Poor project management and mistakes in the development process can ruin even the most promising app idea. At DeepArt Labs, we prioritize high-quality development and understand that proper process is key to the success of a project. That’s why we continuously work to improve our project management methods. Furthermore, we recognize that some products or technologies, like Artificial Intelligence (AI) and Machine Learning (ML), require a customized approach. With our experience in developing projects in these areas, we have developed a tried-and-true process for incorporating the Agile approach in the management of AI software projects.

After developing several projects in those areas, we were able to create our battle-tested process of implementing the Agile approach into project management for artificial intelligence software.

• Agile methodologies fundamental value: individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, responding to change over following a plan.
• Implementing Agile approach in your product development is a guarantee of increased user experience.
• Advantages of Agile include customer-first approach, simplicity, lower costs, flexibility, and shorter time to market.
• The process includes kickoff briefing, AI design sprint, proof of AI development, and AI application deployment in production.
• Do you want to implement Agile in your company? Contact our Experts and harness full potential of Agile.

Why Artificial Intelligence and Machine Learning Need a Custom Project Management Approach?

According to a PwC report, AI is expected to contribute as much as $15.7 trillion to the world economy by 2030. It will be one of the most critical technologies of the upcoming years, influencing our everyday life, business, and politics. "Whoever leads in artificial intelligence in 2030 will rule the world until 2100." Thus, harnessing the full potential of AI for your business is crucial.

Artificial Intelligence or Machine Learning Projects are usually far more complex, expensive, and multidisciplinary than traditional software development. They are considered some of the most challenging projects in the project management world. The product discovery process is usually more intricate and takes much more time. The teams are also more diverse, comprising people from different skills and responsibilities, such as data scientists, developers, designers, psychologists, and user experience specialists. Moreover, many of these projects operate on a high level of innovation where many factors and end results are unknown.

This is why the Agile AI project management approach should be adapted by combining it with the traditional software development process. Agile principles, though originally designed for IT teams, can be modified to meet the particular needs of AI projects. These projects demand a lot of experimentations, and old-fashioned product development methods fail when dealing with such uncertainty.

To succeed in AI, companies need to adopt Agile project management techniques specifically tailored to AI project requirements. Short iterations and continuous testing ensure that AI developments meet customer and business requirements.

What is Agile?

Agile is one of the most popular methodologies for building digital products. It is based on the idea that predicting the final outcome of the product development process at the beginning is impossible. Customer and business needs change over time, making it impossible to design and plan all features upfront. The Agile Manifesto lists its fundamental values:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

The agile approach helps teams with changing priorities through short iterations and continuous testing, ensuring that AI development aligns with customer requirements. The product starts with rapid prototyping, influenced by repetitive, short sprints where the team plans, designs, develops, tests, and collects customer feedback. This iterative process allows for building the product step-by-step, focusing on priority tasks, milestones, and overall simplicity.

Advantages of the Agile Approach

• Customer-first: Guarantee of increased user experience
• Simplicity: Easier to focus only on the upcoming steps or sprints
• Lower costs: Easier to predict the price of the forthcoming sprint than the entire project
• Flexibility: Develop, test, and collect knowledge daily
• Shorter time to market: Ensures quicker delivery of products to market

Disadvantages of the Agile Approach

• Strict and siloed organizations: Agile can be difficult to implement in such environments with inexperienced teams.
• Smaller dedicated teams: Best results are visible in smaller teams, and it can be harder to adopt Agile in larger groups.
• Resource planning: Difficult to predict timeline, costs, and resources due to the uncertain nature of the process.
• KPIs setting: Hard to plan long-term strategies when short-term goals are unpredictable.

How Do You Manage Artificial Intelligence Projects and Why Do You Need Dedicated Project Managers Skilled In AI?

Managing AI projects requires a specialized approach due to their complexity and multidisciplinary nature. An Agile AI Project Manager needs to have an in-depth understanding of AI, its functionalities, opportunities, and challenges. This knowledge enables them to manage resources effectively, ensuring that the AI product meets all requirements and expectations before its release into a production environment.

Data being a crucial part of any AI system necessitates that the project manager has skills and experience in AI data management. The process includes conceptualization, coding algorithms or software frameworks, and training models on labeled data. An AI project manager should guide the AI creation team through the necessary steps for successful product completion ready for production. Moreover, they should be well-versed in AI ethics, including concepts like bias, diversity, privacy, or fairness.

How We Adapted Agile Into the Management of AI Projects?

At DeepArt Labs, we embrace challenges and AI-based projects. We have developed our approach through experience and have successfully tested it in real-world scenarios. Our agile approach allows us to efficiently develop customized AI solutions.

Kickoff Briefing

We start with a short (two hours) kickoff meeting where we learn about your business, your idea, the problems you're facing, and the goals you want to achieve. This helps us understand your expectations and end-user needs.

AI Design Sprint

Our custom workshops, known as AI Design Sprints, are hands-on experiences where we identify potential AI use-cases and explore business opportunities. Within two days, your team, supported by our AI Engineers and Design Facilitators, will learn about emerging technologies, spot AI opportunities, and create new ideas and visions.

These workshops also evaluate data needs, ensuring your organization is at a sufficient digital transformation stage for any machine learning implementation. The AI Design Sprint Outcomes Report blueprints future project work.

Proof of AI Development

In this phase, we develop an AI-based solution through a series of experiments, ensuring it solves your company's problem and meets business needs. We identify data for training AI models and test various AI implementations using a fast iteration approach, minimizing risk while ensuring maximum ROI.

This phase allows you to see the value AI brings to your business, evaluate AI model benchmarks, and decide whether to proceed with AI adoption, all within three months!

AI Application Deployment in Production

If Artificial Intelligence or Machine Learning is necessary for your project, we can take on the full development of the AI system in the production environment. This includes setting up data pipelines, deploying and tuning the AI model, and providing training.

Deployment can take several months to a year, depending on complexity and required integrations. During this time, the solution can be deployed across your product portfolio and supply chain. Our skilled project managers lead all software creation efforts in an iterative approach, ensuring delivered value with direct feedback loops.

DeepArt Labs has experience working with various businesses, including startups and big corporations across industries like Logistics, Healthcare & Pharma, Manufacturing, etc. Our custom project management methodology has proven successful across multiple projects, demonstrating its viability and scalability for product complexity or company growth aspirations.