Software Development Risk Mitigation Using AI

According to the Standish Group’s 2020 CHAOS report, approximately 66% of software projects fail. It is evident that numerous problems can arise during the process of building software products, often more than expected. To tackle these issues promptly and iterate on them, a special scrum process is employed. However, the risk of such problems can be further reduced using AI. In this post, we will explore these issues, divided into two groups: problems rooted in planning and those in building. We will then discuss the consequences of these issues and propose a solution using an innovative AI tool called Project Copilot.

As a Product Manager, What Are the Most Common Problems During Planning and Development?

During the product building process, various issues arise. We can categorize them based on the root cause of the problem, whether it is in planning and specification or in building and testing.

Errors in Planning

  1. Unclear description: When writing a user story, clarity is crucial. An unclear description can lead to confusion and misinterpretation, resulting in incorrect implementation or missing features.

  2. Undefined use case: A use case for a new feature may not be clearly defined or even unidentified. This will likely result in delays or incorrect implementations.

  3. Technical constraints: Sometimes, the chosen solution produces technical limitations that create challenges difficult to overcome. These constraints may not be discovered until the solution is applied and can include issues with hardware, software, or network connectivity, among other things.

  4. Unidentified functional and nonfunctional requirements: After describing the problem and identifying the required acceptance criteria for the change, the solution should be divided into different functional and nonfunctional requirements. If this step is not executed successfully, issues with implementation and delays will occur later.

  5. Failure to properly break down user stories: This point is strictly related to the item above because it’s the consequence of a requirement that hasn’t been identified. Once the problem and solution are described, they can be broken down into different tasks. However, this action might not be properly executed, and the subtasks might have a wider scope than they should because a requirement is identified during the building. Consequently, the assigned person to the task might not conclude it on their own, or it may take longer than expected.

  6. Underestimated requirement: A common mistake is underestimating the complexity of certain features, probably because of a lack of analysis prior to implementation. This leads to unexpected delays in the development process and generates unrealistic time expectations.

  7. Lack of understanding of what the end-user or client expects: Due to miscommunication with users and stakeholders, the requirement may not meet their expectations. Therefore, it won’t get their approval and will be sent back to development to adjust to their needs.

Issues in Implementation

  1. Poor peer review process: If a precise peer review process is not applied, the code solution may present errors or be too complex to understand, which will generate problems and delays in the future.

  2. Poor quality assurance testing process: In case a high standard quality assurance testing process is not applied, at some point, the code will probably introduce bugs in the production environment once the changes are released.

  3. Lack of technical knowledge: If the software engineering team does not possess the required expertise and experience to write flawless code, it would lead to unreliable and unstable software riddled with technical bugs.

  4. Lack of Domain Knowledge: If the developer, product owner or qa is not aware of the industry or domain they are writing software for, it may lead to poor technical requirements compliance, which automatically results in bad software.

Additionally, I would highlight the importance of communication at every stage of development, both in planning and execution. Effective communication between developers, stakeholders, users and other team members is essential for ensuring that everyone is on the same page and working towards the same goals. Poor communication can lead to misunderstandings, missed deadlines and undesired results.

Negative Consequences of These Problems

The problems described above produce negative consequences for the project budget and product reputation. If a feature presents unexpected issues in the building due to errors in planning or execution, the solution might be redesigned, resources might be reallocated and some work will be undone. This generates additional resource costs, poor resource allocation and potential re-work costs, resulting in wasted time and resources, increasing development costs.

Furthermore, if the solution requires technical improvements, additional costs may be incurred to adapt to the technical constraints.

On the other hand, if the problems are not solved correctly, they result in features being implemented incorrectly, incompletely or delayed in time to market, leading to poor user experience and negative feedback. These consequences create a negative effect on the budget in the future due to user turnover and a reduction in user acquisition, decreasing the expected income.

Moreover, these issues generally cause frustration among developers, management and stakeholders. Keeping the team motivated is crucial to building a successful product, failure to do so will likely cause turnover in the team and a lack of productivity.

As an AI Assistant, How Can I Solve Those Issues?

Why do these problems happen? We found three main reasons: the user might not have the time to dig further into each user story, the user doesn’t have the required knowledge or experience to create a correct solution. Also, the user may not gather the team to refine the user story or doesn’t document the resolutions in the user story, because it’s too costly, there is a lack of internal communication or the resolutions aren’t formally written in the user stories.

Can AI solve these problems? Project Copilot is an AI assistant that collaborates in writing both problem descriptions and solutions more deeply and quickly. While we’ll focus on its capabilities in user stories, it’s worth noting that Project Copilot also assists in document creation and code review.

Imagine having an assistant that thoroughly reads product definitions, technical documentation, and related user stories every time you need to write a new user story. This AI assistant not only has the knowledge to draft a clear proposal but also learns from your feedback and corrections. It’s like having a synthetic team member contributing valuable hours to your project.

Additionally, Project Copilot can break down problems into subtasks based on the role of the person assigned and the size of the subtask. It can also generate examples of expected outputs for each scenario and estimate the effort required for each task.

Project Copilot doesn’t stop there; it also contributes to creating and maintaining technical documentation. This ensures that the necessary input for accurately defining tasks is documented and updated as needed.

It’s important to remember that while Project Copilot can significantly improve the quality and accuracy of user stories, it’s still essential to gather your team to review them. The AI assistant is here to enhance human abilities, not replace them. If Steve Jobs called computers “bicycles for our minds,” think of Project Copilot as an engine that helps you ride faster, recommends directions and warns you of potholes in the road.

Are you interested in trying Project Copilot? Join our Discord forum to discuss this post and stay tuned for a demo of this innovative AI assistant.

Joaquin Meza