How to Assess AI Readiness in Technical Reviews

 In the AI era, human interviewers aren’t going anywhere. They’re more important than ever as live human + AI interviews are essential for elevating software engineering quality. Our data has found that over the next three years:

• 63% of companies that use human + AI interviews expect coding errors to decrease. 
• 49% expect the time it takes to bring new products or features to market to decrease.
• 76% expect the number of products and features they release to increase.

How Do You Assess AI Readiness in Technical Interviews?

You assess AI readiness in technical interviews by evaluating how candidates work with AI, not whether they use it. This includes how they frame problems, guide AI output, evaluate correctness, explain tradeoffs, and make decisions under ambiguity. In AI-assisted interviews, process, judgment, and ownership are more predictive than whether the final code compiles. Strong interview rubrics separate outcomes from how candidates arrive at them.

Human + AI interviews are now the clearest way to see who will actually thrive in an AI-enabled workplace, but organizations face an operational challenge to scaling these interviews. Engineers are already overloaded, and the addition of AI doesn’t simplify interviewing. Instead, it exposes gaps in how companies assess competencies. 

Companies often struggle with inconsistent evaluation and assessments that lag behind the evolving tech landscape. This leads to longer time to fill roles, lower confidence in hiring decisions, and lower engineering quality. The stakes are too high to continue conducting technical interviews as you have been, so here’s where you should start.

Should Candidates Be Allowed to Use AI in Technical Interviews?

Hiring leaders tend to fixate on whether to allow AI use in interviews, but that’s not the right starting point. Interviews should reflect the way work happens in the real world, and the reality is that engineers are already using AI in their jobs. When interviews don’t reflect this reality, they magnify the cost of uncritical AI use. 

Even when you ban AI use in interviews, candidates will still try to use it. The more common AI becomes, the more likely this will happen. Weak candidates will game the system and you’ll completely lose any hiring signal.

You may think that a candidate is a great fit because they quickly produced the right code when they actually used AI to feed them the answer and didn’t understand the code. This leads you to hire a candidate who you think will succeed in the role, but they’ll struggle because the world has changed while your interviews haven’t.

Bad hires create ripple effects throughout the entire organization that impact who you can attract and retain, as well as what you can ship over the long term. When you’re trying to ship the best product, increase revenue, or outdo your competitors, hiring engineers who aren’t AI-ready is like stacking the deck. 

Instead of hiring people who will help you achieve your goals, you’ll bring in people who will actively create drag because they don’t know what they’re doing. They’re going to make mistakes and try to cover up their lack of knowledge. When they become hiring managers, they’re going to hire candidates who also don’t meet your hiring bar.

If you want engineers who will innovate, drive your company forward, work efficiently, or support your products in a robust way, you need to assess candidates in a way that reflects how the world works today. 

Not only do you have to reimagine interviews for the human + AI world, but you also need to change your rubrics. Otherwise, you’ll create false positives and false negatives:

• False positives: Weaker engineers can use AI to achieve the correct answer or a working solution, but they don’t have the skills needed for the job.
• False negatives: Strong engineers who use AI effectively get punished because the rubric still rewards memorization and recall that they offload to AI tools.

This is why one of the most important shifts that interviews and interviewers have to make is separating outcomes from process. Whether the final code compiles is no longer an accurate signal when candidates can use AI to get there. How candidates work toward their solution is a more accurate signal of their capabilities. 

What Interviews Must Evaluate in the Human + AI era

The competencies that made someone a great software engineer before AI no longer predict who will be a great engineer today. As humans and AI work together, skills such as problem-solving, communication, and the ability to effectively leverage AI are now more important. Aside from updating to interview content to reflect that, interviewers need to be retrained to assess what matters now: 

• Independence → Ownership: Since work can be completed by AI agents, it’s no longer enough for engineers to complete tasks independently. They also need to take ownership of the full problem, which includes understanding context, anticipating issues, and making tradeoffs. 
• Memorization → Judgment: When engineers can look up syntax in seconds, memorization loses value. What matters now is knowing which approach to take, evaluating trade-offs, and making sound technical decisions under ambiguity. Instead of writing code, strong candidates become the editors and overseers of how the code works.
• Speed → Decision quality: Engineers can use AI to produce a lot of code very quickly, but that’s not effective if it’s the wrong code. It’s more important for engineers to clarify requirements, challenge assumptions, and adjust course when new information appears.

How Technical Interview Rubrics Must Change for AI

If interviewers are going to assess AI readiness effectively, they need rubrics that reflect what actually matters in an AI-enabled workplace. Traditional rubrics were built for a world where engineers wrote every line of code themselves, but that’s no longer how engineers work.

Since a working solution can be the product of strong engineering skills or AI use, what a candidate produces is no longer a predictive assessment of their skills. You need a more sophisticated way to evaluate who will actually succeed in the role, which requires evolving your rubric across several dimensions.

This requires evolving your rubric across several dimensions. 

Outcomes vs. Process

Before AI, a simple “completeness” rating indicating whether the candidate achieved a fully working, mostly working, partially working, trivial progress, or no implementation was enough. Today, candidates can use AI to generate fully working solutions in seconds. 

This means that task completion has become table stakes. To separate skilled engineers from those who use AI as a crutch, how a candidate arrived at their answer is the explicit signal. Rubrics need to separate and score those two things independently. 

In practice, this looks like distinct rubric sections for navigation, decision-making, evaluation, and justification. These competencies must be assessed regardless of whether the code was manually written or generated with AI assistance. 

New AI-Era Competencies

AI has caused skillsets to shift. If you don’t consider what it means to be an engineer in this new era, you’ll continue to use yesterday’s interviews when you actually need to be looking for either different skills or greater strength in some of the skills that were critical before AI. Older skills may also not matter as much anymore. 

One pattern that we’re currently seeing is many hiring leaders still care a lot about the ability to write good code from scratch. This makes sense in the past, as writing code was the most foundational skill that engineers needed to have. In the world that we’re moving to, this is no longer crucial. 

I recently talked to an engineer who said that he hasn’t written code from scratch in two months. He’s more productive than ever because he uses AI agents to produce the initial code. Then he tells agents what he wants changed. Instead of writing code, engineers now need to know what good code looks like, and they have to be able to take potentially flawed code and turn it into good code.

In the AI era, the most valuable engineering work now happens around the code. Engineers need to know what good code looks like, and they have to be able to take potentially flawed code and turn it into good code. To identify these types of engineers, include the following competencies and observable behaviors in your rubric.

To assess AI readiness consistently, interview rubrics must evaluate observable behaviors that show how candidates reason, navigate, and collaborate with AI, not just what they produce.

Making This Easier on Interviewers (Not Harder)

When hiring leaders hear that they need to assess AI-era competencies, they might worry that interviewing requires even more from already overstretched engineers. That doesn’t have to be the case. By creating structure and giving interviewers the proper training and feedback, you can make conducting interviews feel easier. 

1. Emphasize Observations Over Judgments 

Interviewers should be trained to write down what they saw and heard, instead of how they felt about it. The goal is to prevent interviewers from assessing candidates based on “vibes.” For example, instead of noting that the “candidate seemed distracted,” interviewers can write “candidate looked away from the screen several times and needed repeated prompts to return to the problem.” This shift provides more accurate data that can be evaluated consistently across different interviewers and contexts.

2. Use Rubrics and Questions to Reduce Cognitive Load

Unstructured interviews force interviewers to multitask. They have to evaluate the candidate, think about what question to ask next, and manage pacing of the interview. This prevents them from truly listening and being present. With a clear rubric and set of interview questions, interviewers can focus on listening and guiding the candidate when needed. 

Over time, practicing the same questions also helps interviewers recognize patterns, such as what good AI use looks like, where cheating often shows up, and when to step in to keep things on track.

3. Identify Specific AI-Readiness Traits and Behaviors That Interviewers Should Look For

Strong and weak candidates use AI differently. We see strong candidates embrace the opportunities that AI offers. They use the built-in AI to quickly get oriented inside the codebase and immediately start learning about where to make changes, bringing their own product sense to bear on what changes those should be and asking clarifying questions along the way to resolve ambiguity. Those candidates get further, faster, and with better outcomes on assessments.

Weak candidates often copy and paste giant blocks of code or read AI-generated answers off their screen. They can’t effectively explain the tradeoffs they made in their code or what they’d optimize for if they had more time. We see these candidates submit correct answers but without the critical thinking or understanding that should come with it.

4. Educate Interviewers on Candidate Integrity Flags

There’s a range of signs that candidates may be cheating, so interviewers need clear guidance on what to flag and how to handle it. Some are blatant, such as seeing the candidate type the question into a tool and copying and pasting the answer. Others fall into a gray area. You might hear someone talking in the background but it’s difficult to make out what was said. There could have been someone giving the candidate answers, or it could have been a family member who’s doing something else. 

There’s also nuances to cheating that can only be learned over time. With more practice, interviewers can identify suspicious behavior and give candidates an opportunity to show that they’re truly doing the work themselves rather than using some kind of unauthorized assistance. 

Operational Benefits of Training Interviewers

There are many other benefits of equipping interviewers with the right tools and knowledge to assess AI readiness:

• Faster interviews: Because interviewers follow a clear structure and know exactly which behaviors they are looking for, they spend less time figuring out what to ask or how to evaluate responses. This reduces wasted minutes and keeps interviews on track.
• Cleaner debriefs: When interviewers document observable behaviors that are mapped to specific rubric competencies, it’s easier to summarize what happened in the interview. Hiring managers can also quickly see why a recommendation was made and how it ties back to the job description. 
• Fewer opinion-based disagreements: When all interviewers evaluate candidates against the same defined behaviors, disputes in debriefs are based on concrete evidence rather than subjective judgments.

Training Interviewers for Consistency at Scale

At Karat, we’ve conducted over 350,000 technical interviews with our network of 500+ Interview Engineers. When running technical interviews at such a large scale, consistent candidate assessment ultimately comes from properly training interviewers, calibrating constantly, and providing structure.

Enterprise hiring leaders can build a repeatable system that scales across engineering teams by following these principles:

• Consistency comes from the same assessment environment. Letting one candidate go for 60 minutes and another for 75 minutes, or asking a candidate one set of questions and a different set to another candidate creates an unfair playing field. However, that’s how most organizations conduct interviews. They let interviews last as long as it does or allow interviewers to ask whatever they feel in the moment. In order to have a true apples-to-apples evaluation of the signals you’re seeing, interviewers should give all candidates the same, consistent interview. 
• Interviewers that are aligned on the same competencies and criteria produce the same result. When interviewers understand exactly what they’re looking for and why it matters, evaluation becomes more consistent because they’re not relying on gut feel. Rubrics are the best way to achieve this, as they provide interviewers with the competencies that matter and how interviewers should evaluate them. When using rubrics, you should be able to swap out any given interviewer and get the same candidate score. That’s because all interviewer evaluation is based on the same criteria.
• Training shouldn’t be a one-time event. AI tools, candidate behaviors, and engineering workflows will keep evolving. In order to keep your interviews effective and consistent, interviewer training has to be a continuous operational commitment. 

Why This Is an Operations Problem, Not an AI Problem

AI is an incredibly powerful tool, but it’s not what has broken the effectiveness and predictiveness of technical interviews. AI is just the latest in a long series of tools. How software engineers work will continue to evolve over time as new tools come to market, so accurately evaluating candidates has never just been about the impact of specific technology. What’s critical to effective, predictive interviews is structured evaluation and how your interviewers and interview questions adapt to changing times.

The companies that win in the AI era will operationalize judgment by emphasizing objective data, such as observable behaviors, and turning interviews into fair, repeatable systems. They’ll use rubrics to make sure all candidates are asked the same questions and evaluated in the same way, enabling them to increase consistency and scale trust in hiring decisions. 

When you invest in better structure and training interviewers, you won’t just keep up with AI advancements; you’ll also build a hiring engine that can reliably surface the engineers who will move your company forward.