AI projects are riskier than most other corporate projects. A recent research report by RAND states that some failure rate estimates for AI projects exceed 80%. Failure rates for AI projects are also twice as high as for corporate IT projects that don’t involve AI. In this article, we’ll explore how AI-powered solutions can boost projects from development to implementation.
That’s why you should approach your next AI initiative carefully to avoid the common reasons behind AI initiative failures. Those typically include poor data quality, subpar risk controls, soaring costs, and unclear business value.
Here are the six steps toward successfully implementing an AI solution that delivers real value to your business.
1. Identify Your Goals and Use Cases
First, you should determine what problem you are trying to solve with AI; it’s a prerequisite to setting realistic and measurable goals. Those goals, in turn, will dictate the KPIs and metrics you’ll track.
Once you have defined the objectives and KPIs, outline the future system’s use cases and the resources your project will require.
Don’t tackle this step alone or exclusively at the C-level; interview stakeholders across levels to pinpoint the root cause of the problem.
2. Gather and Prepare Data for Training
If there’s one persistent barrier to successful AI adoption, it’s data challenges. They can involve too much data complexity, lack of available data, limited usefulness of data, and data privacy and silos.
You need vast amounts of data to train the AI model, no matter its intended use cases. You can source data from third parties (e.g., via marketplaces like AWS Data Exchange) or use your internal data.
To gather data for training the AI model, identify the type of data to be used (structured vs unstructured) and map data sources (e.g., web scraping, in-house, crowdsourcing). Once the data is gathered, prepare it for use by:
- Ensuring its quality via data cleaning and transformation
- Identifying the most relevant variables for pattern recognition
- Annotating data to make it usable for AI models
3. Select and Develop the Model: AI-Powered Solutions From Development to Implementation
Think back to your goals, KPIs, use cases, and available resources. Based on those, you’ll be able to pinpoint:
- AI model type (e.g., deep learning models are typically used for computer vision solutions)
- Algorithm type (e.g., predictive, classification, clustering, or decision-tree algorithms)
- Project type (developing an AI app from scratch or customizing a pre-built model)
- Programming languages (e.g., Python, R, Java) and tools (e.g., deep learning frameworks like PyTorch)
Based on these requirements, your development team will design the solution’s architecture and develop the model based on it.
Pro tip: Use the tenets of responsible AI to ensure the system’s transparency, explainability, and reliability.
4. Train the AI Model
Once the AI model itself is ready, you’ll need to train it using the data gathered and prepared earlier. When training machine learning algorithms, you can choose from four approaches:
- Supervised learning. The model receives the desired outputs with example inputs to determine the patterns for linking them.
- Unsupervised learning. The model finds patterns in the input without being given specific labels or desired outputs.
- Semi-supervised learning. The model receives a mix of labeled and unlabeled datasets.
- Reinforcement learning. The model is instructed to achieve a specific goal in a dynamic environment, with rewards incentivizing progress toward the goal.
5. Test and Validate the Model: AI-Powered Solutions From Development to Implementation
This is your main opportunity to detect and fix any issues in model performance and accuracy. To do so, your data scientists will test the model and analyze its performance metrics. Those usually include accuracy, precision, F1 score, and recall. Make sure to test your AI model for algorithmic bias, too.
Based on the testing results, you may need to adjust the solution’s algorithms to improve its accuracy or train it on more data.
6. Roll Out and Monitor the Solution
Finally, it’s the finish line. To cross it, you need to integrate your solution into the infrastructure of your choice and deploy it into production. Keep in mind that AI solutions require cloud deployment.
Once your AI solution is rolled out, make sure you have a team to monitor its performance and accuracy. This team will fine-tune and update the AI model in the long run.
AI-Powered Development Solutions In Conclusion
Developing and implementing an AI-powered solution is an endeavor full of underlying risks, from algorithmic bias and data privacy to hallucinations and inaccuracies. That’s why having the right AI and data science expertise to handle the project is crucial.
Don’t have this expertise in-house? Consider reaching out to S-PRO, a development partner with 50+ AI and data science projects for manufacturing, fintech, and more under the belt.