5 Reasons to Embrace Agile in the Era of AI and ML

By Valeria de Albuquerque, SCRUM Master at BairesDev

Moving further into 2023, we will find that the integration of Agile Methodologies in the world of Artificial Intelligence (AI) and Machine Learning (ML) is expected to unlock new heights of innovation and efficiency. AI and ML development teams leveraging Agile will continue to accelerate the deployment of cutting-edge solutions while rapidly adapting to emerging trends and technologies. Enhanced collaboration and communication among team members will drive the creation of more effective and customer-focused AI and ML applications. Ultimately, the marriage of Agile and AI/ML development in 2023 promises to propel many technological advancements.

As Eric Ries mentioned in his book, The Lean Startup (2011), “In an age of rapid technological advancements, Agile methodologies provide a framework for AI and ML teams to pivot quickly, respond to change, and continuously improve their models and systems.” As AI and ML evolve at a staggering pace, it is essential for organizations to have an efficient and adaptable development process. As a project management methodology, Agile emphasizes iterative development, continuous improvement, and rapid response to change, offering a promising solution for AI and ML development.

Time to embrace Agile

This article will discuss the top five reasons why Agile should be applied to the world of AI and ML:

1. Accelerated Development and Adaptation

One of the most significant advantages of Agile is its ability to accelerate the development and adaptation of AI and ML solutions. The iterative nature of Agile enables teams to work in short development cycles, quickly testing, validating, and deploying new algorithms and models. This rapid pace of development allows AI and ML teams to ensure that their solutions are always up-to-date and optimized for performance. One example of this is Microsoft, which has used Agile methodology to develop its AI-powered Cortana virtual assistant. By using an Agile approach, they were able to accelerate this development and also adapt to changes in the market and user behavior. They promptly added new features and capabilities to the product in response to user feedback.

2. Continuous Improvement

Agile promotes a culture of continuous improvement, encouraging AI and ML teams to regularly refine and enhance their solutions throughout the development process. By working in iterative cycles and incorporating feedback from stakeholders and end-users, AI and ML teams can identify and address issues, adjust their algorithms and models, and improve the overall quality of their solutions. The ride-hailing company, Uber, has used Agile methodology to develop its AI-powered pricing algorithm. By using an iterative approach, they were able to continually refine and improve the algorithm, leading to more accurate and effective pricing.

3. Enhanced Collaboration and Communication

Etsy has used Agile methodology to develop its AI-powered search algorithm. By working collaboratively across different teams and departments, the online retailer was able to develop a highly effective search algorithm that considers user behavior and preferences. Etsy fosters an environment of collaboration and communication among team members, which is essential for the successful development of AI and ML solutions. Bringing together diverse expertise and fostering cross-functional cooperation can drive innovation and create more effective solutions.

In his book Agile Software Development: The Cooperative Game (2001), Alistair Cockburn states that “Collaboration, communication, and trust are key components of Agile that drive successful AI initiatives. Working closely with stakeholders, AI teams can create solutions that are both innovative and aligned with the needs of the business.”

4. Responsiveness to Market Changes

Agile empowers AI and ML teams to be more responsive to changes in the market, industry trends, and customer needs. The e-commerce giant, Amazon, has used Agile methodology to develop its AI-powered product recommendations engine. This approach has allowed them to continually improve and refine the product recommendations engine, leading to increased sales and customer satisfaction.

5. Risk Reduction

Shorter development cycles and iterative improvements enable teams to catch and address issues rapidly, reducing the likelihood of costly mistakes and project delays. For the methodology itself, the business approach for 2023 is to anticipate evolution and growth, bringing with it new advancements and improvements that will shape the future of project management and software development.

Mike Cohn, co-founder of the Scrum Alliance, states in his book Succeeding with Agile (2010), “As Agile methodologies mature, we expect to see more organizations embracing a culture of continuous improvement, where teams are empowered to experiment, learn from failure, and adapt their processes to better serve customers and stakeholders.”

The healthcare company, Cerner, has used Agile methodology to develop its AI-powered clinical decision support system. By using an iterative approach, it was able to test and refine the system in a controlled environment, reducing the risk of errors and ensuring that the system was safe and effective for use in clinical settings. This approach also allowed them to respond quickly to changes in regulations and industry standards.

Arising challenges

We can understand why Agile methodology has gained a lot of popularity in the world of software development; however, there are still some problems and mistakes that can arise when applying Agile to AI and ML projects. Here are some of them:

Uncertainty in AI and ML projects: AI and ML’s complexity makes predicting the final outcome difficult, causing uncertainty in project goals, scope, and estimating time and resources in Agile. For example, a team developing a chatbot using AI and ML may struggle to estimate the time needed to accurately understand and respond to user queries.
Lack of technical understanding: AI and ML’s complexity can create a lack of transparency, causing misunderstandings and miscommunication in team collaboration. Non-technical team members may struggle to understand the development process and provide meaningful input, leading to misalignment with project goals.
Difficulty in testing: Testing AI and ML models is difficult due to data and computational resource requirements and the challenge of creating test cases that reflect real-world scenarios. For example, an AI-powered image recognition system may have test cases that do not accurately reflect all possible usage scenarios, leading to quality issues and suboptimal product delivery.
Lack of expertise: AI and ML projects require specialized knowledge and expertise, and the lack of such skills in team members can lead to misunderstandings and misalignment with project goals. This can result in delays and low-quality products, such as a team member without expertise in AI or ML developing a feature requiring such knowledge, leading to poor product quality.
Inadequate data management: Effective AI and ML models require large amounts of accurate, complete, and relevant data, but data management can be challenging, leading to delays and suboptimal product delivery in Agile projects. Proactive measures are necessary to address these challenges and ensure the team has the needed expertise and resources to deliver a high-quality product.

For Project Management

In 2023, AI-powered project management tools are expected to revolutionize the way Agile teams manage their work. Three great tools you might want to take a look are:

GitHub: GitHub is a popular code repository and version control tool that can be used to manage Agile workflows in AI and ML projects. Companies like Google and Amazon have used GitHub to manage their AI and ML projects, allowing them to collaborate effectively with other developers and researchers, and ensure that code changes are tracked and managed effectively.
Rally Software: Previously known as Agile Central, this is a project management tool that is specifically designed for Agile methodologies. Companies like Cerner and Spotify have used it to manage their AI and ML projects, allowing them to track progress, identify and resolve issues, and ensure that everyone is working together effectively toward a common goal.
PyTorch: PyTorch is a popular ML framework that can be used to apply Agile methodologies to AI and ML projects. Companies like Tesla and Facebook have used PyTorch to develop highly sophisticated AI and ML systems, allowing them to iterate quickly and adapt to changes in the market and user behavior.

These tools will automate repetitive tasks, streamline project workflows, and provide real-time data analytics to help teams make informed decisions quickly. Here are some examples of repetitive tasks that AI-powered project management tools can automate:

Task assignment: Automated task assignment allows team members to focus on completing their work instead of administrative tasks. These tools can automatically assign tasks based on team members’ skill sets, workload, and availability, ensuring that tasks are distributed fairly and efficiently.
Status updates: Automated status updates provide real-time updates on task completion and project progress, helping team members stay informed and ensuring that everyone is aligned with project goals and timelines.
Data entry: Automating the process of data entry allows team members to spend more time on creative problem solving. For example, these tools can automatically populate project reports and dashboards with data from various sources, reducing the need for manual data entry.
Meeting Scheduling: Automated meeting scheduling eliminates the need for back-and-forth communication between team members. For example, these tools can use natural language processing (NLP) algorithms to understand meeting requests and schedule meetings based on team members’ availability.
Time tracking: These tools can use ML algorithms to automatically track the time spent on each task, providing real-time data analytics on project progress and resource utilization.

For Decision-Making

The integration of AI and Agile methodologies in decision-making processes will enhance the quality and speed of business decisions in 2023. AI-driven data analysis will enable businesses to identify patterns, trends, and insights from vast amounts of data, which can then be used to inform strategic decisions on market segmentation, product development, supply chain optimization, risk management, and talent management. Big companies worldwide have been able to stay ahead of their competitors and drive growth and innovation. Here are some examples of strategic decisions that can be informed by AI-driven data analysis and facilitated by Agile methodologies, along with some companies that have used this approach:

Market segmentation: AI-identified patterns and trends in customer behavior can be used to inform market segmentation strategies. For example, Netflix uses AI algorithms to analyze user viewing behavior and provide personalized recommendations. This has allowed Netflix to refine its market segmentation strategy and improve user engagement.
Product development: AI-driven data analysis can inform product development decisions, helping businesses identify customer needs and preferences. For example, Coca-Cola uses AI algorithms to analyze social media data and identify emerging trends and preferences among its customers. This results in rapid prototyping, experimentation, and development of new products that are more aligned with customer expectations.
Supply chain optimization: AI-driven data analysis helps businesses identify bottlenecks and inefficiencies in the supply chain. Amazon uses AI algorithms to optimize its logistics network, improving delivery times and reducing costs. By combining AI-driven data analysis with Agile methodologies like continuous improvement and experimentation, Amazon has been able to continuously refine and optimize its supply chain processes.
Risk management: AI-driven data analysis will help businesses identify potential risks and take proactive measures to mitigate them. American Express uses AI algorithms to analyze transaction data and identify potential fraud. By combining AI-driven data analysis with Agile methodologies like continuous monitoring and rapid response, such companies could minimize the risk of fraudulent transactions and protect their customers’ financial data.
Talent management: AI can help identify the best candidates for open positions and improve employee retention. For example, Hilton Worldwide uses AI algorithms to analyze employee data and identify the factors that contribute to employee turnover. By combining AI-driven data analysis with Agile methodologies, Hilton Worldwide has been able to improve its talent management processes and retain high-performing employees.

Where is BairesDev in this picture?

As a leading software development company, BairesDev continues to integrate AI and Agile methodologies to enhance the offered services and optimize development processes. These are some potential methodologies that our company is going to keep pursuing this year:

Talent Acquisition: BairesDev is capitalizing on AI-driven tools, such as our proprietary selection platform. This platform harnesses the power of AI to boost our recruitment and selection processes. With the aid of AI algorithms, we can swiftly analyze and sift through a substantial pool of over 1 million applicants per year, pinpointing top-tier talent that aligns with our specific requirements. This ensures we maintain a highly skilled workforce capable of delivering avant-garde solutions.
Software Development Process: BairesDev is going to continue using Agile methodologies for their software development projects as well as mentoring partners and clients on its best implementation, ensuring flexibility, adaptability, and continuous improvement. By incorporating AI tools within the Agile process, the company can automate repetitive tasks, improve workflow efficiency, and make data-driven decisions more rapidly.
Personalized Solutions: The company will keep using AI-driven analytics to better understand their client’s needs, preferences, and expectations. This information will be used to develop highly personalized solutions that closely align with clients’ requirements.
Quality Assurance: BairesDev will keep integrating AI-powered testing tools into their quality assurance process to streamline testing efforts and identify potential issues more effectively.
Project Management: The company will continue using AI-driven project management tools in an Agile framework to monitor project progress, allocate resources, and address issues proactively. This will allow us to bring to the table more structured project management business models.
Chatbots: BairesDev is using AI-powered chatbots to improve customer engagement and support. By leveraging NLP algorithms, the chatbots can understand and respond to customer inquiries in a highly personalized and efficient manner, improving customer satisfaction and reducing the workload for customer support teams.
Predictive Analytics: BairesDev is also using AI-driven predictive analytics to anticipate and address potential issues before they arise. By analyzing data from various sources, the company can identify patterns and trends that indicate potential problems, allowing them to take proactive measures to prevent them from occurring.
Continuous Integration and Delivery: BairesDev is using Agile methodologies like continuous integration and delivery (CI/CD) to automate and streamline the software development process. By automating tasks like testing and deployment, the company can accelerate the development process, reduce the risk of errors, and improve overall efficiency.
DevOps: BairesDev is also using Agile methodologies like DevOps to improve collaboration and communication between development and operations teams. By breaking down silos and promoting cross-functional collaboration, the company can deliver high-quality software products more rapidly and efficiently.
Cloud Computing: Finally, BairesDev is using cloud computing and serverless technologies to enable more flexible and scalable software solutions. By leveraging cloud platforms like AWS and Azure, the company can quickly provision resources as needed, reducing infrastructure costs and improving overall performance and scalability.

In conclusion

We can say that 2023 promises to be an exciting time for businesses seeking to harness the power of AI and Agile methodologies. The convergence of these technologies will lead to improved project management, data-driven decision-making, customer-centric product development, and enhanced collaboration and communication. Organizations that embrace this synergy will be well-positioned to reap the benefits of increased efficiency, competitiveness, and customer satisfaction, setting the stage for long-term success in an increasingly complex and fast-paced business landscape.

If you enjoyed this, be sure to check out our other AI articles.

———————————————————————————

Valeria is passionate about researching ML and AI applied to Agile. She’s a certified Product Owner and Scrum Master working as a Product Manager at BairesDev since 2020. She’s soon to obtain a Master’s degree in Science of Design, Management, and Direction of Projects of International Cooperation.