Bridging the AI Skill-Gap
The Real AI Skill Gap: Why Systems Thinkers—Not Prompt Engineers—Will Drive Your AI Transformation
👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 1k+ readers who get Looped In delivered to their inbox every Sunday .
One of my client calls started out like this a few days, "We just need to hire more prompt engineers to get our AI transformation going." 🚩
My mind immediately filed this statement away as the 2025 version of "let's just hire more SDRs to fix our pipeline problem."
In the last few days, weeks, heck, months even, I can’t escape it. Every single call I get on is with CMOs or execs who are racing to build out AI capabilities within their orgs. And every single one expressed the same challenge: finding the right talent to drive their AI initiatives forward.
But I strongly believe there’s a fundamental misunderstanding about what "right talent" actually means in this context.
The Illusion of Specialization
The current market obsession with prompt engineers reminds me of when everyone scrambled to hire social media specialists in 2010 or growth hackers in 2017 (let’s please not revisit this can of worms 🙄). We're once again putting our faith in a hyper-specialized (and so new that it’s never-been-proven) role to solve the challenge of efficient growth, competitive market conditions, rapidly changing tech stacks…the list goes on.
Look, I'm not saying prompt engineers aren't valuable. Please don’t leave this newsletter now and run to LinkedIn to tell the world I don’t believe in the power of a strong prompt. Trust me, that’s not the case at all. They absolutely are valuable. But just like hiring an army of SDRs won't automatically generate quality pipeline if your messaging is off or your ICP is wrong, a team of prompt engineers won't magically transform your business if they're operating in isolation and most importantly if they’re operating without first-hand business context.
The real magic—and competitive advantage—comes from the systems thinkers who can connect technical capabilities to business outcomes.
What Makes a Systems Thinker?
What exactly is a systems thinker in the AI context? They're the people who sit at this critical intersection:
Deep knowledge of business processes - They understand the pain points, workflows, and metrics that matter. They know where the friction exists in your customer journey, sales process, operations, etc.
Comprehensive AI knowledge - They grasp what's possible with current AI capabilities—without getting lost in the technical weeds. They can separate hype from reality.
Growth mindset - They're comfortable with iteration, experimentation, and the inevitable failures that come with innovation. They're not looking for the perfect solution, but for continued improvement.
These systems thinkers are the translators—bridging what's technically possible with what creates genuine business value.
History Repeats Itself
This pattern should feel familiar to anyone who's been in marketing for more than a few years.
Remember when companies were rushing to hire specialists for every new channel? Facebook experts, email specialists, PPC wizards, SEO gurus...😐
Those specialists were (and still are) important. But the most valuable people on your team weren't the channel specialists—they were the ones who understood how everything connected to drive revenue. The ones who could look across channels, identify patterns, and orchestrate campaigns that worked together. More of a portfolio approach because no one buys in a direct-response, one-channel motion (at least not for a considered purchase in B2B SaaS).
The same is true for AI transformation.
Your prompt engineer might create an amazing chatbot (might), but your systems thinker will ensure it actually improves customer experience and drives meaningful metrics.
Signals vs. Noise
I've been talking a lot about signal-based marketing in recent posts. This concept applies perfectly to AI transformation too.
Most companies are drowning in AI noise:
"We need a chatbot!"
"Our competitor is using AI for content generation!"
"Let's use AI to analyze customer data!"
These aren't inherently bad ideas. But without systems thinkers who can identify the right signals to act on, you'll end up with a collection of disconnected AI projects that don't move the needle. More tech debt anyone?
The Right Questions
Systems thinkers ask different questions than specialists:
Specialist questions:
"How can we optimize this prompt?"
"What's the best LLM for this task?"
"How can we improve token efficiency?"
Systems thinker questions:
"Where in our customer journey do we have the highest drop-off, and could AI help?"
"How would this AI capability connect to our existing tech stack and workflows?"
"What metrics would indicate success, and how do we measure them?"
"How does this AI initiative support our broader business strategy?"
Both sets of questions are important, but without the systems perspective, you'll optimize for technical excellence rather than business impact.
Real-World Example: MOPs Team at a Growth-Stage SaaS Org
Let me illustrate this with a real example I encountered recently. A $30M ARR B2B SaaS company selling sales enablement software was struggling with their marketing automation and nurture sequences. Their problems included:
Low engagement rates on nurture emails (open rates below 15%)
Poor lead scoring resulting in sales complaining about lead quality
Disjointed buyer journeys across channels (email, website, SMS)
Manual content personalization that couldn't scale
Limited visibility into which content assets influenced pipeline
Their initial instinct? "Let's hire a prompt engineer to build AI-powered content generation for our nurture streams!"
The thinking was straightforward: if they could generate more personalized content at scale using AI, engagement would improve. A prompt engineer could help them craft the perfect inputs for their chosen LLM to generate emails, landing pages, and social posts.
Why this approach falls short:
While better content generation might help, it's addressing a symptom, not the underlying disease. The real issues were systemic:
Their tech stack was fragmented with data silos
They lacked a unified view of customer behavior across touchpoints
Their content strategy wasn't aligned with actual buying signals
Their lead scoring model was based on outdated assumptions
A prompt engineer came in and delivered amazing AI-generated content and recommendations for updating their lead scoring, but these changes were deployed into a broken system. This hire was an AI expert, insanely talented and up to date on all LLMs, but no GTM experience in a SaaS org.
The systems thinker alternative:
Instead, what they actually needed was more of a "Marketing AI Orchestration Lead" – a systems thinker who could:
Map the entire lead-to-customer journey and identify key friction points
Connect their marketing automation platform with their product analytics
Develop an integrated signal-based approach to account tiering and nurturing
Build a coherent data strategy that would power their AI initiatives
Create a roadmap for gradually integrating AI capabilities into their tech stack
Interview questions for this systems thinker role might look like this:
"Walk me through how you'd approach analyzing our current nurture sequence performance to identify opportunities for AI enhancement."
"How would you integrate website behavior, email engagement, and in-app activity into a unified view of customer intent?"
"Describe how you'd evaluate whether an AI solution should be built in-house, purchased, or integrated via API."
"Tell me about a time you successfully bridged the gap between technical capabilities and business outcomes."
"How would you measure the success of an AI implementation beyond the technical metrics?"
What "good" looks like after 90 days:
A comprehensive audit of current marketing technology, data flows, and process gaps
An AI integration roadmap with clear prioritization based on business impact vs. implementation effort
One pilot AI project implemented with measurable results (e.g., new signal-based tiering model with AI components)
Documentation of key customer journeys with identified AI enhancement opportunities
Established metrics framework for evaluating AI initiatives' impact on revenue, not just operational metrics
Cross-functional workshops completed with sales, product, and customer success teams to identify additional AI use cases
The systems thinker approach transforms AI from a siloed technical project into a strategic business initiative that addresses root causes rather than symptoms.
Building Your AI Systems Thinking Muscle
Some practical steps to bring this into your org:
Look for systems thinkers in unexpected places. Some of your best AI strategists might be hiding in customer success, sales enablement, or operations—places where people deeply understand your business processes.
Create cross-functional AI teams. Pair your technical AI talent with business domain experts and facilitate true collaboration (not just status updates).
Start with problems, not solutions. Before investing in any AI capability, clearly define the business problem you're trying to solve and how you'll measure success.
Build for integration from day one. Ensure any AI initiative has a clear plan for how it will integrate with your existing systems, data flows, and human workflows.
Embrace iteration. Plan for multiple phases of deployment with clear learning objectives for each phase (more like a traditional Growth function).
So before you post that job req for another prompt engineer, ask yourself: do you have enough systems thinkers who can connect the dots between your AI capabilities and your business goals?
See ya next week,
Kaylee ✌️
Would love to hear from you! What's been your experience with AI adoption at your company? Hit reply - I read every response and would love to hear your thoughts!
And I’m on the hunt for a marketing AI apprenticeship program (or similar course). If anyone knows of any, drop me a note? Thank you!