AI in recruiting: Practical guidance for leaders — salaries, hiring, and upskilling

In the AI in recruiting podcast, Marcus Sawyerr and Joel Lalgee walk through the immediate levers leaders are pulling to drive efficiency and how recruiting teams must respond. If you care about talent strategy, workforce transformation, or simply want practical next steps, this article lays out a straightforward playbook drawn from their insights.

Why leaders are pushing for efficiency now

Across industries the pressure to do more with less is real. Economic uncertainty plus the rapid maturation of generative AI are creating a unique moment: leaders can either treat AI as a cost-cutting tool that replaces people or an amplifier that redeploys talent into higher-value roles. The short answer from Marcus and Joel is that most organizations are focused on three categories: salaries, hiring, and upskilling.

"There is three categories, salaries... Hiring, and upskilling."

That triad is the decision tree every talent leader is navigating. Do you freeze or adjust salaries? Do you slow hiring or redirect efforts? Do you invest in upskilling to preserve institutional knowledge and increase internal mobility? The right mix depends on your business model and risk tolerance, but a deliberate approach is essential.

Which jobs will be affected first?

One of the clearest takeaways is that the highest-productivity gains from current AI tools are concentrated in two areas: coding assistance and content generation. As Marcus and Joel point out:

"The top use case for productivity today at sixty five percent is coding assistance. ... The second one is content generation and writing assistance of thirty seven percent."

Those numbers matter because they help predict which roles will be reinvented first. Software engineers and content creators are not going away — far from it — but parts of their jobs will be automated or augmented. Tasks that are repeatable, template-driven, or pattern-based will be the first to change. That means recruiters, hiring managers, and learning teams need to anticipate skill-shift rather than simply react to layoffs.

Upskilling is not optional — especially for AI talent

Upskilling forms half of the triage: it’s the proactive way to protect talent and future-proof operations. For AI-related roles, the pressure is twofold. Organizations need people who understand machine learning and prompt engineering, and they also need operational staff who know how to apply AI capabilities in business contexts. The most effective approach is a blended pathway:

• Identify adjacent skills. Find engineers, analysts, and product managers who can transition to AI-related tasks with targeted training.
• Create focused micro-credentials and on-the-job projects. Short, applied courses with measurable outcomes beat long theoretical programs.
• Pair learning with impact. Assign upskilled employees to projects where they can demonstrate measurable productivity gains within weeks.

When deciding who to upskill, prioritize people with domain expertise and adaptability. These employees will translate AI advances into business outcomes faster than hires from outside the company who lack context.

Hiring: when to pause, pivot, or accelerate

Hiring decisions should align with your AI adoption strategy. Marcus and Joel emphasize that while some roles will be reinvented, new opportunities will emerge — but not always in the same titles. Consider these practical rules of thumb:

1. Pause hires for roles that are primarily task-based and likely to be automated in the next 12 months.
2. Accelerate hiring in roles that orchestrate AI-human workflows: AI product managers, tooling engineers, data governance specialists, and learning designers.
3. Pivot job descriptions to emphasize adaptability, systems thinking, and AI literacy rather than just narrow technical skills.

Recruiting teams should rework scorecards and interview rubrics to evaluate candidates’ ability to work alongside AI systems. Ask for examples of tooling adoption, automation they’ve implemented, or how they measure human-AI collaboration outcomes.

Pricing talent: the salary question

Compensation is the third lever and arguably the trickiest. When leaders think about salary adjustments, they must balance market realities with internal fairness. A few practical guidelines:

• Use transparent pay bands tied to demonstrated impact, not just title or tenure.
• Create incentive structures for employees who acquire AI skills and apply them to measurable productivity gains.
• Offer internal mobility options before considering external hiring at higher market rates; this often reduces total cost while preserving knowledge.

Salary adjustments alone are a blunt instrument. The more strategic approach is to connect compensation to outcomes enabled by AI — for example, bonuses for teams that reduce cycle time or increase customer throughput using AI-assisted workflows.

Practical playbook for leaders

Here’s a step-by-step playbook you can use this quarter to align your talent strategy with AI adoption:

1. Map roles by exposure to AI automation (High / Medium / Low).
2. For High exposure roles: design upskilling pathways, identify internal candidates to transition, and pause redundant external hires.
3. For Medium exposure roles: pilot augmentation projects that pair employees with AI tools, measure productivity gains, and iterate.
4. For Low exposure roles: invest in reskilling only where strategic advantage exists; otherwise maintain hiring plans.
5. Update job descriptions and interview rubrics to reflect AI collaboration skills.
6. Measure ROI: track time saved, error reduction, velocity improvements, and employee retention after AI interventions.

This playbook is meant to be adapted. Your priorities—speed, cost reduction, or talent preservation—will determine emphasis, but the structure helps avoid knee-jerk decisions.

Recruiters: how to adapt to the new reality

Recruiters are at the frontline of this transition. The best recruiting teams will evolve from transaction-driven hiring to strategic workforce shaping. Practical steps for recruiting teams include:

• Build talent pipelines for hybrid roles, e.g., software engineers with domain experience or content strategists with prompt-engineering skills.
• Offer “skill preview” programs: short paid projects or hackathons that let candidates demonstrate their ability to leverage AI tools.
• Partner with L&D to create internal apprenticeship models that reduce time-to-impact and preserve institutional knowledge.
• Use AI tools responsibly in screening and outreach — but keep humans in the loop for final cultural and ethical decisions.

Remember: tools like coding assistants can change the evaluation baseline. A candidate’s ability to use AI effectively will be a differentiator, not a replacement for critical thinking and collaboration skills.

Measuring success: metrics that matter

To avoid vanity metrics and focus on business outcomes, track a small set of KPIs tied to AI-enabled productivity:

• Time-to-completion for key workflows (e.g., feature delivery, content production).
• Error rates or quality measures before and after AI augmentation.
• Internal mobility rates for upskilled employees.
• Hiring velocity for new hybrid roles.
• Employee engagement and retention in teams adopting AI.

Quantifying impact helps justify continued investment in upskilling and ensures that hiring and compensation decisions are evidence-based.

Ethics, transparency, and the human side

Any transition that involves AI and workforce change must be handled transparently. Communicate early and often about why changes are happening, what support will be available, and how success will be measured. Key commitments to make:

• Clear upskilling roadmaps and funding for employees impacted by automation.
• Transparent criteria for role changes, promotions, and compensation adjustments.
• Guardrails for responsible AI use in hiring and performance evaluation.

Treating people with respect during transition isn’t just ethical — it’s strategic. Organizations that manage transitions well retain institutional knowledge and move faster.

Why you should listen to the AI in recruiting podcast

If you want more practical discussions like this, the AI in recruiting podcast with Marcus Sawyerr and Joel Lalgee is an excellent weekly resource. They break down high-level trends into tactical actions for leaders, recruiters, and people managers. Episodes focus on real-world examples, tool recommendations, and a clear-eyed view of where AI is creating opportunities — not just risk.

For more resources and to subscribe, visit: https://marcussawyerr.substack.com/podcast

Also consider joining communities that focus on applied AI and people operations. One example is the EQ.app AI Community, a place for people to unlock opportunities by harnessing the power of AI. It’s a practical way to stay current and connect with peers solving the same problems.

Final takeaway

The emergence of AI in recruiting and across teams is not a single event but a wave of reinvention. The first jobs to be reinvented will be those created by computers: coding and content tasks are at the top of the list. That means software engineers and content creators will see parts of their workflows change first. Leaders who act now with a balanced strategy — thoughtfully adjusting salaries, pausing or pivoting hiring, and investing in upskilling — will create far better outcomes than organizations that react later.

Start by mapping your roles, defining upskilling pathways, and measuring the impact of AI pilots. Recruiters should reimagine scorecards and candidate experiences for hybrid roles. Above all, treat people with transparency, provide clear pathways, and focus on outcomes. If you want a practical, no-nonsense guide to doing that, subscribe to the AI in recruiting podcast with Marcus Sawyerr and Joel Lalgee and use the frameworks shared there to build your own playbook.

"The jobs have been created by computers are gonna be the first jobs that are gonna be reinvented." — a reminder that automation often changes roles rather than simply eliminating them.