Something had to change in the way companies hire tech talent. Not gradually. Fundamentally.
Since the pandemic, hiring has become mostly virtual. Interviews moved to video calls. Technical assessments went remote. Take-home challenges replaced whiteboards. In many ways, it was a step forward: remote hiring expanded the talent pool across regions and time zones, and helped companies access specialists that were hard to reach before.
Then AI arrived and rewrote the rules again.
Today, it is no longer realistic to hire tech talent as if we were still in 2023. The signals that used to work do not tell the full story anymore. And pretending otherwise is becoming one of the most expensive mistakes companies make.
At Inclusion Cloud, we saw the wave coming early because we were already dealing with these situations daily, across hundreds of interviews each month. So instead of playing defense, we went on offense and rebuilt our recruiting engine around the new reality: AI is a real advantage, but it also changes the hiring signals. It’s harder to verify identity, detect proxy interviews, and separate genuine expertise from undisclosed AI assistance.
That’s how we landed on inMOVE™ by Inclusion Cloud and why we’re proud to say it’s proven, reliable, and built for how hiring works now.
This article explains why the change became necessary, what problem it solves, and what hiring looks like when AI becomes part of both the work and the hiring process itself.
When Everyone Looks Like a “Strong Candidate”
A candidate joins a remote interview. Camera on. Confident tone.
They breeze through the technical test. The code runs. The answers sound polished.
Everything checks out.
Until the interviewer asks a follow-up that requires real reasoning:
Why did you choose this approach?
What happens when traffic spikes?
Where does this break at scale?
There is a pause. The explanation becomes vague. Architectural reasoning disappears.
This isn’t always a bad intent. The market is competitive, and the pressure is high. AI tools are powerful, affordable, and widely available. Many candidates use them to prepare for interviews or keep them close as a safety net in case they go blank.
The real problem is not AI usage.
The problem is not knowing when AI is being used as a crutch instead of a multiplier.
As Pablo Bucci, Recruiting Lead at Inclusion Cloud, puts it:
“Anyone can generate working code. The real value is understanding why that code exists, what trade-offs were made, and how it behaves inside a real system.”
That gap is exactly what traditional hiring processes often fail to catch.
Mariano Baca-Storni, Inclusion Cloud’s CEO, frames it like this:
“The challenge today isn’t just finding people who can use AI. It’s figuring out who can think beyond the prompt. Who understands systems, anticipates vulnerabilities, and designs for scale. That’s what separates someone who codes with AI from someone who builds with AI.”
When code becomes cheap, judgment becomes scarce
There is a deeper shift happening beneath the surface of tech hiring.
In a world where almost anyone can generate working code in seconds with AI, the usual hiring signals are no longer enough. Functional code, clean demos, and confident explanations do not guarantee real understanding.
And this is not limited to software developers.
The same pattern is emerging across enterprise technology roles: ERP consultants, functional analysts, integration specialists, automation engineers, and solution architects. AI makes it easier to produce artifacts, configurations, scripts, and recommendations that look correct at first glance.
What it does not guarantee is judgment.
Enterprise systems rarely fail immediately. They fail later: during scale, audits, integrations, peak loads, and regulatory reviews. That is when assumptions collide with reality and shallow understanding becomes expensive.
We are not against AI. Quite the opposite. We see it as a productivity accelerator when used by people who understand the systems they are working with.
The risk is AI-substituted understanding.
Knowing how to configure a module is not the same as understanding how it behaves inside a financial close. Generating an integration flow is not the same as reasoning about data consistency across systems. Producing automation is not the same as understanding operational risk.
As AI lowers the cost of producing outputs, the value shifts toward professionals who can reason about architecture, trade-offs, and consequences.
That shift is what forced us to rethink how hiring works.
Why did this become a structural hiring problem
This is not a temporary distortion. It is structural.
Remote hiring expanded access and speed. GenAI reduced the cost of producing convincing artifacts.
And at the same time, organizations are under pressure to move fast: modernize platforms, migrate core systems, integrate AI into operations, deliver under tighter budgets and timelines.
That creates a paradox:
Companies now need deeper specialist judgment for those high-complexity projects at the exact moment it has become hardest to tell what comes from the person and what comes from AI.
The Two AI Hiring Risks Companies Now Face
Through research conducted at AXIS, our insights lab, we examined how AI is altering hiring dynamics and where organizations are becoming more exposed. We identified two primary risk categories:
1) Fake candidates
The first (and most harmful) risk involves deliberate attempts to deceive the interviewer. A growing ecosystem of tools and services now exists to help candidates cheat their way into jobs: proxy interviews, real-time AI teleprompters, deepfake voices, and bait-and-switch tactics.
This is fraud, and it requires process-level defenses.
2) AI-inflated candidates
This one is tougher to spot. In this case, not all candidates are trying to deceive. But the pressure of the process often nudges them to rely on AI without saying so. They use it to polish answers, speed up take-home tests, or generate clean-looking work that appears solid at first glance.
The problem is not the tool.
It’s the lack of disclosure.
When AI use is explicit and allowed, the interview can shift accordingly. You can ask different questions. You can explore how the candidate uses AI, which tools they rely on, where they draw the line, what they trust the model with, and what they never delegate. That’s a productive conversation.
But when AI is used quietly, the question becomes unavoidable:
Does the candidate actually own the reasoning, or are they just copying and pasting AI-generated, generic solutions?
Should AI be allowed in job interviews?
There is no single industry consensus. Some companies want interviews to reflect real working conditions, where AI tools are part of daily workflows. Others prefer to restrict AI use during interviews to evaluate baseline skills.
Both positions are understandable.
But the core point remains: the interview exists to validate real capability. Not which tools a person can access, but whether they can solve problems when the tools are not enough.
The goal is not to remove AI from the process. The goal is to design interviews that still surface depth, even when AI is present.
Our Answer: inMOVE™ by Inclusion Cloud
inMOVE™ by Inclusion Cloud was designed around a simple idea:
Speed without depth increases risk.
Depth without speed slows execution.
We built a recruiting engine that assumes AI is present and raises the bar accordingly.
It combines AI-driven discovery at scale with human validation at the points where judgment, context, and technical experience matter most.
How inMOVE™ by Inclusion Cloud Works
Step 1: AI Screening
We start with speed. inMOVE™ by Inclusion Cloud uses AI to search and match across a database of over one million profiles, identifying candidates based on skills, certifications, domain experience, and cultural fit. This AI-driven step is fundamental to keeping the process agile and to filtering candidates who truly match the client’s needs and the role’s requirements.
Step 2: HR Behavioral and Credential Screening
Speed without validation is risky. We pair AI screening with human oversight. Our HR team validates credentials, background, communication skills, and collaboration, while also watching for red flags such as undisclosed AI use that may misrepresent a candidate’s actual capabilities.
Step 3: Technical Interview Led by Senior Engineers
This is where depth is tested. Candidates go through live technical interviews with our technical leaders, focused on architecture thinking, problem-solving under pressure, real-world trade-offs, and how they use AI (if they do) inside real workflows.
We do not just evaluate what candidates produce.
We evaluate how they think while producing it.
Why delivery speed still matters
Raising the bar does not mean slowing down.
In enterprise environments, slow staffing becomes slow delivery. Delays in hiring often translate into project delays, higher costs, and shortcuts taken under pressure.
By combining AI-driven discovery with structured human validation, inMOVE™ by Inclusion Cloud allows us to deliver certified specialists in as little as 72 hours, without compromising on the quality of the resources.
Final Thought
Hiring has become a strategic capability in the AI era.
AI did not eliminate the need for experts. It changed what expertise looks like, and made it harder to validate with old methods. The gap between “working” and “reliable” has never been more expensive.
And inMOVE™ by Inclusion Cloud is our answer to this new market reality.
This isn’t a story about avoiding AI. Far from it. Ignoring this wave would mean falling behind. The real challenge is balancing speed with sustainability. Innovation with sound engineering. Hype with what the business actually needs to scale and grow. That’s why rethinking how we hire has become a critical part of any AI strategy. Starting fast without the right experts in place can turn into a maze of spaghetti code, apps that barely work, and millions lost to broken trust, rework, and avoidable vulnerabilities
If you want to see how inMOVE™ by Inclusion Cloud can support your roadmap with certified specialists in 72 hours, let’s talk.
