In a world where gaining that competitive edge over others is the key to survival in business, organizations are resorting to adopting more of artificial intelligence (AI) and its intelligent capabilities. Consequently, the global AI market is growing exponentially, with estimations indicating that it is likely to touch the $191 billion mark by the year 2025.
Now, undertaking AI software development or automation projects require enormous efforts and meticulous judgment. To successfully develop AI-enabled business applications, organizations need to take on a comprehensive approach and carefully consider the various phases of the AI software development life cycle.
The AI project life cycle
The different phases of AI software development bear generic similarities to the classical SDLC but with slight differences.
Let us take a look at the different stages of AI software development below.
• Understanding “why” AI is needed
The fundamental step is to define and understand why AI is needed. For this, the inputs should ideally be sourced from the organization’s employees who directly deal with customers. The ultimate aim is to offer convenience to customers, so the frontline employees can better understand “why” AI needs to be incorporated in business processes and how it can transform and improve existing customer-centric operations.
• Identifying “what” needs AI transformation
The next step is to identify the business processes where AI will be implemented. The target is to achieve maximum organizational efficiency and reap greater value through intelligent functionalities, but not every operation needs to undergo automation. Carefully identifying specific areas for AI transformation will ensure streamlined progress towards the desired target, prevent gaps and unnecessary overheads of finances and resources.
• Selecting data sets for AI solution
Implementing AI essentially means training applications to process data, gain experience and operate based on the learnings. So it is essential to choose which data sets are suitable to train the AI application. This is an extremely vital step since the final output quality will entirely depend on the quality of data used. And no matter how well the design is, low-quality data will never yield proper results. Data must be gathered from relevant channels, and it should be high-quality, well-structured, and credible real data. Also, the higher the volume of data better will be the performance of the AI software.
The AI software must be “trained” using relevant and meaningful data, so preprocessing data plays a significant role here. The accumulated data needs to be scrubbed, reformatted, analyzed, and labelled before the AI system is deemed suitable for use.
• Choosing “which” AI capabilities are required
Most AI software solutions are a combination of two or more AI capabilities. Studying and selecting the right capabilities based on the targeted objectives make all the difference here. AI capabilities to choose from include-
- Machine Learning (ML), which comprises deep learning, unsupervised and supervised algorithms
- Natural Language Processing (NLP), which includes extraction of content, classification, answering questions, machine translation, and generating text
- Computer Vision, which includes machine vision and image recognition
- Speech, which constitutes capabilities for speech to text conversion and vice versa
- Planning, robotics, and expert systems
• Deciding on the right SDLC for the project
Since AI software development projects are undertaken to achieve high-value targets, deciding the requirements and finalizing them upfront can save effort, time, and funds. Going by this ideology, the waterfall SDLC method is considered most suitable for AI projects. The waterfall model consists of the following phases-
- Requirement analysis phase
- Design phase
- Development phase
- Testing or QA phase
- Deployment phase
- Maintenance phase
The greatest benefit of adopting the waterfall SDLC model is that it permits timely reviewing of the development work after every crucial phase to avoid costly mistakes and the initial requirements do not keep changing
1. Requirements analysis phase
Business analysts are appointed in this phase to gather detailed requirements. This task involves direct communication with the client, to better understand what exactly they are looking for. There are established industry protocols for requirements analysis. However, in AI software development projects, certain best practices should also be adopted.
There are various factors to consider for effective requirements analysis. These include customer empathy, experiments, smaller components or modules within the AI software, among others. It is also important to look for real-life scenarios, gather user complaints and consider employee observations to understand the exact requirements and propose solutions.
During this phase, the financial aspects such as the budget estimate of the project and the ROI, should also be evaluated.
2. Software design phase
The design phase in AI software development involves extensive planning for designing all the different software versions – for the web and different mobile platforms like iOS and Android. In this phase, the low-level design of the AI solution is carried out and diverse parameters like sources of information, process stages, dynamic behaviours, target action conditions, and more are analyzed.
The entire process can be expedited using development platforms that come with ML, NLP, automation, vision, speech, and other AI capabilities and employing a strong cloud setup.
3. Application development phase
Depending on the project, the AI application development phase can range anything from a few weeks to several months. Forming the development team, giving it structure, checking necessary access to tools, processes, and data for software development, and ensuring seamless collaboration with other teams is critical to success.
Various AI development platforms offer detailed documentation that the development team can use for reference. The most popular AI development platforms include – Microsoft Azure AI Platform, IBM Watson Developer Platform, Google Cloud AI Platform, and many others.
4. QA phase
Along with the fundamental concepts of the QA or software testing phase, there are some extra considerations in the case of AI projects. They are-
- Data sets for AI projects can be highly voluminous and complex
- Data validation is crucial to eliminate biases in selected data sets and prevent adverse outcomes in testing
- ML and AI algorithms should be tested keeping in mind model validation and effectiveness of algorithms
- Testing for regulatory compliance is indispensable for sensitive data, along with performance testing
- Test data sets must include relevant subsets of data that will be used to train the AI system
- Creating test suites for validating ML models is mandatory
5. Deployment phase
The deployment phase is when the fully developed AI software goes live- this is where it starts showing the real-life real-time results after launch. There might be surprises, affirmations, and even shockers in the way, so all involved teams need to remain alert.
6. Monitoring and maintenance phase
Maintenance involves offering support after deployment and warranty support. It requires a smooth collaboration among the development and maintenance teams to understand the AI system thoroughly and work on bug fixes, performance tuning modifications, and feature add-ons. Monitoring and maintenance is essentially a long-term, ongoing task that continues as long as the AI software runs in the live environment.
Apart from following the fundamental AI SDLC of a project, it is critical to adhere to security conventions to ensure the privacy of sensitive data. Moreover, since AI is still evolving, it is crucial to team up with experienced professionals from project management and architecture to development, testing, and support teams.