Part of this architecture is also the RAG functionality, which, among other things, ensures that Copilot knows his current context. For example, the AI acts as a writing assistant in Word and supports you in Excel when writing formulas or creating diagrams. More details: How Copilot for Microsoft 365 works: A deep dive