More and more businesses in every industry are adopting artificial intelligence (AI) to transform business processes, but the success of their AI initiatives depends on more than just data and technology. It also depends on whether the right talent comes on board.
#An effective enterprise AI team is a diverse group that includes more than just data scientists and engineers. Bradley Shimmin, principal analyst for AI platforms, analytics and data management at consulting firm Omdia, said successful AI teams should also include a lot of people who understand the business and are trying to solve problems.
“The technology and tools available to us increasingly require us to provide support and authorization for those field professionals, business users or analytics professionals to directly use and be responsible for AI within the company. ”
Carlos Anchia, co-founder and CEO of AI startup Plainsight, agrees. He believes that the success of AI depends largely on building a comprehensive team of people with various advanced skills. team, but it’s challenging.
“It may seem easy to pinpoint what makes a high-performing AI team, but when you look at the detailed responsibilities of each person on a successful AI team, you’ll quickly come to the conclusion that building such It's very difficult to be a team," he said.
To help you build an ideal AI team, let’s take a look at these 10 key roles that the team should have.
The data scientist can be said to be the core of any AI team. They are responsible for processing and analyzing data, building machine learning (ML) models, and drawing conclusions to improve ML models that are already in production.
Mark Eltsefon, a data scientist at TikTok company, said that data scientists are a mixture of product analysts and business analysts and have certain machine learning knowledge.
“Their main goals are to understand which key metrics have a significant impact on the business, collect data to analyze possible bottlenecks, implement visualizations of different user groups and metrics, and make recommendations on how to improve these metrics. and develop various solutions," he said, adding that when developing new features for TikTok users, without data science, it is impossible to understand whether the feature is benefiting or alienating users.
“You don’t know how long you should spend testing features and what aspects should be tested. For all these problems, you have to use artificial intelligence methods.”
Data scientists can build machine learning models, but it is machine learning engineers who implement those models.
Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems, said: "Machine learning engineers are tasked with packaging machine learning models into containers and deploying them into production environments—usually as microservices. The role requires specialized back-end programming and server configuration skills, as well as knowledge of containers, continuous integration and delivery deployment, Rao said. "Machine learning engineers also need to participate in model verification, A/B testing and production monitoring."
He said that in a mature machine learning environment, machine learning engineers also need to test service tools, and service tools only require a small amount of Experiments will help you find the model that performs best in a production environment.
Data Engineer
He said: "Data engineers build data pipelines to collect and aggregate data for downstream consumption, and in a DevOps environment, they build pipelines to implement the infrastructure that runs these data pipelines."
Data engineers are the foundation for both machine learning and non-machine learning projects, he said. "For example, when implementing a data pipeline in a public cloud, data engineers need to first write scripts to launch the necessary cloud services, which then provide the calculations required to process the data."
IT SERVICES Matt Mead, CTO of SPR, said that if you are building a team for the first time, you need to know that data science is an iterative process that requires large amounts of data. Assuming you have enough data, "about 80 percent of the work will be related to data engineering, and about 20 percent will be actual work related to data science."
Because of this, he said, there are only Very few people work in data science. "Other members of the team need to identify the problem being solved, help interpret the data, help organize the data, output integration into another production system, or present the data in a presentation-ready manner."
Data Manager
Ken Seier, national practice leader for data and AI at technology company Insight, said data stewards ensure data scientists are getting accurate data and that everything is repeatable and clearly labeled in the data catalog.
The person holding this position will understand data science and have the communication skills to collaborate across teams and work with data scientists and engineers to ensure data is accessible to stakeholders and business users.
Data stewards also enforce the organization's policies on data use and security. "Data stewards need to ensure that only those who should have access to secure data do so," Seier said.
Domain experts have in-depth knowledge of a specific industry or subject area Understanding is an authority in a certain field, can judge the quality of available data, and can communicate with the expected business users of the AI project to ensure that the project has real value.
Max Babych, CEO of software development company SpdLoad, said domain experts are essential because technical experts developing AI systems rarely have expertise in the domain in which the system is targeted. “Domain experts can provide critical insights that allow AI systems to perform at their best.”
When SpdLoad developed a computer vision system to identify moving objects on autopilot to replace LIDAR technology, They started the project without domain experts. Although studies proved the system was effective, what SpdLoad didn't know was that car brands preferred lidar over computer vision because of the technology's proven reliability, and they didn't have the opportunity to purchase computer vision-based products. .
“One of the key pieces of advice I’d like to share is that you think about the business model and then engage domain experts to judge whether this is a viable way to make money in the industry before moving on to more technical issues ."
Ashish Tulsankar, head of AI at education technology platform iSchoolConnect, said domain experts can be important liaisons between clients and AI teams.
“This person can communicate with customers, understand their needs, and provide a series of directions for the AI team. And domain experts can also oversee whether the company implements AI in an ethical manner.”
The AI designer is responsible for working with developers to ensure they understand the true needs of human users. This role envisions how users will interact with the AI and creates prototypes to demonstrate the new AI. Function usage scenarios.
AI designers also ensure that trust is established between human users and AI systems, ensuring that the AI can learn and improve from user feedback.
Shervin Khodabandeh, co-leader of BCG’s North American AI practice, said: “One of the difficulties organizations encounter in scaling AI is that users don’t understand the solution, don’t buy in, or can’t interact with it. Those The secret sauce for organizations that are getting value from AI is actually that they can do human-computer interaction in the right way.”
The Boston Consulting Group follows the 10-20-70 rule: 10% of the value is Algorithms, 20% are technology and data platforms, and 70% of the value comes from business integration, or tying it into corporate strategy in business processes.
"Human-computer interaction is absolutely critical and an important part of 70% of the challenges," he said, adding that AI designers will help you achieve your goals.
The product manager is responsible for discovering customer needs, responsible for product development and product marketing, while ensuring that the AI team makes favorable strategic decisions.
"In an AI team, the product manager's job is to understand how to use AI to solve customer problems and then translate that into a product strategy," said Dorota Owczarek, product manager at AI development company Nexocode.
Owczarek recently worked on a project to develop an AI product for the pharmaceutical industry that would support human review of research papers and documents using natural language.
“This project required close collaboration with data scientists, machine learning engineers and data engineers to develop the models and algorithms needed to power the product,” she said.
As a product manager, Owczarek is responsible for implementing the product roadmap, estimating and controlling budgets, and handling collaboration between the technical, user experience, and business aspects of the product.
She said: “In this particular case, since the project was initiated by business stakeholders, it was especially important to have a product manager who could ensure their needs were met while focusing on the overall goals of the project. ," she said. AI product managers should have both technical skills and business acumen.
“Product managers 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. ”
AI product managers also need to understand the ethical considerations related to AI, Owczarek said. “They are responsible for developing internal processes and guidelines to ensure that the company’s products comply with industry best practices.”
An AI strategist needs to understand how things work at the enterprise level and coordinate with the executive team and external stakeholders to ensure the company has the right infrastructure and talent to Achieve AI program success.
To be successful, AI strategists must have a deep understanding of their business domain and the fundamentals of machine learning; they must also know how to use AI to solve business problems, said Dan Diasio, global AI leader at EY Consulting.
"A few years ago, technology was the harder part, but now, technology is reimagining the way we connect different businesses to make full use of the AI capabilities or AI assets we build." He added that AI Strategists can help companies think about how to use AI with a transformational mindset.
“To change the way (companies make) decisions, it will take people with significant influence and vision to drive the process.”
AI strategists can also help enterprise organizations obtain The data you need to drive AI effectively.
“Today, the data that enterprises have within their systems or within their data warehouses actually represents only a tiny fraction of the data they need to build AI capabilities. Part of the role of an AI strategist is to Look to the future and see how you can acquire and leverage more data without violating privacy rules."
The Chief AI Officer is the primary decision-maker for all AI initiatives, Responsible for communicating the potential business value of AI to stakeholders and customers.
“Decision makers are those who understand the business, the opportunities and the risks,” said iSchoolConnect’s Tulsankar.
He said that chief AI officers should know what uses human AI can have and which ones can bring the most important economic benefits, and they should be able to articulate these opportunities to stakeholders.
"They should also discuss how these opportunities will be realized iteratively. If there are multiple customers or multiple products that require the application of AI, the chief AI officer should be able to separate the customer-agnostic and customer-specific parts of the implementation."
The executive sponsor needs to be a C-level executive who plays an active role in ensuring that AI projects achieve results and is responsible for obtaining funding for the company's AI initiatives.
EY Consulting’s Diasio said executives play an important role in helping drive AI projects to success. "The biggest opportunities for companies often come from areas 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 team responsible for sales and a marketing team, "The biggest and best opportunity to apply AI to transform the business is related to all four functions, so to achieve these changes, strong leadership from the CEO or top management is needed."
Shervin Khodabandeh of BCG said that unfortunately, many company executives do not fully understand the potential of AI.
“Their understanding of AI is very limited, and they often regard AI as a black box and throw it directly to data scientists, but they do not really understand what new methods are needed to use AI.”
He said that if a company does not understand how the AI team operates, how the roles operate, and how to obtain authorization, adopting AI will be a huge change in corporate culture. "99% of traditional companies that adopt AI think this is a difficult thing."
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