Qorums Events and Webinars

Webinar Transcript Follows

Megan: I am Megan Metcalf Draghi. I’m the Vice President of Customer Success at Qorums and joining me. Today we have our esteemed panelist Christine Eckhaus, and we’ve got David Andrews who is a co-CEO of Qorums who is going to be helping me co-host. 

How this all came about is for my company, Qorums, our hiring process management system. We wanted to implement AI into our software in some way and probably like most of you, we had a lot of questions we weren’t really sure where the areas of opportunity were and so when it came time to really think about it we called in Christine, who is, I’ll let you introduce yourself. Christine, and let everybody know who you are and in your background.

Christine: Hello everybody! Thanks for joining us. I am the chief business and people officer at a company called CLIPr. CLIPr is a video intelligence platform, and what we do is essentially index your video content so that you can easily search and query and find what you’re looking for in all the video that we’ve been bombarded with over the last couple of years. 

Megan: I got my slides working just in time! Let’s look at the agenda. We  will start with the basics: What is the traditional hiring process look like, and where are their areas for opportunity, how we can use AI to up skill or interview teams key strategies for tracking and utilizing interview data challenges and concerns with hiring. I know that we all have those concerns so we’re gonna talk about that a little bit, and then we’re gonna look to the future of AI and hiring. Christine talk to me about what everyone is used to doing. What is the current situation in the hiring process of the stands today? 

Christine: Yes, so I think there are a couple key elements right that sort of remain the same regardless but it’s sourcing talent, and your screening and selecting talent, and then you’re going into your offer management and onboarding processes. So I think one of the areas that has been consistently hard to address is the screening and selection process. For sourcing you have some tools you’ve got, of course locked in there’s other tools like SeekOut, you know all that front end stuff is out there. They have some companies like Gem that are CRM that help you get out reach out there, so that’s kind of you know in the process of people getting their arms around and you also have search firms that can help you source the right, but screening and selection is tough and every company sort of does it differently right and has different technology deployed. It’s sort of this you know crystal ball, magic, ball and nobody’s hiring process is100% accurate.

It’s like we used to say nobody but 1000, right?  So you’re always trying to get the highest quality hirer you possibly can and you develop depending on your hiring team. So the folks who are interviewing that candidate to figure out through a 30 minute, 45 minute, 16 minute interview whether or not this person is a good fit for the role, the team and the overall organization, which is really hard to do without some data. So now enter the world of AI, and we have these wonderful, wonderful tools. They can help us wrap our arms around that hiring process, assess the interview team, get at the quality of the interview and then track that through to the higherr quality of the hirer and that is super exciting is one of the things I love most about Qorums.

Megan: Well thank you for that, and let’s talk about that a little bit…the areas for opportunity and where those really exist, because you mentioned that some things will never change, right? That oversight of how people manage the beginning of the hiring process and sourcing. Where do you think, when you incorporate AI within the interview process itself,  in the hiring process itself? Where do you think the biggest opportunities for data collecting data utilizing that data as we move forward? Where are the biggest opportunities in the process as it sits right now? 

Christine: Yeah so, what I meant to say is the sort of the process, the elements of the process right so seeing screening and selection offer management and onboarding that is unlikely to change how we do it and what we do inside those sort of boxes, if you will. If the opportunity is right, and so I think some of the biggest opportunities are around that process in the middle. Where the sourcing tools are really good at finding talent, and then companies typically have decent tools with offer management,  but then in the middle is this interview section where you’re really depending on the recruiter and/or the hiring team to usher that candidate through the process, and that’s where we have issues. Candidates get ghosted, interviews get scheduled and rescheduled, and it’s difficult to keep track of what’s going on within companies. 

Some of them are still using spreadsheets. 

There’s all kinds of tracking systems that are out there with varying degrees of efficacy, and so really getting you know the secret to using AI and data effectively is to have good data. So really getting your arms around that process is going to help you have good data to draw from. The humans make these important decisions. 

Megan: That’s really interesting and a lot of people are using spreadsheets right to manage their hiring process, that’s probably pretty much across-the-board, and when you talk about collecting data for AI, I’m just gonna ask the question I already know the answer to, but what’s the problem with spreadsheets? If everyone is used to using them, and everybody’s been doing it that way, and you’ve got your standardized naming convention you manage to keep up with it? 

Christine: The problem with the spreadsheets and managing data that way is that the data that you have inside the spreadsheet is limited, right? And the data you’re able to capture, search, and utilize is also limited. So maybe you have name contact information, and you know the salary, and who interviewed them all in this massive spreadsheet, but it’s really difficult to make any sense of that at scale. If you have only five jobs to hire or something like that, one recruiter or five jobs, something like that OK, but still not the best because you’re not retaining any sort of data. You’re spending all this time and effort and energy interviewing this person, 4 to 6 employees inside your organization, are spending their time asking this person questions and developing their understanding of this individual’s experience, and that’s not in the spreadsheet. There’s no real way to put that in there. And so I think it can work, there’s nothing wrong with it, but there’s a huge opportunity to do things so much better. And it benefits everyone: the company, the hiring team, the recruiter, and most importantly the candidate, and it gives the candidate a really great experience versus being bounced around because the spreadsheet person didn’t track correctly and they got lost in the sauce. 

I’m gonna go off a tangent, but for a candidate, the hiring process is a window into the company. So if you’re hiring process is bumpy and disjointed and kind of all over the place, the candidate has got to be thinking to themselves, “What’s gonna happen if I take this job? Am I gonna be able to get the things done that I’m trying to get done to be successful here, or is it gonna be like pushing a boulder uphill all the way there?

Megan: Like going to check the bathroom in a restaurant before you eat there when you get there? It’s not gonna get much better after that if it’s messy. Because you are you are being interviewed too, and especially when you’re looking for top talent. When you’re looking for the best that’s out there, which is what we all want, but sometimes companies feel like they can’t compete with other companies. But the more streamlined you can make processes, and allow people to do their actual job without a lot of start-stop, tedious tasks, and outdated ways of doing things… I mean, if I’m a candidate, looking for an innovative company to join, and their hiring process is tedious and long, and it’s probably long because of all of the backend processes that they’re doing, then it says a lot. So with that, beyond the candidate experience, let’s talk about using that data to make your interviewers better. 

Right now I think about hiring teams that I’ve been part of and there’s little recordkeeping involved. When I think about how I’ve done things in the past, after I would conduct an interview, I’d type up my summary, things that stood out, things that maybe were red flags, and then you send it over to HR and they collect the information and use it to make a decision. But other than that, the information becomes sort of lost, and it doesn’t tell a lot about my personal interviewing skills or other people who are part of hiring teams, and so I want to talk about beyond just sourcing the candidates, and the candidate experience, but also about how will AI will start to impact how we train our our hiring teams, and and how we make those better. 

Christine: I’m sure you know a lot of companies are worried about the future of work, right? We’ve got all this hybrid back to the office work from home task going on right now, and people have a wide degree of feelings about all of that. There’s been massive layoffs, there continue to be layoffs, and so why are we sitting here talking about the recruiting process? It’s kind of going the other way and people should feel lucky to have a job. That’s one point of view. The other point of view is, OK if you’re not deploying your recruiting resources, and if your machine is not at full capacity, now is a terrific time to envision the future and put it in place right start to practice how this is going to be. Like, “here’s what we’re doing today. It’s OK, we’re accomplishing what we need to accomplish inefficiently, whatever, but what  if we threw out everything we know today and envisioned an amazing way to do this? What would that look like, and then what do we need to do to get there, and can we get there and how much can we do?”

In that direction maybe it’s a phase approach; maybe you don’t have all the resources to deploy to get here today. And so if you think about that, I think the big piece in the middle that has kind of always remained unaddressed, is the quality and the skill of the interview team. So you’ve got all kinds of people from all kinds of backgrounds bringing their experience into the interview, and if you’re a larger company with more resources, maybe you have training for individuals on how to interview people. A lot of the big tech companies have that.

Megan: I don’t remember having formal interviewing training before becoming a member of hiring team. It’s been here’s the résumé, here’s the job description, I’m looking for you to vote on them based on this certain criteria, but most of the time what happens is you’ve been added to a hiring team, and you’re like “I’m a team player, I’ll be part of an interview team” and it becomes part of a hectic day. It kind of gets lost in the shuffle. And so you get an alert on your phone, we’ve all been there, where you’’ve got an interview in 10 minutes, and you’re like where is the résumé? What is the job that they’re even interviewing for? Is it on zoom? Are they coming in person? You know there’s all kinds of things that you’re trying to do in that span of maybe 10 minutes. That’s not really preparing in a meaningful way. So sometimes it’s not for lack of vision, it’s just lack of execution, because there’s not some centralized location for the data for the interview process to take place itself. 

So, I want to talk about and think about opportunities, because that’s what Qorum does, and this webinar is to be educational, but it’s educational for us too in understanding exactly where we fit in. One of the things that we wanted to do was make everything in the interview process and hiring process related part of one system. so I want to talk about the opportunities for if you were able to get your entire team to interview in a place where everything’s in one location. So with Qorums, you login and you conduct the interview right there in the system. So with that collection of data, how can you use that to mitigate biases? And this isn’t just with Qorums, this is using AI and hiring. How can you use that tool to mitigate biases to find areas for opportunity? 

Christine: Yes. So now you’re at the step where you’re envisioning the future. Like right now, whatever we’ve got is whatever we’ve got. And, “wow if we could do this we would be slick”, right? So if you think about it, a lot of companies can do this manually to some extent, but again we have the question of, hiring will come back will happen again, we will be hiring at scale again one day, and what are we gonna do if that happens? So think about having all of that information, and what that place is. Ff you think through the manual process of best practices and that vision is right: your interviewers know what they’re doing, they’re prepared for the interview, they’re not searching around “my God where is that calendar invite with a person’s résumé and the job description attached, and I’m reading it on my phone in the elevator. I hope I don’t lose service, where’s the conference room? I’ve never been in this building before”, right? So now with virtual interviews, maybe that’s less of a problem, but anyway, you get the idea. 

So if you have it all in one place, ideally you have a pre-brief where everybody on the interview team is brought together, and the hiring manager says with the recruiter in the room so everybody’s got the same point of view: here’s the position, here’s why we’re hiring for this position. Here are the skills and talents and qualities that we are looking for in the position for our team in the overall organization and this is the ideal candidate. You’re part of the interview process, and we want this person to interview on these couple of competencies, and this person to interview on these couple of competencies, this person to interview on these couple of competencies, and that’s essentially why you’re here, right? And then there’s sometimes a dialogue about “oh what do you mean about this?” “What do you mean about that?” blah blah blah and everybody sort of comes to “OK I know what we’re trying to accomplish together here within this hiring process”, right? So you’ve got step one down.  Step two is now candidates are coming into the system, and everybody sort of knows “OK this is my job is to interview this person on these couple of competencies” and so on and so forth for everybody on the team, right?

What does that really mean? 

Because if you have hundreds of people in the organization that are interviewing people on this competency, is their mindshare there? There might be mindshare on this interview team, they’ve  got it, but what about the seven teams over there that are interviewed for something similar, and the same competency? How do we ensure that that is aligned right? 

Megan: Can you elaborate on mindshare for the audience?

Christine: Well consistency, right? Are we looking for something similar or are we when we say that?  Let’s say python skills, are we looking for something similar, or is one person is super hard, grader on python skills, and the other person sort of a more lenient grader on python skills? So if this person interviews, good luck getting your candidate through, and if this person interviews even somebody who doesn’t really know how to code is going to get it.  So what’s the variability there and how do we kinda close the gap on that and get some consistency? Everyone’s gonna have their style, that’s the beauty of being human and that’s also benefit. But how do we close the gap on that, and that’s one of the things that is really hard to do, and it’s hard to have a really tight hiring process if you and use data if you don’t have that if there’s so much variability in your process that there’s no consistency across the board, whether your companies huge or whether your company is small, the more consistency you can bring across-the-board, the better your data is gonna be.

And that’s one of the things about it is that it is scalable, because it provides not only the context of the interview: here’s what the position is, here’s what each person is interviewing for, and also suggest questions that the interviewer can ask, right? It’s genius! It is things I’ve seen done manually at some of the best companies in the world that do hiring right, and then there’s a feedback loop from the quality of the hire, assuming the hiring manager puts the information in on how this person is doing in 30, 60, 90 days or whatever the intervals are that they want bake  into the system. And then the questions: was this valuable question to ask? Was the answer we were listening for resonate with what we needed for the role? Did this help predict that? 

And then you can go into the skills-based hiring even deeper and start to really create a system where you are hiring the highest caliber person for the role, and your interview team knows how to do that and that is what we’re talking about. How you plan to hire in the future is based on what you’ve set up.

Megan: So you know, I guess looking at a recursive review in three months and six months and seeing how people voted and seeing how the candidate who was hired how they performed at the job and using that analysis to to build out the rest of your process… 

Christine: Yeah, so I think I got it and interrupt me if I’m going in the wrong direction, I’m gonna try. So I think that would happen is, the more you use the system like that the more data you have, and the more inputs you have around using the example of the question, when we ask this question, and we got a certain type of answer, that we know we’re looking for, we ended up with a very high caliber candidate, 80% of the time that says “wow this is it”. This is a really well baked interview question that helps us understand whether or not the person is going to be a good fit for the role,and it can go across the organization. But the neat thing about about something like Qorums or you know any systemized approach to this type of process is that it can also be on the team. So every team is gonna be a little bit different. The organization is gonna have its culture and its norms and everything else. Every team is gonna be a little different, and every dynamic is gonna be a little different. 

And so if the hiring manager can start to learn from this data, “Wow you know these are really telling questions about that help predict the future performance for someone on my team with the vibe we have going on and with the skills that we have together”  as they continue to grow and scale, this feedback loop continues to guide them on. “Hey, this is what is resonating for your group at this time, and here’s how you can effectively build and scale your team and keep them performing at high-level as they are now.” 

David: I actually wanted to add a little angle to what you’re talking about. This whole thing is how you get AI to leverage all these great ideas. How do you take the output of these interviews? How do you get it into a format that makes sense? And if you got a lot of people being interviewed, got a lot of feedback, a lot of video if you’re recording it, text, and everything else. Surely that’s where the AI comes into play here that you can take reams and reams of stuff, filter down and then make a decision based upon a summary of it. That doesn’t mean that you don’t need to sometimes read everything, but at least you’ve got some really useful data, and I think that’s where you’re going with this, am I right? 

Christine: Yeah! It’s all about the data and having the data in a sort of consistent organized fashion, and then that feedback loop actually works right so if you think about a system like forms you have. The pre brief sort of organized in that system, the position, whatever everybody’s interviewing for. What questions that we know are pretty good to ask and what’s your listing for, and then you have the candidates background, and any sort of phone interviews that have happened recruiting-wise. For feedback, are there the scores? Are there any pre employment testing if it’s a software engineer? And they took a test, the scores are all there, and then once all of the interviewers do their thing, the scores are all there. And then you have the debrief baked in where you’ve got everyone’s feedback, everyone’s scores, all the questions that were asked notes on the answers or video of the answers in addition to the transcripts once once CLIPr and Qorums solidify their partnership, and so it’s all right there. 

And then manager/recruiter, whomever can go in and easily review if there is a question. If you do have a debrief meeting where everyone comes and talks about the candidate, what did they say about that thing? Did anybody get a signal? I’m not sure it’s all there. You don’t have to try to remember because of the full scale hiring process you might’ve done. You know, three interviews a day last week I don’t really remember that so I just sort of context of it, and I did my job. I know what I said to this person in general but I don’t remember this other stuff. And so I think that the data that’s there and that’s captured and the patterns that the AI can bring to bear are where the value is. 

And then not even to mention, let’s say you find two great candidates and you have a budget for one in an ATS system or spreadsheet. It’s hard to find that person again. 

Megan: Well, I was gonna say like not only can you even find them, if you can’t remember their name you’re not gonna find them, they’re gone. So beyond just being able to find them, but what about the fact that they’ve already been interviewed before? When you think about what makes people who are sourcing candidates really good, really good recruiters it’s that they 1) Have been doing it for a while, and they know the people who are on the market, and they keep up with that, but they’ve already had discussions with them, and they know exactly what area to point them, and what direction to appoint them, and so if you can get all of that information into a system that uses AI to analyze the data and algorithms to produce scoring for them, that all remains into a system that can utilize again. It becomes part of a pre-vetted, pre-interviewed pool rather than just a data warehouse with a bunch of information that’s almost impossible to find.

Christine: So you can’t search your query, right?  Now with Qorums, the more you use it the better it gets. You can search esoteric criteria, or you can search on scores. If I’m a recruiter, I’m facing challenges in getting people to engage with me or finding candidates who meet the required standards even after going through my recruiter screening process. My hiring managers are disappointed, and I find it frustrating not to have a starting point in the system. Having a tool in place to identify potential candidates quickly is crucial. We all know the importance of initiating conversations promptly, and having a system to kickstart the process allows us to track hiring metrics and conversion metrics efficiently. By feeding this data into the system, we can adjust our approach to achieve the desired KPIs. This tool is a game-changer for recruiters, providing more than just a mechanism for KPIs; it’s an opportunity to enhance our strategies. However, it’s important to acknowledge that, despite the promises of AI making our lives easier, there are challenges and downsides. As someone who’s already using AI tools, I’ve noticed some hurdles in utilization.

Megan: They still need a supervisor, correct? I was attempting to create a presentation with a cool app, and while I think most of the struggles are due to user error on my part, the app does have some limitations. You can prompt it to create your entire presentation for you, which I attempted to do, but the results aren’t quite there yet. It’s like an alien pretending to be human and creating a presentation—it’s a whole different ball game. When considering the future of AI in hiring, there are numerous challenges, especially when it comes to mitigating biases. Let’s walk through some of those challenges. 

Christine: You’re never going to remove the human aspect from human resources; that’s one of my mottos. Even though we might call it people or recruiting, it’s still a business about people, right? Hiring is all about people and context. So, while AI can be excellent at suggesting questions, helping interviewers refine their skills, scoring candidates, and recognizing patterns, it ultimately empowers humans to focus their time and efforts in the right direction. It’s really good at recommending.

Hey, you know, maybe you should try this, maybe you should try that, and augment the activities that humans do. But it can’t make decisions. It doesn’t understand, it can’t reason, it lacks an understanding of context, trade-offs, and all the executive functions that come into play. It doesn’t comprehend negotiation, doesn’t grasp building relationships. Just like I was saying, if the hiring process is disjointed, the person thinking about taking the job will feel more connected if the interview team, hiring manager, and recruiter are doing the right things. You anticipate their needs, like thinking, “Wow, we haven’t talked to this person in two days. Let’s give them a ring or send them a note.” Not an AI-generated note that says, “We’re thinking about you,” or “We’re in the process of doing something with your candidacy. Please give us a minute.” You can send these updates automatically, sure, but you also need to inject humanity into it so people feel connected. 

The future of work is grappling with this issue—people don’t feel connected; it’s just a job. You go in, punch a clock, work 9 to 5, make a decent salary, and go home. It’s an entirely different world than it was five years ago. Human connection is crucial, and using AI to focus attention and energy in the right direction is an incredibly powerful tool. Using AI to make choices and decisions for you requires careful management. There’s a lot of fear surrounding the future of work, but from this perspective, managing AI will free you from tedious tasks, allowing a focus on the core aspects of your business. It’s an amazing tool when used properly. However, if used improperly and with the wrong expectations, it can make mistakes—mistakes that are 100% avoidable when using this powerful technology responsibly. For any talent or HR professional navigating the future of work and integrating AI tools, consider if the company, product, or individuals you’re working with are using AI responsibly. Are they thinking forward? What are the downsides, and how do we mitigate risks? How do we use this in the right direction and create products that align with our goals?

Driving people to take the right actions involves warning them about potential risks, pitfalls, and mistakes. Some companies are mindful of the magnitude and power of AI technology and are proactively considering the potential for things to go wrong. Responsible use of AI is crucial in any business aspect. Recognizing the significant downside, it’s important to remember that it’s not just about embracing the positive aspects of AI but also being vigilant about potential challenges and pitfalls.

Megan: With that being said, as we’re nearing the end of our time, we want to share some suggested resources before diving into the Q&A. Before we proceed, considering the rapid pace of change in the AI landscape, it’s fascinating to reflect on the future. It feels like just yesterday I learned about ChatGPT, and now there’s an AI tool for practically everything. It’s been a year since my first encounter with ChatGPT. Looking ahead, both in the next year and the next 35 years, where do you think AI will stand in the realm of hiring? Do you anticipate widespread adoption, or will there be late adopters? What outcomes do you foresee for companies moving in the direction of AI in hiring, and what about those who choose not to embrace it? Let’s explore these intriguing questions as we delve into the future of AI in the hiring landscape.

Christine: I can share my perspective, I don’t have a crystal ball to predict precisely what companies will do. They don’t necessarily follow my advice. Drawing from my extensive experience in talent acquisition and management, my focus has been on the significant underserved areas within the hiring process and transition management. I find the thoughtful and scalable addressing of these aspects truly exciting.

Firstly, in hiring process management, there’s a noticeable gap that tools like [tool name] are addressing in a remarkably scalable way. This aspect excites me tremendously.

Secondly, the transition phase is another challenging area. For example, a software engineer transitioning into a product manager role might face uncertainties. Questions arise: How do I become a product manager? What skills do I need? How can I navigate this transition within my current company? This is an intricate puzzle for both managers and employees, and companies often grapple with understanding and supporting these transitions effectively. Having tools that guide employees through learning paths, offer step-by-step guidance, and help address skills gaps for both individual growth and organizational alignment is crucial.

Additionally, when someone gets promoted, there’s often uncertainty about the new expectations and the skills needed in the new role. Having learning guides and clear paths can be immensely helpful in ensuring a smooth transition for both the individual and the organization.

In essence, I believe significant advancements can occur in talent acquisition, ensuring the highest quality hires through a combination of data, strategic thinking, and human effort. Moreover, the potential for increased time, energy, and investment in the development of these high-quality hires is immense, especially when supported by robust talent management plans. This not only includes making high-quality hires but also facilitating their growth, transitions, and promotions within the organization. Having clear roadmaps and tools that guide individuals and managers through these processes could lead to remarkable outcomes. I’m genuinely thrilled about the possibilities in talent management and acquisition.

Megan: We can actually have a whole other conversation about this topic in itself. 

Christine: Yeah, it’s a hot topic! 

Megan: OK that’s maybe the next webinar!

David: You mentioned a crucial element that everyone seems to be considering in the realm of AI, and it pertains to the education of AI itself. Currently, it’s akin to how GPS gathers information from the entire Internet and synthesizes it, resulting in AI “knowing” everything in a general sense but lacking personalized understanding. As you pointed out, the data gathered in this process is pivotal, especially when considering career advancements. The real potential lies in AI not just understanding a company in a generic sense but comprehending the nuances of “your” company. Moreover, by understanding individuals on a personalized level through feedback from various sources, the AI engine can be educated and leveraged more effectively. This aligns with my perspective, and it’s a direction I believe holds significant promise for the future of AI.

Christine: I’m so happy to hear you say that you have the power to build it! 

Megan: Before we dive into that, I want to recommend a couple of resources. If you’re currently focused on talent search and recruitment, Seekout is an exceptional AI tool tailored for that purpose. While Qorums primarily operates on the interview process side of things, Seekout specializes in enhancing the talent search and recruitment experience. Seekout has a great reputation, and Christine I know is very familiar with the company.

Christine: Additionally, let me just interrupt you for one sec. Sorry it’s a sourcing tool, and you know we talked about responsible use. They are very very thoughtful about developing their products and their platform in a way that you know they’re thinking about the risks and helping companies wrapping their arms around the responsibility is really great. 

Megan: Ok, let’s talk XOR. If you find yourself dealing with a large number of interviews, managing communication can be a lot to manage. In fact, there are instances where candidates ghost us because of a lack of engagement. Now, XOR isn’t here to eliminate the human element; instead, it steps in to automate processes and enhance candidate engagement. So, if you’re on the lookout for assistance in streamlining communication and boosting engagement, XOR has a great reputation. 

And then before we get into Q&A, I do want to mention that Qorums is sponsoring the webinar, and it really is to just add value and bring information. Anybody that deals with hiring or hiring software is looking to the future of what we can really do, and how we can be helpful and productive. And so what Qorums does is it leverages AI and proprietary algorithms to streamline the interview and decision making process, and helps you make data driven decisions. My one little pitch here is that I’d love to show you a demo of Qorums if you’ve not seen it yet, so feel free to reach out to me, and I’d love to set you up with that. 

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