INBOX INSIGHTS: Why You Need a Skills Matrix, Skills Inventories (2025-02-12) :: View in browser
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Why You Need a Skills Matrix Immediately
Here’s what’s happening in companies right now, and I bet this sounds familiar: Teams are staring at their project plans, realizing that about 40% of what they did last year is either automated or completely transformed by AI. The specialized technical skills they hired for? Many are now handled by tools. The capabilities they thought would matter most? Rapidly shifting. And the new skills they suddenly need? Those weren’t even on the radar eighteen months ago.
I’m seeing this pattern everywhere. CTOs are discovering their data teams need entirely different capabilities. Marketing leaders are scrambling to understand what skills actually matter in an AI-augmented workspace. And HR teams? They’re trying to figure out how to hire for roles that seem to change every quarter.
The speed of this transformation is, frankly, overwhelming. But here’s the thing – the biggest risk isn’t AI disrupting your workflows. It’s not knowing what capabilities your team actually has right now to handle that disruption.
What Exactly Is a Skills Matrix?
Let’s simplify this, since it’s really straightforward. A skills matrix is essentially a visual snapshot of your team’s capabilities. Think of it as a spreadsheet where:
- The rows list each team member’s name
- The columns show different skills relevant to your business
- The intersection points indicate proficiency levels (usually on a scale of 1-5 or Novice to Expert)
For example, in our marketing team’s matrix, we track things like:
- Data Analysis
- Content Creation
- AI Tool Proficiency
- Project Management
- Client Communication
- Technical SEO
Each team member gets rated on these skills, which gives us a clear picture of our collective strengths and gaps. I learned the hard way that letting people self-rate works better than trying to rate everyone myself. The key is to provide specific examples of what each proficiency level looks like.
Why a Skills Matrix Matters Now More Than Ever
Remember when we could plan team development on a yearly basis? Those were simpler times. Now, the skills your team needs are shifting faster than ever, and without a clear picture of your current capabilities, you’re essentially navigating in the dark.
A skills matrix isn’t just another business document to create and forget about. It’s your strategic radar for:
- Identifying critical gaps before they become problems
- Understanding where AI can actually help (versus where you need human expertise)
- Making smarter hiring and training decisions
- Ensuring your team stays relevant and valuable
Creating Your Skills Matrix
Here’s how I approach this with our team.
Start with your current state:
- List all the skills currently needed for your team’s success
- Create clear definitions for each proficiency level
- Have team members self-assess their proficiency levels
- Note which skills are becoming more or less important with AI adoption
Then, look forward:
- What new skills are emerging in your industry?
- Which current skills need upgrading?
- Where can AI augment existing capabilities?
Pro Tip: Setting Up Your First Matrix
Here’s a practical way to start (this is exactly how we did it):
Step 1. Create a simple spreadsheet Step 2. List team members down the left side Step 3. Add skill categories across the top Step 4. Define proficiency levels:
- 1 = Awareness (understands the basics)
- 2 = Basic Application
- 3 = Competent
- 4 = Advanced
- 5 = Expert/Can Teach Others
Include a notes column for additional context or certification details. (This has saved me countless times during resource planning conversations.)
The Real Value (Beyond the Obvious)
The true power of a skills matrix isn’t just in the document itself. It’s in the conversations it sparks and the clarity it provides. When we implemented ours, we discovered that:
- Some team members had valuable skills we weren’t fully utilizing
- Areas where we thought we needed to hire could actually be addressed through internal training
- Certain AI tools could fill gaps we hadn’t considered before
Making It Work for Your Team
Start small. Really. You don’t need to map every conceivable skill in your first iteration. Focus on:
- Core skills that drive current business value
- Emerging skills needed for upcoming projects
- Areas where AI is creating the biggest impact in your industry
Remember to revisit and update your matrix quarterly. (I literally put it in my calendar because we all know how easy it is to push these things off.)
The Bottom Line
You can’t manage what you can’t measure, and you can’t prepare for the future if you don’t understand your present. A skills matrix gives you both the insight and the foresight you need to keep your team competitive and confident in an AI-transformed landscape.
What’s your next step? Block an hour this week to start your skills inventory. Begin with just 5-10 core skills for your team. Trust me, your future self will thank you.
I’d be remiss if I didn’t mention that this exercise only works if your skills matrix is aligned with your business goals. If your business goals and roadmap are not clearly defined, start there. Otherwise, you’re guessing at what you’ll need from your team. If you need a starting point, I’d recommend the Trust Insights 5P Framework.
We’ll be building a skills matrix on this week’s livestream. Join us at 1 p.m. EST this Thursday or catch the replay on our YouTube channel!
(And hey, if you’re feeling stuck, you can always reach out – I’ve got some templates that might help you get started.)
Have you created a skills matrix for your team? I’d love to hear about your experience and what you’ve learned in the process. Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.
– Katie Robbert, CEO
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of a skills matrix assessment in today’s rapidly evolving work environment. You’ll understand what a skills matrix assessment is and why it’s essential for navigating today’s rapidly changing work environment.
Watch/listen to this episode of In-Ear Insights here »
Last time on So What? The Marketing Analytics and Insights Livestream, we covered using generative AI reasoning models to do scenario planning and disaster planning. Catch the episode replay here!
This week we’re going to walk through how to build a skills matrix assessment. Are you following our YouTube channel? If not, click/tap here to follow us!

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What’s My AI Tech Stack? (2025-02-09)

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In this week’s Data Diaries, let’s talk about the skills of artificial intelligence. As Katie highlighted in this week’s podcast and the opening section, one of the things we need to understand is what skills are even available. If we don’t know that, then we’re not able to determine if we have a skills gap.
Imagine if you were running a kitchen in the 1840s, and suddenly you were transported to the present day. Even though you might be a master chef, not knowing that things like blenders, microwaves, or refrigerators even existed would handicap what skills you might look for in a soup chef.
This is where things like job listings – usually a rich source of data – aren’t very helpful. Job listings like those on Indeed.com, LinkedIn, etc. are trailing indicators, often written by hiring managers and HR professionals who simply can’t make the time to keep up on what’s happening in AI.
So how do we even get our arms around a set of skills in something like AI that’s changing too rapidly? This is where we can use AI to understand AI. If you were to take data from discussion forums, YouTube, Slack, LinkedIn, or other places where real people are discussing real challenges, you’d have a large collection of problems.
From those problems, most of the comments offer a variety of solutions. For the purposes of a skills inventory, we don’t need to quantify the skills, we just need to note their existence. By feeding all that data into a tool like NotebookLM, we can extract the possible skills list:

With this, we have a better idea of the skills someone in our organization COULD have; the next step in the process would be determining how relevant the skills are to our specific 5Ps around AI, then follow Katie’s process for doing the skills matrix.
Where do the discussions about skills you need for your industry live? How could you get those discussions into the AI tool of your choice so that you have a complete, comprehensive list of what skills are even possible for the people you have or might be hiring?

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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.