Quality Over Quantity: It’s Time to Hire Better | Featured Image

Quality Over Quantity: It’s Time to Hire Better

Quality Over Quantity: It’s Time to Hire Better | Main Image

These days, there is rarely a technology that can’t be mimicked, a service that can’t be purchased, or a system that isn’t for rent. Big organizations mostly use essentially the same services from Microsoft Office to ATS databases. With so much homogeny, what separates successful companies from the rest? The people are the secret sauce. Even with proprietary software or patent-protected techniques, no company can truly thrive without one extremely important element: effective and creative teams.

Despite all our technological advancements, it’s humans who truly make the difference at an organization. In our 21s t century reality – where technology is ubiquitous – talent acquisition professionals become one of the most important departments at a company, because they are responsible for the most important competitive asset: new hires.

Unfortunately, we don’t always realize how important our talent acquisition processes are. In fact, many companies remain focused on the wrong metrics, concentrating on hiring quickly, rather than zeroing in on finding the right candidate.

Some organizations are already making the shift. Where most recruiters are encouraged to fill roles as quickly as possible, forward-thinking organizations are focused on quality, tasking their recruiters to fill the roles with the best possible candidate.

What caused this shift? That’s easy – organizations are realizing that emphasizing speed in hiring sacrifices quality. And filling a role quickly with the wrong person is extremely costly to an organization.

For the organizations not yet making the shift and slower to realize they are doing it wrong, it’s not all bad news. The fact is, best practices around making hiring decisions have been understood by academics for years. And they are not that difficult to implement. There are new and exciting talent-acquisition tools that are enabling companies to reform their practices and overhaul processes to create something much better.

With artificial intelligence (AI) capabilities, technology can play an important role from the get go . For example, it can help someone write a better job description. This first step in the hiring process would then invite a diverse pool of candidates with capabilities that match companies’ needs. Cloud and mobile computing solutions facilitate better communication between recruiters and hiring managers. Nudge technology and access to data allows decision-makers to move away from hiring based purely on gut-decisions and shift to data-driven choices.

Research has identified five hiring best practices that span the talent acquisition process – from writing targeted job descriptions that invite the best candidates to blind resume reviews to conducting structured interviews. These best practices make hiring more effective and yield stronger teams, happier employees, and improve the candidate experience, which reflects on the company at every step. The talent acquisition industry has technology that can facilitate all of these strategies and transform hiring systems to be both more effective and more equitable. What we need now is a change of mindset.

As an industry, let’s forget the incomplete idea that talent acquisition is only about filling an open position. It’s about strategically finding creative and effective team members that fit the company culture and will drive the business forward. As new markets emerge, and old sectors are rapidly transformed, it’s the employees, the human element, who contribute to a company’s success and it’s competitive differentiation.

Instead of pressuring talent acquisition professionals to be faster, or to collect more resumes, true improvement will come from creating processes that prioritize hiring best practices and finding the right hire. This change in focus from the fast hire to the right hire will succeed only if it is organization-wide and reinforced at every level, from senior leadership team and executive suite to the hiring manager and recruiters.

The data is there: the hiring process is broken. We have the tools and the strategies to change. It’s time to start changing our priorities and focusing on the metrics that really matter. It’s time to hire better.

About the Author:

Laura Mather, Founder and CEO, Talent Sonar

Laura Mather, CEO and Founder of Talent Sonar, is an expert on hiring, AI, and the future of work. Her innovative technology, Talent Sonar, is the only comprehensive hiring platform to improve hiring at every step from incorporating values into the hiring process to conducting blind resume review and structured interviewing. She was honored as one of Fast Company’s Most Creative People in Business and as one of Fortune’s Most Powerful Women Entrepreneurs. She is a featured speaker at Fortune’s Most Powerful Women Next Generation Summit, HR West, and Ad Week, among others.


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Artificial Intelligence

Trends Shaping Machine Learning in 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world is Artificial Intelligence (AI). The two technological concepts, Machine Learning and Artificial Intelligence, are often used as interchangeable terms. However, it is important to understand that both technologies supplement each other and are essentially different in terms of their core functions.

It is often predicted by technology enthusiasts and social scientists that human beings in the workforce will soon be replaced by self-learning robots. It is yet to be seen whether there lies any truth in these predictions or not but for 2017, the following trends have been prominent in the development of Machine Learning.

Giant companies will develop Machine Learning based Artificial Intelligence systems

In 2016, we saw many prominent developments in the domain of Machine Learning, and numerous artificial intelligence applications found a way to our phone screens and caught our attention. In the previous year, companies just touched the tip of the iceberg and in 2017, we will continue to see more developments in the field of machine learning. Big names such as Amazon, Google, Facebook and IBM are already fighting a development war. Google and Amazon launched successful apps, which include Amazon Echo and Google Home, at the beginning of the year, and we have yet to see what these tech giants have in stores for their customers.

Algorithm Economy will be on the Rise

Businesses greatly value data to take the appropriate actions, whether it is to understand the consumer demand or comprehend a company’s financial standing. However, it is not the data alone they should value because without an appropriate algorithm, that data is worth nothing. Peter Sondergaard, Senior Vice President of Gartner Research, says that, “Data is inherently dumb and the real value lies in the algorithms which deduce meaningful results from a cluster of meaningless data”.

Algorithm Economy has taken center stage for the past couple of years, and the trend is expected to follow as we expect to see further developments in machine learning tools. The use of algorithm economy will distinguish small players from the market dominators in 2017. Small businesses that have just entered the transitional phase of embedding machine learning processes in their business models will be using canned algorithms in tools such as BI, CRM and predictive analysis. On the contrary, large enterprises will use proprietary ML algorithms.

Expect more Interaction between Machine and Humans

Google Home and Amazon Echo received an exceedingly positive response from the audience which made it evident that consumers perceive human-machine interaction positively. Innovative technologies embedded with machine learning processes prove to be helpful under various circumstances; for example, helping people with eyesight issues to navigate. But will they completely replace human-human interaction? Maybe 25 years down the road, but we do not see that happening anytime soon. Machine learning has made it increasingly possible for machines to learn new skills, such as to sort, analyze and comprehend. But nevertheless, there are certain limitations to it. Automated cars have frequently been tested, and even with modified algorithms and advanced technologies, the chance of an error is still present. This example alone is enough to convince that machines will not completely replace humans, at least not anytime soon.

Conclusion

Machine Learning and Artificial Intelligence is a promising field with much potential for growth. We have seen some recent developments in the sector which, not long ago, people believed were not possible. Therefore, we cannot give a definite verdict regarding the industry’s potential for growth. However for now, intelligent machines are only capable of handling the repetitive tasks and can follow a predetermined pattern. It lacks the skill to figure out things which are out of the ordinary, and we still require human intervention for keeping the chaos at bay in such situations.

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read Ronald van Loon future posts then please click ‘Follow‘ and feel free to also connect on LinkedIn and Twitter to learn more about the possibilities of Big Data .

This article was originally posted on SimpliLearn

Source: Trends Shaping Machine Learning in 2017 | Ronald van Loon | Pulse | LinkedIn

HR Your Way

HR Your Way

How the next-generation digital workplace can power a deeply personalized HR customer experience

Business disruption is rampant—new business models, new technologies, a challenging economic environment, and the overall quickening pace of business are all disruptive to “business as usual.” Workforce demographics and trends—retiring boomers, high-expectation millennials, workforce-on-demand models, team-based work—are another disruption. It is incumbent on HR to find ways to “hack” these disruptions for their customers, leveraging the digital workplace to customize the HR customer experience according to each individual’s unique needs in the face of this almost constant change.

To better understand how the next-generation digital workplace can counter disruptions by powering a deeply personalized HR customer experience, let’s flash forward about 10 years to 2027. This is when we could see the first cohort of Gen Z employees—engage in their organization’s open enrollment process for benefits.

Our Gen Z futuristic scenario envisions three hypothetical levels of digital workplace “chatbots” at increasing levels of sophistication:

  • Workflow Adviser—assists the HR customer through the life or work event workflow using natural language, while automatically gathering data from disparate systems and tapping into available training, research, and operational services support resources.
  • Solution Adviser—“understands” desired outcomes and leverages all available internal and external data to design and propose an optimized solution for the HR customer.
  • Human Adviser—“empathizes” with the human emotions and feelings likely involved in the HR customer’s decision process, and provides support—or referral to an actual human—as required.

Future forward to Gen Z

Jamie, an employee and a new mom, along with her husband, Liam, kick off the enrollment workflow in Jamie’s digital workplace and are greeted by the chatbot who will be assisting them through the workflow.

The chatbot explains that, set at the level of Workflow Adviser, it has the capability to listen, understand natural language, and talk back, and is also able to interpret the context of Jamie and Liam’s questions in order to suggest relevant training, research, or operational services assistance as they work through the open enrollment process.

As a bonus, the chatbot explains, it has recently been upgraded to a beta version of the Solution Adviser level. So if Jamie would like to explore this advanced level of digital workplace engagement, the chatbot will be able to understand desired outcomes and leverage Jamie and Liam’s demographic, health, and financial data, as well as cloud-based benefits solution provider data, to effectively personalize a recommended package of benefits.

Jamie authorizes the chatbot to use its Solution Adviser capabilities for her open enrollment process. After a structured conversation driven by the chatbot, she is rewarded with a customized portfolio of company benefits that are customized for her family’s unique health needs and financial resources. After a discussion with the Solution Adviser chatbot to clarify the details, Jamie verbally accepts the recommended portfolio of benefits and completes the open enrollment process.

Toward a true AI model for HR

So, what’s going on behind the scenes in our futuristic scenario, and how far are we from being able to deliver this hyper-personalized experience? Let’s drill a bit deeper into the chatbot’s capabilities at the Solution Adviser level by considering one element of the benefits package—long-term disability insurance—the chatbot recommended.

At the Solution Adviser level, the chatbot was permitted to leverage Liam’s personal health records, (which included information about a mild attack of unexplained vertigo that sent him to the ER six months prior), as well as financial income and liabilities information (indicating the couple was living paycheck-to-paycheck with very little savings). By leveraging this information, along with the context gathered through a structured conversation with Jamie and Liam, the chatbot was able to conclude with a reasonable degree of probability that covering a portion of Liam’s expected future income in the event of an unexpected disability made sense for the couple.

Impressive to be sure. But this ability to use natural language to understand context in order to make reasoned judgments about desired outcomes isn’t even the end of the line. Interestingly, and perhaps just a bit frighteningly, true AI is reserved for what we call the Human Adviser level. Here, the chatbot actually understands the human situation, demonstrates empathy with HR customer feelings, and even engages in humor opportunistically to build a deeper bond of understanding with those it has been designed to serve. Of course, at this level of sophistication, the chatbot would also discern, given the nature of the HR customer’s questions, when a referral to an actual human on the operational services team may be in order.

Hacking the disruption

While the advanced cognitive and empathetic capabilities we are ascribing to our next-generation Solution Adviser and Human Adviser digital workplace chatbots are in the infant stages today, we are making rapid advances at the Workflow Adviser level of sophistication for Deloitte’s own digital workplace solution.

As we increase digital workplace capabilities, however, we may find that the process of benefits enrollment itself has become disrupted by our technology advances, and a complete rethink of how benefits are packaged, priced, and administered will likely not be far behind. After all, disruption tends to breed more disruption—which, by the way, is why achieving sustainable HR is so imperative.

About the Authors:

Michael Gretczko is a principal with Deloitte Consulting LLP and the practice leader for Digital HR & Innovation. He focuses on helping clients fundamentally change how they operate, often working with large, complex, global organizations to guide transformation programs that enable HR organizations to reinvent the way they leverage digital to improve the employee experience and business performance.

Daniel John Roddy  is a specialist leader with Deloitte Consulting LLP and a member of the Digital HR & Innovation team. He focuses on leveraging his decades of global HR transformation experience to develop and promote thought leadership that helps create breakthrough opportunities for our clients. 

Copyright © 2017 Deloitte Development LLC. All rights reserved.


Source: HR your way | Michael Gretczko | Pulse | LinkedIn

5 Machine Learning Startups To Improve Your Recruiting Workflow

5 Machine Learning Startups To Improve Your Recruiting Workflow

Screen Shot 2017-06-14 at 7.20.58 PM
This list was originally published on Product Hunt here. Below is an abbreviated version.

Sam DeBrule co-founder of Journal and voice of the Machine Learnings Newsletter has curated a list of top startups using Machine Learning to automate work-related tasks. I’ve pared this down to my favorites for simplifying recruiting and team building efforts.

1. Textio | Spell checker for gender bias and more

Job descriptions are often vague and unintentionally biased, which affects the quality and diversity of applicants applying to your jobs. By generating insights from your job posts, Textio teaches you how to better message an open job role in a way that is both non-discriminatory and eye-catching to applicants.

2. Slack | Real-time messaging, archiving & search

Slack facilitates quick, real-time communication using ML-powered search, allowing you to chat with your team and candidates without the lag-time between emails. It automates many internal status updates and meetings regarding candidates as they move through the pipeline. Additionally, with hundreds of groups, it’s a great place to source candidates and learn tactics and best practices from other recruiters.

3. Wade & Wendy | AI chatbot for engaging & interviewing candidates

Wade & Wendy has developed an Applicant Experience Chatbot, Wendy. She serves as a first-round interviewer and candidate engagement tool. By chatting with applicants at the top of the funnel, recruiters and hiring teams can spend more time building relationships with candidates and sourcing hard-to-fill positions.

Disclaimer: I work at Wade & Wendy! 😎

4. Grammarly | Clear, effective, mistake-free writing everywhere you type

With nearly 1 in 3 employees searching for new opportunities, many often communicate with recruiters when a few spare minutes arise while at their current job. When candidates have a small window of time, recruiters need to move fast with their communication. Grammarly is a seamless way to side-step embarrassing typos when quickly emailing (or Slack-ing) back and forth.

5. X.aiAn AI personal assistant who schedules meetings for you

Between, texting, calling, emailing and messaging candidates, it’s tough to keep your calendar straight. X.ai uses AI scheduling assistants to automate this process. Cc’ing Amy to emails eliminates the time-consuming task of scheduling phone calls, interviews and coffees with candidates.

Any other tools keeping your recruiting efforts on track? Drop them in the comments section.👇

About the Author:

Bailey Newlan is the Content & Growth Marketer at Wade & Wendy, a New York City-based startup on a mission to make hiring more human. Wade & Wendy is a conversational engagement platform for recruitment automation. To connect, reach out to Bailey via LinkedIn, Twitter or Medium and join the private beta list.


If you want to share this article the reference to Bailey NewlanWade & Wendy and The HR Tech Weekly® is obligatory.

Leveraging the Best of AI for Outstanding Hiring Results

Leveraging the Best of AI for Outstanding Hiring Results

Written by Laura Mather, Founder and CEO at Unitive, Inc. (Talent Sonar).

Laura Mather, Founder and CEO at Unitive, Inc. (Talent Sonar)

Every hiring team is asking the same question: is this candidate the right person for the job? This should be a fairly simple question to answer, but after the resume review and the interview are over, it’s become pretty clear that humans don’t always have the best intuition. Although we sometimes do get it right, sometimes just isn’t enough. Bad hires are hugely expensive for any organization of any size. Tony Hsieh, the CEO Zappos has estimated that bad hires cost the company “well over $1 million.” The US Department of Labor has estimated that a bad hire can cost a company at least 30 percent of that employee’s first-year earnings.

While many companies are feeling pressure to scale and expand quickly, no company can afford to absorb these losses, especially when you factor in the time and energy your current employees will expend hiring and training them.

Ineffective hiring techniques hurt your chances of finding great hires in numerous ways. Not only will you miss great applicants, or let qualified candidates get lost in the shuffle, bad hiring techniques can also translate into bad candidate experiences, meaning that you may be losing great candidates to competitors just because your hiring process was tedious or confusing.

LinkedIn Talent Solutions found that a shocking 83 percent of applicants said a negative interview experience changed their opinion about a role or a company they had once thought of positively. Not only can a bad experience influence a candidate but a good experience can have an even stronger reaction: 87 percent of respondents to LinkedIn said that a good interview experience improved their opinion of a company they had previously doubted.

When an unstructured and unreliable hiring process leaves candidates feeling confused, frustrated, or even disappointed, this can damage both our hiring outcomes and your company’s reputation. One study found that 72 percent of candidates who had a poor hiring experience shared that experience publicly on sites like Glassdoor.

So how can you leverage the best in people analytics to create a hiring system that consistently yields great hires while also maintaining a positive candidate experience? The answer lies in the careful calibration of human intuition and machine learning. While our “gut instincts” are often wrong, good HR teams are able to combine those human reactions with great data and software that guide hiring decisions but don’t dictate them.

For companies of any size, in any sector, the key to consistently successful hiring isn’t automation alone: it’s structure throughout the process and alignment at every level of the team from executives to managers and recruiters. Software can help combine these crucial components, ensuring teams are guided by the same principles and priorities so that candidates have uniform, positive experiences. Software can also stitch machine learning and AI tools into every step so they become an intuitive part of the process, instead of a cumbersome addition.

Although AI has mostly been used during resume review, this technology can and should be expanded to rest of the process, guiding how managers draft job descriptions so that they are accurate, communicate the most important aspects of the position, and will appeal to a wide range of candidates, ensuring your applicants represent the full pool of potential talent that can succeed in this role.

AI can also help continually guide HR teams back to the qualities and capacities that matter most to this position. That can mean helping interviewers create questions that are relevant, behavior-based, and consistent with other interviewers so that every candidate has a consistent experience. It can also mean scoring candidates so that HR teams can see, without a doubt, which applicants are qualified and why.

Whether you are a Fortune 100 powerhouse or a nimble and growing startup, whether you are looking for a C-Suite executive or a daring creative, your needs remain the same: find great candidates with proven abilities to succeed and convince them to work for you and not your competitor. While the objectives are clear, the task is herculean. With the structure, support, and guidance of AI hiring technologies, HR professionals are finally fully empowered to create meaningful interviews, build positive relationships with candidates, and make great decisions and find the perfect hire every time.


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5 HR Tech Trends Shaping Your Business | Featured Image

5 HR Tech Trends Shaping Your Business

5 HR Tech Trends Shaping Your Business | Main Image

Technology continues to drive and disrupt today’s talent management strategies. As we move closer to the halfway point of 2017, we take a look at 5 key HR tech trends shaping your business.

Cybersecurity skills challenges

The widely publicised global data breach that affected the NHS last month highlights the very real risks to all businesses. After the talent shortage, PWC notes that cybersecurity is the second highest ranked concern for CEOs, with three quarters (76%) citing this it as a significant challenge in its annual CEO Survey. A UK government report also found that half of all businesses have experienced at least one data breach or cybersecurity attack in the past year, rising to two thirds of medium and large businesses. Your ability to secure your data is an increasing issue and the pressure is on HR to source talent with vital cybersecurity skills. A report from Experis found that demand for cybersecurity professionals is at an all time high, echoing an earlier survey from Robert Half, Technology and Recruitment : The Landscape For 2017 which found that sourcing tech talent with cybersecurity skills was a priority for over half of all hiring managers this year.

The ongoing debate over AI

Predictions of a jobless world have thrown the debate over AI sharply into focus but AI and automation offer a number of benefits for hiring teams. Writing in the Harvard Business Review, Satya Ramaswamy describes ‘machine to machine’ transactions as the ‘low hanging fruit’ of AI rather than ‘people displacement’.

Elsewhere, Gartner predicts that by 2022 smart machines and robots could replace highly trained professionals in sectors including tech, medicine, law and financial services, transforming them into ‘high margin’ industries resembling utilities. But it stresses the benefit that AI brings in replacing repetitive, mundane tasks and offering more meaningful work. The key is to create the right blend of AI and human skills, which HR is ideally positioned for. Gartner suggests that a further benefit of AI is the alleviation of skills shortages in talent starved sectors.

A beneficial and immediate use of AI for HR is the automation of mundane and repetitive tasks in the recruitment cycle through HR technology, allow hiring teams to focus on creating the effective candidate and employee experience that their business urgently needs.

Chatbots in hiring

Today chatbots are emerging as an essential tech tool for high volume recruitment, engaging with candidates via messaging apps with the aim of creating a more interactive and engaging hiring process. The AA was one of the first brands to feature this smart technology and this year it is predicted that chatbot Stanley will interview 2.5 million candidates. As the skills shortage continues, the chatbot offers a more direct and effective way of engaging with sought after millennials or graduate talent. Chatbots are also predicted to make HR’s life easier through simple interactions via mobile devices for both candidates and employees.

Dark data

While still in the exploration stage, dark data can offer vital insights into talent sourcing. Up to 80% of the data created is ‘unstructured’ or ‘dark’ data found in, for example, e-mails, text messages, spreadsheets and pds. At present it is not usable in analytics but AI can be leveraged to organise it into a more usable form. Last month it emerged that Apple have acquired a machine learning based company to strengthen its own capabilities in the area of dark data. Deloitte’s Global Talent Trends report for 2017 reports that only 9% of businesses have a good understanding of the talent dimensions that drive performance. Dark data may help to illuminate those dimensions.

Moving to predictive analytics

It’s not a new or emerging HR tech trend but the transition to predictive analytics is one that HR must eventually (reluctantly?) make as the skills gap in the UK widens and the availability of qualified and digitally able candidates continues to fall. Applying people analytics improves hiring outcomes, reduces the level of early departures from your business and enables HR to begin to predict and plan for future hiring needs. The first step towards predictive analytics is for tech-averse hiring teams to relinquish manual recruitment systems in favour of HR technology and begin to understand the key metrics affecting your hiring process.

Advorto’s recruitment software provides workflow and structure across the entire hiring process, offering a dynamic database of candidates and analytics. Used by some of the world’s leading organisations, it provides a straightforward first step into AI, HR analytics and big data. Start your 30 day free trial today.


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The Conversation Paradox: Why 100% of Interviews Are Biased

The Conversation Paradox: Why 100% of Interviews Are Biased

In a recent New York Times article, The Utter Uselessness of Job Interviews, Jason Dana, Assistant Professor of Management and Marketing at the Yale School of Management, explores the biases surrounding the unstructured interview process. He observes that:

“…interviewers typically form strong but unwarranted impressions about interviewees, often revealing more about themselves than the candidates.”

Throughout the article, Dana cites, Belief in the Unstructured Interview: The Persistence of an Illusion, a study he conducted in 2013 with 140 student subjects. To test the effectiveness of interviews in predicting a student’s GPA, Dana broke students into two groups. While both sets of students used past GPA and course schedule to make predictions, only one group was interviewed. The results of the study showed that GPA predictions were more accurate for the students not interviewed. In other words, the interviews muddled the data and negatively impacted the decision-making process. 

Regression analyses of the accuracy GPA predictions

Conversations Are Biased

Something occurred during the interviewing process that led the interviewer to misidentify which interviewees were best qualified and thus most likely to succeed. This ‘something’ is the collection of biases that often come up through the course of conversation or what we, at Wade & Wendy, refer to as conversational bias.

Conversational bias is the set of biases that influence the quality and quantity of data extrapolated during the course of a conversation. At a high level, it includes two key components:

  • Set of biases refers to external factors, including everything from confirmation biases and preconceived notions to physical environment and mood, that influence how a person engages in a conversation.
  • The quality and quantity of data refers to the information learned during the course of a conversation and how helpful it is in facilitating good decision-making.

The data learned through conversation is inherently incomplete and/or misleading due to the external factors and biases that influence engagement and perception. This is clearly demonstrated in the study above, where subjects were better able to identify future success for students whom they had never met over students that they had met. While not explicitly referred to as ‘conversational bias,’ the issues it perpetuates have been studied time and time again.

Interviews Are Biased

There is information asymmetry between the data learned in a job description and the data learned from a resume. Former SVP of People Operations at Google, Laszlo Bock, says about this paradigm:

“[having] a taxonomy of skills and abilities that are hard to articulate, and resumes don’t do a good job of capturing them. Employers have a set of jobs, but are terrible at both articulating what they need, and actually filtering candidates.”

Essentially, the two forms (resume and job description) used to determine a job seeker’s ability to fulfill the requirements of a job both contain incomplete data. It is for this reason that a conversation — often in the form of an initial phone screen or a first-round interview — is necessary to resolve this asymmetry. This initial conversation allows candidates to better understand the requirements of the job and allows hiring managers to gather information not found in the resume.

It is at this point in the hiring process that conversational bias comes into play.

For example, imagine a hiring manager has a full day of interviews lined up. Throughout the day, he/she becomes increasingly fatigued and, as a result, asks poorer questions and takes fewer notes as the day goes on. Because the conversation and the subsequent data gathered about each candidate is different, it becomes impossible to compare candidate to candidate accurately.

The Problem

In Dana’s Belief in the Unstructured Interview study, GPA, course schedule and an interview were used to predict future success. Results showed that the assessments were less accurate when interviews were included in the decision-making process. In effect, the interviewers were counterproductive.

The Other Problem

To fill the information gap that exists between resume and job description, a conversation must take place. Applicants need clarification on the requirements of the role, just as hiring managers need to gather information not found within the resume.

The Paradox

These problems present two interesting concepts: 1) Conversations are biased and 2) Conversations are necessary. This is what we, at Wade & Wendy, call “The Conversation Paradox.”

Looking Ahead

While the very act of conversation has been proven to introduce numerous biases, it remains a critical part of the hiring process. To date, many solutions have been proposed, such as Dana’s suggestion to use structured interviews, but these solutions do not go far enough. Rather,

  • What if there were an artificially intelligent tool smart enough to have a conversation without bias?
  • What if there were an artificially intelligent tool agile enough to converse with 100% of candidates 100% of the time?

At Wade & Wendy, we are eagerly working on this solution. To join the conversation, chat with us on Twitter… We’re passionate about conversation, after all: @wadeandwendy.

About the Author:

Bailey Newlan is the Content & Growth Marketer at Wade & Wendy, a New York City-based startup on a mission to make hiring more human. Wade & Wendy’s artificially intelligent chatbot personalities bring clarity and simplicity to the hiring process. Wade is an always-on career guide for job seekers, while Wendy assists hiring managers throughout the recruitment process. To connect, reach out to Bailey via LinkedIn, Twitter or Medium and don’t forget to join the beta list.✌️


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Current and future state of HR and employee appreciation – Interview with William Tincup

Written by João Duarte, Content Director at Tap My Back.

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William is the President of RecruitingDaily. At the intersection of HR and technology, he’s a Writer, Speaker, Advisor, Consultant, Investor, Storyteller & Teacher. He’s been writing about HR related issues for over a decade. William serves on the Board of Advisors / Board of Directors for 18 HR technology startups. Many say his words dictates and predicts the future of managing people and teams.

Tap My Back, an employee appraisal software, recently managed to have an interview with Tincup about the current and future state of HR focusing topics such as performance reviews and the use of AI. This article is sort of a compilation of the main ideas he went through on this interview.

One of the most interesting topics Tincup spoke was about the way he feels HR managers currently should have more responsibilities than ever before. Following his thoughts we’re moving from era where employee engagement was the main worry of HR managers onto one where there’s the need to manage the full experience staff go through on the workplace.

He even says that engagement is the same as recycling, everyone already recognizes the value it provides but still many prefer to ignore it.

According to William, the reason why performance reviews stopped producing the outcome they used to is related with the fact that many times managers who conduct those are not honest with the employees about whose interest this process serves. As society currently values highly aspects such as transparency, HR staff conducting performance reviews should be clear to people and say something “hey, this actually for us, so that we do better, so that we make sure that we’re on the right track and we get the most out of you because we want the best version of you while you’re with us. We’re going to train you, we’re going to help you, we’re going to throw some stuff in but at the end of the day we want the best version of you while you’re with us”.

Regarding AI and Machine Learning, William provides an interesting opinion, stating that these tools will make insights that used to be remarkable to become commonplace, a commodity. Following his reasoning these tools will turn dump databases into something capable of providing insightful conclusions, sparing human brain of analyzing raw data.

William, with his typical charismatic way of being, finishes the interview with an advice for every entrepreneur, “Grow, comma grow the right way” referring to the fact that the ambition to grow should never overlap the way managers treat people

End note: You can hear and read the full interview here.


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Solving the Job Application Black Hole with Chatbots

Written by Bailey Newlan, Content & Growth Marketer at Wade & Wendy.

ATS Black Hole

Applicant Tracking Systems (ATS) are not inherently bad — for the hiring manager. They are critical to managing massive amounts of resumes and establishing an efficient workflow. However, the candidate experience suffers. A survey conducted by CareerBuilder found that 52% of employers responded to less than 50% of candidate applications. With such little communication, candidates are left frustrated and unsure of where they stand. This is referred to as the “ATS Black Hole.”

By incorporating Conversational Intelligence into the existing process, better engagement, better communication and transparency can be realized.

Conversation with Wendy in Facebook Messenger screenshot
This is how a conversation with Wendy, our conversationally intelligent chatbot, begins in Facebook Messenger.

Here’s How the ATS Fails Candidates

When an individual applies for a job, his or her resume is sent into a company’s ATS. Through matching algorithms and keyword extraction, a shortlist of candidates is generated for the hiring manager to review. These algorithms fail to take into account spelling errors and deviances in word choice (explained in more depth here). Because matches are generated exclusively through one-dimensional data, hiring managers’ understanding of candidates is distorted.

The result: Very few qualified candidates make it past the ATS and to the interview stage.

This problem is further compounded by the ease of the application process. In response to mounting candidate frustrations with lengthy applications, many employers now offer “Quick Apply” or “1-Click Apply” options. While this significantly lowers friction for applicants on the front-end, they are actually worse off in the long run. Employers are receiving more and more resumes, but, due to the simplicity of new application processes, they now have less data from which to draw conclusions.

In a world where candidates expect engagement and transparency, they are getting less and less.

On average, a single corporate job opening receives 250 applications. With an influx of resumes to review and no uptick in resources with which to process them, hiring managers cannot possibly respond to each individual applicant. In fact, of those 250 applications, only four to six will be called in to interview. As a result, most candidates receive zero communication, experiencing what has ubiquitously been labeled the “ATS Black Hole.”

Here’s Where Conversational Intelligence Comes In

Conversational Intelligence transforms the application process from something static to dynamic. At Wade & Wendy, we believe artificial intelligence is at its best when used conversationally. Our two chatbot personalities are built with this in mind. By creating a space in which conversations can occur, chatbots have the power to drastically improve the application experience.

Chatbots can engage every single applicant at any point in time.

Immediately following submission of their resume, candidates are directed to have a conversation with a chatbot through either text or Facebook Messenger. This introduction allows for a much friendlier first point of contact. Rather than receiving a “Thank You for Your Application” message from a “do not reply” email address, you meet Wendy. Here, candidates can inquire further about the company and the job itself.

At Wade & Wendy, we have designed each of our chatbot personalities to be conversational and inviting. Conversational Intelligence has the power to make a notoriously stressful and automated process fun and distinctly personable, especially when emojis are involved 🙌.

Chatbots give every candidate an equal chance at landing an interview.

Chatbots provide context and depth around the static data gleaned from the ATS. Because every candidate can be engaged via chatbot, algorithm mismatches, various misspellings and differences in keywords no longer hinder a strong candidate from getting in front of the hiring manager. Chatbots, like Wendy, allow candidates to provide context to their resume; they have an opportunity to explain properly a successful project that would otherwise be summed up in a mere bullet point.

Candidate Chats with Wendy
Here, the candidate is able to give Wendy more details about her experience with open source projects.

A candidate’s experiences and skills cannot always be properly communicated in a resume. On top of that, the ATS responsible for gauging a candidate’s ability to do a job utilizes flawed algorithms and thus provides flawed recommendations. Conversational Intelligence allows candidates to best communicate who they are and what they can do, while also overcoming algorithm flaws within the ATS.

About the Author:

Bailey Newlan, Content & Growth Marketer at Wade & Wendy

Bailey Newlan is the Content & Growth Marketer at Wade & Wendy, a New York City-based startup on a mission to make hiring more human. Wade & Wendy’s artificially intelligent chatbot personalities bring clarity and simplicity to the hiring process. Wade is an always-on career guide for job seekers, while Wendy assists hiring managers throughout the recruitment process. To connect, reach out to Bailey via LinkedIn, Twitter or Medium and don’t forget to join the beta list.✌️


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How Machine Learning is Revolutionizing Digital Enterprises

How Machine Learning is Revolutionizing Digital Enterprises

According to the prediction of IDC Futurescapes, two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today.

If you want to stay ahead of the game, then you cannot afford to wait for that to happen. Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.

In short, ML will connect intelligently people, business and things. It will enable completely new interaction scenarios between customers and companies and eventually allow a true intelligent enterprise. To realize the applications that are possible due to ML fully, we need to build a modern business environment. However, this will only be achieved, if businesses can understand the distinction between Artificial Intelligence (AI) and Machine Learning (ML).

Understanding the Distinction Between ML and AI

Machines that could fully replicate or even surpass all humans’ cognitive functions are still a dream of Science Fiction stories, Machine Learning is the reality behind AI and it is available today. ML mimics how the human cognitive system functions and solves problems based on that functioning. It can analyze data that is beyond human capabilities. The ML data analysis is based on the patterns it can identity in Big Data. It can make UX immersive and efficient while also being able to respond with human-like emotions. By learning from data instead of being programmed explicitly, computers can now deal with challenges previously reserved to the human. They now beat us at games like chess, go and poker; they can recognize images more accurately, transcribe spoken words more precisely, and are capable of translating over a hundred languages.

ML Technology and Applications for Life and Business

In order for us to comprehend the range of applications that will be possible due to ML technology, let us look at some examples available currently:

  • Amazon Echo, Google Home:
  • Digital assistants: Apple’s Siri, SAP’s upcoming Copilot

Both types of devices provide an interactive experience for the users due to Natural Language Processing technology. With ML in the picture, this experience might be taken to new heights, i.e., chatbots. Initially, they will be a part of the apps mentioned above but it is predicted that they could make text and GUI interfaces obsolete!

ML technology does not force the user to learn how it can be operated but adapts itself to the user. It will become much more than give birth to a new interface; it will lead to the formation of enterprise AI.

The limitless ways in which ML can be applied include provision of completely customized healthcare. It will be able to anticipate the customer’s needs due to their shopping history. It can make it possible for the HR to recruit the right candidate for each job without bias and automate payments in the finance sector.

Unprecedented Business Benefits via ML

Business processes will become automated and evolve with the increasing use of ML due to the benefits associated with it. Customers can use the technology to pick the best results and thus, reach decisions faster. As the business environment changes, so will the advanced machines as they constantly update and adapt themselves. ML will also help businesses arrive on innovations and keep growing by providing the right kind of business products/services and basing their decisions on a business model with the best outcome.

ML technology is able to develop insights that are beyond human capabilities based on the patterns it derives from Big Data. As a result, businesses would be able to act at the right time and take advantage of sales opportunities, converting them into closed deals. With the whole operation optimized and automated, the rate at which a business grows will accelerate. Moreover, the business process will achieve more at a lesser cost. ML will lead businesses into environs with minimal human error and stronger cybersecurity.

ML Use Cases

The following three examples show how ML can be applied to an enterprise model that utilizes Natural Language Processing:

  • Support Ticket Classification

Consider the case where tickets from different media channels (email, social websites etc.) needs to be forwarded to the right specialist for the topic. The immense volume of support tickets makes the task lengthy and time consuming. If ML were to be applied to this situation, it could be useful in classifying them into different categories.

API and micro-service integration could mean that the ticket could be automatically categorized. If the number of correctly categorized tickets is high enough, a ML algorithm can route the ticket directly to the next service agent without the need of a support agent.

  •  Recruiting

The job of prioritizing incoming applications for positions with hundreds of applicants can also be slow and time consuming. If automated via ML, the HR can let the machine predict candidate suitability by providing it with a job description and the candidate’s CV. A definite pattern would be visible in the CVs of suitable candidates, such as the right length, experience, absence of typos, etc. Automation of the process will be more likely to provide the right candidate for the job.

  • Marketing 

ML will help build logo and brand recognition for businesses in the following two ways:

  1. With the use of a brand intelligence app, the identification of logos in event sponsorship videos or TV can lead to marketing ROI calculations.
  2. Stay up to date on the customer’s transactions and use that behavior to predict how to maintain customer loyalty and find the best way to retain them.

How Enterprises Can Get Started Implementing Machine Learning

Businesses can step into the new age of ML and begin implementing the technique by letting the machines use Big Data derived from various sources, e.g. images, documents, IoT devices etc to learn. While these machines can automate lengthy and repetitive tasks, they can also be used to predict the outcome for new data. The first step in implementation of ML for a business should be to educate themselves about its nature and the range of its applications. A free openSAP course can help make that possible.

Another step that can bring a business closer to ML implementation is data preparation in complex landscapes. The era of information silos is over and there is an imperative need for businesses to gather data from various sources, such as customers, partners, and suppliers. The algorithms must then be provided open access to that data so they can learn and evolve. The Chief Data Officer of the company can oversee the ML integration process.

To start with completely new use cases for Machine Learning is not easy and requires a good understanding of the subject and having the right level of expertise in the company. A better starting point for many companies would be to rely on ML solutions already integrated into standard software. By that it will connect seamless with the existing business process and immediately start to create value.

Lastly, businesses should start gathering the components necessary for building AI products. Among the requirements would be a cloud platform capable of handling high data volume that is derived from multiple sources. The relevant people are as important to this step as are the technology and processes. After all, they would be the ones who will be testing the latest digital and ML technologies.

If you want more information on SAP Machine Learning, then go here to subscribe to the webinar on Enabling the intelligent Enterprise with Machine Learning.

The presenters include Dr. Markus Noga: VP Machine Learning Innovation Center Network, SAP SE. You can follow him on Twitter. Ronald van Loon is the other presenter for the webinar. Mr. van Loon is counted among the Top 10 Big Data expert and is an IoT Influencer. You can also follow him on Twitter.


Source: How Machine Learning is Revolutionizing Digital Enterprises | Ronald van Loon | Pulse | LinkedIn