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Stop Vending-Machine Hiring: Build the Candidate Your Business Actually Needs · The ROI Online Podcast

• 0:01
  SPEAKER_00: AI Made Simple.
• 0:03
  SPEAKER_00: I'm Steve R Y Brown.
• 0:07
  SPEAKER_00: Why are you here?
• 0:09
  SPEAKER_00: Well, you're here because we talk about how to think about artificial intelligence and how to apply it in your job and your leadership in your organizations.
• 0:22
  SPEAKER_00: It's changing expectations.
• 0:26
  SPEAKER_00: There are things that are available now that weren't available just months ago.
• 0:30
  SPEAKER_00: The other day I was walking through an airport and I was hungry.
• 0:34
  SPEAKER_00: So I stopped by a vending machine.
• 0:36
  SPEAKER_00: And if you've ever been hungry and you've uh gone to the vending machine and you look at it, and what do you have?
• 0:43
  SPEAKER_00: You have a very small options to pick from.
• 0:50
  SPEAKER_00: So you put your money in and you pick the worst, the least worst option.
• 0:56
  SPEAKER_00: Sometimes I've been in a gym, I've been hungry, you go look, and what is there?
• 1:02
  SPEAKER_00: It's like, oh, there's there's sugar, there's sugar, there's sugar, and then there's artificial chemical things that are arranged in so-called snacks.
• 1:12
  SPEAKER_00: And it's like, it's not what I want.
• 1:16
  SPEAKER_00: And the other day I was talking with a client, and we're thinking about uh getting a a someone someone that will be dedicated to helping them be successful.
• 1:30
  SPEAKER_00: And you know, immediately the your mindset goes to, hey, what what are we looking for?
• 1:37
  SPEAKER_00: Right?
• 1:38
  SPEAKER_00: And if you just start there, what are we looking for?
• 1:41
  SPEAKER_00: Or what what are my options?
• 1:44
  SPEAKER_00: You're shorting yourself.
• 1:46
  SPEAKER_00: There's a better way to go about it instead of going up to the vending machine and just seeing what the the least worst option is.
• 1:55
  SPEAKER_00: How about we how about we change that?
• 1:58
  SPEAKER_00: Okay.
• 2:00
  SPEAKER_00: And so AI as a thinking partner, the framework for custom hiring is like stop vending machine hiring and let's build a candidate your business actually needs, right?
• 2:11
  SPEAKER_00: And so that to avoid your um your vending machine hiring mistakes, you know, hiring a mistake in hiring is probably one of the most uh costly things that you're going to do in your business.
• 2:24
  SPEAKER_00: And you have to do it all the time.
• 2:26
  SPEAKER_00: And guess what?
• 2:28
  SPEAKER_00: We all know that we're probably we're probably not an expert at hiring, interviewing, identifying, placing onboarding, and setting up for success, both your business and and your new candidate.
• 2:43
  SPEAKER_00: That's just hard.
• 2:44
  SPEAKER_00: And and I hear it every single time.
• 2:48
  SPEAKER_00: The the biggest complaint of business owners is that the struggle they have and the insecurity they have of not being able to solve this problem why can't I find good people and get them in place and keep good people.
• 3:04
  SPEAKER_00: Well, I think we need to start at the beginning and we need to think about what is our destination.
• 3:11
  SPEAKER_00: You know, last week we talked about, you know, we've got this robot now that can drive us around through traffic safely, better than a human.
• 3:22
  SPEAKER_00: You know, there's all the all the data shows that.
• 3:26
  SPEAKER_00: So here we are running businesses with our hands on the steering wheel.
• 3:30
  SPEAKER_00: Well, when we look at what we're doing here, why not why not back up and reduce our risks and start about this in a different way?
• 3:41
  SPEAKER_00: And that is instead of going, all right, what do we need?
• 3:44
  SPEAKER_00: I need a receptionist.
• 3:45
  SPEAKER_00: All right, go find a receptionist template job description and let's get it posted.
• 3:51
  SPEAKER_00: And we'll customize it a little bit.
• 3:53
  SPEAKER_00: But imagine having AI as a thinking partner and that we get more specific and that we increase our opportunities and we increase our choices of who we can bring in, that would be a great fit in our organization, would be successful.
• 4:10
  SPEAKER_00: Well, that means that we have to look at the data in our organization to create that bespoke position, that that bespoke job description, so that we have our odds, our success rate goes up as far as placement expectations.
• 4:30
  SPEAKER_00: So they're today, we're going to talk about that.
• 4:33
  SPEAKER_00: So, in essence, what are we going to learn today?
• 4:35
  SPEAKER_00: We're going to learn how to define your vision, we're going to learn how to uh lock AI into our reality, and then we're going to learn how to study and extract that so that you can come out with um better assets and a better job description and increase your odds.
• 4:54
  SPEAKER_00: So the trap of vending machine hiring.
• 4:58
  SPEAKER_00: And by the way, if you're getting value out of these, you know, subscribe, share this.
• 5:06
  SPEAKER_00: And yeah, every week we talk about these things that can help you be more successful in your responsibilities there as a leader in your organization.
• 5:16
  SPEAKER_00: So subscribe, share, like, tell your friends and neighbors.
• 5:22
  SPEAKER_00: All right.
• 5:22
  SPEAKER_00: Most business owners treat hiring like a transaction.
• 5:25
  SPEAKER_00: You drop a blank template into the machine and expect a perfect employee to pop out.
• 5:31
  SPEAKER_00: But that transactional inputs guarantees disastrous outputs.
• 5:36
  SPEAKER_00: And we've all done it.
• 5:37
  SPEAKER_00: We've been in the heat of the moment.
• 5:39
  SPEAKER_00: We just needed to fill that position.
• 5:41
  SPEAKER_00: We went on our gut, we made a decision.
• 5:45
  SPEAKER_00: It's good enough.
• 5:46
  SPEAKER_00: Let's get them in here.
• 5:47
  SPEAKER_00: And then what do we find out 90 days into it?
• 5:51
  SPEAKER_00: That's not a good move.
• 5:54
  SPEAKER_00: Wasn't a good move for the person you brought in, and it wasn't a good move for your customers, wasn't a good move for your other valuable, dedicated teammates.
• 6:05
  SPEAKER_00: And ultimately, it cost you dearly.
• 6:09
  SPEAKER_00: So what we're putting in a generic prompt, if you will, and then you're you're getting a generic output.
• 6:16
  SPEAKER_00: And so what are we doing?
• 6:18
  SPEAKER_00: We're attracting people who aren't a good fit.
• 6:20
  SPEAKER_00: It's not that they're bad, it's that we've failed on bringing in and setting everyone up for success.
• 6:28
  SPEAKER_00: Why?
• 6:29
  SPEAKER_00: Well, it takes a lot of work, and so let's talk about that work and let's talk about how to make it easier.
• 6:34
  SPEAKER_00: So let's design this architecture that we can do.
• 6:39
  SPEAKER_00: So, pillar one, we want to do the human work first.
• 6:42
  SPEAKER_00: Where are we going?
• 6:44
  SPEAKER_00: What's the vision?
• 6:45
  SPEAKER_00: What's the destination?
• 6:46
  SPEAKER_00: What's the aspirational outcome here?
• 6:49
  SPEAKER_00: And then we're going to take our data that we have in-house.
• 6:53
  SPEAKER_00: We have all this great data.
• 6:55
  SPEAKER_00: Imagine that we're designing a job description, but we don't rely on all of the inputs that we could consider.
• 7:03
  SPEAKER_00: Well, why do we not do that?
• 7:04
  SPEAKER_00: Well, we don't have a lot of time, we don't have the bandwidth, we don't have the cognitive energy.
• 7:12
  SPEAKER_00: Yeah, and maybe we don't even have the temperament to sit out and be detailed and go through all these things and study and look for insights.
• 7:19
  SPEAKER_00: Well, that's where Gemini Notebook in particular, in this case, or AI, can is graded all of that.
• 7:27
  SPEAKER_00: So let's turn it loose.
• 7:28
  SPEAKER_00: But we got to turn it loose on data that we bring in-house, and it becomes a walled garden.
• 7:34
  SPEAKER_00: It's only going to consider that.
• 7:35
  SPEAKER_00: And what are we going to do?
• 7:36
  SPEAKER_00: We're going to get a very bespoke job description based on observations of how we work.
• 7:42
  SPEAKER_00: So we'll let the bot do that, extract it, and then put it into clear language so that we can set proper expectations and ultimately help us ask better questions to identify the folks that would be a good fit.
• 7:59
  SPEAKER_00: So today we're going to look at the actual problem.
• 8:01
  SPEAKER_00: That's what that is.
• 8:03
  SPEAKER_00: So here we go.
• 8:04
  SPEAKER_00: Step one.
• 8:06
  SPEAKER_00: Imagine, imagine that we uh we use a Gemini notebook.
• 8:11
  SPEAKER_00: I'm going to pop that up at the moment.
• 8:13
  SPEAKER_00: So hang on a minute.
• 8:14
  SPEAKER_00: Let me get there.
• 8:17
  SPEAKER_00: All right.
• 8:18
  SPEAKER_00: I'm going to share this screen.
• 8:20
  SPEAKER_00: Where'd you guys go?
• 8:21
  SPEAKER_00: Here we go.
• 8:23
  SPEAKER_00: Hang with me.
• 8:25
  SPEAKER_00: We're going to bring her in.
• 8:28
  SPEAKER_00: I'm going to share the screen.
• 8:30
  SPEAKER_00: We're going to go to Gemini Notebook.
• 8:35
  SPEAKER_00: Okay, so we're looking at Gemini Notebook here.
• 8:38
  SPEAKER_00: When we look at this, on the left side is where we're going to bring in all our transcripts.
• 8:43
  SPEAKER_00: And so in this case, I'm showing you the actual notebook that I use to create the show today.
• 8:50
  SPEAKER_00: So I brought in all my messy thinking.
• 8:52
  SPEAKER_00: I got it organized and I brought it in as a source.
• 8:56
  SPEAKER_00: So here's where we can interrogate it.
• 8:59
  SPEAKER_00: So you know what is what is this source area?
• 9:04
  SPEAKER_00: I heard a great term today.
• 9:05
  SPEAKER_00: This is where we bring everything in quarantine.
• 9:08
  SPEAKER_00: What does quarantine mean?
• 9:09
  SPEAKER_00: Well, if you're sick, well, you quarantine yourself.
• 9:13
  SPEAKER_00: And what does that mean?
• 9:13
  SPEAKER_00: That means you separate yourself from everything else so that you don't spread your sickness.
• 9:19
  SPEAKER_00: Well, when you think about Gemini Notebook, you're going to bring everything in here.
• 9:24
  SPEAKER_00: Why?
• 9:25
  SPEAKER_00: Because you only want it to be considered.
• 9:27
  SPEAKER_00: You don't want the sickness, all the slop, all the unrelated data to be considered in your outputs.
• 9:35
  SPEAKER_00: Here's where we have our boardroom or where we uh brainstorm, interrogate, clarify our thinking, clarify what we want to say.
• 9:46
  SPEAKER_00: That's what happens in here.
• 9:48
  SPEAKER_00: But what's it looking at to give you the answers?
• 9:51
  SPEAKER_00: It's looking at the source over on the left.
• 9:53
  SPEAKER_00: And over on the right, it's going, this is the output that we can direct.
• 9:58
  SPEAKER_00: Very clear, specific output, out assets, job descriptions, whatever it may be on the right, is what we can expect to be clear, pithy, and helpful.
• 10:11
  SPEAKER_00: So back to the show.
• 10:13
  SPEAKER_00: Here we go.
• 10:14
  SPEAKER_00: So that's where we're going to put in what's our destination, what's our vision, what's our output.
• 10:19
  SPEAKER_00: This is where we're going.
• 10:22
  SPEAKER_00: And that helps us, that helps us end up with clear assets, clear questions, clear expectations, clarity wins, right?
• 10:32
  SPEAKER_00: So then we're going to, like I described, we're going to lock that AI in that walled garden in that source aspect of Gemini Notebook.
• 10:42
  SPEAKER_00: So you bring in, here's what we did in this case.
• 10:46
  SPEAKER_00: In this specific instance that I was working on, the problem was we don't have a specific job description written yet for this particular position, but this position is new.
• 10:59
  SPEAKER_00: Okay, so there's no template.
• 11:01
  SPEAKER_00: So what I what I did is I went and got several months of project meeting transcripts.
• 11:09
  SPEAKER_00: So that was equivalent to maybe 12 meetings.
• 11:13
  SPEAKER_00: You know, these are hour-long meetings.
• 11:16
  SPEAKER_00: I brought these transcripts into that uh area, your source area, and then I asked Jim and I to go through and pull out the unique aspects.
• 11:28
  SPEAKER_00: Look at this, and we're designing a job description.
• 11:30
  SPEAKER_00: I gave it the aspirational destination.
• 11:33
  SPEAKER_00: I would need a job description for this particular support role for this particular client.
• 11:38
  SPEAKER_00: But go look at the various look how we work, look about what we talk about, look about at what's falling through the cracks, and let's create a job description that is very clear.
• 11:51
  SPEAKER_00: And what did it do?
• 11:52
  SPEAKER_00: It gave me great content, it didn't give me hallucinations, it didn't go out and find other job descriptions, it leaned into best practices and and it popped out some stuff that was really good.
• 12:05
  SPEAKER_00: So I have uploaded several months of project meeting transcripts.
• 12:09
  SPEAKER_00: I want you to read how we actually work and strictly use those provided resources.
• 12:14
  SPEAKER_00: Build this description out of that.
• 12:17
  SPEAKER_00: So, what did we do?
• 12:18
  SPEAKER_00: We just took all that messy stuff.
• 12:21
  SPEAKER_00: The truth is, our work is messy.
• 12:22
  SPEAKER_00: We've got all these conversations and rabbit trails and all these details that I wanted to have considered.
• 12:30
  SPEAKER_00: Then we focused our energy on those, and guess what?
• 12:34
  SPEAKER_00: We came out with this really cool, clear job description.
• 12:38
  SPEAKER_00: It was very, very specific, clear expectations.
• 12:45
  SPEAKER_00: And it was amazing.
• 12:47
  SPEAKER_00: So now what do we have?
• 12:48
  SPEAKER_00: We'll be able to attract, we'll have based on our real habits and needs.
• 12:52
  SPEAKER_00: Now, what am I doing?
• 12:53
  SPEAKER_00: I'm increasing my odds of bringing someone who will be a good fit in this organization.
• 12:58
  SPEAKER_00: I'm increasing the odds that I'm bringing someone that's starting to think like my team thinks and understands the workflow innately based on how we talk about it, how we presented it, what what we're looking for to shore up and fill, yeah, fill in uh missing areas that we don't have in the team.
• 13:21
  SPEAKER_00: So we're we're we're designing a methodology.
• 13:26
  SPEAKER_00: So we're moving from a vending machine hiring with very few options and picking the the least worst of those options to designing a blueprint, and we're using these messy transcripts, and we're clarifying our vision, we're analyzing, and we're extracting the exact requirements.
• 13:50
  SPEAKER_00: So it brings us into we're moving from uh into an exact reality.
• 13:55
  SPEAKER_00: We have the candidate here that is more able to see what we're doing, is more able to like uh not not present themselves as a option based on the sp specifics of what we're requiring.
• 14:13
  SPEAKER_00: And the interviewer is set up for success, you're set up for success in that you can ask now you can ask questions that can be more specific to the unique aspects of it, instead of just like, hey, what would be a good question to ask in this moment?
• 14:31
  SPEAKER_00: And so by using this methodology, you can start using AI as a thinking partner, and you've got a framework for customize hiring, right?
• 14:43
  SPEAKER_00: So let's define your vision, let's lock in your reality, let's study and extract, and let's use AI to do all that work that releases you of having the need the energy and the cognitive uh input and the focus that day to create a job description, improve the odds to solve one of the most expensive, costly things that most businesses are shooting themselves in the foot at.
• 15:11
  SPEAKER_00: And that's that is you can hire better, you can hire more clearly.
• 15:18
  SPEAKER_00: You know, clarity wins, especially in this situation.
• 15:22
  SPEAKER_00: All right, AI made simple, that's what we're doing.
• 15:25
  SPEAKER_00: Use this QR.
• 15:29
  SPEAKER_00: You need help designing a great job description.
• 15:33
  SPEAKER_00: Maybe you want to talk about what implementing a version of uh Notebook LM or an equivalent system into your organization.
• 15:43
  SPEAKER_00: I can help you.
• 15:44
  SPEAKER_00: My name's Steve R.
• 15:46
  SPEAKER_00: Y.
• 15:46
  SPEAKER_00: Brown, and this has been AI Made Simple, and that's a wrap.