11 key roles of the hottest AI team

Set up AI team: 11 key roles

5 AI ethicists and sociologists

AI ethicists and sociologists may play a crucial role in some sectors (especially health care or government departments), but they seem to become increasingly important in a wide range of use cases

McGehee said, "an important part of artificial intelligence system is to understand how it affects people and whether underrepresented groups are treated fairly. If a system has unprecedented accuracy, but does not produce the expected social impact, it is doomed to fail."

6. Lawyer

mcgehee said that in this emerging field, we also see a separate and related demand for legal expertise to set multiple experimental parameters. McGehee said, "the gdpr regulation sets a precedent for formulating regulations around algorithm decision-making. As countries around the world become more and more aware of the application of artificial intelligence in industry, more laws are expected to be introduced. Lawyers who are proficient in this field may be a valuable asset."

due to the importance of industry experts, as stated by kranc and McGehee, it is necessary to study some specific examples of industry fields, including technical and non-technical fields. These areas should be part of AI team building, depending on the specific goals and use cases of the enterprise

Costenaro of

company pointed out, "since AI is usually only an enabling layer to enhance existing business use cases, team members who have supported this use case in the past are still valuable and essential for the same reason."

costenaro provides five examples of roles that may be valuable contributors to AI, and explains how to adjust and enhance existing roles in an AI environment

7. Executives and strategists

costenaro said, "senior executives will need to consider which business models can be automated and improved through artificial intelligence, and weigh new opportunities and risks from the following teams, such as data privacy, human-computer interaction, etc."

8. It executives

don't be confused about the value of non-technical roles: without it, the enterprise's AI strategy will not go too far. Costenaro pointed out that the IT team needs to solve the following problems: "if a large amount of data is being accumulated and stored for model training, how will it ensure the privacy and security of the data? In addition, how will it store and provide services quickly and reliably from the server to the customer's devices?"

Costenaro added that this will also drive the growing demand for Devops professionals and professionals with cloud native technologies such as containers and orchestrations. It departments have the opportunity to use AI tools such as chat to simplify internal services

9. Human resources leader costenaro said, "similarly, the human resources department also has many opportunities to provide services to customers by using AI tools such as chat robots, so as to improve efficiency." Stretch experiment

in addition, human resources seem likely to become an important participant in evaluating the impact of artificial intelligence in organizations, which is not different from McGehee's inclusion of ethicists, lawyers and other roles

10. Marketing and sales leaders

as kranc pointed out, if an enterprise's AI plan is related to revenue generation, it should consider adding domain expertise from areas such as sales and marketing

Costenaro also pointed out that as part of the AI project, sales and marketing professionals may need to use technologies such as sales automation tools and robotic process automation (RPA) to enhance their existing skills and processes

11. Operations experts

throughout the IT department, operations and Devops professionals have specific domain expertise to implement AI programs. Costenaro cited the following questions as examples of where expertise needs to be applied:

· what can be automated and improved

· if machine learning models are used, how will new data collection processes be created to continuously train and improve these models

· can you get ready-made, pre trained models and/or datasets from the open source repository, so as to gain a huge advantage? Will the API services provided by third-party vendors consider some tasks and use cases

although artificial intelligence can solve some major problems, it will also produce new challenges. This is the fundamental reason for forming a diversified team

mcgehee said. "It is useful for people with different backgrounds and personalities to focus on different project details and constraints, because it increases the likelihood of all important details and provides a holistic approach to identifying solutions."

what inspiration can musicians, chemists and physicists bring to the artificial intelligence team of enterprises? There will be many. People need to understand a series of skills and roles of AI, including non-technical skills and roles, which will promote the successful application of AI

the success of AI programs may depend on art and philosophy, as well as data science and machine learning. This is because the effective deployment of AI in enterprises requires the establishment of a comprehensive team, including personnel from various backgrounds and skill sets, as well as non-technical roles

ness moshekranc, chief technology officer of digital engineering company, said, "Any AI plan requires the combination of IT experts and industry experts. IT experts understand the machine learning Toolkit: which algorithm series are most likely to solve specific problems? How to adjust specific algorithms to improve the accuracy of the results? And industry experts bring specific domain knowledge: which data sources are available? How dirty is the data? How good is the recommendation quality of machine learning algorithms? If there is no input from industry experts , it experts may not be able to answer these questions. "

therefore, the conclusion is that the success of AI really depends on the team, not any individual or role

Keith Collins, executive vice president and CIO of SAS, said, "when building an effective AI team, we need to seek industry experts or super teams, and teamwork will win. Diversified disciplines are the key to the success of AI."

four core types of AI talents

Collins believes that AI teams need four core types of personnel:

· people who understand business processes are critical to establishing real scenarios and valuable results

· people who understand machine learning, statistics, prediction and optimization and use them correctly

· people who know where the data comes from, how the quality is, and how to maintain safety and trust

· AI architects who understand how to implement analysis through results

Collins points out that, like other IT leaders and AI experts, these core disciplines or roles can draw inspiration from a variety of backgrounds. He took music, chemistry, physics and other disciplines as examples

he said, "these disciplines encourage people to understand scientific processes and thinking from complex interactive systems. They are usually good at building critical thinking skills required for good experiments and applying the results of machine learning."

the value of diversified AI teams

the value of diversified teams has a wide range: for example, it can help enterprises better deal with AI bias. It is also important to solve business problems (including the biggest and thorniest problems), which may be one of the reasons why enterprises first formulate AI strategies

Jeff McGehee, senior data scientist and IOT practice director of very company, said, "it is widely believed that diverse opinions are essential to solve all complex problems. Diversity is related to life experience, and professional background is an important part of most people's life experience. It can add dimensions to AI projects and provide a new perspective for finding innovative solutions."

McGehee also pointed out that the establishment of artificial intelligence or other different teams requires the active efforts of enterprises as part of recruitment and employment practices. Enterprises will find that achieving diversity may not be a feasible team building strategy

considering this, we need to understand a series of experts and roles that are valuable to AI teams, including non-technical roles

1. Domain experts

people can regard these roles and personnel as subject matter experts. No matter which term is used, we need to understand their importance to the AI plan of the enterprise

mcgehee said, "developing AI systems requires an in-depth understanding of the field in which the system operates. Experts who develop AI systems rarely become experts in the actual field of the system. Industry experts can provide key insights to make AI systems perform at their best."

Kranc of ness pointed out that these experts can solve the problems of enterprises and strategies in their fields

he said that the type of industry experts depends on the problem to be solved. Whether the required insight is in revenue generation and operational efficiency or in supply chain management, industry experts need to answer these questions:

· which insights are the most valuable

· can the data collected about the industry field be used as the basis for insights

· are the insights meaningful

some industry-specific examples will be introduced below, but first, let's learn about some other key roles in the AI team

2. Dave costenaro, director of AI research and development of data scientist

Company, said that this is the first of the three key needs of the AI team in the new project that operators and observers 1 must work outside the safe alert line. Examples include chat agents, computer vision systems, or prediction engines

Costenaro said, "data scientists have various backgrounds, such as statistics, engineering, computer science, psychology, philosophy, music, etc., and usually have strong curiosity, which forces them to go deep into the system to find and use patterns, such as what they can provide for artificial intelligence projects, determine what it can do, and train it to do this."

3. Data Engineer

costenaro said, "programmers get ideas, models, and algorithms from data scientists, and turn them into reality by normalizing code, running it on the server, and successfully talking to appropriate users, devices, APIs, etc."

4. Product designer

costenaro said that the final results of the three key requirements also illustrate the value of the non-technical expertise of the AI team

he said, "product designers also come from various backgrounds, such as art, design, engineering, management, psychology, philosophy. They have developed a roadmap for what is needed and useful."

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