Recruiters
June 22, 2020

How the Startup Community is Adopting AI: A Closer Look

AI and Machine Learning are being utilized and helping startups growing by creating vast efficiencies and making the lives of employees a little bit easier. Here are some developments in the startup space as of late.
Source: Unsplash

The use of AI and Machine Learning now goes way past the cybersecurity realm and is helping firms, small businesses, and startups across the globe realize their mission and organizational goals. ML in particular is helping small businesses in recruiting, talent management, monitoring business performance, and has actually risen to the task of helping hospitals and healthcare workers during the Covid-19 pandemic. Let’s take a look at some of these companies and how ML is helping them accomplish their missions:

Anodot is the first on the list. This organization is an industry leader in Business Monitoring, and uses an AI-driven approach that empowers businesses to safeguard their revenues and costs, digital partners, and audience journey, experience and engagement. By leveraging AI to constantly monitor and correlate business performance, Anodot identifies revenue-critical issues, providing real-time alerts and forecasts.

Breaking this down, two sectors that Anodot works in to provide business monitoring via AI solutions are the transportation industry and FinTech (financial technology). Simply put, Anodot helps transportation companies know exactly how many drivers they will need on a given day of the week, and helps to optimize revenue by not wasting drivers. Their Machine Learning algorithm in this sense has inputs such as weather forecasts, holidays, and other major events that helps spit out important logistical information.

The next is Eightfold AI, a startup in the talent management industry whose:

“deep learning technology uses billions of data points from more than 100 million talent profiles, providing highly accurate recommendations to recruiters, hiring managers, and candidates. Finding insights of people’s capabilities and career paths; not just what they have done in the past.”

While perusing the website, they don’t exactly say which exact data points they are using, which is some cause for concern, but they do state they are closing the talent gap and claim a 25 percent reduction in regrettable attrition. This basically means they uncover at risk employees early in the recruitment phase using AI.

Covid-19 and AI

While the above organizations are making use of their speed and efficiency through the use of AI and ML to boost program performance, they don’t really have a direct link with Covid-19 efforts. But Olive, a startup that was founded in 2017 and since been covered in Forbes magazine because of their funding award of $51 million as of March 31st, has utilized AI in a different manner.

Olive’s tagline is that they “automate healthcare’s most robotic processes, so your employees don’t have to.” They state on their website that the healthcare industry is facing challenges like workflow inefficiencies, leaked revenue, and burned out employees in a time when healthcare margins are already too thin. One major task that Olive completes with their ML is claim checks.

Efficiencies at the Task Level

Perhaps the most interesting part about all of this is how ML is being used to complete tedious and time-consuming tasks in general so that humans don’t have to. When this happens, it frees up more time for employees, regardless of the sector they work in, to be more creative and to potentially have more job satisfaction as they are able to delegate their working hours towards processes that are more meaningful, respective to their industry.

There are some things to be worried about, however, with regard to ML at the startup level, and this includes an institutional drift whereby ML processes are not updated when workloads become too heavy or when a big project neglects how real-time or up-to-date an ML system. Because ML is data driven and not hypothesis driven, it means that organizations and startups will need to understand, at the managerial level, when and how to tweak their AI systems to fit the changing supply and demand of their industry.

TagsStartupAIMachine Learning
Michael Robbins
Writer
Michael is a writer that helps organizations align their mission and values to a wide audience.

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RecruitersJune 22, 2020
How the Startup Community is Adopting AI: A Closer Look
AI and Machine Learning are being utilized and helping startups growing by creating vast efficiencies and making the lives of employees a little bit easier. Here are some developments in the startup space as of late.

The use of AI and Machine Learning now goes way past the cybersecurity realm and is helping firms, small businesses, and startups across the globe realize their mission and organizational goals. ML in particular is helping small businesses in recruiting, talent management, monitoring business performance, and has actually risen to the task of helping hospitals and healthcare workers during the Covid-19 pandemic. Let’s take a look at some of these companies and how ML is helping them accomplish their missions:

Anodot is the first on the list. This organization is an industry leader in Business Monitoring, and uses an AI-driven approach that empowers businesses to safeguard their revenues and costs, digital partners, and audience journey, experience and engagement. By leveraging AI to constantly monitor and correlate business performance, Anodot identifies revenue-critical issues, providing real-time alerts and forecasts.

Breaking this down, two sectors that Anodot works in to provide business monitoring via AI solutions are the transportation industry and FinTech (financial technology). Simply put, Anodot helps transportation companies know exactly how many drivers they will need on a given day of the week, and helps to optimize revenue by not wasting drivers. Their Machine Learning algorithm in this sense has inputs such as weather forecasts, holidays, and other major events that helps spit out important logistical information.

The next is Eightfold AI, a startup in the talent management industry whose:

“deep learning technology uses billions of data points from more than 100 million talent profiles, providing highly accurate recommendations to recruiters, hiring managers, and candidates. Finding insights of people’s capabilities and career paths; not just what they have done in the past.”

While perusing the website, they don’t exactly say which exact data points they are using, which is some cause for concern, but they do state they are closing the talent gap and claim a 25 percent reduction in regrettable attrition. This basically means they uncover at risk employees early in the recruitment phase using AI.

Covid-19 and AI

While the above organizations are making use of their speed and efficiency through the use of AI and ML to boost program performance, they don’t really have a direct link with Covid-19 efforts. But Olive, a startup that was founded in 2017 and since been covered in Forbes magazine because of their funding award of $51 million as of March 31st, has utilized AI in a different manner.

Olive’s tagline is that they “automate healthcare’s most robotic processes, so your employees don’t have to.” They state on their website that the healthcare industry is facing challenges like workflow inefficiencies, leaked revenue, and burned out employees in a time when healthcare margins are already too thin. One major task that Olive completes with their ML is claim checks.

Efficiencies at the Task Level

Perhaps the most interesting part about all of this is how ML is being used to complete tedious and time-consuming tasks in general so that humans don’t have to. When this happens, it frees up more time for employees, regardless of the sector they work in, to be more creative and to potentially have more job satisfaction as they are able to delegate their working hours towards processes that are more meaningful, respective to their industry.

There are some things to be worried about, however, with regard to ML at the startup level, and this includes an institutional drift whereby ML processes are not updated when workloads become too heavy or when a big project neglects how real-time or up-to-date an ML system. Because ML is data driven and not hypothesis driven, it means that organizations and startups will need to understand, at the managerial level, when and how to tweak their AI systems to fit the changing supply and demand of their industry.

Startup
AI
Machine Learning
About the author
Michael Robbins -Writer
Michael is a writer that helps organizations align their mission and values to a wide audience.

Related Articles