How CTOs and CIOs should interview candidates in the age of AI: Six things that matter most

The pace of change in technology has never been faster. New AI tools, agent frameworks, code-generation platforms and automation capabilities are reshaping how engineering, data and platform teams work. Skills that were cutting-edge two years ago have already shifted, and the half-life of technical competencies is shrinking.

For CTOs and CIOs, this creates a new challenge: how do you interview candidates when the job they’ll be doing tomorrow may not look like the job they’re doing today? Traditional interview techniques such CV reviews, theoretical questions, static coding tests aren’t enough anymore.

To identify the talent that will thrive in 2026 and beyond, technology leaders should focus on six key areas.

1. Assess learning velocity, not just experience

In a world where new frameworks, infrastructure models and AI-driven platforms emerge at speed, the ability to learn quickly is a critical differentiator. You’re not just hiring for what someone has done, you’re hiring for what they can become.

What to look for:

• Concrete examples where the candidate acquired a new skill or domain within the last 12 months, ideally without formal instruction.
• Evidence of self-directed learning: personal projects, open source contributions, new tool adoption, participation in AI communities.
• A mindset of continual learning: how they stay current with AI / agent-tech developments, how they approach “unknown unknowns”.

What to ask:

• “Tell me about a technical skill you learned in the past year. Why did you choose it? How did you go about mastering it? What was the outcome?”
• “What is the newest AI tool or development concept you adopted, and how did it change your workflow or perspective?”
• If a candidate cannot clearly articulate recent learning, their ability to keep up with the rapid change your organisation faces is questionable.

2. Evaluate AI-readiness and tool fluency

With AI accelerating nearly every technical discipline, candidates don’t need to be AI researchers but they must be comfortable working with AI: integrating it into workflows, understanding its limits, validating its outputs and using it to amplify productivity.

What to look for:

• The candidate can talk about how they would use AI tools (e.g., code-generation, automating testing, prompt engineering) to accelerate a task.
• Awareness of pitfalls: recognising hallucinations, understanding that AI requires human validation, knowing where governance or risk must be applied.
• Willingness to experiment: candidates who talk about how they trialled a new AI-enabled workflow, measured results and iterated.

Interview test idea:

• Give them a small, ambiguous problem — for example: “Design a simple micro-service that takes data from X, processes it using an AI tool, and outputs Y.”
• Ask them two things: (a) How would you use an AI tool in that process? (b) What checks, validations, governance steps would you build in?
• Their answer reveals whether they are truly AI-native (or merely AI-aware) and how they balance speed with risk.

3. Prioritise problem-solving over coding syntax

While syntax, algorithms and coding are still important, the real value today is in how candidates think: how they decompose problems, clarify assumptions, assess trade-offs and pivot when things change.

What to test:

• Give them a real-world scenario (from your environment) with ambiguity or constraint. Request a walkthrough of how they’d approach it - first in discussion, then in a short hands-on portion if relevant.
• Watch for how they frame the problem: Do they ask clarifying questions? Do they identify risks (performance, security, cost, integration)?
• Do they communicate their logic, not just their code? Because in the real world the team will have to understand and maintain their work.

Key traits to assess:

• Structured thinking (breaks down a large challenge into manageable pieces)
• Trade-offs awareness (not just the “right” answer but the “right for now” answer)
• Adaptability (how they respond when new information arrives)
• Communication (how clearly they describe what they’d do and why)

This style of assessment provides far more insight into how the candidate will behave in your evolving environment, rather than purely how fast they can code.

4. Test collaboration and communication

Modern technology teams don’t operate in silos. Your candidate will need to work across product, architecture, operations, data and business teams, and increasingly with AI-augmented workflows. In that context, communication becomes a critical skill.

What to look for:

• Ability to explain technical ideas simply to non-technical stakeholders.
• Comfort discussing the “why” (business impact, user value) as much as the “how” (technology stack).
• Agile communication across modes: synchronous meetings, async messages, documentation, prompt engineering dialogues.
• A mindset of feedback: how they receive input, adapt their work, collaborate with peer teams.

Interview exercise:

Ask them to explain a complex technical concept or recent project to two different audiences:

1. A Chief Financial Officer (CFO) or business executive (focus: value, risk, time to market)

2. A junior engineer or intern (focus: clarity, mentorship, knowledge transfer)
If they can naturally switch tone and level of detail, they’ve got strong communication flexibility and will likely integrate well into your cross-functional environment.

5. Understand whether their mindset fits your culture

Culture fit in today’s environment is less about “will they fit in” and more about “will they thrive and help shape the culture”. Because with AI-driven change, the wrong mindset isn’t just a hiring mistake, it’s a strategic liability.

What you want to assess:

• Curiosity and growth-orientation (what was their last stretch project?)
• Ownership and autonomy (did they drive something end-to-end?)
• Comfort with ambiguity (projects where the path wasn’t clear, tools changed mid-way)
• Low ego, high collaboration (seeks input, shares credit)
• Respect for governance, risk and quality (especially important in AI / security contexts)
• Adaptability (example: “I started this project one way, but it shifted, here’s how I handled it”)

Suggested questions:

• “Tell me about a time you disagreed with a technical decision. How did you handle it, and what happened?”
• “Describe a project that was outside your comfort zone or that required you to learn something entirely new. How did you approach it?”
• “What is the most recent tool, method or paradigm you changed your mind about? Why?”

The answers you get, not just the stories, but the tone of how they talk about learning, failure, iteration, will give you insight into whether they’re going to be a cultural accelerant or a drag.

6. Use realistic, role-specific work simulations

The fastest way to judge whether someone can do the job is to give them a piece of the job (or as close as you reasonably can), not a generic algorithm puzzle.

What to design:

• Short, focused simulation (30–60 minutes) based on a real challenge your team is or will be facing. For example: “Here’s our current API contract, what would you improve from a scalability or AI-integration perspective?”
• Or: “Here’s a failing data pipeline scenario. Talk through how you’d troubleshoot it, then sketch how you’d use an AI-assisted approach to detect issues faster.”
• Or: “Here’s a product requirement with constraints (cost, latency, data governance) - sketch a high-level architecture and how you’d assemble a team or use on-demand talent.”

What you’ll learn:

• How they confront real complexity vs toy problems
• How they think under time pressure and ambiguity
• Whether they can apply both technical and business context
• How they use or propose to use on-demand or external talent (if relevant)
• How quickly they can articulate trade-offs and highlight next steps

Because you’re hiring not for “what they’ve done” but “what they’ll need to do”, this kind of simulation gives you the highest fidelity insight.

Why the right hire, and the right hire process, matters more than ever

Hiring mistakes aren’t just inconvenient - they’re expensive. Studies estimate that making the wrong hire can cost anywhere from 30% of the employee’s first-year salary to three or four times the annual salary for mid-level roles.

Given the speed of skill change and the strategic importance of AI-driven initiatives, hiring too quickly or without rigorous process now increases the risk of misalignment, slow delivery and operational disruption.

That’s why many organisations are shifting towards pre-vetted talent models. Pre-screened engineers, data scientists and AI specialists who have already passed rigorous technical, behavioural and culture-fit assessments reduce hiring risk, accelerate onboarding and allow your internal team to focus on what matters most.

At BrightBox we’ve embedded this model into our ecosystem: through our BrightBox Associates programme, we connect you with pre-vetted, high-calibre tech specialists who have already been assessed for skill, agility and culture fit. This allows you to reinforce your core team quickly, minimise hiring risk and focus your internal energies on strategy and governance rather than recruitment overhead.

Final suggestion for you

The old hiring model rewarded deep expertise in a specific stack. The new model rewards:

• Fast learners
• Curious by nature
• AI-native thinkers
• Adaptable problem solvers
• Excellent communicators
• Truly collaborative teammates
• People who elevate your culture, not just fit into it

CTOs and CIOs who update their interview process now, taking the time to hire the right candidate, not just the available one, will build teams that can evolve continuously, rather than becoming obsolete as AI reshapes the technology landscape. A thoughtful process, augmented by pre-vetted on-demand talent like BrightBox Associates, is no longer optional, it’s strategic.