According to statistics, AI projects fail at a high rate. With us, experience working with various clients has taught us that AI projects necessitate an entirely different strategy than normal mobile/web apps. This article defines the high-level method for effectively designing successful AI-powered applications.
The International Data Corporation (IDC quotes as half of all Artificial Intelligence (AI) efforts fail. This is not the only accusation made by the IDC.
When contrasted to the numbers presented by Pactera Technologies, the conclusions of their data research and advisory firm appear conservative.
As per the studies, more than 80% of AI programs failed to generate the envisioned results when they were initiated.
Alarmingly, AI programs fail despite the firms’ best efforts that initiate them. So, why do the majority of AI programs fail? Dimensional Research provides some answers in a paper.
As per the report’s findings, the problems that cause AI projects to fail include a lack of quality data, incorrect labelling of the data needed to train AI, and a lack of confidence in the AI model.
However, the issues do not stop there. According to the IDC analysis, two major factors contribute to the high failure rate of AI projects: unreasonable expectations and skill shortages.
Despite the high failure rate, the number of AI initiatives being implemented has increased. As a result, Thoumany AI projects will fail in the future. However, many others will be successfully executed.
Businesses can secure their success by adhering to a high-level process designed to develop AI applications efficiently.
1. Artificial Intelligence-Focused Business Analysis
This is a business analysis centred on Artificial Intelligence.
The questions given below must be answered as part of the key parts of AI-focused business analysis:
- What are the company’s primary requirements and goals?
- What makes Artificial Intelligence (AI) vital for this project?
- How will AI assist in meeting such needs?
- What are the criteria for success and avoiding failure?
The AI-focused business analysis must begin focusing on the business’s key needs and objectives.
Although your company will have a variety of demands and goals, you should concentrate on those that necessitate the use of Artificial Intelligence. This is the project’s defined success criteria.’
To determine the success criterion for the AI project, you must first examine your company’s current condition and comprehend the cognitive requirement.
After you’ve set the success criteria, the next step is to figure out how AI can help you meet them.
At this point, the AI Go/No-Go Assessment is required. This is a measure for quality control that will assist you in deciding whether or not to continue with the project.
Suppose there are sufficient criteria to move forward with AI implementation. In that case, it is vital to determine the appropriate AI pattern, which will drive the execution technology stack, strategy, and machine learning model selection.
The industry has identified seven unique patterns:
- Hyper-personalization
- Recognition
- Detecting trends and anomalies
- Human interaction and conversation
- Self-contained systems
- Goal-oriented systems
- Decision-making and predictive analytics
- Finally, all of these efforts contribute to business analysis.
It is centred on the AI project and determining its possibilities of success.
2. AI-Driven Data Analysis
The second step of the AI application development process focuses on data analysis relevant to the created AI application.
The following are the essential questions that must be answered as part of this phase:
- What are the primary goals and requirements for data?
- What are data sources relevant to AI?
- What kind of data is accessible to meet the business’s objectives?
- What is the quality of the data available?
This analysis must begin with an awareness of the primary objectives and data requirements. Once these needs have been identified, the following stage is to identify the data sources relevant to the AI project.
Furthermore, you must determine the type and quality of data accessible to achieve the business objectives defined during the AI-focused business study.
The AI-relevant data is the information used for AI training. Before you begin using this data for AI training, make a few essential decisions, such as:
- It doesn’t matter if the information is structured, unstructured, or semi-structured
- The quantity and quality of training data that is currently available
- Whether there is a sufficient supply of well-labeled, clean data for testing and validation
It would help investigate pre-trained models and whether they can be employed for the AI project to expedite project completion.
By conducting this analysis, you will determine whether you have the necessary quantity and quality of data to construct a successful AI solution.
3. Data Preparation Using AI
This phase aims to prepare data by the AI project’s requirements and scope. The essential components are as follows:
- Data must be in a state that permits it to be used to meet the demands of the business.
- Combination, intake, and selection of data
- Data preparation, cleaning, and labeling
- Data creation, formatting, and enhancement
The data employed in the AI project must answer the business’s needs. As a result, data must not only be prepared, cleansed, and categorized, but it must also be correctly formatted and enhanced.
The latter is significant and can be accomplished through data engineering and augmentation. However, before you do so, ensure that trimming and optimizing data will result in more effective and accurate modelling.
After preparing the initial data set, the method for subsequent data set iterations must be developed.
If you ensure the above, you will have enough data to go to the next phase.
4. Development of Machine Learning Data Models
This phase seeks to define the critical processes that will aid in the creation of the project’s ML data model.
This phase’s essential components are as follows:
- Algorithm selection
- The modeling assumptions
- Creating test designs
- Model development and iteration
- Model evaluation and iteration
Some significant factors to consider when developing an ML data model are as follows:
- The approach and application of ensemble methods
- Model tuning or hyperparameter optimization
- The process for training and scaling the model
- The procedure for developing a re-training model
- A pre-trained model’s extension
- Identification methods include supervised, unsupervised, and reinforcement learning.
Taking all of these factors into account will allow you to develop an acceptable ML data model for your AI project.
5. AI Model Validation (Testing)
As the name implies, this phase involves testing the constructed AI model.
The following are the essential questions that must be answered as part of this phase:
- Is learning occurring for the training model?
- Is it possible to incorporate the validated data in the model?
- Is the model correct and incorrect in acceptable ways?
- The first question in AI model testing is whether or not the trained model learns. Learning curves are a tool that can be used for diagnosis.
Once the testing results are available, the model should be assessed for accuracy, performance, and alignment with the business KPIs.
If the model fails to match the criteria, it must go through another iteration to acquire a better result.
Finally, this phase will result in an ML/AI model ready for use.
6. AI Operationalization
After you’ve tested and fine-tuned your ML model, it’s time to integrate it with other web or mobile application components and deploy it to the production environment.
Typically, the procedure begins with a comparison of model operationalization needs. For example, it would help if you chose the following deployment strategy for your ML model:
Cloud AI – the model is carried out by one of the cloud AI providers (AWS AI Services, Azure AI, Google AI, IBM Watson)
Server — the model is deployed to a server (cloud or on-premises) and performed using a bespoke machine learning framework (i.e., TensorFlow)
Edge – the model is run straight from the mobile device.
The target environment must be configured based on the technique chosen, and end-to-end integration with the rest of the program must be thoroughly tested.
Establishing a monitoring plan and a model governance strategy is the final stage in this phase.
AI Application Development Process That Has Been Battle-Tested
The AI-focused software development process described in this article tackles most challenges that cause AI project failures.
This process has been battle-tested on numerous projects completed successfully by Achieving for clients of diverse sizes and sectors.