While it may seem like artificial intelligence (AI) has hit the big time, a lot of work needs to be done before its potential really come to life.
copyright by dataconomy.com
While it may seem like artificial intelligence (AI) has hit the big time, a lot of work needs to be done before its potential really come to life. In our modern take on the 20 th -century space race, AI developers are hard at work on the next big breakthrough that will solve a problem and establish their expertise in the market. It takes a lot of hard work for innovators to deliver on their vision for AI, and it’s the data that serves as the lifeblood for advancement.
One of the biggest challenges AI developers face today is how to process all the data that feeds into machine learning systems, a process that requires a reliable workforce with relevant domain expertise and high standards for quality. To address these obstacles and get ahead, many innovators are taking a page from the enterprise playbook – where alternative workforce models can provide a competitive edge in a crowded market.
Alternative Workforce Options
Deloitte’s 2018 Global Human Capital Trends study found only 42 percent of organizations surveyed said their workforce is made up of traditional salaried employees – and employers expect their dependence on contract, freelance and gig workers to dramatically increase over the next few years. Accelerating this trend is the pressure business leaders face to improve their workforce ecosystem as alternative workforce options bring the possibility for companies to advance services, move faster and leverage new skills.
While AI developers might be tempted to tap into new workforce solutions, identifying the right approach for their unique needs demands careful consideration. Here’s an overview of common workforce options and considerations for companies to select the right strategy for cleaning and structuring the messy, raw data that holds the potential to add rocket fuel to your AI efforts:
In-house employees – The first line of defense for most companies, internal teams can typically manage data needs with reasonably good quality. However, these processes often grow more difficult and costlier to manage as things progress – calling for a change of plans when it’s time to scale. That’s when companies are likely to turn to alternative workforce options to help structure data for AI development.
Contractors and freelancers – This is a common alternative to in-house teams, but business leaders will want to factor in extra time it will take to source and manage their freelance team. One-third of Deloitte’s survey respondents said their human resources departments are not involved in sourcing (39 percent) or hiring (35 percent) decisions for contract employees, which “suggests that these workers are not subject to the cultural, skills, and other forms of assessments used for full-time employees.” That can be a problem when it comes to ensuring quality work, so companies should allocate additional time for sourcing, training and management.[…]
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read more – copyright by dataconomy.com
While it may seem like artificial intelligence (AI) has hit the big time, a lot of work needs to be done before its potential really come to life.
copyright by dataconomy.com
One of the biggest challenges AI developers face today is how to process all the data that feeds into machine learning systems, a process that requires a reliable workforce with relevant domain expertise and high standards for quality. To address these obstacles and get ahead, many innovators are taking a page from the enterprise playbook – where alternative workforce models can provide a competitive edge in a crowded market.
Alternative Workforce Options
Deloitte’s 2018 Global Human Capital Trends study found only 42 percent of organizations surveyed said their workforce is made up of traditional salaried employees – and employers expect their dependence on contract, freelance and gig workers to dramatically increase over the next few years. Accelerating this trend is the pressure business leaders face to improve their workforce ecosystem as alternative workforce options bring the possibility for companies to advance services, move faster and leverage new skills.
While AI developers might be tempted to tap into new workforce solutions, identifying the right approach for their unique needs demands careful consideration. Here’s an overview of common workforce options and considerations for companies to select the right strategy for cleaning and structuring the messy, raw data that holds the potential to add rocket fuel to your AI efforts:
In-house employees – The first line of defense for most companies, internal teams can typically manage data needs with reasonably good quality. However, these processes often grow more difficult and costlier to manage as things progress – calling for a change of plans when it’s time to scale. That’s when companies are likely to turn to alternative workforce options to help structure data for AI development.
Contractors and freelancers – This is a common alternative to in-house teams, but business leaders will want to factor in extra time it will take to source and manage their freelance team. One-third of Deloitte’s survey respondents said their human resources departments are not involved in sourcing (39 percent) or hiring (35 percent) decisions for contract employees, which “suggests that these workers are not subject to the cultural, skills, and other forms of assessments used for full-time employees.” That can be a problem when it comes to ensuring quality work, so companies should allocate additional time for sourcing, training and management.[…]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
read more – copyright by dataconomy.com
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