In 2022, Artificial Intelligence will become one of the most sought-after business technologies. Companies are increasingly adopting new technologies to retain users and remain competitive in the market. As Every company is a technology company, says Gartner. Using the right technology to get the most out of the benefits it offers can be a real difference maker.
AI-enabled work is different from conventional software development work. Managing AI projects therefore requires a special approach. When developing a traditional software solution, a team of developers is responsible for writing the app’s logic. In other words, AI apps are not needed clearly written meaning. ML algorithms can learn important patterns.
But they still have something in common. As with any other technology businesses looking to power their businesses with AI, they need to start with Proof of Value (PoV). PoV ensures that your AI-powered solution meets the expectations of your business. Here’s what you need to know about it Managing AI projects.
Managing AI Projects: The Basics
The following six are the key steps you need to cover in the AI project management process. They include:
Identify business problems
Testing appropriate problem-solving
Data collection and preparation
Choosing the right AI-based algorithm
Training the algorithm
Deploying the product
As you can see AI project management is a bit different from managing traditional software development processes. Despite the popularity of AI technology, business solutions can be unnecessarily complex with AI components. Before you start developing an AI-enabled software solution you need to clearly understand why you need AI.
Understand how AI will be used in YOUR company
Make sure you understand the requirements and what you are trying to address with an AI-enabled solution. There must be an obvious problem to solve or value to create. This is the area where most teams fail. In this case, there are two possible outcomes. A group of characters can be a specific machine learning model with specific characteristics. Or another team might create models that don’t provide any value because the project already has all the data and insights provided by the model.
Understanding real business needs and challenges is a key factor that should not be overlooked when designing AI-enabled software solutions. Make sure your team has all the information. Otherwise, they won’t be able to self-assess and prioritize projects (what data to use, what is to be predicted, how to analyse).
Know the architecture of the AI project
As you build AI-powered solutions you need to understand the overlap. As with any other software project, AI apps consist of a front-end component (how users see the app) and a back-end component (how the app works). But they also have an ML component (how an app learns and generates predictions).
Let’s take Netflix as an example of an AI-enabled platform. The back end allows users to watch their favourite movies and movies while managing different users. Its front-end component identifies the player and allows users to interact with content. And the ML part for what a user might want to watch based on their previous choices and interests.
But not all AI systems are so simple. The infrastructure becomes more complex when integrated into other real-time systems, such as self-driving cars. There are several ML components (one to monitor the environment in real time, one for tracking one to avoid collisions with objects etc.)
Understanding the software development lifecycle for AI projects
Generally, a software development project goes through several stages according to the software development life cycle. But if you’re building an AI or ML-enabled app, there’s another step that your team shouldn’t overlook in the timeline. Here are some other tasks to consider with your AI the team during the process:
Understand the business problem
Collect and maintain clean data
Build and train the model
Deploy the model
Experiment and analyse the performance of the model
Note that your team may need to repeat the previous step. When building an ML model, you may also find that you need additional data. Also collecting and managing data can take more time than you originally planned.
There is a good chance that your development team may need to go back to previous steps. In this case it is best to use a flexible sprint schedule. Going without running at all can make any software development difficult and even burdensome. A plan needs to be developed which organizes the goals and tasks of the group.
The team needs to establish metrics and milestones that can be targeted when developing AI solutions. Apart from the usual machine learning metrics such as accuracy, recall precision etc., there should also be some performance metrics. Customers succeeded after generating revenue and market penetration is done top business metrics to focus on. Of course, you can set your own metrics associated with the project.
Know when it is time to scale up
In most cases, it is wise to start small and gradually scale up the project rather than deploying the system at scale. There is no need to throw everything over the wall to develop a comprehensive AI solution for your business.
It’s best to start small and test your ideas without investing a lot of time and money. Scaling is less risky than deploying AI-enabled systems at scale.
According to data, 87% of data science projects fail without going to production. The main reason for this failure rate is lack of leadership support and collaboration. Project managers, developers, and data scientists don’t know every trick in business books. No masterclass or online course can teach all the ins and outs of business without actually owning it.
Planning and managing an AI project requires close cooperation between the project sponsor (business owner) and the development team. Ensuring that the goals of an AI project fully meet business needs and requirements is every project manager’s primary goal. This is why as a business Owner; you need to be ready to share your business insights and tell your team as much as possible about your workflow to deploy AI systems that exceed your business needs.