The True Scope of an AI Project
Artificial Intelligence (AI) is often seen as a cure-all for business problems. However, before an AI project can begin, there are essential steps that go beyond the technology itself. These initial phases not only determine the feasibility of the project but also assess the actual need for AI compared to traditional solutions. The success of an AI project largely depends on careful planning and preparation.
- 17. October. 2024
Patrick Ratheiser
CEO & Founder
Karin Schnedlitz
Content Managerin
Definition and Evaluation of the AI Use Case
The initial phase of an AI project focuses on the precise definition of the AI use case. At this stage, it is critically assessed whether AI is truly the best solution or if traditional digitalization approaches would suffice. This decision is crucial, as it determines whether further investments in specialized AI solutions are justified.
Data Acquisition and Analysis
Once the use case is defined, the next phase is data acquisition. This step involves examining whether there is enough suitable data to develop and train an AI model. The challenges in this phase are often greater than anticipated, as data may be incomplete, unrepresentative, or simply unsuitable. This analysis is crucial for the feasibility of the entire project.
See for yourself the power of our AI solutions.
Request your free trial access now!
Model Training and Integration
If the data analysis is positive, an AI model is developed and trained. However, this technical aspect is only part of the challenge. The most extensive work begins with integrating the model into the existing infrastructure. This involves software development, setting up interfaces, frontend development, and managing user permissions. These steps require extensive software development expertise.
Operation and Maintenance of the AI System
The operation of the AI model represents the final major step and primarily involves traditional software tasks, including DevOps responsibilities. This phase is crucial for the long-term use and efficiency of the system. It becomes evident that most of the work in an AI project is indeed traditional software development and that the role of AI is often overstated. This structure provides a comprehensive overview of what an AI project truly entails, highlighting that, in addition to the AI itself, the surrounding processes and software development are key.
70% of the work in an AI project is dedicated to software development, while only about 30% is focused on actual AI development and training.
The Ratio of Software Development to AI in Projects
Although artificial intelligence often takes center stage in public perception, practice shows that up to 70% of the work in an AI project is dedicated to software development, while only about 30% of the efforts are directly related to the development and training of AI models. These figures highlight that the role of AI is frequently overstated, while the actual effort and importance of software development are underestimated.
A key factor often overlooked is the quality of the data. Once the data is well-prepared and validated, the actual AI model can often be developed and trained relatively quickly. However, the majority of time and resources are spent on integrating the AI model into the existing software infrastructure. This includes tasks such as connecting interfaces, developing user interfaces and dashboards, and implementing security and privacy measures.
Additionally, ongoing maintenance and adjustment of the software surrounding the AI model are crucial for the long-term success of the project. Software development is not only essential for initial integration but also for the continuous adaptation and improvement of the overall system in operation. These aspects of software development ensure that AI is effectively integrated into daily operations and provides real value.
The realization that software development constitutes the lion’s share of an AI project should prompt adjustments in resource planning and project management. Teams undertaking AI projects should therefore include not only AI experts but, more importantly, experienced software developers. This ensures a smooth integration and allows the AI to deliver its full impact.
The Critical Distinction Between ML/Data Science and Software Development
A common misconception in planning and executing AI projects is treating Machine Learning (ML) and Data Science as synonymous with software development. This confusion often leads to problems or even project failure. ML and Data Science primarily focus on analyzing data, developing algorithms, and training models to recognize patterns and make predictions. These disciplines require specialized knowledge in statistics, mathematics, and data analysis.
In contrast, software development encompasses the design, implementation, and maintenance of software solutions that must be integrated into an existing IT infrastructure. This includes writing code, developing functional and user-friendly interfaces, and ensuring the scalability and security of applications. These tasks demand a deep understanding of programming principles, system architecture, and software engineering.
The differences in the knowledge and skills required for ML/Data Science and software development can lead to significant communication and collaboration barriers within teams. When data science team members do not work effectively with software developers, it can result in the developed models being poorly integrated into the application landscape.
Moreover, a lack of software development expertise among providers who specialize primarily in AI and ML can cause difficulties. Without adequate know-how in robust software development, there is a high risk that the project will not meet operational requirements or be technically feasible.
Projects that fail to clearly distinguish between these two disciplines and do not ensure close collaboration are more likely to fail. It is crucial that both AI experts and software developers understand and respect their respective roles and that project management implements mechanisms to foster interdisciplinary collaboration.
Start your AI project securely with our expert team.
Book your free initial consultation now!
 Tips for a Balanced AI Project
For project managers and teams working on AI projects, it is crucial to find a balance between software development and AI-specific tasks. Here are some helpful strategies:
- Form Interdisciplinary Teams: Ensure your team includes both experienced software developers and AI experts. This mix promotes knowledge sharing and helps set realistic goals.
- Integrate Quality Management: Invest in robust quality management and testing procedures specifically for AI applications. This includes validating data quality and continuously testing the behavior and performance of models under various conditions.
- Apply Agile Methods: Utilize agile development methods to ensure flexibility throughout the project and to respond quickly to challenges. Iterative cycles allow early insights from tests to be incorporated directly into development.
- Promote Communication: Regular meetings and clear communication channels between data scientists and software developers are essential. This ensures that both sides understand each other’s requirements and limitations and work together on solutions.
- Education and Training: Provide continuous education opportunities to sharpen the technical skills of all involved and deepen understanding of each other’s disciplines.
Conclusion: A Realistic Perspective on AI Projects
AI projects are complex and require more than just technological expertise. The key insights from the previous sections highlight the need for a balanced approach that addresses both software development and the specific demands of artificial intelligence. Maintaining realistic expectations is essential. It is crucial to recognize that successful AI projects necessitate a deep integration of technological innovation with solid software development. Emphasizing a strong foundation in software development, combined with proficient use of AI, will ultimately make the difference between a successful project and one that fails to meet its goals.
Have you heard about our KI>Inside Podcast?
The podcast not only offers intriguing insights into the mechanisms and potential of AI but also explores what it truly takes to make an AI project successful.
Â
Â