AI Team – AI is quickly becoming an important aspect of the IT strategy, which was once a niche research specialty for many businesses. The growth of machine learning tools and data science, as well as the quick implementation of the machine learning platforms, in the cloud, has been fueling the businesses to explore new ways to gain high business value, from the accumulated data.
But, not every organization can work like tech giants, Google and Facebook and can hire top AI talents. And it is not just data science PhDs that companies are looking for. To build a fully-fledged highly productive AI team, businesses are looking for professionals who possess subject matter expertise, the ability to translate learning algorithm into actual business value, and software engineering skills.
The infrastructure and the tools that top organizations built may not hold relevance for SMBs. They made those tools to solve issues that big organizations face. You can end up spending a lot of capital and time on those tools, but it might turn out to be a huge waste. So, it is better to know the difference and then deploy the right algorithms for the tasks.
So, instead of hiring high-level PHDs to create new models organizations are looking for developing blended teams to get the right data, and opt the right models to get the right decisions.
Here’s how organizations are building AI teams to solve business issues. And how advances in the data science are changing the requirements of the baseline skills that are needed for growth and success.
When building an AI department, it is very vital to know that successful AI requires multiple roles with different skillsets.
AI Team – The Data Specialist: Big Data Analysis
Artificial Intelligence has the ability to identify patterns through large sets of data, and also enhance learning patterns. Artificial Intelligence is primarily based on seed data. AI need to process a sample data in order to work on the real data. This implies that an AI team requires big data analyst who specializes in handling a large amount of data sets.
Managing data is the core foundation of machine learning. Companies built all the learning upon the foundation of data. AI will not have the required information to make decisions.
UI Developer: User Experience
User Experience is Artificial intelligence’s engine. The present programs are moving heading towards artificial intelligence software that can anticipate the user’s needs. Rather than learning and exploring a new toll every time when there is an issue, the user just needs to provide the information and the software itself will do everything on its own. You can easily see this in personal assistants – Siri, Alexa, and Cortana.
An expert UI developer can concentrate on the goals of the user. Companies can easily achieve this through user stories and comprehensive user testing.
The Core Developer: Programmer
Developers are the ones who will integrate everything all together in terms of data and user experience. Even though the data scientists can direct the developers on how to analyze data and which algorithm will help create the structure of the software.
Though, the UI developer will actually design the interface, it is the programmer who is going to implement it. The developer needs to be an all-rounder in this game as he is the one who should have the knowledge of both UI and data science. And most importantly the skills to bring all these elements alive. Therefore, the programmers who already have the skills in AI are always preferred for these applications.
Fundamental roles of a successful AI Team
A balanced team requires three key people:
- Data Engineer – Will gather all the organization’s information and turn it into data that AI can process.
- Data Scientist – An expert who will test out variety of algorithms to see which one suits the best, and then implement them to get worthwhile predictions.
- Programmer –The one who can incorporate all these actions into an application.
The skillsets that companies require for AI projects include not just data science skills. But also requires project management, user interface design, software engineering, and project marketing. A highly-skilled cross-functional team is required to deliver this kind of technology.
Also, many organizations can fill the core AI roles through training and education of existing employees and hiring of others. Companies can also accomplish employee education through a combination of in-person and online professional courses in addition to hands-on-experience. The hiring of experienced experts to work with employees on delivering the first use cases is highly recommended.
Building an in-house AI team is expensive but considering the current scenarios, this investment can help organizations in the long-run market space. Please contact Decondia if you are interested to implement AI in your business.
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