If you’re thinking about building an AI app or adding AI features to your current product, there’s one thing you should know: success depends on two things working well together, the software development lifecycle (SDLC) and project management (PM).
Here is how they work: while SDLC takes care of the technical side, the project management keeps everything on track by providing strategic planning and stakeholder collaboration. In this article, we’ll look at how these two sides come together to turn great ideas into successful, scalable AI apps.
AI innovation needs more than code
When people hear “AI app,” their minds often go straight to something like ChatGPT. We get it, ChatGPT is the standard in the AI world, but getting an app to that level of sophistication requires a very well-defined structure and a robust development process.
An important thing to is that AI technologies evolve at a rapid pace. That means your application must be built with adaptability in mind. A properly implemented software development life cycle (SDLC) provides the foundation for that adaptability, helping your product evolve without losing stability or scalability over time.
Applying SDLC to AI projects
ChatGPT-like app planning? Let’s find out what it looks like. Although the SDLC flow remains the same (planning, analysis of requirements, design, development, testing, deployment, and maintenance), for AI applications, each stage includes specific steps and requirements.
Planning
At this stage, we define the objectives. For AI projects, this also involves identifying specific problems where AI can offer more effective solutions than traditional methods.
Analysis of requirements + Dataset preparation
In general, this stage is about getting clear on what the application needs to do. In the case of AI, it also includes figuring out what data you need and where you can get it from to train your model. At this point, it’s important to consider that not all data can be freely used (some may require consent, for example). This is when such boundaries should be clearly established.
Design
In AI, design is more about cleaning and organizing data. In this stage, the errors are removed, missing values are filled in, formats are transformed, and the database is divided into sets for training and testing.
Development & AI model training
As the name suggests, this stage is about application development, where programmers write the code. In the case of AI applications, code is written to build and train a machine learning model.
Testing
In the SDLC for AI, testing means evaluating how well the model has learned to recognize patterns. This is done using a separate dataset to check how well the model performs on new data.
Deployment
Once the AI model is built, it needs to be deployed on the cloud or on local servers. After that, it’s usually connected to other applications through APIs.
Maintenance
In building AI apps, continuous training of the model is very important if we want to maintain its performance. Language evolves, people evolve, and this must be reflected in the model’s performance.
Where project management meets technical execution?
The use of Agile methodologies, which promote close collaboration between PMs and technical teams, leads to a 64% increase in project success rates (according to moldstud.com).
This is why the Agile development process and Scrum methodologies have become industry standards. They enable continuous , rapid review of priorities, and transparent collaboration between stakeholders.
The lack of a common vision and good communication between the software development and project management teams can lead to errors, missed deadlines, scope creep, and even burnout.
BEE CODED’s method: Combining process with adaptability
Experience has taught us that a successful project starts with a well-defined strategy. Our approach is based on the SDLC, adapted to meet the unique demands of AI development. Our clients are also involved at every stage, from the product discovery process to deployment, to ensure our visions are aligned. We are agile-ready, and we have integrated teams that collaborate closely every day.
If you want a better understanding of what our step-by-step process looks like, we recommend our complete web app development lifecycle guide.
Conclusion: SDLC + PM = scalable results in AI
In conclusion, a well-executed software development life cycle and effective project management in software development:
- ensure clarity in delivery
- enable sustainable scaling
- reduce risks of errors
- facilitate collaboration across roles and teams.
Our BEE CODED team offers the best of both worlds: deep technical expertise and agile, flexible processes tailored to your needs. Explore a discovery session to plan your AI product!
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