Artificial intelligence - successfully implementing AI projects

For readers in a hurry:

• From theory to practice: Find out how to successfully implement AI projects and which basic principles are crucial for this.
• Avoid mistakes, maximize success: Discover the most common stumbling blocks in AI projects - and how you can elegantly avoid them.
• Step by step to perfection: Learn how to successfully implement your AI projects with clearly defined goals and MVP strategies. 
• Practical tips: From smart data quality to continuous optimization - use proven strategies for sustainable AI success.

Artificial intelligence (AI) opens up completely new possibilities for value creation. From intelligent chatbots that ensure round-the-clock customer service to sophisticated forecasting systems that support business-critical decisions - the potential applications seem almost limitless.

But while the temptation to implement AI projects quickly is great, practice shows that many of these ambitious projects never reach their full potential or even fail completely. The good news is that the most common stumbling blocks can be elegantly avoided with the right preparation and approach.

Successfully implementing AI projects

Before you embark on your AI adventure, it is essential to understand how AI projects differ from traditional IT projects. The fundamental difference lies in the nature of the technology itself: While traditional IT systems work according to hard-coded rules, AI systems are based on learning processes. They develop continuously and become more efficient the higher the quality of the data with which they have been trained and are continuously trained. The result is always subject to probability. In other words, there is no such thing as 100% certainty with AI.

For you as a project manager or decision-maker, this means that careful strategic planning of use cases is at least as important as technical implementation. Let's take a look together at the most common mistakes you may encounter when implementing your AI projects.

Mistake 1: Insufficient definition of the content task

A key obstacle to the success of a project often becomes apparent as early as the analysis phase. A typical mistake in AI projects is an imprecise or unclear definition of the task. If the goal of the project is not clearly formulated, misunderstandings can arise, both within the team and with stakeholders. This often leads to inefficient use of resources and time.

You often come across objectives such as: "We want to integrate AI into our processes" or "We also need something with artificial intelligence". Such vague formulations are a sure way to a dead end. After all, AI is not a panacea for inadequate human thinking about goals and tasks.

Imagine you were building a house and simply said to the architect: "Build me something nice." The likelihood of the end result meeting your actual needs would be vanishingly small. It's the same with AI projects.

The professional solution

In order to successfully implement your AI projects, you should invest sufficient time in formulating specific, measurable goals. Use the proven SMART methodology for this:

If it is clear which specific task or tasks within a use case the AI should solve, then you have taken the first step in the right direction.

Error 2: Overly ambitious range of functions

The initial enthusiasm for AI often leads to excessive enthusiasm: "While we're at it, we might as well..." However, this understandable euphoria harbors considerable risks. Too much functionality is like trying to sprint a marathon - you quickly run out of breath before you reach your actual goal.

In addition, AI is not yet powerful and "intelligent" enough to deal with comprehensive tasks and problems. AI platforms simply lack analytical capabilities, even if OpenAI GPT o1 suggest this.

The structured approach

Use the concept of the minimum viable product (MVP) as a guide. Focus initially on a core function that already offers measurable added value. Define clearly definable tasks that are to be fulfilled with the help of AI. As soon as this has been successfully completed, the project can be expanded step by step. 

Our practical example shows how a step-by-step AI-supported system can be developed to meet the challenges of email management.

Phase 3: Integration of a sentiment analysis (sentiment analysis)

Mistake 3: Neglecting technical aspects critical to success

Imagine erecting a high-rise building on sandy ground - without prior soil surveys. It is just as risky to underestimate the basic technical requirements for AI projects. The three most critical factors are:

Since large language models are the basis of generative AI and are not themselves a search engine, you absolutely need good data to which you can apply these language models. Make it clear what action you want the AI to perform with your data and check whether the AI approach you have in mind is the right one.

You should also clearly formulate the success criteria so that you can check the technical implementation backwards from these and identify and resolve any gaps at an early stage.

Mistake 4: Confusing AI and automation

Another common misconception is to see AI as a kind of "all-in-one solution" for all automation processes. Many companies try to mix automation systems and AI platforms together without understanding their fundamental differences. To make the difference clear, you can think of the following analogy:

Automation is like a precise assembly line in a factory: it performs the same predefined steps over and over again without deviating from them. The processes are predictable to a certain extent and are based on previously defined rules and processes. The strength of automation lies in its efficiency and reliability for clearly structured tasks.

AI on the other hand, is similar to a flexible, adaptive assistant: it recognizes patterns, processes data intelligently, adapts to new situations and can make independent decisions. Its ability to learn makes it particularly suitable for complex, dynamic requirements. However, AI does not offer you 100% certainty in terms of results, as its core is subject to probability.

Explanation using a practical example

Applied to the example of e-mail management, the difference can be illustrated as follows:

Automation: An automated email confirmation with standardized text - fast and efficient, but less customized and without consideration of the specific context.

AI: An intelligent analysis of the query with the ability to create personalized responses - context-aware, individual and equally efficient. However, the result does not follow an exact predetermined solution path. This means that the answer can be somewhat surprising and look different each time it is run.

In practice, the use of AI is part of an automation solution. The semantic capabilities of AI, i.e. the understanding of language and patterns in all forms, are used to interpret content and take the right actions with the recognized content. The AI also understands which interfaces and "tools" it needs to use and how. This is the core of so-called AI agents.

Mistake 5: The "set-and-forget" syndrome

Perhaps you are familiar with the situation: the project has been successfully implemented, everyone is happy - and then? Many companies treat their AI systems like an autopilot. But even the best autopilot needs a vigilant pilot.

Errors must be detected, corrected or escalated automatically. Otherwise, even a low error rate will lead to unsatisfactory results, which jeopardizes the successful implementation of AI projects.

Practical implementation strategies

To successfully implement your AI projects, we recommend the following procedure:

Preparation phase

• Conduct a thorough functional needs analysis: What exactly do you want to achieve? What does the target state look like?
• Definition of measurable success criteria: What facts and figures do you use to determine whether the project is a success for you?
• Putting together a qualified project team: Who brings functional internal knowledge of the business processes? Who provides technical information and enables access to IT systems and data?

Implementation phase

• Development of a prototype: If there is uncertainty about criteria that are crucial to success, testing and ensuring these functions is the ideal solution.
• Iterative improvements: Focus on the essential desired functionalities that are technically demanding. Perfection can always be achieved later.
• Continuous quality assurance: Are the results satisfactory? Are the success criteria and expectations being met? If not, escalate, analyze and correct at an early stage.

Operating phase

• Regular performance review
• Optimization and fine-tuning of the system: This is often only possible during operation, because only then are all the data and environmental parameters available and have an effect.
• Adapting to new requirements: Hunger comes with food - that's a promise.

Conclusion

AI projects offer enormous potential for companies to reduce costs and expenses and significantly increase the productivity of both core processes and administrative work. However, they also pose challenges. If you avoid these typical mistakes, you can significantly increase the chances of realizing a successful AI project. Clearly defined functional and technical goals, a focused functional scope, technical expertise in the various AI technologies and platforms, an understanding of the difference between AI and automation and how they complement each other, and continuous support for the AI models are crucial to achieving long-term success. With a well-planned and carefully executed implementation, you can fully exploit the potential of AI and minimize risks at the same time.

About Business Automatica GmbH:

Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.