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I started a creative writing class recently. No business application, just some fiction for fun. (Translation: I love writing. But only doing recruiting rants…my God there’s gotta be more to life 😂.)
Coursera. Great curriculum. Atrocious feedback. I digress.
I’ve heard the phrase “show, don’t tell” a thousand times in my life. I never understood it though. Isn’t all writing “telling’ on some level?
The most straightforward explanation of difference (in fiction): telling is describing something, like an object or place. Showing is advancing the plot or writing dialogue that describe those things along the way.
“James’ computer is a 4 year old, oversized Thinkpad.”
Vs
“Why do I bother? No one is gonna read this crap,’’ James cursed himself as he hammered the keys of his dying Thinkpad.”
A vanilla info dump vs a story putting the same info into relatable context.
Naturally, it comes back to real world application.
There are less buyers of recruiting services right now. Tech products, too. The ones who remain have tighter budgets. More scrutiny from leadership. Higher expectations of an ROI.
No one can afford to light money on fire with a bad buy. It’s not just the company’s money. That individuals a$$ is on the line.
With that in mind, we’re seeing 2 trends in buying:
1. People buying from people they already know.
Existing relationships. And secondarily, referrals. People who have already proven to the buyer that they can do what they say. “Customer Led Growth” is the first chapter of everyone’s rewritten playbook. (If it’s not, you have some work to do.)
File this under: Duh. Onto the point of today’s rambling…
2. Buyers expect more data and specifics. Especially if you’re new.
Data Driven Storytelling™️, my favorite personally overused buzzword from 2021 (besides calling everything “garbage” – still great btw) is coming back in a big way.
Tell: We recruit for paid digital specialists.
Show: We recently filled 3 paid media specialists roles at a digital agency client. Metrics from that project:
Days to first submission: 4
Candidates to Fill Ratio: 5:1
Average Days to Offer & Acceptance: 9
Average Days to Start: 24
(Expect to see an annoying amount of metric driven client success stories from our team as we finalize our new BI reporting.)
But…are those metrics good? Great? Just ok?
It really comes down to what your clients have to say. (That one was extremely happy). How do you put that in front of your new potential buyers?
Case studies and client success stories. With not just metrics (easy to do with one sheet and slide decks), but with their participation (video.)
An example, our case study with Evive, where the client spoke about our work there themselves.
It means something if you say it. But a lot more if they do.
Partner at Hirewell. #3 Ranked Sarcastic Commenter on LinkedIn.
Over the last year, hiring teams have started seeing a wave of new job titles pop up across tech, sales, and operations.
Some are legitimate new roles.
Others are existing jobs with a slightly different name.
And many of them have one thing in common: AI is suddenly part of the job description.
From Go-to-Market Engineers to AI Specialists, companies are experimenting with new roles as they figure out how automation and AI fit into their teams.
But most of these positions aren’t entirely new. They’re evolutions of existing roles.
One role that’s gaining traction is the Go-to-Market Engineer.
Depending on who you ask, it’s either:
In practice, it’s a bit of both.
We recently worked on a role called an Outbound & Go-to-Market Specialist. Instead of traditional RevOps work like reporting and CRM management, the focus was on:
The goal wasn’t just managing sales data. It was accelerating pipeline generation through automation.
In other words, the role was designed to help SDRs and AEs move faster.
One trend is becoming clear: companies aren’t replacing entire departments with AI.
Instead, they’re changing how existing roles operate.
Sales teams still need pipeline.
Marketing teams still need content.
Engineering teams still need to build software.
The difference is that employers now expect candidates to use AI tools as part of the workflow.
That’s why we’re seeing so many job titles that start with “AI.” But that may not last forever.
Right now, AI still feels new enough that companies highlight it in job titles.
But eventually, AI will likely become a baseline expectation, not a specialty.
Think about it like cloud technology or data analytics.
At first, companies hired “cloud specialists.” Now most engineers are expected to understand cloud infrastructure.
The same shift will likely happen with AI.
Instead of hiring “AI-enabled marketers” or “AI engineers,” companies will simply expect employees to know how to work with AI tools.
One challenge with these emerging roles is simple: there aren’t many candidates with real experience yet.
Many of these positions didn’t exist two years ago.
In one recent search, we started looking for a candidate locally in Chicago. Eventually we expanded nationwide because the pool of people with relevant experience was extremely limited.
This is a common issue with emerging roles:
That gap will likely persist for the next few years.
Another noticeable shift is that roles are becoming more hybrid.
Instead of hiring for narrow responsibilities, companies are combining multiple functions into one position.
For example:
Forward Deployed Engineers
A model popularized by Palantir, these engineers:
That used to involve several roles: product managers, engineers, and solution architects.
Now, AI tools allow one person to cover more ground.
Similar changes are happening in other functions as well:
Automation removes repetitive tasks, leaving more strategic work behind.
For employers, the takeaway is straightforward.
Job descriptions need to evolve alongside technology.
Instead of focusing only on traditional experience, hiring managers should consider:
Because in many cases, the perfect candidate with the exact title simply doesn’t exist yet.
We’re currently in a transitional phase in hiring.
AI is changing how work gets done, which means job titles, responsibilities, and expectations are shifting quickly.
But most of these “new” roles aren’t entirely new professions.
They’re existing jobs adapting to new technology.
And as companies continue experimenting with AI, the titles may keep changing.
The work itself, however, is likely to look familiar.