So What? Developing a Skills Matrix for Your Organization

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to build a skills matrix to identify the strengths and weaknesses of your team. Using a skills matrix, you’ll discover how to pinpoint critical skill gaps and develop a plan to address them, whether through training or hiring. You’ll also learn how generative AI can help accelerate the skills matrix development process. Finally, discover how a skills matrix can empower you to make informed decisions about resource allocation and team development.

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In this episode you’ll learn:

• What a skill matrix is
• Why you and your organization should create one
• What to do with a skills matrix

Transcript:

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Katie Robbert: 00:25
Well, hey everyone! Happy Thursday! Welcome to So What?, the marketing analytics and insights live show. I am Katie, joined by Chris and John. Howdy, fellows!

Christopher Penn: 00:35
Hello.

Katie Robbert: 00:37
On this week’s show, we are talking about developing a skills matrix for your organization. One of the goals of 2025 for Trust Insights is to go back to our roots—to make sure that our foundational pieces are solid and that we’re also sharing that knowledge with our audience, our customers, and pretty much anyone who’s willing to listen. Because without a strong foundation, all of the fancy, shiny new objects aren’t really going to do a whole lot. So today we’re going to be talking about a skills matrix. And before we get into it, have either of you ever done this kind of exercise in past roles or organizations? Is it something—was it a brand new concept?

Christopher Penn: 01:20
We’ve done skills assessments. I mean, we’ve all gone through like the whatever, Strengths Finder thingy and those things. But not something as formal as like, okay, well, what exactly can you do?

Katie Robbert: 01:36
What about you, John?

John Wall: 01:37
Yeah, no, we’ve done some stuff. I had done—a million years ago when I was in the insurance industry—we had done some stuff as far as, like, okay, these are the positions and these are the skills that are needed for that kind of stuff. But yeah, we also wrote letters and I carried a briefcase and wore a suit. So that’s hopelessly out of date.

Katie Robbert: 01:55
Well, what’s interesting is—Chris, you started with skills assessment. And so, we’ve all probably done like the Myers-Briggs and all those other assessments that tell us what we’re individually good at. But that’s not what this exercise is. So, yes, you need to know what you bring to the table. But more to what John is saying is—and what’s needed by the organization—and that tends to be a disconnect when we think about a skills assessment. It’s great to know what we’re all good at, but what does the organization actually need? And so a Myers-Briggs is like a fun thing for, like, oh, these five or six people all rate as leaders. But we don’t need six leaders on the team.

Katie Robbert: 02:43
We need individual contributors and that part of the conversation tends to get missed. So because we wanted to use some generative AI to put this together, Chris, where would you like to start today?

Christopher Penn: 02:54
Well, I suppose it would help to define what is a skills matrix. Like, what does it even look like, Katie?

Katie Robbert: 03:04
A skills matrix—I think the word matrix trips people up—so it can be a checklist, it can be a Venn diagram, it can be a document, but at the end of the day, a skills matrix—whatever you call it—is: here’s what I do, here’s what the company needs and what matches and what’s missing. So it’s—in some ways—a gap analysis. And so we can do a gap analysis with our market, with our audience, with our customers. We need to also do it with our teams and sort of understand: here’s what we have, here’s what we need, what’s the overlap, or are we over-indexed on certain things? And I think on the podcast, Chris, the example that I gave was: we have one development project to 10 developers. We’ve skilled up for the wrong thing.

Katie Robbert: 03:53
Especially as the needs of a company change. We need to be able to be more flexible with our team members as well. And that’s hard because having that kind of agility with people is really sticky.

Christopher Penn: 04:09
Okay, so where I would start if I was going to do this and I had nothing else to begin with, is I would actually start by saying to them, what am I doing? So if you would start with something along the lines of your management consulting expert and you know, skills assessment, skills matrix, gap analysis, skill gap, etcetera. Let’s talk about what we need to build a skills matrix. So our first thing is purpose and scope, which is good. Like, why are we doing this thing? Which is amusingly and completely unsurprisingly reminiscent of the 5P Framework.

Katie Robbert: 04:50
It’s weird how that works. Yeah, you know, I’m glad it started there because just to go through the exercise of putting a skills matrix together, it’s not a small exercise. So you have to have a reason to do it. If the reason is we just kind of want to do a gut check, like, okay, but is that a strong enough reason to put—to make everyone sort of stop what they’re doing and do this? Or is it that you have clear business goals and you want to make sure that you’re aligned for today and aligned for six, 12, 18 months from now?

Christopher Penn: 05:25
So, for the purposes of our example today, Katie, what is our purpose?

Katie Robbert: 05:30
So let’s say the purpose is that our purpose for this skills matrix is to grow our development practice over the next 12 months.

Christopher Penn: 05:48
Software development.

Katie Robbert: 05:50
Software development.

Christopher Penn: 05:51
Okay. The next 12 months we want to—

Katie Robbert: 05:58
Grow it from 0.5 resources to 5 full-time resources.

Christopher Penn: 06:10
Okay, so that’s our purpose, it says, and what business problem does that solve?

Katie Robbert: 06:18
So in this very fictional business problem, we are going to take our internal code and make it more of a SaaS product. So we want to start offering off-the-shelf products.

Christopher Penn: 06:38
Katie is offering off-the-shelf software products. Okay, let’s continue down the checklist because we have these questions here. Who is the target audience for the skills matrix? Is it—who do—who wants this?

Katie Robbert: 06:56
The target audience is going to be me, the CEO, HR, and our current head of development—which is half of you. I don’t know how you want to phrase that—our current technology lead.

Christopher Penn: 07:21
What level of granularity do we need? Do we need broad skill categories or very specific technical skills?

Katie Robbert: 07:27
Let’s start with broad skill categories because I’m curious to see what—what kind of questions are going to come up.

Christopher Penn: 07:37
Okay, let’s see. Next, identify and define the skills. What skills are needed and how do we describe them?

Katie Robbert: 07:46
So go ahead.

Christopher Penn: 07:49
I would say what you got?

Katie Robbert: 07:51
I would say probably a lead architect. Lead software architect.

Christopher Penn: 07:59
Okay.

Katie Robbert: 08:03
You need—I would go with two developers because I’m going from 0.5 to 5.

Christopher Penn: 08:11
Okay, well these are the skills it’s asking about—like what skills are relevant and how do we describe the technical skills? Hard skills, soft skills, domain-specific skills.

Katie Robbert: 08:23
Okay, well let’s keep going with the way that I have it, though. Sure. And then we can get into each of those. One QA engineer.

Christopher Penn: 08:33
Okay.

Katie Robbert: 08:35
And one database architect.

Christopher Penn: 08:38
Okay.

Katie Robbert: 08:38
One DBA. And so the reason I wanted to list out the roles first is because it’s very easy to get into the weeds of: We think we need this. We think we need this. But listing out the roles—at least the way that I think about it—at least helps you start to constrain it because I could start to get into the weeds of: Well, I need someone who’s good at project management. Was that a developer? Is that a project manager? And I might need those skills. But if I’m talking about—if my goal is to start offering SaaS products—I need people who can build them and maintain them.

Christopher Penn: 09:19
Okay.

Katie Robbert: 09:21
So lead architect. I’m looking at that person as the one who’s basically outlining—how do I say it—like basically they’re creating the technical requirements. They’re creating the blueprint for each of the products.

Christopher Penn: 09:43
Okay.

Katie Robbert: 09:45
The developers are actually executing, creating the code.

Christopher Penn: 09:51
Any specific languages?

Katie Robbert: 09:55
Not to my knowledge. I mean, that’s not something that you know—that’s a good question. I don’t know the answer to that question, to be honest. I know that Python is one of the more common. I know that there’s R, so I don’t have a good answer to that question.

Christopher Penn: 10:14
Okay, what about the QA engineer?

Katie Robbert: 10:20
A good working understanding of how code works and attention to detail.

Christopher Penn: 10:29
Okay. And the DBA?

Katie Robbert: 10:37
Ability to build and join secure databases for data collection.

Christopher Penn: 10:50
Okay. Now, in terms of the skills list itself, if we were to start putting that together, I would say for the hard skills in today’s environment—given that we’re talking about generative AI and such—probably the primary language would be Python because generative AI is the most fluent in Python and we would have a secondary language of a web language of some kind. So PHP or Ruby would be good choices there because you’d want to know: can anyone help us put our thing on the web? We don’t have the perfect idea for databases. The gold standard these days is MySQL, which is the most common one. But again, going to the world of AI, if we’re going to be using AI with this, we probably want vector databases like Weaviate or Milvus or ChromaDB, that would be good choices. Or LanceDB.

Christopher Penn: 12:03
Actually, for the development environment you should probably know VS Code as well as AI-assisted coding like Cursor, GitHub Copilot, and Tabnine. Those would be good hard skills to have. And then for generative AI, you should probably know ChatGPT, Claude, Gemini, DeepSeek, etcetera. For the database architect, for architecting stuff, you should probably know any DB structural tool.

Katie Robbert: 12:53
Like SQL Workbench. So those are the hard skills. What are the soft skills, Katie, that we would be thinking about?

Katie Robbert: 13:05
Number one, communication.

Christopher Penn: 13:07
Okay.

Katie Robbert: 13:11
Soft skills would be time management.

Christopher Penn: 13:19
Okay.

Katie Robbert: 13:22
And I mean those are really the two things that I look for. Number one, I feel like everything else can be taught, but if you’re not a great communicator, that’s going to be a struggle.

Christopher Penn: 13:34
Okay, so now for the sake of—

Katie Robbert: 13:37
These, those are the only two that we need to list.

Christopher Penn: 13:40
Sure. How, in terms of skill assessment, what does proficiency in the skill—these skills—look like?

Katie Robbert: 13:50
So typically what you would do is you would have some kind of a rating scale. So if we’re saying these are the skills we need, then we would then put the skills—the hard and soft skills—into what you would call like a rubric or even just like a quick feedback survey of: if I wanted to assess the team today, then I would give them a survey listing out each of these things. Like, on a scale of 1 to 5, 5 being the most proficient, how proficient are you in database schema? How proficient are you in Python? And I already know the answers to that would be very—not great.

Christopher Penn: 14:33
Exactly.

Katie Robbert: 14:34
When I think about the team as a whole. And. But that’s what you do. Start with the self-assessment. So first you need to list what you need and then you can build that into the assessment to see what you currently have.

Christopher Penn: 14:45
The model also suggests things like manager assessment and 360-degree feedback and skills tests and certifications and qualifications, too.

Katie Robbert: 14:53
Yeah, I mean, there’s a lot of things that you do, but for the sake of the live stream, we can keep it to the skills assessment.

Christopher Penn: 15:01
Okay, so you’ve got the numerical scales. I could just—and then descriptive scales and then choosing a matrix format. How do we visually represent the data?

Katie Robbert: 15:12
Honestly, that really comes down to preference. I think a spreadsheet is fine. Like I said, I feel like the term matrix trips people up and it sounds more complicated than it needs to be, but it can literally be like: this is how Chris rated overall, this is how John rated overall. And based on what you need, here’s the gap.

Christopher Penn: 15:37
Okay, so with this, we have enough to at least say to the model: here’s what we’ve got for what we want to do. Let’s do this. Here’s our answers to many of your questions so far. In terms of the information we’re providing, is there anything that is glaringly obvious missing from this—these answers? We put our answers in and let’s see what it comes back with. If it says like, you totally missed the boat on this and that. Okay, it’s a great start. Definition of proficiency levels. You’ve opted for a 1 to 5 proficiency scale, which is perfect, but you haven’t decided what each number actually means. What does level 3 proficiency in Python mean?

Christopher Penn: 16:29
Can someone at level 3 build a specific type of application without clear definitions, doesn’t know what to do, specifically of soft skills definitions there as well, and then implicit versus explicit skills. You haven’t explicitly defined what each skill encompasses in context of your SaaS product development, things like Django and Flask, for example. Now, what we could do is say: let’s have you, based on the knowledge you have of skills assessment, fill in the missing pieces for the critical feedback you’ve highlighted above, infer based on our purpose, what the missing pieces would be, such as skill definitions. In the absence of this information, we could at least rely on the model’s long-term knowledge and see if it comes up with anything interesting.

Katie Robbert: 17:38
And when you do this as an organization, there is a lot of upfront work. So we’re doing a bit of the shortcut and we’re letting generative AI fill in the pieces. But to do this properly, Chris, if you came to me and said: I’m the client, I want to do a skills matrix because our goals are to have 70% of our organization using generative AI by the end of the year. We would start with: deconstructing everything. So what are the goals? What do you currently have? What does that mean? And we would dig really deep into the detail and that would become the knowledge block before we get to this step. So you’d want to build all of that—like, who are you as a company? What do you offer today?

Katie Robbert: 18:24
What do you want to offer—six, 12 months from now—as service offerings? Who are your clients, what do you do for them? And have all of that background information as those knowledge blocks. And so on other episodes of this live stream, you often start with the knowledge block that you’ve already written out of: who’s Trust Insights, who’s Chris Penn, who’s John Wall? That’s the kind of work that you want to do up front before getting into this part of it. So I just wanted to acknowledge that we’ve skipped over a couple of steps for the sake of the time crunch, the live stream.

Christopher Penn: 18:59
Exactly. So we’ve got the scale definitions like: awareness of basic understanding, developing proficiency, applied learning, proficient or independent application, advanced or expert master, leading. Now, reading these descriptions here, in terms of skill level at Python in particular, I would rate myself a level 1. I can barely—I maybe could do a couple of things in level 2, but not really. What I find interesting is how I use and how I’ve experienced working with generative AI tools, particularly reasoning models. I would easily put them at a level 4, if not a level 5.

Katie Robbert: 19:39
But that’s a different skill that we haven’t even talked about—is being able to use generative AI to create the code. We’re talking about actual people who do the things as designed. And that’s where it gets tricky because when you start off with: well, what is the purpose of this? It’s: I want to find people who can use generative AI to do the thing versus I want to find people who do the thing. Two very different paths to go down. But you have to define that first.

Christopher Penn: 20:08
So, Katie, not only have you got half of a resource, you’ve got half of a level 1 resource.

John Wall: 20:14
I would say you’re at level 2, I think.

Christopher Penn: 20:17
No, not in Python.

Katie Robbert: 20:23
And so that’s okay because it’s not your primary job. It’s like—you know—for full transparency, you do it because it’s a necessity of the company, not because that’s what you set out to do or were trained in.

Christopher Penn: 20:37
Exactly. We have—so it went through and then made all the definitions for all of these things. So if this was what we wanted to roll with: here’s how we define problem solving, here’s how we define adaptability, learning, attention to detail demonstrated by writing well-tested code, identifying, education, some meticulous reviewing work. So again, you’ve got a level 1.

Katie Robbert: 21:01
I’d put you at a 0.5 for that one, respectfully.

Christopher Penn: 21:09
It’s absolutely true. Well, it’s funny—just as a quick aside—I had Gemini Reasoning evaluate one of my old scripts and it’s like, wow, like none of the best practices that we recommend are in here. So I’m just going to go ahead and fix this for you. So now we’ve got a toy version of a skills assessment rubric. In terms of the things that we’re looking for, what do we do next?

Katie Robbert: 21:37
Next we put it together in a way that people can actually respond to it because we’ve defined at the top: here’s what we want to do, here’s what we’re looking for. Now we have to see what we currently have. So, for the sake of the example, the three of us are going to be filling it out and we can do it pretty quickly. But that’s what you would do next is you would say: okay, great, I know what I need, here’s how I’m going to assess it. Now I need to actually do the assessment. And John, as our chief statistician, you can certainly check our math on these scoring rubrics.

John Wall: 22:17
Make sure the spreadsheet’s right.

Katie Robbert: 22:19
Exactly.

Christopher Penn: 22:21
Okay, so we’re here comes the spreadsheet. Here’s the instructions on how to fill it out. Self-rating, optional comments. And so this is our assessment.

Katie Robbert: 22:33
One of the things that I learned the hard way—and I mentioned this in the newsletter this week—is you have to let people do the self-assessment. Don’t try to assess their skill sets on their behalf. And you may feel like you know based on your perspective, but long story short, it’s not going to end well. And so at least give people the option to do the self-assessment. You may find surprising things like: you may not know—oh, you know what, John is actually a really good database architect. We’ve just never asked him to do that. So it’s never come up in conversation.

John Wall: 23:14
Yeah, no, I’m right with Chris on that. Like I have built a database in the past, but yeah, there’s no bragging about any of that.

Christopher Penn: 23:24
All right, so how do you want to do this assessment, Katie?

Katie Robbert: 23:28
So we can just do one for each of us? I would say ideally, yes, but you know, given the time constraint, let’s just do one and just sort of go through it to do the demonstration.

Christopher Penn: 23:40
Okay, so we’ll just do me here. Yeah, so proficiency in Python, I would rate myself a 1. Let’s do a 1.5.

Katie Robbert: 23:48
Okay.

Christopher Penn: 23:48
Working knowledge of PHP or Ruby, that’s a 4. Sequel, that’s a 5. Vector databases, 2. IDE, that’s a 5. Generative AI, that’s a 5. DB architect, I had to make myself a 3 because I’m kind of at it. Soft skills: problem solving, I’m good at solving problems. Collaboration, that’s a 1. Adaptability is a 5. Attention to detail, that’s a 1. And proactive communication, ownership, that’s a 2. So how do we do? All right, now what? Now what do I do?

Katie Robbert: 24:21
Now we have to give it. Because we’re using generative AI to do this. We say: okay, we’ve done, we’ve had the team go through the self-assessment, now let’s start to find the gaps because theoretically you would have a lot of these—you know—not just one person. Because if it’s just one person, yeah, I already know I need to hire a bunch of people because it’s not sustainable to try to have half a person do the role of five people. But if I had a lot of people, let’s say I had a team of 20 marketers, then I would want to use generative AI and have every single assessment and then give that back to this model and say: great, we’ve done the assessment, help us understand the gap analysis now.

Christopher Penn: 25:08
And now. So let’s say, let’s review it and now build a gap analysis based on our goals and needs of becoming a SaaS company versus the human talent we have in-house. So let’s take our—I’m going to take a good old-fashioned screenshot. Drop this in right here and let’s see if generative AI says, yeah, you may as well just fire Chris. Says: okay, let’s analyze Chris’s self-assessment. Build a gap analysis. Chris is the head of software development. Strengths: MySQL, IDE. Yep, significant gaps. Python programming, collaboration, teamwork, attention to detail, proactive communication, ownership, moderate gaps. So gap level critical. Python program urgent and primary. Requires significant immediate and significant upskilling. May need to hire a strong Python expert first to lead the initial Python development. Well, Chris, upskill and I think—

Katie Robbert: 26:16
That’s a solid recommendation.

Christopher Penn: 26:21
Consider leadership teamwork training for Chris. Focus on building a collaborative culture from the outset. Many to provide support and mentorship and team leadership and collaboration. Being mindful of team dynamics as you hire and build the team.

John Wall: 26:33
Let’s see.

Christopher Penn: 26:34
Attention to detail, urgent and important. Adjust proactively in strong code review processes, testing frameworks and quality assurance practices from the start. Pair Chris with someone who’s strong in QA and so on and so forth. Then we go down the list here. So, in terms of this, is this the skills? This is just one person’s feedback, but is this essentially the skills matrix?

Katie Robbert: 26:57
It is. Okay, it is. And I think again the word matrix trips people up. It doesn’t have to be complicated because what you’ve just done, Chris, with the help of generative AI, is build a hiring roadmap to get you from where you are today to where you want to be. And so obviously you know, you have next steps: discuss skills gap, develop, you know, the development plan. It’s focused on you. However, the way that it outlined all of those pieces, that’s your roadmap.

Christopher Penn: 27:30
Should we just fire Chris?

Katie Robbert: 27:36
So what’s interesting though is we’re looking at it from the lens of software development. We haven’t looked at Chris holistically as a full 360 of. You know, if you were going to do this with an organization, you would have more than just that one particular role. You might have a few rounds of marketing and data science and this and that. So we’re focused specifically on software development. You were never meant to be our primary software developer. And so this all makes sense. But if you go—if you scroll back up, Chris—to the—yeah, that part—if you look at that last column, that is—that’s what I would then look at and be like: okay, this is my roadmap. So in parallel of developing Chris’s skills—if that’s a priority to us—this is where I start to hire.

Christopher Penn: 28:31
Okay, let’s just as a mock thing, I’m going to add in, have do John’s here and let’s do rand between 1, 5. So we will go through and we’ll just randomly assign characteristics for John and then let’s do Kelsey and do another rand between here and do this and now. So what you’re saying is we could add in these two extra people and—

Katie Robbert: 29:14
That recommendation list is going to change.

Christopher Penn: 29:17
Let’s add in our other two resources, perform the analysis and gap analysis for each, then take a look at the company overall and provide a department-level gap analysis of skills for our team as a whole.

Katie Robbert: 29:47
The bigger picture of a skills matrix is to aid with what your business goals are. So obviously, like when you’re hiring people, you know you’re doing it with a purpose because they need to fill certain gaps. But when you do it for using this methodology as a more holistic view of things with the current people and what you need, hiring and onboarding people is very expensive. It takes a lot of time. If it doesn’t work out, a lot of people have like that 30, 60, 90-day window to figure out if it works. That’s still costing you money and it’s costing the team time and resources. And so really trying to get as much of the upfront research done as possible so that you can have a clearer roadmap.

Katie Robbert: 30:33
We could say: all right, well we need to immediately hire a database architect when really we do need to hire a developer or a development lead to then help with the additional hiring and setting up things correctly. If we start with the database architect, we’re getting it wrong and we’re not doing ourselves a service to get us closer to the goals that we have.

Christopher Penn: 30:56
Yep. So the fictional development skills of both Kelsey and John have been put in. It did the initials and now we’re looking at the department overall. And it’s—and this is really interesting—overall team critical gap. This no one has Python skills. Like, it’s like this is your department-wide, mission-critical problem and then it goes through the rest of the skills. So now as a manager, Katie, with your entire team in view, this is—

Katie Robbert: 31:23
What you’ve got, which I think is incredibly helpful because a lot of organizations find themselves in the situation of trying something new, trying to diversify their offerings, trying to offer something that maybe isn’t necessarily in their wheelhouse, but one person might be kind of good at it. But when you look at it through this lens, number one, we don’t have anyone on the team who can actually build the thing, let alone maintain it, let alone have confidence that we can sell it to anybody. Like that makes me—if I were looking at this, if this were a real example, I’d be like: are we even moving in the right direction? Is it going to cost us a lot of money to reach this goal? Is it even going to be worth it?

John Wall: 32:08
I quit today.

Katie Robbert: 32:11
But if the number one thing that you need is Python programming and we don’t have any of that is offering, you know, SaaS to our clients the right goal. Those are things, those are conversations that this is going to bring up, which I think is really helpful.

John Wall: 32:34
She’s been underrated in her Python.

Christopher Penn: 32:36
Yes. Sorry. Well, no, see if we go down, it’s funny because even though I used a random number generator on that, it does say in collaboration, teamwork. While Kelsey is a strength, Chris’s low self-assessment is a concern for team dynamics. Right. Building proactively build a collaborative culture. Department-wide team-building activities and workshops. Focus on collaboration, communication. Establish clear communication. Shall use Kelsey’s strengths to foster teamwork. Provide leadership coaching for Chris and collaborative leadership. I mean that’s for random number generation. That’s not entirely inaccurate.

Katie Robbert: 33:08
I mean I’m seeing some trust falls between Kelsey and Chris in our future.

Christopher Penn: 33:14
That’s not going to go well. Look how many sharp things I’m wearing.

Katie Robbert: 33:19
Let’s see. But what I like about this is that because when we think about adding a new team member, oftentimes we’re hiring for like one specific role. So let’s say—we hadn’t done this exercise—we might just be looking for a developer who’s proficient in Python because we’ve just thought: okay, that’s the gap. Like I could probably handle some QA, you know, John can pick up some of the slack on the communication or whatever the thing is. But this is telling us a different story and this is telling us a more complete picture of not just the hard skills, but also the soft skills, but also what happens when you’re missing them completely—the team as a whole. And that’s why I like a skills matrix.

Katie Robbert: 34:07
It is more work, but overall it’s going to set you up for more success within the organization.

Christopher Penn: 34:14
Now what can I do with this? What do I do next with this? Do I just immediately go and hire a Python developer? Like do it? Yeah. It feels like if I think back to the days when I managed a team, if you would handed me this, I would like—so what should I do next?

Katie Robbert: 34:33
I think, number one, I think that’s a great question to ask the model, but if you’re asking me as the person, I would look at this and re-evaluate again. I would look at the goals of: are we—did we—number one, did we have enough information to do this analysis? Are we missing anything? And number two, are we re-evaluating our goals? And number three, let’s start writing a job description for the first person we need to hire.

John Wall: 35:04
If you don’t just decide to scrap the project completely.

Katie Robbert: 35:07
Exactly. But that to me is the number one, is: are you scrapping this all together?

John Wall: 35:12
Right?

Christopher Penn: 35:13
Well, yeah. Okay, so we’ll say the model: yep, this is useful. We look at our goals. Did we assess the right things for those goals? We’ll ask the model to essentially almost assess itself. It says largely, yes, you did assess the right categories of things to your goals. Where your goals are growing a software development practice and building a SaaS product. The skills matrix is relevant and valuable. Here’s why it’s largely aligned nuances. Some SaaS-specific skills, cloud platform DevOps and things are missing and those cloud platform in fact would be very useful. And so that would be something we might want to go back and redo. Granularity, project management skills. Next steps, immediate steps, action now, Python emergency. Katie, we have a Python emergency.

Katie Robbert: 35:58
We have a Python emergency.

Christopher Penn: 36:06
Start this week. Time is of the essence for closing this critical gap. Recruit Python experts, start hiring immediately. So we need to hire that, discuss gap analysis and plans with the team and then short-term steps: implement Python mentorship, MySQL development plan, team building and collaborative initiatives. Process implementation of quality, attention to detail, monitor training progress, medium-term reassess skills gap matrix and gap analysis in three to six months. Expand training, performance management integration, team growth and continue hiring.

Katie Robbert: 36:41
So if you were still the team manager, Chris, would this version make more sense to you? Would you then know where to start?

Christopher Penn: 36:50
Yes, I mean just having—say you have an emergency, do this week—is super helpful. It sounds—you know—bombastic and stuff, but I mean that just—you can’t say any more clearly: do this now.

Katie Robbert: 37:07
I like how it has everything prioritized. The other thing that I like that it commented on was you need to redo the skills assessment. So let’s say you follow this roadmap, you hire all the people, you start doing the thing, you need to then reassess: Did we do the thing? Is it actually working? Because once you get people in place and they start working together, the chemistry is going to change. The way in which people work is going to change. And so you want to do this at least twice a year.

Christopher Penn: 37:41
Just for fun, I’m going to do a randbetween and we’re going to change things up here and we will do—let’s do randbetween three to five there and do one more for Kelsey. And now what we’re going to say is it’s now six months later. Unfortunately, we’ve been unable to hire as all the talented people have fled the country. However, here’s the assessment of our team today. Compare it to six months ago, what’s improved, what’s declined. Show the gap analysis and we’ll put in all three revised evaluations. All right, so then versus now. Kelsey has positives on something. She went down on IDE and generative AI and attention to detail. John went down on attention to detail but is up 3 points in Python stuff. So things are doing well.

Christopher Penn: 39:20
So summarization, Chris, significant improvements, Python notable declines, generative AI adaptability, learning, problem solving and so on and so forth. The gap analysis, this is pretty useful. So then versus now, here’s the department level changes.

Katie Robbert: 39:36
That is incredibly useful, especially as teams are trying to fight for more resources or more dollars for the people they have on their team. This is incredibly helpful.

Christopher Penn: 39:50
It says massive improvement. The critical Python gap has largely been closed. Department level declines. Department average has declined slightly. No significant change in IDE overall, remarkable progress. MySQL skills broadened and so on and so forth. So remaining gaps update. So as—again now as the manager—I’m like: okay, you all let your generative AI skills decline. So we need to do some more training on that and things. But this is now pretty useful. So I might say—as the team manager—build me a work plan for the next six months for growing my team’s skills based on this new skills matrix. Break it out month by month and then team member by team member. Let’s see, let’s see if we can come up with a professional development plan. Okay, we have overall work, playing goals. Oh, it’s really—

Christopher Penn: 41:02
It looks like it actually got jammed on some of its formatting there. So it’s good, it’s having a moment.

Katie Robbert: 41:09
We all have those, it’s all right.

Christopher Penn: 41:14
But what you get a general sense of: here’s the team member, here’s Chris needs a half-day workshop on generative AI. Kelsey needs to attend the same workshop as Chris and also do some IDE training for—or that sort of thing. I might try and regenerate this, just see if it’ll format properly. But that to me—as a not-great manager—is even more helpful because I can now look at this and go: oh, here’s what I need to do for my team month by month.

Katie Robbert: 41:47
Well, you know, and it’s—what strikes me as interesting is about the way you said that is: we often—were talking just this morning—about what can we do to help people feel like generative AI is more approachable. But the flip side of that is: what can we do for people who aren’t strong team managers to make managing feel more approachable with their team? And it’s the same thing. You just give them a checklist, you give them a recipe to start doing the thing. So if I had to step out and you were filling in for Chris as the lead of the company, the lead of the teams, this would be your to-do list.

Christopher Penn: 42:27
Yep.

Katie Robbert: 42:27
Don’t get hit by a bus. I mean, it’s classic Katie getting hit by a bus, man.

Christopher Penn: 42:34
Yeah, well, I mean, for those folks who don’t get the reference, that was last week’s live stream, but you can watch on our YouTube channel. But yeah, no, this is actually pretty decent and I could see this being useful. Even if your company doesn’t do this, there’s nothing saying you couldn’t do this for yourself and sit here and say: what do I need to do to grow my career if I know that I want to be a successful generative AI expert? What are the generative AI skills you need? Right. You can go on to anywhere and get a list of those and then you do a self-assessment of yourself and then have it build your own personal professional development plan.

Katie Robbert: 43:16
Oh, absolutely. That’s really all this is. Kelsey asks a really good question. You recently did an episode on business continuity planning, and I’m curious, can a skills matrix help with those gaps? Absolutely. So when—if—for those who want to watch the last live stream episode, TrustInsights.ai YouTube, go to our So What? playlist on business continuity planning. If you find within your business continuity plan: here’s all the things that I have to do in order to make sure that there is a contingency plan. Run it through the skills matrix as your next step to say: here’s where we are today. Here’s all the things we need to cover to keep the business moving forward. If someone suddenly can’t perform their duties, what does that look like? So it may be all right.

Katie Robbert: 44:11
John needs to bring on a sales admin in order to make sure those gaps are filled. I need to bring on an EA. Chris needs to bring on—you know—a junior data scientist, whatever the thing is. But using the skills matrix is going to help you put that plan together because it can feel very daunting, like: oh, we have a big gap, what the heck do we do? Create the to-do list.

Christopher Penn: 44:34
A follow-up process. Question: How do you deal as a manager with these types of assessments with Dunning-Kruger syndrome, where like, I am a Python God and I can know you’re 1, maybe a 1.5?

Katie Robbert: 44:48
So there’s something to be said for letting people do the self-assessment. But then you still need to look at the self-assessment and say: does this jive with what I think I’m seeing? And so it’s another opportunity to have the conversation. So what I wouldn’t do is have people do the self-assessment and you—as the person, the manager—don’t look at it and just give it to the model. You should still be reviewing everything. So, Chris, if you said: I’m a 5 on communication and attention to detail, I would go: all right, well, let’s talk about that. Explain, you know, help me understand why you’re giving yourself that rating.

Katie Robbert: 45:30
Or if John said: you know, I’m a 1 on communication or attention to detail, I would take the opportunity to be like: well, John, what I see is that you’re more of like a 3 or a 4, because here’s what I’m seeing and maybe it’s just a matter of expectation, setting and making sure that people are clear. But if you do have someone on your team who is very unaware of their skills, then that’s a whole different episode and conversation.

Christopher Penn: 45:56
I think another thing you could add to that is: let’s create a 1 to 5 scale definition for communication skills as you did for Python. Because I think you could work with the team, maybe even showcase it in front of the team, say: here’s how we’re going to do the assessment. So when you do rate yourself, you can think about what does this mean? And in this way, this goes back to other episodes we’ve had in the past. I know myself as a human being. I would have biases like: oh, well, you know, Katie is always taking over my meetings and stuff like that. She’s got bad communication skills. Like actually she doesn’t. I just have butt hurt because I was an insecure manager. So by having the machine define it, this is how you rate yourself. You know, if on—

Christopher Penn: 46:56
Am I truly a 1 on communication skills? Do I provide minimal input meanings? No, actually, I talk a lot, meanings. Is my written communication unclear, grammatically incorrect, or lacking necessary detail? Actually, it’s not too bad. Do I frequently ask for clarification, simple instructions sometimes? Do I miss important information? Shared verbal? Yeah, that I do. Reluctant to share updates to raise concerns practically now I’m pretty clear about that. And I do talk over people. So I would say I have some of the observable behaviors, but not all of them, for here, other ones, you know, this scale is because a machine spit it out is a little more objective than me as a bad manager deciding, well, I think great communication means you are always praising me and you’re never criticizing me.

Katie Robbert: 47:40
Right. And so it’s definitely—and this is why I say it’s a lot of work, because when you’re working with people, it’s never as straightforward as you think it is, because we’re allowed to have our own valid feelings and opinions about things. And, you know, I might say I’m an amazing leader. I think everything I do is the right decision. And Chris and John might be like: yeah, no, we just go along with it because we don’t want to get yelled at.

John Wall: 48:11
Yeah, you totally hit a point there. That you see that all the time. The first time you do this with a group is one thing, but if it’s a group that’s done five or six projects, the coworkers are like: yeah, no, he can’t do that. You know, it’s easy to correct that stuff.

Katie Robbert: 48:27
But you know, Chris, you bring up a good point, is bringing the team into the process of creating the skills matrix because they’re on the ground, they’re going to know more of the detail. But also it’s helpful for them to get your perspective on the way that you’re looking at it. And that way everybody can have that same shared understanding, which is going to make the skills matrix analysis more meaningful and more actionable.

Christopher Penn: 48:54
Yep. I would even go so far as to have a meeting like this one, record it and take the transcript and incorporate it into the work. With the generative model. Say: like, this is what the team—we discussed what we want to get out of the skills matrix. We’ve discussed what we want to accomplish as a team, and here’s our feedback. And if you incorporate that—which is going to be like 6,000 words of text—the model is going to come up with a much less generic, much more focused set of assessments.

Katie Robbert: 49:22
Yeah. The more detail, the more information, the more knowledge blocks that you have up front, the better the skills matrix is going to be. And like a lot of things that we do, you can build it once and reuse it. So once you have the pieces built, once you have the skills assessment defined and created, you just need to rerun it on a more consistent basis and then just say: okay, what’s changed? What do we need to do next? How do we adjust? Here’s what we’ve done so far. And you’re going to be set up for more success to reach the goals that you want to because you’re doing right by your people.

Christopher Penn: 49:57
Exactly. And John, it says you are Python expert now. So I’ll be calling you after this meeting for some help with some coding.

John Wall: 50:04
Scored Expert. Fantastic. That’s what happens when you get the right D20 role.

Katie Robbert: 50:12
I understood that reference.

Christopher Penn: 50:17
That’s going to do it for this week’s episode, folks. Thanks for tuning in. We will talk to you on the next one. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at TrustInsights.ai/podcast and a weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw in today’s episode? Join our free analytics for marketers Slack group at TrustInsights.ai/analytics-for-marketers. See you next time.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.