本文作者Steven Jiang CEO and Co-Founder of Hiretual
人工智能简化招聘流程的能力 - 特别是采购和过滤候选人的初始阶段 - 将使人力资源团队能够花时间处理更有价值的任务，例如了解简历或在线档案背后的人。
Why 2019 Will Be The Year Talent Acquisition Moves From Data-Driven To Intelligence-Driven
Historically, the human resources industry has been fraught with time-intensive tasks, identifying the top candidates and coordinating their interview and screening processes. One study found that the time to hire in 2017 was, on average, 23.8 days, an increase of 13 days since 2010 as the marketplace has grown ever more crowded.
Artificial intelligence-powered technology opens up nearly limitless possibilities, and 2019 is poised to be a year of innovation and high adoption across thousands of industries. HR, recruiting and talent acquisition are no exception and will benefit greatly as the adoption of artificial intelligence (AI) becomes more ubiquitous.
The coming year will see vast amounts of HR teams relying heavily on AI and machine learning (ML) to take care of much of the behind-the-scenes work that goes into talent acquisition. Thanks to this shift, recruiters will have the ability and time to engage with and build strong relationships with their top candidates on a deeper level — instead of studying and digging through a huge quantity of resumes and profiles for a significant part of the day.
Talent Acquisition Powered By AI And Machine Learning
I believe one of the most exciting shifts we will witness in 2019 is AI leading the change in recruiting from data-driven to intelligence-driven. In fact, 42% of executives in one survey believe AI will be of critical importance within the next two years. Thanks to the powerful forces of AI and ML and their incredible ability to gather and sort information intuitively and shorten the time to insights, HR specialists will spend less time sifting through data and more time acting on it.
For the moment, this will require fundamental changes and upgrades from a company’s existing talent acquisition (TA) system and tech stacks by adopting and deploying an AI engine into their existing systems. The whole workflow and process will be reshaped as HR efforts become dramatically more proactive as they are enhanced by AI. The new year will bring upgrades to infrastructure, software and operations as TA teams are retrained and processes redefined.
AI technology will also evolve both the recruiter's and candidate's relationships with the sourcing process. Organizations will enlist the same AI technologies to handle both active-job-seeker and passive-job-seeker pipelines. Because of the high levels of adoption anticipated in 2019, I also foresee companies hiring significantly more technical architects to help design and build scalable HR stacks. AI will take center stage and people will become much more knowledgeable about it in the coming year, which they will be forced to do in order to ensure they find and achieve ongoing success.
Heightened Candidate Engagement
In the next 12 months, more HR and recruiting teams will wave goodbye to many of the tedious tasks that have historically occupied a significant amount of their valuable day and eaten up too much of their time. In their place will be tools and platforms to accelerate the talent acquisition process. It will be a year of widespread adoption of augmented sourcing and writing and engagement technologies.
As sourcing and finding proactive and passive candidates becomes more efficient, we will see a shift in attention from sourcing to the next phase of the process, encouraging potential employees to talk to recruiters, also known as "heightened candidate engagement."
The ability of AI to streamline the hiring process — especially the beginning stages of sourcing and filtering candidates — will allow HR teams to spend time on more valuable tasks like getting to know the person behind the resume or online profile.
Improving The Candidate Experience
Thanks to unprecedented technological insights and abilities, recruiting methods are already highly intuitive and will continue to become even more so. As more and more processes start to take care of themselves, the candidate experience will rise to the next level. Candidates will have better, more comprehensive experiences as they apply for and express interest in positions. Gone are the days (and weeks) of waiting for recruiters to personally contact each candidate with the necessary information to simply move forward with the next step in the application process.
Through advanced matching and sourcing technology, candidates will receive more relevant and nuanced job suggestions and opportunities, while recruiters will benefit from being matched with the best candidates for the job at hand. Once candidates find jobs in which they’re interested, technology will play a part in the next stages of the process as well.
AI-powered chatbots and scheduling bots will enable candidates to ask questions and have them answered in real time. Chatbots will continue to be integrated into a company’s website, freeing HR team members to handle more pressing and complicated requests and have time to play an even more thoughtful role in the hiring process. Chatbots can lead applicants through the application process in its entirety, guiding the candidate from page to page to ensure accuracy and completeness. We will also see AI-matching technology flourish.
The new year will bring improved practice, unparalleled candidate and recruiter experiences and some of the highest levels of technology adoption the HR industry has seen in a decade. The impact of AI and ML will be felt by job candidates and recruiters alike. The candidate experience will continue to modernize, allowing companies to treat their applicants like customers, while giving recruiters more time and energy to devote to the human side of recruiting: engaging with talented candidates. Companies that embrace AI in 2019 will take their talent acquisition team to the next level.
然而，同一份题为"The future of HR 2019: In the Know or in the No"的报告中，88％的高管称他们的人工智能投资是值得的。
管理咨询公司The Hackett Group Inc.表示，一些收入超过10亿美元且运营良好的大型企业正在使用自动化来减少人力资源人员。
招聘聊天机器人仍然处于起步阶段，但他们在处理更复杂的问题方面变得越来越好，例如“家庭政策中公司的工作是什么？” Capterra（一家面向买家的商业软件咨询公司）的高级人力资源和人才管理分析师Brian Westfall表示。
加拿大人力资源咨询公司Morneau Shepell 在其报告“2019年人力资源趋势”中确定，将员工敬业度提升为2019年人力资源领导者的首要任务。67％的受访者在对356个组织的调查中引用了它。
原文链接：HR automation tops 2019's six big trends
英文赏析：Will a Chat Bot Be Your Next Learning Coach?
By Margie Meacham
Eighty percent of major companies expect to be using artificial intelligence by 2020, but their training departments are likely to be the last places you’ll find it. We need to fix that.
A recent survey of Millennials revealed that 40 percent of them interact with a chat bot, a program that simulates a human conversation, on a daily basis; another survey indicates that many people prefer chat bots over humans for certain types of customer support transactions.
While other industries are already developing AI, the learning industry seems to be lagging behind. It’s pretty hard to implement something you don’t understand, so let’s start there.
Artificial intelligence, or AI, is a branch of computer science that aims to create intelligent machines, capable of performing problem-solving, pattern recognition, and learning without explicit programming.
AI requires vast amounts of data to create intelligent machines, and Big Data requires intelligent machines to perform the massive calculations necessary to find meaningful patterns and connections. For this reason, you will often find Big Data and AI are employed together and support each other.
“Big Data” refers to data sets that are so voluminous and complex that traditional data processing application software packages are inadequate to deal with them. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.
Big Data analytics examines these massive, varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that drives artificial intelligence.
3 Dimensions of Big Data
There are three dimensions to Big Data: velocity, variety, and volume.
Data is coming at us from all directions, and it is coming faster every day. To benefit from Big Data insights, companies must be able to capture, analyze, and use this massive amount of information as quickly as it is coming in. Human beings alone could never keep up with this firehose of information, so Big Data solutions must include strategies to control and keep up with the speed of incoming data. Bring in the smart machines!
Consider your own experience as a digital consumer. In a single hour, you may read an email on your PC, send a text on your phone, download a podcast, watch a video, and post a tweet. Each requires different strategies for capture and analysis—and these are only a few examples of the diversity of data available online today.
VolumeHere is just a snapshot of the sheer volume of data that came at us every day of 2017:
456,000 tweets on Twitter
50,926 videos viewed on Buzzfeed
3,607,080 Google searches.
The amount of data coming from your learning management system (LMS) and performance management software is puny compared to the onslaught coming from social media; but it is part of the Big Data mosaic, and most of us are simply not taking advantage of the information we have readily available.
Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
In other words, machine learning focuses on the development of computer programs that can access large amounts of data and change their behavior or programming based on that information, without human intervention. Uses for machine learning in talent development include:
Assess and predict job performance.
Predict the competencies that will be needed in 10 years so learners can develop relevant skills today.
Provide personalized conversation about new information, performance coaching, or motivation on a 24-hour basis, without the need for a human coach.
Identify learner competencies and gaps to make better training and education suggestions that are truly personalized to the individual.
Examples of AI in Talent Development
Here are just a few examples of education-focused AIs that are already in use. Many early adopters are in the higher education arena, but the ideas work equally well in corporate training or K-12 education.
Jill Watson, the virtual teaching assistant at the University of Georgia, communicates with students via email.
Virtual tutors can help each learner move at a pace that is right for them.
Penn State is using chat bots to help teachers gain confidence handling difficult conversations, like bullying or hate language in class.
Think grading essays requires the human touch? Think again! At Stanford, an AI grading system achieved an 81 percent accuracy rate when compared to essays graded by humans.
Beware These Beginner Mistakes
Because some AI applications are still in the early days on the hype cycle, I interviewed an AI expert at one of my client organizations to find out what common mistakes she sees in chat bot projects led by early adopters.
Here’s a summary of her list.
Garbage In/Garbage Out (GIGO)
Many projects fail because project managers forget to check data quality, or do not have the right approach to identify and resolve these issues. When we analyze incomplete or “dirty” data sets, our AI ends up making decisions and recommendations based on a poor foundation.
Apples and Oranges
Comparing unrelated data sets or data points will result in inferring relationships or similarities that do not exist.
Overly Narrow Focus
Some projects are designed to consider one data set without considering other data points that might be crucial for the analysis. For example, a project set up to analyze learner pass/fail rates while ignoring the course completion rate may inflate performance results.
Cool but Useless
Some AI projects are quick to deliver but fail to make a significant impact on the learner’s everyday experience. Ensure that you have the right strategy to deliver the most value to your learners, and avoid giving them something cool that doesn’t really help them learn.
My advice is to just get on with it. Make a point of learning something about AI and machine learning every day, always with an eye to how you might be able to use it in your own organization. Here are a few suggestions:
Check out datascience.com for a huge list of data science resources.
Take this course from Google on Udacity—it’s free, and quite well done.
Brainstorm some ideas with colleagues. There are some great ideas here, and even more ideas here.
Build a Bot
There are dozens of platforms that let you create free chat bots for specific messaging apps without any special skills or coding knowledge. Snatchbot, for example, can be used on Facebook Messenger, Slack, WeChat, Skype, and more. It’s easy to use, and the interface is probably already familiar to many of your users. And Botsify has a variety of bot templates to get you started, including a whole list of education bots. Looking for more do-it-yourself tools? Here’s a nice list from business2community. com.
Engage With Colleagues
You might be surprised how many of your colleagues are eager to test the waters with a chat bot or other educational AI application. You won’t find them unless you join the conversation. One place to start is by attending the ATD 2018 International Conference & Exposition (for example, Elliott Masie will talk about some innovations changing workplace learning during the session, Learning Trends, Disrupters, and Hype in 2018) or any of our other conferences designed to educate, engage, and inspire you.
Will You Be Replaced by a Chat Bot?
While there is a vast difference of opinion on how AI is shaping the very near future of work and learning, one thing I know for sure: Those of us who are not part of the disruption will become lost in the dust that the disruptors kick up. I plan on being in front of it
Margie Meacham is an adult learning expert with a master of science in learning technologies and more than 15 years of experience in the field. A self-described “scholar-practitioner,” Margie collaborates with like-minded instructional designers to find practical applications of neuroscience to instructional design.