How to build a dedicated development team for AI projects

 

The arrival of artificial intelligence has meant a revolution for companies, transforming numerous processes in order to increase productivity and efficiency in various sectors. For example, through AI, many businesses have been able to automate repetitive and routine tasks, freeing up employees to focus on more strategic and creative activities.

 

In addition, AI has improved decision making by providing more accurate data analysis and more accurate predictions, allowing companies to streamline their operations and offer more personalized solutions to their customers. All these benefits and features that we have named have also changed the way in which a dedicated development team is created for AI projects.

 

 

Artificial intelligence projects are complex and sophisticated initiatives that require careful planning and consideration by companies. That is why, in the first place, it is necessary to have a dedicated multidisciplinary development team, which guides the development of the project and injects all the experience it has to achieve the highest quality in the project.

 

That is why, in this article, we will cover how to build a dedicated development team for AI projects, so that you avoid future pitfalls or inconsistencies.

 

The transformation in the creation of dedicated development teams with AI

“A combination of culture change, job restructuring, hiring, reskilling and onboarding contract workers will provide the necessary equipment for a successful AI talent ecosystem", Deloitte said in a white paper on this topic, noting that the organizational landscape driven by AI is based on humans and machines working side by side.

 

In this same analysis, they concluded that building dedicated development teams that can succeed in this AI landscape requires companies to reexamine everything:

 

1) The teams themselves, including individual job descriptions, mosaics, and career paths.
2) Team structures, including organizational design, internal alignment, and integration of skills and abilities, particularly with the increasing reliance on external talent.
3) Team enablement, including culture, communications, collaboration, continuous learning, updating and upskilling.

 

They are also realistic in recognizing that AI is a new field, so a lack of qualified personnel is completely normal. In fact, the lack of personnel is not only due to the fact that there are few professionals trained in AI, but the global demand is also very high.

 

“What is needed is a thoughtful approach to build not only the best dedicated AI development team to meet the needs of the business, but also the structure in which they must work to be successful”, they emphasized in the Deloitte study.

Things to consider when building a dedicated development team for AI projects

• Above all, planning

In the technology industry there is always a new trend to try, but that does not mean that all companies have to rush towards it. The important thing is to have a technology partner that knows about AI or a dedicated development team that has the necessary skills for this kind of project.

 

“Carefully analyze your current position, allocate costs, determine the most important problems, and launch a pilot project. All these steps require proper planning and evaluation. Your business needs a step-by-step AI implementation plan, from laying a solid foundation to creating a vision for the future," they explained in a Forbes article.

 

• Roles of dedicated development team members

When building a dedicated development team, it's not just about finding the best software engineers. It is necessary to create a list of priorities and areas to attend to in the project, to create the team based on those needs, especially in the case of AI projects.

 

The most critical AI roles in a dedicated development team are typically:

 

- Data modelers.
- Deep learning specialists.
- Data engineers.
- Software engineers.
- Domain experts.
- Product designers.
- Sociologists and AI ethicists.
- IT leaders.
- Applied machine learning engineers.
- UX or graphic designer.

 

Skills you must have in an AI team

As a base level, companies often look for professionals with a college degree in mathematics, data science, statistics, or computer science. The demand for professionals with studies and doctorates in computer science or cognitive sciences is also increasing.

 

Additionally, companies are highly valuing these skills in dedicated development teams for AI projects:

 

• Programming
• Linear algebra, probability and statistics
• Big data technology
• Algorithms and frameworks
• Communication and problem solving

 

Experience in AI projects

If possible, try to ensure that the profiles you hire to assemble your dedicated development team have some kind of experience in AI projects, as this will improve the understanding of the tasks and challenges of your business project and the best practices in this field. area are key to making informed decisions and avoiding common problems.

 

Data management

Data availability and quality are critical to successful AI projects. The dedicated development team should have data management experts who can access, clean, and prepare the data needed for model training.

 

There are 5 areas in which AI plays an important role in data management, they refer to in an MITSloan article:

• Classification
• Cataloging
• Quality
• Security
• Data integration

 

Ethics and privacy

Dedicated development team members must take ethical and privacy aspects into account when developing AI solutions. This involves ensuring that the data is used responsibly and that the models do not introduce bias or discrimination.

 

Flexibility for problem solving

AI can pose complex challenges, and the dedicated development team must be prepared to solve technical and analytical problems effectively and efficiently.

 

How to successfully lead an AI project with a specialized dedicated development team

Before you start, make sure you clearly understand the domain in which the AI will be applied and the specific goals of the project. This will allow you to set realistic expectations and determine which type of AI is best suited for the problem. In this task, the help of a dedicated development team is very appropriate to guide you in this project definition stage.

 

Here are ways to lead an AI project:

 

Build a talented and diverse team

Assemble multidisciplinary profiles and create a powerful dedicated development team, including AI experts, data scientists, software engineers, and domain experts in the problem you are tackling. Diversity of skills and perspectives will enrich the process and allow you to approach challenges from different angles.

 

Careful planning

Develop a detailed plan for the project, including clear objectives, realistic timelines, necessary resources, and potential obstacles. Break the project down into smaller, more manageable stages for easier tracking and management.

 

Data collection and preparation

Data quality is critical to the success of an AI project. Make sure you collect relevant and clean data that is representative of the problem you want to solve. Proper data preparation is also essential, as AI relies on available information.

 

“While it may seem obvious to know the problem to be solved, the data that can be useful in building a predictive model, and how that model will be used within the organization. This is actually an area in which equipment often fails", they warned in an article on the Data Science PM portal.

 

Choosing suitable algorithms and models

Select the most appropriate AI algorithms and models for the problem at hand. You can use supervised, unsupervised, or reinforcement learning techniques, as appropriate.

 

Iteration and continuous improvement

AI often involves a process of iteration, where models are gradually improved as more data and feedback is obtained. Don't be afraid to adjust and refine your approaches as the project progresses.

 

Risk management

Identifies and manages potential project risks. This can include data privacy issues, model bias, lack of resources, etc. Being prepared to address these issues will help you minimize roadblocks.

 

Effective communication

Maintain clear and constant communication with all team members and stakeholders. Explain the progress, challenges and results in an accessible way so that everyone understands the progress of the project.

 

Ethics in AI

Always consider the ethical and social implications of your AI project. Make sure technology is used responsibly and for the benefit of society.

 

Constant Learning

AI is a constantly evolving field. Stay up to date with the latest trends and advances in the field, and foster a continuous learning environment in your team.

 

Do you need a dedicated development team for your next AI project? At Rootstack, we have +12 years of experience supporting companies in their digital transformation. Contact us!

 

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