Ten key roles to fully realize the business value of artificial intelligence

More and more businesses in every industry are adopting artificial intelligence to transform business processes. But know that the success of an AI program depends not just on data and technology, but also on including the right talent.

Bradley Shimmin, principal analyst for AI platforms, analytics and data management at consulting firm Omdia, said an effective enterprise AI team should be a diverse group that includes more than just data scientists and engineers. There is a range of people who understand the business and try to solve the problems.

Carlos Anchia, co-founder and CEO of AI startup Plainsight, agreed, adding that success in AI largely depends on building a well-rounded team with a variety of advanced skills. , but doing so is extremely challenging. He explains, “Determining what makes an effective AI team may seem easy to do, but when you look at the detailed responsibilities of individuals within a successful AI team, you quickly conclude that building such a team is very difficult. .”

To help you build your ideal AI team, here are 10 key roles that are essential in today’s well-run enterprise AI teams:

Data Scientist

Data scientists are the core of any AI team, responsible for processing and analyzing data, building machine learning (ML) models, and drawing conclusions to improve ML models that are already in production.

TikTok company data scientist Mark Eltsefon said that data scientists are a mixture of product analysts and business analysts, and also have a small amount of machine learning knowledge. Their main goal is to understand the key metrics that have a significant impact on the business, collect data to analyze possible bottlenecks, visualize different user groups and metrics, and come up with various solutions on how to increase these metrics. For example, when developing a new feature for TikTok users, without a data scientist there is no way to understand whether the feature will benefit or harm users.

Machine Learning (ML) Engineer

Data scientists can build ML models, but implementing them requires ML engineers.

Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems, said, "This type of role is tasked with packaging ML models into containers and deploying them (usually as microservices) into production environments. They tend to Professional back-end programming and server configuration skills are required, as well as expertise in containers and continuous integration and delivery deployment. In addition, ML engineers are also involved in model validation, A/B testing and production monitoring."

In Mature In the ML environment, ML engineers also need experimentation service tools that can help ML engineers find the best performing models in production with minimal experimentation.

Data Engineer

Data engineers are responsible for building and maintaining the systems that make up the organization's data infrastructure. Erik Gfesser, director and chief architect at Deloitte, said data engineers are critical to AI initiatives. They build data pipelines to collect and assemble data for downstream use. In a DevOps environment, they build pipelines to implement the infrastructure to run these data pipelines. .

He added that data engineers are the foundation of both ML and non-ML initiatives. For example, when implementing a data pipeline in one of the public clouds, data engineers need to first write scripts to launch the necessary cloud services that provide the computation needed to process the ingested data.

Matt Mead, chief technology officer of information technology services company SPR, said if you are building a team for the first time, you should understand that data science is an iterative process that requires large amounts of data. Assuming you have enough data, about 80% of the work will be related to data engineering tasks and about 20% will be actual work related to data science. Because of this, only a small percentage of your AI team will be working in data science. Other members of the team will be responsible for identifying the problem being solved, helping to interpret the data, organize the data, integrate the output into another production system, or present the data in a presentation-ready manner.

Data Steward

The Data Steward oversees the management of enterprise data and ensures its quality and accessibility. This important role ensures data consistency across enterprise applications while ensuring the enterprise meets ever-changing data laws.

Data stewards ensure that data scientists get the right data and that everything is repeatable and clearly labeled in the data catalog, said Ken Seier, practice lead for data and artificial intelligence at technology company Insight.

The individual in this role will need a combination of data science and communication skills to collaborate across teams and work with data scientists and engineers to ensure data is accessible to stakeholders and business users.

In addition, data stewards are responsible for enforcing the organization's policies around data use and security, ensuring that only those who should have access to secure data get that access.

Domain expert

A domain expert has in-depth knowledge of a specific industry or subject area. This role is an authority in their field, can judge the quality of available data, and can communicate with the intended business users of the AI ​​project to ensure it has real-world value.

Max Babych, CEO of software development company SpdLoad, said these domain experts are essential because technical experts developing AI systems rarely have expertise in the actual domain in which the system is being built. Domain experts can provide critical insights to enable AI systems to perform at their best.

For example, Babych’s company developed a computer vision system to replace lidar (LIDAR) for identifying moving objects on autopilot. They started the project without domain experts, and although studies proved the system worked, what his company didn't know was that car brands preferred LIDAR over computer vision.

Babych said, "The key advice I want to share in this case is to think about the business model, and then engage domain experts to understand whether this is applicable to your industry, and then discuss in detail the implementation of this feature." Technical issues."

In addition, Ashish Tulsankar, head of artificial intelligence at education technology platform iSchoolConnect, said that domain experts can also become important liaisons between customers and artificial intelligence teams. He can communicate with customers to understand their needs and provide next steps for the AI ​​team, while domain experts can also track whether AI is being implemented in an ethical manner.

Artificial Intelligence Designer

Artificial Intelligence designers work with developers to ensure they understand the needs of human users. This role envisions how users will interact with AI and creates prototypes to demonstrate use cases for new AI capabilities.

AI designers also ensure that trust is established between human users and AI systems, and that the AI ​​can learn and improve from user feedback.

Shervin Khodabendeh, co-leader of the AI ​​practice at consulting firm BCG, said, "One of the difficulties companies encounter when scaling AI initiatives is that users don't understand the solution, don't identify with it, or can't interact with it. Those who start from artificial intelligence The secret of companies that gain value from intelligence is actually the correct implementation of human-computer interaction."

BCG's thinking model follows the "10-20-70" principle, that is, 10% of the value is algorithms , 20% is technology and data platforms, and 70% of the value comes from business integration or linking it to company strategy in business processes. Human-computer interaction is absolutely key and is an important part of 70% of the challenges. Artificial Intelligence Designer will help you achieve this goal.

Product Manager

Product managers identify customer needs and lead the development and marketing of products while ensuring the AI ​​team makes beneficial strategic decisions.

Dorota Owczarek, product manager of artificial intelligence development company Nexocode, said, "In the artificial intelligence team, the product manager is responsible for understanding how to use artificial intelligence to solve customer problems and then turning it into a product strategy."

Owczarek was recently involved in a project to develop an AI-based product for the pharmaceutical industry that would support human review of research papers and documents using natural language processing. The project requires close collaboration with data scientists, machine learning engineers, and data engineers to develop the models and algorithms needed to power the product.

As a product manager, Owczarek is primarily responsible for implementing product roadmaps, estimating and controlling budgets, and handling collaboration between product technology, user experience, and business aspects. She said, “Since the project was initiated by business stakeholders, it is particularly important to have a product manager who can ensure that the needs of the stakeholders are met while also focusing on the overall goals of the project. Moreover, the artificial intelligence product manager must also Possess technical skills and business acumen. They should be able to work closely with different teams and stakeholders. In most cases, the success of an AI project will depend on collaboration between business, data science, machine learning engineering, and design teams .”

Owczarek added that artificial intelligence product managers are also responsible for developing internal processes and guidelines to ensure that the company’s products comply with industry best practices.

Artificial Intelligence Strategist

An AI strategist needs to understand how the business operates at the corporate level and coordinate with the executive team and external stakeholders to ensure that the company has the right infrastructure and Talent to help artificial intelligence programs succeed.

Dan Diasio, global AI leader at EY Consulting, said that to be successful, artificial intelligence strategists must have a deep understanding of their business domain and the basics of machine learning. At the same time, they must also know how to use AI to solve business problems.

If you want to change the way companies make decisions, you need people with significant influence and vision to drive the process. Artificial intelligence strategists are people who can help companies think about transformation. Additionally, they can help businesses gain access to the data they need to effectively drive AI.

Diasio said, “Today, the data that enterprises have within their systems or data warehouses actually represents only a small part of what they use to differentiate themselves when building AI capabilities. Part of the strategist’s role is Look to the future and see how you can capture and leverage more data without touching on privacy issues.” The person responsible for communicating the potential business value of AI to stakeholders and customers.

iSchoolConnect’s Tulsankar said decision-makers are people who understand the business, opportunities and risks. The chief AI officer should understand the use cases that AI can solve, where the most important benefits lie, and have the ability to articulate these opportunities to stakeholders. Additionally, they should discuss how to implement these opportunities iteratively. If there are multiple customers or multiple products that require the application of AI, the chief AI officer can split the “customer-agnostic” and “customer-specific” parts of the implementation.

Executive Sponsor

The Executive Sponsor should be a C-level manager who plays an important role in ensuring positive outcomes for AI projects and is responsible for obtaining funding for the company's AI initiatives .

EY Consulting’s Diasio said executive leaders play an important role in helping drive AI projects to success. Know that the biggest opportunities for companies are often where they break out of specific functions. For example, a consumer goods manufacturer has a team responsible for research and development, a team responsible for supply chain, a sales team, and a marketing team. Applying artificial intelligence can help transform all four of these functions to realize the business’s biggest and best opportunities. Only a CEO or C-suite with strong leadership can help bring about these changes.

Unfortunately, senior management at many companies have a very limited understanding of the potential of artificial intelligence, often viewing it as a “black box.” They're used to throwing it to data scientists, but don't really understand the new methods required to use AI.

Adopting AI will be a huge cultural change for many companies that don’t understand how effective AI teams work, how their roles work, and how they are empowered. Moreover, this is a very difficult thing for 99% of traditional enterprises that adopt AI.

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