Agile AI Project Management. From AI Design Sprint and Proof of AI to Production Solution

Dorota Owczarek - June 23, 2021

The history of app development is full of stories where even the most fabulous idea is not transformed into a great project due to the mistakes made in the development processes and improper project management.

At nexocode, we believe that the correct process guarantees high-quality development, and it is one of the critical factors of the final project’s success. That is why we are constantly working on improvements in the way we manage projects.

We are also sure that some of the products or technologies need a tailor-made approach. We believe that Artificial Intelligence (AI) and Machine Learning (ML) are one of them. That’s why 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.

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

AI Design Sprint in progress - mapping the confusion matrix

We are sure that it will be a decade of Artificial Intelligence - AI will contribute as much as $15.7 trillion to the world economy by 2030, according to a PwC report. It will be one of the most critical technologies of upcoming years, influencing our everyday life, business, and politics - “Whoever leads in artificial intelligence in 2030 will rule the world until 2100”.

Artificial Intelligence or Machine Learning Projects are usually far more complex, expensive, and multi-disciplinary than traditional software development. They are considered one of the most challenging projects in the project management world. The process of product discovery is usually more intricate and takes much more time. The teams are also more diverse – connecting people with different skills (also from outside IT-bubble) and responsibilities like data scientists, developers, designers, psychologists, and user experience specialists. Moreover, many of those projects operate on a high level of innovations where a lot of factors, as well as end results, are unknown. This is why the Agile AI project management approach should be adopted by combining it with the traditional software development process.

Even though agile principles are originally designed for IT teams – there is no reason to ignore them in AI projects. The only difference can be found in its interpretation: some practices may need different implementation and adjustments than they would have when applied to a typical IT or SaaS (Software As A Service) project.

Also, the projects need a lot of experimentations, and it’s not easy to use old-fashion product development methods with this level of uncertainty – like, for example, clean Agile. To succeed in AI, companies need to adopt agile project management techniques, but they need to match the particular needs of AI projects. Agile methods such as short iterations and continuous testing are required to ensure that the Artificial Intelligence is developed according to the requirements of customers and business needs. A custom approach should be applied when it comes to AI projects - they require a particular culture within an organization, new skillsets for developers who will work on them, and well-defined processes around product development.

What is Agile?

Agile is of the most popular methodologies of building (not only) digital products. It is constructed around the idea that at the beginning of the product development, you cannot predict how will look the result of the whole process. Customer and business needs change with time, so it’s impossible to design and plan all features upfront. 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 by providing short iterations and continuous testing, ensuring that AI development follows customers' requirements. The product starts with rapid prototyping and will be influenced by repetitive, short sprints – during them, the team is planning, designing, developing, testing, and collecting the customer’s feedback.

The product will be updated through iterations and cycles where stakeholders review the results. The Agile process is iterative - meaning that you take small steps to build up a project over time without necessarily knowing everything ahead of time.

Iterative approach to software implementation

The Agile approach lets you build the product step-by-step, focusing on the priorities (most important tasks are the first ones), milestones, and overall simplicity. The whole approach is focused on customers. The goal of implementing Agile is to improve the end-users' experience and increase the business value added by the project.

Advantages of the Agile Approach

  • Customer-first – implementing Agile approach in your product development is a guarantee of increased user experience;
  • Simplicity – it’s easier to focus only on the upcoming steps or sprints than to control the whole project at one time;
  • Lower costs – it’s much easier to predict the price of the forthcoming sprint than the cost of the entire project (primarily when the end results are not known);
  • Flexibility – you develop, test, and collect knowledge daily. That means that if you see that something is not going in the right direction, you can easily implement the changes;
  • Shorter time to market – processing the product development is a guarantee that you will be able to deliver your product to the market in a shorter time;

Disadvantages of the Agile Approach

  • Hard for strict and siloed organizations – sometimes you might face problems with implementing Agile methodologies, especially with inexperienced teams. Agile can be difficult for those who are not used to the concept of iterative development. The organizations may experience more stress when they need to adapt their processes to meet market demands or new technological developments;
  • Work distributed among smaller dedicated teams – the best results of implementing Agile methodologies are visible in smaller teams. It is harder to adopt Agile in larger groups, but you can find many Agile forks dedicated to them;
  • Hard to plan resources – the Agile idea of not knowing the result before the process makes it hard to predict the timeline, costs, and resources for what you will need to finish the project;
  • Hard to set proper KPIs – It’s hard to plan long-term strategies if you don’t know what your short-term goals are going towards. As the results are not known, it is hard to set the right KPIs. It is hard, but not impossible. For proper KPIs or OKRs setup, project managers need to change their mindset to be leaner;

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

There are many project management approaches and tools that can help you. However, if AI is a relatively new area for your company or team, it’s best to start with an agile approach explicitly tailored for AI projects. You need Agile AI Project Manager who will be able to figure out the best way of managing resources in order not only to finish the AI product but also make sure it fulfills all requirements and expectations before getting released into the production environment. Your project managers need to have in-depth knowledge about AI - how it works, what are the opportunities for AI systems, what is the feasibility and complexity of various models and approaches, what are the challenges, how the AI development process looks like, and how AI is different from other IT projects.

As data is a crucial part of any AI system, it is equally essential that your project manager has AI data management skills and experience. The process is more than just data - it includes conceptualization, coding algorithms or software frameworks (like TensorFlow or PyTorch), training models on labeled data, etc. Your project manager needs to understand the whole process as a subordinate and as an expert who can guide the AI creation team through necessary steps for them to finish their product successfully and be ready for the production environment.

Your project managers should help you with AI Ethics and concepts like bias, diversity, privacy, or fairness are also essential considerations when machine learning is involved. In addition, they need to understand and be able to explain to stakeholders topics like explainable AI or black-box models.

How We Adapted Agile Into the Management of AI Projects?

At nexocode, we love challenges as well as AI-based projects. So far, we have helped many companies to develop their own Artificial Intelligence based solutions, and during those implementations, we were able to create our approach. We successfully tested it in battle, and we are sure that implementing it guarantees to deliver the highest possible quality of projects. How we develop AI projects in Nexocode?

Nexocode’s goal is to deliver great products and make sure they fulfill company expectations by delivering them ahead of time, on budget, and with the highest quality AI development. ​We understand the importance of AI project management, and that’s why we offer not only the work of machine learning engineers and data scientists but also a whole battle-tested process that works every time. This way, we cover all aspects from strategy through implementation. The company’s agile approach will enable us to develop AI solutions tailored for your needs even more efficiently than before!

The project management process for implementing AI-based systems

Kickoff Briefing

We usually start from a short (two hours) kickoff meeting with the customer, where you can explain to us every side of your business – we learn about your idea, the problems you are facing, the goals you want to achieve. Then, we sit down with our clients (in person or via video call) and start brainstorming about the best way to develop an artificial intelligence solution for them using AI tools such as machine learning. This briefing lets us understand the expectations of you and the end-users of your product. It is also the moment to go through our processes and project management approach.

AI Design Sprint

Our custom workshops are the most exciting part - Nexocode’s AI Design Sprint is a hands-on experience where, together, we identify potential AI use-cases for your business and explore the business opportunities available to you. We have created a set of tools for each step of the design-thinking process to help our clients turn AI into social, user, and business value. Within just two days, your team, supported by our AI Engineers and Design Facilitators, can learn and understand the power of emerging technologies, spot AI opportunities, and create new ideas and visions.

At nexocode, we understand the importance of iterative, learning experience and that’s why we offer AI Design Sprint workshops where our experts will help you find out what AI solution would work best for your company or organization. This way, before investing more time in developing AI products, you’ll know whether it actually makes sense to proceed with this idea at all.

AI workshops are also the very first step to evaluate data needs and haves on the client side. As data, and straightforward access to it, is essential for any machine learning implementation, we make sure the organization is at a sufficient digital transformation stage on the path towards AI maturity. These sessions result in an AI Design Sprint Outcomes Report, which is then used to blueprint future work on the project.

AI Design Sprint materials - cards and canvases for design thinking methodology

Proof of AI Development

In the next phase, you will be able to face reality – over a series of experiments; we will develop an AI-based solution that solves your company’s problem, covers your business needs, and collects and analyzes feedback from testers. We will be working with you to identify what data you have available to train AI models on and which AI algorithms will be most applicable in solving a particular problem or use case for your company. Most importantly, we’ll test out various AI implementations using our fast iteration approach that ensures only robust AI products get deployed into production environments - minimizing risk while ensuring maximum ROI!

It is an excellent chance for you to see what value AI can bring to your business, decide if it is the right technology for your project, and develop it further. In addition, you will be able to evaluate what benchmarks the AI model can achieve, which will help you decide whether to proceed with AI adoption.

Thanks to this phase, you will be able to reduce the risk of failure – with lower time and money involvement comparing to building a fully functional product. It usually takes from 4 to 12 weeks – that means that you can verify your AI-based idea in three months!

AI Application Deployment in Production

Suppose you decided that Artificial Intelligence or Machine Learning is something that you need in your project. In that case, we can take on the full development of the AI system in the production environment. We will build data pipelines​, deploy and tune the AI model, as well as provide training.

AI application deployment in production includes: data pipeline infrastructure setup (including the integration of AI with existing systems), AI model tunning to real-time data and large datasets, AI-based system testing, preparing self-learning features, and adjusting AI training model versions. After deploying your AI model to a cloud or on-premise environment, we will also offer you all required maintenance after completing project delivery.

The production deployment can take several months, up to a year, depending on solution complexity and all the integrations needed. During this time, you can deploy the solution across your company’s product portfolio and supply chain or service offering. We will lead all software creation efforts in an iterative approach, with sprints that increment delivered value and a direct feedback loop. All work is supported and leads by skilled project managers.

Nexocode team has experience working with many different types of businesses from startups to big corporations across various industries, including Logistics, Healthcare & Pharma, Manufacturing, etc., providing engineering solutions tailored specifically to each company’s needs. Our custom project management methodology has been successfully implemented across several projects so far - from business intelligence solutions over automated document processing with NLP to visual recognition systems, proving its viability and scalability for product complexity or company growth aspirations.

Summary

AI projects are usually far more complex than standard ones. That’s why old-fashioned project management methods are not working so well for them and need a tailored approach. The methodology developed at nexocode provides a complete end-to-end project management process for developing AI projects with an Agile approach – we are sure that it’s the right way of delivering the highest quality project management to every AI project, and we proved it during the successful cooperations with our previous AI customers.

AI Design Sprint workshop materials

To learn more about the initial phase of our process, you can visit our dedicated page on AI Design Sprints. In addition, you’ll find there a free-to-download ebook (no email needed) on AI Design Sprints with a more in-depth description of the workshops, preparations, and outcomes. So make sure to get it!

About the author

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams. She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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