Project Manager Preparation for AI Project Development

Over the course of your career as a project manager, you’ve certainly worked on a variety of IT projects, from intricate monolithic buildings to SaaS web applications. However, as artificial intelligence and machine learning develop, a growing number of new projects with a variety of needs and issues are appearing.

It is now more important than ever for technical project managers to have a positive connection with these ideas as a result of the development of these technologies. By 2020, AI will create 2.3 million jobs, outnumbering the 1.8 million it will eliminate, according to Gartner, and will add $2.9 trillion in value to the economy by 2021. The CEO of Google even goes so far as to state, “AI is one of the most significant projects that mankind is working on. Deeper than fire or electricity, it is.

Technical PMs who can take advantage of this opportunity must be aware of how AI project management differs from other forms of project management and how they can best prepare for the shifting business environment. Artificial intelligence applications are already disrupting industries ranging from finance to healthcare.

Theory: AI vs. ML What It All Means

It’s critical to have a clear knowledge of what artificial intelligence (AI) is before delving further. Let’s start with the meanings of the most often used words as many of them are frequently used in the same context.


Pattern recognition, learning, and generalization are a few examples of issues that may be solved by artificial intelligence (AI), a branch of computer science.

Artificial general intelligence (AGI), which refers to self-aware computer programs with true cognition, is a phrase that has been overused in recent years. However, for the foreseeable future, the majority of AI systems will be what computer scientists refer to as “narrow AI,” which means that they will be created to execute a single cognitive job exceptionally well rather than to really “think” by themselves.


A branch of artificial intelligence known as “machine learning” use statistical methods to enable computers to learn from data without explicit programming.

Due to the success of various machine learning techniques in the area of AI, many businesses now use the terms AI and ML interchangeably. To be clear, artificial intelligence includes learning along with other capabilities, while machine learning refers to a program’s capacity for learning.

The Difference Between AI and Standard Algorithms

One important lesson from AI is that its algorithms make extensive use of data to modify their internal structures so that new data is classified in line with previously provided data. Instead of rigidly following the classification instructions contained in the code, we refer to this as “learning” from the data.

Imagine that we want to create software that can distinguish between trucks and autos. In the conventional method of programming, we would attempt to create a program that searches for certain, suggestive properties, such as larger wheels or a longer body. The appearance and location of these features would have to be specified in the code, explicitly. Such software is incredibly challenging to create, with a high likelihood of producing false positives as well as false negatives, to the point where it may not even be useable in the end.

In situations like these, AI algorithms are quite helpful. Once an AI algorithm has been trained, we can provide it with a large number of instances, and it will adapt its internal structure to begin looking for characteristics that are important for accurately classifying the images rather than depending just on static, predetermined feature definitions.

Project Management for AI in Action

Data Is The Most Important

Because we are not very good at handling vast amounts of data, the sheer amount of data that is accessible to us sometimes prohibits us from utilizing it directly. AI systems can help in this situation.

The idea that AI systems’ predictions are only as accurate as their data is fundamental to the field. An algorithm with a million data points, for instance, will perform better than one with 10,000 data points. Additionally, according to BCG, “many firms do not comprehend the significance of data and training to AI performance. In many cases, better data is more important to developing an intelligent system than better-naked algorithms, similar to how nurture often prevails over nature in people.

With this information in hand, the project process will increasingly include preparing and cleansing data. Since most organizations lack the necessary data in the right forms, this stage is often the most labor-intensive one of developing an AI system. As a result, data analysts may take some time to finish this crucial step.

Additionally, compared to typical software development, the construction of the data infrastructure and the data cleaning tasks are considerably more linear, necessitating the use of a distinct project management approach.

In conclusion, developing the appropriate data infrastructure and getting the data ready for use might take far longer than constructing the machine learning model that will process the data. Project managers must take this into account as they manage teams, determine the scope of AI, and estimate project costs.

Additionally, fresh data should be regularly added to the dataset. The ability to access distinctive datasets may be the primary element determining which ML solution is most effective. To get the optimum performance for your ML project, even after launch, it is important to keep up to current on this.

The Lifecycle of AI Development

The standard systems development lifecycle (SDLC) and how various approaches and technologies are influencing it will be recognizable to the majority of you. It is important to emphasize that the discipline will face new hurdles as AI develops. The phases of AI development may be broken down into ideation and data discovery, prioritizing MVPs, and transforming MVPs into finished products.


The end-user of the ML product and the accessible data pools should be the primary areas of attention at this early stage.

These methods may assist a project manager in swiftly reducing the ML product options accessible inside a corporation by tackling the issue from two distinct angles. Top PMs may take use of this phase to better comprehend the complexity of a given challenge by drawing on their understanding of the machine learning space. In the world of machine learning, things move quickly, and recent advancements in research may greatly simplify certain challenging issues.

The data must be cleansed and processed after it is found, as was already indicated. Although they may be pushed into sprints, this particular work is often carried out in linear phases that do not cleanly fit into traditional project techniques like Agile or Waterfall. Data cleaning is often carried out iteratively by progressively enlarging datasets and preparing them concurrently with other development processes.


When it comes to machine learning products, the adage that it is preferable to have a functional prototype of a smaller product than an incomplete huge one still holds true. Prioritize new ML MVPs according to how quickly they can be delivered and how valuable they are to the business. It might be a rapid victory for the whole team if you can supply items quickly, even if they are minor ones; you should give these products top priority.

It is a good idea to prepare these MVPs in the traditional Agile manner, and the development team should concentrate on producing ML models based on the independently created, continuously improving datasets. The data team does not necessarily need to operate under the same Sprint organization as the team creating the MVP, and this is a crucial difference.


At this stage, data infrastructure becomes crucial. You should now think about how you can expand the infrastructure to support the ML product if your ML product demands high-frequency API access from all over the world.

Here, modifications to the ML modules must be carefully considered in order to prevent degrading the functionality of the present product. Prior to live deployment, extensive testing is necessary since retraining the ML modules with new methods or datasets does not necessarily result in a linear performance gain. Although generative adversarial network (GAN) attacks and testing for edge cases in ML modules are still in their infancy, project managers should bear this in mind when using a live ML product.

Significant Positions in the AI Development Lifecycle

The development of ML applications requires a lot of data, which introduces new responsibilities into the SDLC of AI products. Data scientists, data engineers, and infrastructure engineers are the three jobs that you need to be highly acquainted with in order to be a successful project manager in the area of ML applications. It’s critical to comprehend these key roles and how they affect the development of machine learning (ML) systems, even if they are sometimes referred to by other names as machine learning scientists, engineers, or infrastructure specialists.


The folks who create the machine learning models are data scientists. They combine concepts based on their in-depth knowledge of applied statistics, machine learning, and analytics, and then use their discoveries to address actual business issues.

Sometimes, data scientists are compared to more experienced data analysts. However, data scientists often have good programming abilities, feel at ease processing huge volumes of data from many data centers, and are knowledgeable in machine learning.

They must also be able to independently carry out exploratory activities, look at data to identify preliminary hints and insights, and have a solid grasp of data infrastructures and big data mining.

Python, R, Scala, Apache Spark, Hadoop, Machine Learning, Deep Learning, Statistics, Data Science, Jupyter, and RStudio are examples of fundamental programming languages.


Software engineers with a focus on developing the infrastructure and software needed for the operation of ML products are known as data engineers. While they may not be specialists in machine learning, analytics, or big data, they need to have a solid grasp of these subjects in order to test their software and infrastructure. They prefer to concentrate on the overall architecture. This is required in order to correctly apply and expose the data scientist’s machine learning models to the actual world.

Python, Hadoop, MapReduce, Hive, Pig, Data Streaming, NoSQL, SQL, Programming, DashDB, MySQL, MongoDB, and Cassandra are examples of fundamental skills.


Infrastructure engineers look after the infrastructure layer, which is the foundation of ML products. While part of this infrastructure may be constructed by data engineers, it is often built on top of the layer that has been established and approved by the infrastructure team.

In order to create a scalable and effective environment where ML applications may grow to serve millions of users, infrastructure engineers may collaborate with members of various ML teams. In addition to looking after platforms’ software needs, infrastructure engineers work closely with data center partners to make sure everything is operating as it should, from the physical placement of hosted data to hardware. Infrastructure engineers are increasingly significant in AI-driven firms as these factors become more crucial for ML initiatives.

Kubernetes, Mesos, EKS, GKE, Hadoop, Spark, HDFS, CEPH, AWS, cloud computing, and data center operations are fundamental skills. Infrastructure for end-to-end computing, IT infrastructure, and service management

Common Issues of Today

Project managers may anticipate to encounter both well-known and wholly new issues as AI and ML-based technologies become more prevalent. Top project managers are intensely aware of these possible problems throughout the whole process, from project scoping through conclusion.


The likelihood is that the issue you are seeking to resolve doesn’t need a complex AI solution, despite the popularity and promise of AI.

Many prediction issues may be resolved using statistical regression models that are more straightforward and, in some situations, more accurate. Before beginning a project, it is crucial for a PM to do a sanity check to make sure the situation really calls for machine learning.

Sometimes it makes sense to use a machine learning-based solution alongside a basic statistical model. For instance, it would be a good idea to start with a simple solution and a shorter development cycle if you are constructing a recommendation engine. This will provide a solid baseline that the later ML model should beat.


The most frequent reasons for scope creep in ML projects have to do with attempting to handle too many tasks at once and underestimating the time required for data preparation.

Manage the stakeholders to realize that it’s preferable to start with little victories rather than ambitious objectives in order to solve the initial issue. As you construct and test the project, consistently communicate this strategy.

Start by defining and testing simple, tiny, atomic features. If you are faced with challenging work, attempt to divide it into smaller projects that serve as solid stand-ins for the larger ones. What these tasks were meant to achieve should be simple to explain.

For instance, you may attempt to guess if a person would completely ignore an advertisement before trying to forecast when they will click on it. This method simplifies the issue and makes it easier for the present ML models to foresee and handle it. Facebook has created a fantastic series that delves further into this subject, concentrating more on the ML pipeline from model creation through model distribution.

Make sure you are capable of producing the data to support your ML efforts to solve the second cause of scope creep. The biggest error PMs make when beginning ML initiatives is believing they already have the data required in the format required. Managing this stage is crucial since it is often the most time-consuming phase of the ML project process. Before developing any ML features, make sure your data scientist has access to the appropriate data and is able to evaluate its accuracy and validity.

As the project may always need better and more data, be prepared to undertake data labeling and cleaning as a continual activity throughout the project, not only as a starter. Because this stage is not the most interesting assignment, divide it into sprints so your data team can see the results of their labor rather than dealing with an infinite backlog of tickets.

Companies may contract with other organizations to label data. This might save time and money upfront, but it can also result in incorrect data, which eventually prevents your ML model from working as intended. Use the multiple overlap strategy to prevent this, in which each piece of data is examined by many individuals before being utilized only if their findings agree.

When planning your project, provide adequate time for the data team to make changes in case your labeling specifications alter mid-project and new labels need to be applied.

Check whether your data can be utilized with current ML techniques rather than developing new ones, since doing so may significantly lengthen the project’s timeline and scope. Be aware that there is a strong possibility you will fail if you attempt to tackle an ML issue that has not previously been solved. Despite the popularity of machine learning and the volume of research papers that have been written about it, it may be exceedingly challenging to solve ML challenges. Instead of attempting to create anything new, it is usually simpler to start with an area of machine learning (ML) that already has many strong instances and methods and strive to enhance them.


Every PM should be prepared to consider how best to lead the team producing the AI products they are generating as well as the user experience of those products. In a fantastic article, Google discussed its views on UX and AI, putting a focus on human engagement.

This notion is particularly crucial if your machine learning solution must work with operators or possibly replace them. The system’s operators and users should only experience the absolute least amount of stress as a result of the design. For instance, chatbots often rely on machine learning, but a human operator may easily take control at any time.

Additionally, it’s possible that users of machine learning solutions may want far more of them than they are capable of providing. It is crucial for the project manager to establish realistic expectations since this is often an issue caused by the excitement that the media generates when writing about AI technologies.

Make careful to fully explain the capabilities of the AI tool to your stakeholders so that you can control their expectations before they try the technology. It is crucial for any PM concerned to manage these and inform their stakeholders about AI and its true capabilities since good UX cannot satisfy consumers who have false expectations.


In its present state, it is a relatively young discipline. Deep learning is now being used by more applications than ever before to accomplish their objectives. There are unique difficulties associated with these new advancements, notably in testing.

Testing machine learning models thoroughly, particularly those created using neural networks, is far more challenging than testing typical software that has a defined “rule set” developed by humans. The majority of machine learning (ML) models are currently evaluated by data scientists alone, and there aren’t many agreed-upon procedures for testing ML products with traditional QA teams to make sure they don’t fail in unexpected ways.

Comprehensive model testing will become more and more crucial when novel methods to alter the outcomes of the known models, like GAN attacks, are developed. Many ML projects will make this a top goal, and in the years to come, we’ll see more ML model “integration” testing. It’s crucial to keep this in mind if you are developing a mission-critical machine learning product even if it may not yet be an issue for the majority of straightforward projects.


Since the initial study was presented at the USENIX Security conference in 2016 and this Wired story was published, it has become clear that it is possible to steal a live machine learning model.

Although it’s still challenging to do this successfully, it’s crucial to be aware of the potential if your model is running via a widely accessible API. Theoretically, a party with enough access might train their own network based on yours and essentially duplicate your predictive power.

Although the likelihood of this happening is still rather low, if your project is affected, be sure to engage with your team to develop a preventative plan for potential assaults.


The present need for top-tier AI expertise is driving up the cost of hiring the appropriate people. In fact, according to a New York Times investigation, top-tier AI professionals may earn up to $1 million annually by working with significant Silicon Valley tech giants. Be mindful of these dynamics as a PM while you search for AI specialists to join your team. They might have an influence on your recruiting processes, spending, or work product quality.

The lack of top-notch data scientists and engineers goes beyond the creative brains behind the more recent deep learning algorithms.

In machine learning contests like Kaggle, where participants may compete for prizes of up to $100,000 for solving challenging machine learning tasks, many of the most skilled people participate. It is advisable to explore unconventional solutions, such as employing specialist contractors remotely or hosting your own Kaggle competition for the most challenging ML tasks, if it is impossible to find ML professionals locally.


The use of AI in project management presents two distinct legal and ethical problems.

The data used to train the ML models are the source of the first set of difficulties. Knowing the source of the data you use is crucial, as is knowing whether you have the legal authority to use it and the necessary licensing.

To resolve such issues before deploying a model trained on data for which you may not have the proper sort of license, it is always vital to speak with your attorney. PMs should make sure that their teams are only accessing datasets that they have the right to use since this subject is still in its infancy and many of the solutions to these questions are unclear.

Here is a useful selection of datasets that are freely accessible for training machine learning algorithms.

The second set of difficulties derives from controlling the emergence of systematic bias in your system. There have been several instances of these issues recently, where one camera business was forced to acknowledge that its smile recognition system only recognizes persons of a certain race since it was trained only on data comprising faces from that race. Another example is a prominent software business that had to remove its self-teaching Twitter bot after a few days of learning because a group of online trolls had worked together to make it produce racist insults and regurgitate bizarre theories.

When creating systems that are crucial, PMs should make sure that they take into account such possibilities and avoid them as early as feasible since the severity of these issues may range from minor to project-destroying.

Solid Structures are Built on Solid Foundations

In conclusion, the next AI revolution will usher in a range of intriguing, dynamic projects that often include a changed development method, a different team typology, and fresh difficulties.

Top technical project managers have an intuitive sense of the complexity of each project stage and what is really feasible to achieve with their team in addition to having a solid knowledge of the fundamentals of AI. Since AI is not a commercially available off-the-shelf (COTS) solution, even businesses that want to buy specific ML solutions will still have to spend money testing new ideas and properly managing their infrastructure and data.

It is obvious that the development of AI is altering the sorts of software products and the methods for producing them. Future machine learning products will be greatly aided by project managers who are able to understand and put these new ideas into practice.

Additional Readings by the Author

DLs and NNs are two theories.

Project managers may benefit from being aware of further distinctions between deep learning (DL) and neural networks in addition to the more often used terms artificial intelligence (AI) and machine learning (ML) (NN).


In contrast to traditional task-specific algorithms, deep learning is a member of a larger family of machine learning techniques focused on learning data representations.

Though they may use a variety of additional techniques, the majority of contemporary deep learning models are built on an artificial neural network.


Neural networks are interconnected mathematical structures with biological inspiration that allow AI systems to learn from data that is provided to them.

These networks may be thought of as millions of tiny gates that, in response to our data intake, either open or shut. The development of GPU processing power in recent years has made these approaches successful by enabling us to swiftly modify more of those “little gates” within neural networks.

There are several varieties of neural networks, and each has a unique set of applications and degree of complexity. Different forms of a neural network architecture may be referred to using names like CNN (convolutional neural network) or RNN (recurrent neural network).

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