AI is everywhere but while at it so are its risks.
Grand View Research stated that from 2023 to 2030, there would be a monumental 37.3% of growth in the Artificial Intelligence market. It means more and more businesses are rushing into AI development but all of this without any proper plan or guidance.
Which is why projects stall, overspend and ultimately fail.
While AI promises its involvement in software development it raises whether it will replace software developers or not but one major question is that: would the AI software development process actually benefit a project?
Too many questions but don’t you worry, let’s get you covered in this blog!
Why Do Most AI Projects Fail Without a Defined Process?
We are being real here. AI is not a magic tool or a genie that will do everything on its own. It requires an insane level of effort and getting it to work right is tough. Cue: AI needs human work!
A staggering study by Gartner claimed that 30% of generative AI projects will be abandoned before even reaching full implementation by 2025 and some other reports suggested that over 80% of AI initiatives fail outright.
That’s worrisome.
But why does it happen? The problem starts with the leaders who often misjudge what AI can realistically deliver or push teams toward vague or overly ambitious goals. As said before, AI is not a genie. Then comes the dotty costs of building and maintaining AI systems that add to the pressure. It doesn’t end here. We have performance issues. Hitting 75% accuracy is easy but hitting that critical 90% benchmark is no piece of cake. Sometimes, even if AI is working fine, getting teams to actually use it is another battle.
All of this points to one clear truth: without a strong AI software development process, projects are more likely to crash than deliver.
What Is the AI Software Development Process?
The whole AI software development process journey is a structured one.
First things first: Define your project goal. Whether it is improving customer experience or predicting behaviors or automating decisions. Once the vision is clear and goals are set in place, focus on data. Good AI needs great data be it structured like spreadsheets or unstructured like videos and text. Make sure to collect, clean and prepare this data for machine learning. Techniques like feature selection and data annotation—labeling text, images, or audio—play a vital role in helping the AI “understand” the information it processes.
For model building you have to choose the accurate algorithms. Train these algorithms on your data, and fine-tune the results. Python language and tools like TensorFlow or PyTorch come handy to use a pre-trained model and adapt it for your needs.
Phase 1: Business Goals & Data — Laying the Groundwork
Like any other project, your AI project needs a clear direction. Start your project with asking yourself: “What are we trying to achieve with AI?” Your AI system needs a solid business goal. Skipping it means building a house before deciding how many room it should have.
The next step is data and this is where a smart ai software development process shows its actual value. You have to start by doing a data audit:
- What data do we already have?
- Is it clean, complete, and relevant?
- Do we need more, and if so, where will it come from?
One important thing: AI is not the solution for every single problem. Make sure whether your business even needs AI or not. if AI is the problem to solutions then align your data and business goals from day one for long term success.
Phase 2: Building AI Infrastructure the Right Way
Once the goal and data are set you have to now build the foundation your AI will run on. This is called building AI infrastructure. It is an extremely critical yet overlooked part of the AI software development process.
Infrastructure includes:
- Where your data is stored (cloud vs on-premise)
- How your data flows (data pipelines)
- How much computing power you need (CPUs, GPUs, memory)
You must choose the right setup for your business. Make sure your AI has a strong, stable ground to stand on.
Phase 3: Choosing the Right Model — Don’t Just Build an AI Model, Built AI Smart
The model is the brain of your AI project. This is where you actually build an AI model that can learn from data and make smart decisions. You have two main options:
- Use a pre-trained model: These are already built AI systems trained on huge datasets (like ChatGPT or image recognition tools). They save time. But may need some fine-tuning.
- Create a custom model: Best for complex or unique business problems, but takes more time, data, and expertise.
Phase 4: From Code to Execution — Testing, Scaling, and Deploying AI
The model is ready. Start off by testing the AI with real scenarios so you can catch errors or any unexpected results. Once you’re satisfied that it is working well, scale it so it can handle more users and data without crashing.
Then comes deployment which is putting AI into your live system. You can run it on the cloud or your own servers. Just make sure it’s secure and integrates well with your other tools. After launch, keep monitoring the AI. It should improve over time, but only if you keep feeding it the right data and updates.
Why You Need an AI Step by Step Guide Creator
AI projects aren’t simple checklists — they’re moving parts that need careful planning. That’s why you need more than just tutorials or scattered advice. You need a clear, tested roadmap — and someone who knows how to guide you through it.
At OCloud Solutions, we act as your AI step by step guide creator. From defining your business goals to deploying a reliable model, we’ve done it all. Our team of experts stay with you from planning till adaptation.
Final Thoughts: Don’t Just Chase AI—Do It Right
AI is great for your business but you cannot be hasty with it. It all seems exciting in the beginning but in the longer run you will face issues. Please don’t skip the AI software development process as it will lead to wastage of time and failure.
If you’re serious about results, you need a partner who knows the journey inside out. That’s OCloud Solutions — your roadmap, tech team, and AI support all in one.
Let’s build AI that works — together.