From Point Of Values to ready-to-use solution- All you need to know about AI Project Management

Artificial Intelligence , Posted by on 2020/10/30 0     Comments

Once Gartner said ‘every company is a technology company. And in 2020 we are better understanding the meaning of this quote. More and more businesses are adopting sophisticated technologies to reach their goals. 

Artificial Intelligence is one of those heartthrobs that simply wins the hearts of businesses. Today, it has become a solid business opportunity in 2020. If a company can master to apply new AI approaches to work at scale and enable an amazing competitive edge then that business is going to create a real difference in the market. 

Without any doubt ai project management is a bit different. Here you have to apply different techniques and strategies to ensure the best benefits or success. 

According to experts, AI is at its very native stage yet it has come with a handful of benefits. In other words, we can say that businesses are yet to recognize the best AI use cases as well as their effectiveness. 

The hope of amazing benefits and a vague idea of implementing AI have given birth to a lot of queries among enthusiastic entrepreneurs. Today, we at Vyrazu Labs are going to answer the most common yet very important question that revolves around ai project management. 

So, without more ado let’s get started with how to successfully manage ai projects-

When it comes to managing ai projects, we prefer to be a bit different than others. We believe that every organization is unique and has some unique management styles that let them top the market. In the below sections, we will explain how we do ai project management at Vyrazu Labs along with the infallible steps that effectively transform a POV (proof of value) to the stunning Ai solutions and services. 

Difference between traditional projects and artificial intelligence projects

Well, how ai projects differ from traditional projects that we should clear at the first move. According to experts, differences between ai projects and traditional projects are manifold. But today, we will only discuss the core to keep the discussion brief and crisp. 

When it comes to mobile app development, here the solution is specified. If it seems difficult to properly specify a solution, the result will be uncertain and risky at the same time. We can call this type of development top-down programming. 

On the other hand, when it comes to the proof of the value of ai projects, we follow the bottom-up approach. In this way, AI will be solely responsible to bring conclusions by applying its own rules. Ai will also apply its working process along with the extensive data set.

Over time, the AI development landscape offers more opportunities. In other words, we can say that in order to be completed, an ai project will go through various stages of exploration and trials. In this way, the projects will be error-free as well as revenue friendly. It can add the expense of the development but the development time will be reduced for sure. 

Lastly, when it comes to AI project management, we need to improve change management in the agile process as a mandatory task. In order to make it possible, ai project managers follow the fail-fast technique. I mean they prefer to explore fast and fall at the very first step of the wrong beginning. If the techniques fail after time and in the middle of the process, the mess will be larger than imagination.   

To make the process error-free from the very start, we need to explore the reasons for failure. We have seen that some entrepreneurs claim that they have embedded some form of artificial intelligence but they failed to get the desired benefits. 

Generally, these ai projects start with a proof of value. But in some cases, the POV does not work in the desired path. Why? We will explore that below-

Reasons for POV (Proof of Values) failure

Unaware of the actual requirements- it has been noticed that POV or ai projects when the requirements are not properly understood. We always suggest properly understand the requirements and tally that with the implementation. I mean the implementation is perfect for the specified goal or not. 

Experts say that the proof of the value is not the best place to resolve the problem. But when you are trying to solve the problem, the problem, as well as the solution, needs to be prominent. 

Improper leadership- if you are working with external parties, you have to make the communication clear and logical. As a part of the development, you have to engage multiple department stakeholders to ensure the wide adoption of your AI solution. Here prominent leadership for each team or department is very much important. You can enable buy-in but at the same time, you also have to decide the best person for the final say. 

Improper change management- poor change management is a common reason for POV failure. In order to avoid such a kind of failure, you have to properly understand which teams are going to be affected by the AI system implementation. At the same time, you also have to focus on the long term vision and objective. The people who are going to use the product should be engaged early as they either can be the affected persons or the ones who will impact the POV directly. 

Error in Data modeling- the gap between generating the input data and doing it in a continuous mode is important. We need to generate input data for the machine learning model for the proof of value. But often we make mistakes to understand the time and energy needed for the data modeling.

Here all types of data should simplify reality. You will see that some fields remain lost always in the process. And that’s why you need to ensure the drop in accuracy while heading to production. Along with this, you should also be equipped with the right toolsets and teams to properly maintain the system especially when it will deal with its new environment. 

Delay in endpoint- just after achieving the defined objects, the proof of value should end. You should refrain your team from gold-plating the achievement to make it work after being unsuccessful. In order to avoid any kind of misunderstanding, you should be prominent about the goal so that all the teams and their members can have a clear goal in their minds. 

You need to make sure that you are not following any of the mentioned mistakes while your ai project management. The process should start with understanding the category of the project. Here we can give two categories of ai projects. The first category is about projects that are pretty common in nature. For example, we can say that language translation or image to word conversion falls into the first category of the project. The second category is a bit more complex. The second category is able to handle difficult tasks like detecting heartbeats or sleep monitoring. 

These two categories require two solutions such as existing AI incorporation and developing custom yet effective artificial intelligence project management solutions. 

Two categories of AI project management

Existing artificial intelligence- today, there are some places where the implementation of artificial intelligence has become a pretty common practice. And that’s why there are some ready to use AI tools available in the market. All you need to do is just integrate the required AI tool into the application. Some of the popular AI tools or platforms are Google AI platform, Microsoft Azure AI, Amazon Machine Learning, and so on. 

Custom AI solutions- there are some projects that need advanced automation. In order to satisfy that kind of requirement, companies prefer to use custom AI solutions. In this way, they not only improve automation but also make it completely error-free. Some big brands in the industry are continuously trying to enable more and more custom AI solutions to make the machine learning apps easily rin on the devices. 

We have already mentioned that AI is still at its native stage. It needs more days to fully flourish. But we have some tools or ways in our hands that we can use today to smoothen the process of AI flourishment in near future. 

But in order to stay competitive in the future market of AI, companies should set the best AI strategies. Creating a successful AI strategy is not an easy job especially when the entire scope is not properly defined yet. But there are some pillars for the support that can ensure durability. I mean various changes can take place over time, but those pillars will help your AI solution to stay competitive in the market. What are those pillars? Let’s explore below-

Six pillars of a strong AI strategy

Do required experiment with your thoughts- we can say that machine learning is an iterative as well as an exploratory process. More and more companies are mass producing the core algorithms. So, customizing the project based on the business context and data will not be a problem anymore.

When you will run an experiment, some hypotheses will become wrong at the initial stage. At that time, new data will be needed. In order to solve that, both decision-makers and team members should learn a machine learning test to know how to successfully establish the data analysis. 

Generally, an iterative process comes with more flexibility and agility. As a result, faster progress evaluation becomes possible and you will also be able to determine the need for an alternative approach. 

Work closely with the data science team- just by investing in machine learning you are not going to get the best results. Along with the investment, you also have to make sure that your team is strong enough or has the right people to deal with data science. 

You should have a dynamic team model. The model will help you in involving various experts with data, technical aspects, and business. They will rightly assess the important data and bring it into the spotlight to make wise business decisions. Along with this, you will also need a dedicated IT team for deploying and maintenance of the technology ecosystem. 

You also need to ensure that all the teams are working closely and there is no miscommunication or misunderstanding between teams. 

Create an effective data strategy and ecosystem- When it is about machine learning, we can get sure that now the system will be dealing with a large amount of data. So, the process should be strong enough to deal with sufficient identification, procurement, and delivery, access to quality data and resources. 

In order to make it possible, experts suggest that the government guidelines and the data ecosystem should be able to support the exploratory and production environments. Here you may need to enable a multi-level approach to properly align access flexibility and access even without sacrificing privacy, security, and quality. 

More risk-tolerance power- it has been noticed that often machine learning cannot run with the traditional approaches for quality assurance and proper risk management. Actually, sometimes, test data needs to be replaced by a productive data set. So, you need to be prepared with the true validation process to deal with a new data set or approach. 

More adaptation of established processes- machine learning is disruptive no matter it is about automating an existing decision-making point or offering a new service or product. Here you should assess the potential impact on the existing process, functions, and roles as these are truly important. 

Here you do not have to design the potential outcome even before starting. But an effective quick check can save you from spending a lot of time and money restructuring everything afterward. 

The ability for embracing new IT practices- you should also make sure that after deployment of AI tools and machine learning, both iterative modeling and tuning of machine learning should continue at the same pace. Here the update intervals can be unpredictable and may not follow the traditionally planned deployment patterns. 

On the other hand, machine learning needs systematically different QA and delivery models. 

These are the 6 pillars that can strengthen the strategy and without any doubt that will be beneficial for successful ai project management. 

Now we know the ways that can strengthen the strategy yet there will be some challenges that companies will face while working on artificial intelligence projects. We have already discussed why the POV fails. Now it’s time to explore the steps to successfully manage the ai projects. 

How to do AI project management

Project management artificial intelligence: Recognize the problem

When it comes to ai project management, we have to start the process by identifying the problem. The problem and the solution both should be cross-checked various times. 

AI will not work as a solution. We should use it just as a tool. Along with AI, we can take other tools in the process to achieve the goal. 

Project management artificial intelligence: Testing the solution

This stage is about how to start artificial intelligent projects. But even before starting, checking the outcome is important. I mean the development will help the users or not or it will get a solid marketplace or not. 

At this stage, leading companies prefer to problem-solution fit tests through a lot of techniques like a product design sprint or traditional lean-approach. 

Project management artificial intelligence: Data preparation and management

When you know that there is a need for your product or service in the market, you can accelerate the development process. You can use machine learning for better data preparation and management. 

You can start by dividing the data into various categories. You can take the help of data engineers if your database is really large. 

Project management artificial intelligence: set the right algorithm

On the basis of learning, there will be different types of AI algorithms. And you need to choose the best one for your artificial project management. Supervised learning and unsupervised learning are the most common types of algorithms you will be facing soon.  

Project management artificial intelligence: Train the algorithm

After selecting the right type of algorithm, you should start training the model. The process is quite simple as all you need to do is input data into the model and maintain the importance of model accuracy into consideration. 

We believe that setting the minimum acceptable threshold and enabling statistical discipline are the best steps for better management of artificial intelligence projects. 

AI project management: project deployment 

When it comes to artificial intelligence project deployment, we always prefer to use ready-made machine learning as a service for the product. And suggest the same to our clients. These platforms not only simply AI but also facilitate the deployment stage of ai projects at the same time. They also come with cloud-based analytics that we can use to enable various languages and algorithms. 

Conclusion

We have discussed a lot about the successful management of artificial intelligence projects. It is always not that AI projects are better but projects can be of more quality with AI inclusion. But sometimes, it can also be complex. So, we will suggest you be sure about the use case and added value by AI, before planning the roadmap of the development.