📚 Weekend Read — Training the AI Factory Knowledge Worker
This week, PwC shared a glimpse of what they believe the future of entry-level work will look like.
Within 3 years, their junior associates — today’s entry-level accountants — won’t spend their days reconciling ledgers or building spreadsheets. Instead, they’ll manage AI systems that do the grunt work. The human’s job will be to guide the system, validate its outputs, and deliver insight with context.
It’s not the death of the entry-level role. It’s the evolution of it.
Why This Matters to Enterprise IT — and Why Platform Readiness Comes First
In IT, we’ve long treated entry-level positions as proving grounds:
Reset passwords.
Close tickets.
Apply patches.
Escalate when things get tricky.
But AI will soon do most of that faster, cheaper, and at scale.
Here’s the critical point: You can’t train people for AI-augmented work if your platforms aren’t ready to support it.
If your systems lack the automation hooks, orchestration capabilities, and integration points AI requires, then even the best-trained talent will be stuck working at human speed. Platform readiness is the foundation — without it, training programs will stall.
If your organization isn’t there yet, that’s exactly the gap we close with The CTO Advisor’s Advisor Bench async subscription and services.
📩 keith@advbench.com — Let’s assess your platform maturity and get you AI-ready.
Skills for the AI-Augmented IT Associate
Once the platform is ready, the next-generation IT associate will work in an environment where the following capabilities are already built into the stack:
Prompting & Guiding AI: Crafting precise inputs that drive automation tools to resolve incidents efficiently and contextually.
Validating Outputs: Detecting and correcting AI-driven misconfigurations or anomaly misreads before they cascade into outages or compliance failures.
AI Workflow Integration: Seamlessly embedding automation into ITIL or DevSecOps pipelines via tools like Ansible, Terraform, or AI-powered orchestration engines.
Insight Translation: Converting AI-detected anomalies — like deviations in network telemetry — into actionable operational or business changes, such as rerouting traffic or scaling capacity.
If these capabilities aren’t present in your IT platforms today, you’re not just behind on training — you’re behind on operational readiness, and that will directly impact your ability to compete.
A Practical Example: The Network Engineer in an AI World
Pre-AI:
An entry-level network engineer tasked with bringing up a new 10Gbps link to support a business partner connection needed to:
Understand they were working at Layers 1–3 of the OSI Model.
Log into the Cisco CLI (or ACI for larger environments).
Configure the switch ports, VLANs, and routing.
Perform link testing and troubleshoot if the connection failed.
This required both conceptual knowledge (the OSI model) and hands-on command syntax mastery (CLI/ACI).
With AI:
The desired outcome — a functioning 10Gbps link — hasn’t changed.
But how you get there has.
Now, the engineer might:
Still identify that they’re working at Layers 1–3 of the OSI Model.
Use an AI-powered network assistant (e.g., a model trained on Cisco configs + Ansible playbooks) to generate and validate the configuration commands.
Run automated tests where AI interprets results, surfaces anomalies, and suggests fixes.
Instead of memorizing every CLI command, the skill shifts toward describing the problem in precise terms, validating AI-generated outputs, and knowing when and how to override automation.
The key point: AI changes the execution layer, not the understanding layer. Foundational knowledge still matters — but it’s applied in a more abstracted, AI-assisted way.
The Catalyst Problem — and How to Create One
In accounting, the Big Four move in concert. When one shifts, the others follow.
Enterprise IT doesn’t have an equivalent.
That means waiting for a market-wide catalyst is risky. Leaders can create their own by:
Pilot AI-Driven Workflows: Start small — for example, automate patch validation for a non-critical application and have junior staff validate results.
Designate AI Champions: Select team members to go deep on AI orchestration and act as peer mentors, refining processes as they go.
Embed AI From Day One: Integrate AI-assisted tasks into onboarding labs so that new hires build AI fluency immediately.
These steps ensure your platform capabilities and team skills evolve together, avoiding the trap of having one outpace the other.
Weekend Takeaway:
The organizations that will win in an AI-first IT world won’t just train better — they’ll operate faster, safer, and closer to the business.
By aligning platform readiness and workforce skills now, leaders can:
Reduce operational risk.
Accelerate delivery of business-facing initiatives.
Retain talent that thrives in an AI-augmented environment.
If your platform readiness is holding you back from building these skills, that’s what we solve at The CTO Advisor through the Advisor Bench async subscription and services.
Read the full PwC story here:
https://www.businessinsider.com/pwc-ai-training-changing-the-job-accountants-jenn-kosar-2025-8