DEV Community: Ainur Baigozha

Why Business Schools Are Teaching AI Like a Core Engineering Skill

Ainur Baigozha — Mon, 03 Aug 2026 10:06:41 +0000

A lot of engineers dismiss MBAs as irrelevant — reasonably, given how many of them used to be two years of case studies with no connection to how software actually gets built or shipped. That's changing faster than most engineers have noticed, and the direction it's changing in is worth paying attention to if you're thinking about a move into product, strategy, or eng leadership.

The data point that surprised me

According to a Graduate Management Admission Council (GMAC) survey, only 22% of business schools haven't integrated AI into their curriculum in some form. That means roughly 8 out of 10 already have — and it's not a single "Intro to AI" elective bolted on. Top programs are restructuring around it.

What "restructuring" actually looks like
Wharton launched a dedicated AI MBA major in 2025 — a full specialization track, not a course.
Chicago Booth created an Applied Artificial Intelligence concentration with courses like "AI Essentials" and "Machine Learning in Finance," backed by an internal Center for Applied AI.
UVA Darden added courses like "AI for Customer Growth" — embedding AI inside existing disciplines (marketing, finance, ops) rather than isolating it.
INSEAD, LBS, and HEC Paris made similar moves in 2025, with a notable addition: AI framed not just as a technical tool but as a module on ethical and strategic deployment risk — the kind of thing engineers building these systems rarely get formal training in either.

If you build ML systems and have ever sat in a room where a non-technical stakeholder made a call about your model that made no sense given the actual constraints — this is the gap these programs are trying to close from the other side.

An honest caveat, because hype-checking matters here too

GMAC's Application Trends Survey shows full-time, in-person MBA applications actually growing in 2025, while flexible and fully-online formats declined for a second year. So "the traditional MBA is dying" isn't supported by the data — if anything the format trend runs the opposite direction from what you'd expect. What is growing consistently is demand for one specific specialized master's category: business analytics. Nearly every other narrow specialization lost applicants for the second year running. Translation: people aren't optimizing for format, they're optimizing for whether the content is actually about data and applied AI.

Why this matters if you're an engineer, specifically

The WEF's Future of Jobs 2025 report lists big data and AI/ML specialists among the fastest-growing roles, with 90% of employers expecting AI-skill demand to keep rising. That's the technical side. The business-school shift is the other half of the same signal: companies increasingly need people who can sit between the model and the P&L — and right now that population is thin on both sides. Pure technical depth with no ability to translate constraints to leadership caps out your ceiling; pure business fluency with no grounding in what the system can and can't actually do produces bad roadmaps. The overlap is where the leverage is.

Where SITE fits into this, and where it doesn't

I work at SITE (Swiss Institute of Technology in Education) in Geneva, and our MBA/EMBA follows the same pattern as the schools above — AI embedded inside finance, strategy, and marketing rather than as a standalone course, plus mentors from PwC, Deloitte, Amazon, Google, Meta, and Microsoft. If you're an engineer weighing whether an MBA-style program is worth it at all, that's the filter I'd actually apply — not "does it have an AI course" but "is AI load-bearing across the curriculum, or decorative."

I'll be straight about where we're not comparable: we're young (first cohort was September 2024), so we don't have Wharton or Booth's alumni track record, and I'm not going to pretend otherwise. What is verifiable: programs are accredited by QAHE and EQAC, ISO 21001:2018 certified, and SITE holds CEEMAN and ECBE membership.

What to actually check, regardless of school
Is AI embedded inside core disciplines (finance, marketing, strategy), or does it exist as a bolt-on elective?
Does the curriculum update on a cycle faster than accreditation review — and how, mechanically?
What's the placement data for the AI-adjacent specialization specifically, not the program-wide average?

For engineers specifically, I'd add a fourth: does the program assume you already know how models work, or does it re-teach you things you learned in your first ML course? The good ones assume the former and spend the time on the translation layer instead.

Which Engineering Roles Are Actually Disappearing (and Which Are Just Evolving)

Ainur Baigozha — Mon, 03 Aug 2026 10:05:47 +0000

Every few weeks there's a new thread about AI "replacing developers." Usually it's either pure panic or pure dismissal, and neither is that useful. Let's look at what the actual labor data says is happening to technical roles specifically — not the hot-take version.

The aggregate numbers first

The World Economic Forum's Future of Jobs Report 2025 (1,000+ employers, 14M+ workers, 55 countries) projects ~170 million new jobs globally by 2030 against ~92 million lost — a net gain of roughly 78 million. Tech isn't exempt from the "lost" column, but it's heavily overrepresented in the "created" one: big data specialists and AI/ML engineers are named among the fastest-growing roles in the entire dataset.

The more interesting number for engineers specifically: 39% of core skills are expected to change or become outdated by 2030. Not roles — skills. That distinction matters a lot for how you should actually plan your career.

What's actually getting automated vs. what isn't

Based on what the growth/decline pattern in the data actually rewards, here's the practical split I'd draw:

Getting commoditized: boilerplate CRUD generation, routine test-writing, first-draft documentation, simple bug triage, basic data pipeline scaffolding — the stuff that's pattern-matchable from a large training corpus.

Getting more valuable, not less: system design, architecture trade-off decisions, debugging genuinely novel failure modes, translating ambiguous business requirements into technical specs, and — this is the one people underrate — cross-functional communication with non-engineers.

That last point isn't a soft-skills platitude. The WEF report specifically names analytical thinking as a critical skill for 70% of employers — ranked above any single named technical skill. The winning trait across the dataset isn't deep expertise in one tool; it's the ability to reason about new systems fast and make calls under uncertainty. Which, if you think about it, is a decent description of what senior engineers already do and juniors are still building.

The retraining reality

Zoom out to the labor market as a whole: out of every 100 workers globally, 59 need some form of retraining by 2030. For engineers, this isn't really new information — you've been "retraining" every 2-3 years since your first job just to keep up with framework churn. What's changing is the pace and the stakes.

One data point worth flagging separately: LinkedIn reports that the number of professionals listing AI skills on their profile has grown 20x since 2016. That's not a projection — it's already reflected in who you're competing against for the next role.

What this means for how you position yourself

The roles growing fastest — AI/ML engineering, data engineering, applied cybersecurity, fintech infrastructure — sit at intersections, not inside single narrow specialties. A pure "I know framework X" positioning is more exposed than "I know how to design systems that incorporate X, understand the business constraint driving it, and can explain the trade-off to a non-technical stakeholder."

I see this pattern from the education side too — I work at SITE, and our "AI and Entrepreneurship" bachelor's track is deliberately built at that intersection (technical + business) instead of as a CS degree with one bolted-on business elective. It's not a guarantee of anything — no program is — but it reflects the same signal the labor data shows: intersection skills are what's compounding in value right now.

Practical takeaway

Instead of asking "will AI replace my job," ask two more useful questions: how much of what you do today is pattern-matchable and routine — and how deliberately are you building the parts of your skill set that sit at an intersection rather than inside one narrow lane. Roles rarely vanish overnight. They quietly change shape, and that catches off guard the people who optimized for the job as it exists today rather than where it's clearly heading.

How Developers Should Actually Choose a Learning Format in the AI Era

Ainur Baigozha — Mon, 03 Aug 2026 10:04:25 +0000

If you're a developer, you've probably had this argument with yourself at 1am: bootcamp, online courses, a formal degree, or just keep grinding LeetCode and side projects? I get a version of this question from people outside tech too, but for engineers specifically, the stakes are a bit different — your stack has a shorter half-life than most careers.

TL;DR: the format matters less than how fast it updates. Here's the data behind that, and how I'd think about it if I were choosing today.

The half-life problem is real, and it's measurable

According to the World Economic Forum's Future of Jobs Report 2025 (a survey of 1,000+ employers representing 14M+ workers across 55 countries), about 39% of core skills held by today's workforce will change or become outdated by 2030. Scale that to 100 workers globally, and 59 of them need some form of retraining.

For engineers this isn't abstract — you've lived it. The framework you learned in year one of your CS degree is probably not the framework your team ships with today. The gap between "what a formal program teaches" and "what the job actually requires" is exactly what makes this a format problem, not just a content problem.

Three formats, three different failure modes

Traditional CS degree / university. Deep, structurally sound, respected by hiring managers who still gatekeep on credentials. Failure mode: curriculum update cycles are slow — sometimes literally bound to accreditation cycles that take years. You can graduate having never touched the tools your first job actually uses.

Bootcamps and online courses (Coursera, edX, etc.). Fast — a new course on a specific framework or tool can exist within months. Failure mode: fragmentation. You end up with a stack of certificates instead of a coherent body of knowledge, and for roles where a degree is still a hard filter (enterprise, some visas, some markets), that's a real gap.

AI-native / continuously-updated programs. The newer category — where the curriculum and career tooling are treated more like a living system than a syllabus. Full disclosure: I work at one of these (SITE, Swiss Institute of Technology in Education, Geneva), so take this with the appropriate grain of salt. What's genuinely different about this model isn't marketing language — it's mechanical: our internal AI career-matching layer updates against live job market data daily instead of being reviewed once a year by a curriculum committee. That's the actual thing that solves the staleness problem, not the "AI" label on the brochure.

What I'd actually check before picking one

If you're evaluating any program — ours or someone else's — as an engineer, skip the marketing copy and ask three mechanical questions:

What's the actual update cadence of the curriculum? Not "we use AI" — literally, how often does content change, and is that process automated or manual?
Is the credential portable? Accreditation matters for visas, enterprise hiring filters, and grad school pipelines even if it doesn't matter for your first startup job.
What does the placement data look like for people with your specific target role, not the program average across all majors?

For what it's worth, on the credentialing question: our programs are accredited by QAHE and EQAC, ISO 21001:2018 certified, and SITE holds CEEMAN and ECBE membership — worth checking for equivalents wherever you're looking.

The honest bottom line

None of the three formats above is universally correct. If you want deep, slow-changing fundamentals (compilers, algorithms, distributed systems theory), the traditional degree still wins. If you want a specific skill fast, targeted courses win. If you're optimizing for staying current across a fast-moving stack without going back to school every two years, that's the gap the newer model is trying to close — worth evaluating on the mechanics, not the pitch.

We Built an AI Career Matching Tool as a Student Project — and Ended Up Solving Our Own Career Anxiety

Ainur Baigozha — Tue, 14 Jul 2026 16:01:35 +0000

A 102-question diagnostic, a mock interview coach, and a CV that fixes itself. Here's what I learned building AI Career during my studies at SITE, an AI-native university in Geneva.

I still remember the exact moment the idea clicked. A classmate showed me her CV, three different versions, none of them tailored to anything in particular, and asked me which one "sounded more employable." Neither of us knew. We had grades. We had a few projects. What we didn't have was any real signal connecting what we were learning to what employers actually wanted from us.

That gap — between academic performance and career readiness — became the starting point for AI Career, a project I built as part of my studies and collaboration with SITE, an AI-native online university based in Geneva, Switzerland. It started as a coursework idea. It became something I now think every university should have.

The Problem: Career Services Weren't Built for How Students Actually Search

Traditional career centers work on a simple model: post the vacancy, let students browse, hope for a match. It's a static, manual process, and it puts the entire burden of self-assessment on the student. You're expected to somehow know your own strengths well enough to filter through hundreds of listings and figure out which ones you're actually qualified for.

Most students can't do that. Not because they lack ability, but because nobody ever gave them a structured way to translate coursework and personality into career direction. So they either apply to everything, or they apply to nothing.

I wanted to build the layer that was missing: a system that understands the student first, and only then shows them the market.

Starting With the Student, Not the Job Board

The core of AI Career is a career preferences diagnostic — 102 questions, about 17 minutes, built around eight disposition scales: things like conscientiousness, adaptability, collaboration, and grit. Instead of asking students to self-report ("Are you a leader?"), it uses paired comparisons — for each block of statements, you pick what fits you most and what fits you least. That forced-choice format produces a much more honest signal than a standard survey, because you can't just rate yourself highly on everything.

At the end, the system maps you to an archetype — mine came back "Organizer," with "Catalyst" as a runner-up pattern — and shows your full profile as a shape across all eight scales, so you can see at a glance where you actually stand out versus where you're simply average.

That profile becomes the input for everything downstream: which vacancies get surfaced, which skills get flagged as gaps, and what the AI recommends you work on next.

AI Career Matching, Not Just Job Listings

Once a student has a profile — diagnostic results, academic record, stated interests — the platform matches them against live vacancies and shows a percentage match for each one, along with salary range, work format, and role tags like entry-level or fast-paced. It's a small design choice, but it changes the psychology of job searching completely: instead of scrolling a wall of listings wondering if you're even qualified, you see your fit score before you decide whether to invest time in an application.

This is the same idea behind SITE's broader AI career matching engine, which continuously aligns student learning with live employer requirements — AI Career was, in effect, the practical, hands-on version of that concept, built by a student, for students.

Where the AI Actually Does Work: CV, Cover Letters, and Interviews

The diagnostic is the foundation, but the part students seem to like most is the AI assistant layered on top of the profile. From "My Profile," you can ask it to optimize your CV against actual feedback, generate a cover letter tailored to a specific vacancy pulled directly from the platform, or plan your portfolio — mapping which projects and certificates would actually move the needle for your target role.

There's also an AI mock interview module, so instead of walking into a real interview cold, students can rehearse against a simulator that responds to their answers, and a technical case simulator for roles that expect you to demonstrate applied skills rather than just describe them on paper.

None of these are generic AI wrappers bolted onto a job board. Each one reads from the same underlying student profile, so the CV advice, the interview prep, and the vacancy matches are all pulling from the same data instead of treating career prep as five disconnected tools.

Why I Built This at a University, Not a Startup

I built AI Career as a student project during my studies at SITE, an AI-native online university registered in Geneva, Switzerland, where the curriculum is built around applying AI to real problems rather than just studying it in theory. That context mattered more than I expected. Being inside a university, with direct access to actual student profiles, actual course data, and actual questions students were asking their advisors, meant the product could be shaped by real friction instead of assumptions about what students "probably" need.

It also meant the project didn't stay a private experiment for long. It was originally built for SITE's own students, but the architecture — diagnostic engine, matching layer, AI career assistant — isn't specific to one institution. It's now open for collaboration with other universities and companies that want to give their own students or candidates the same kind of structured, data-driven career support, instead of a static list of job postings and a PDF template for CVs.

What I'd Tell Any Student Thinking About Building Something Similar

The instinct when you set out to build a "career platform" is to start with the job listings — scrape vacancies, build filters, ship it. I'd argue that's backwards. The listings are the easy part. The hard part, and the part that actually creates value, is building an honest picture of the person first. Once you have that, matching, CV help, and interview prep all become downstream features instead of separate products you have to sell independently.

I also learned that forced-choice diagnostics (pick the one that fits most, pick the one that fits least) produce much better signal than open self-rating scales. Students are bad at rating themselves in isolation. They're much better at comparing.

Where AI Career Goes From Here

Right now, AI Career lives as a working prototype built and tested within SITE. The next steps are the obvious ones: more employer partnerships feeding live vacancies into the matching engine, deeper integration with academic records so the diagnostic updates as students actually grow, and validating the approach with more universities outside SITE's own student base.

If you're a student building something in this space, or a university or company curious about integrating a diagnostic-first career matching engine into your own programs, I'd genuinely like to hear from you.

About SITE
SITE is an AI-native online university registered in Geneva, Switzerland. We offer accredited BBA and MBA programs powered by a proprietary AI career matching engine, gamified learning platform, AI-driven content personalization, and access to an international exchange network across 20+ partner institutions. Accredited by QAHE and EQAC, ISO 21001:2018 certified, CEEMAN and ECBE member. 1,200+ students enrolled from 20+ countries since September 2024.

Learn more at sitegeneva.com · info@sitegeneva.com · Chemin Louis-Hubert 2, 1213 Petit-Lancy, Geneva, Switzerland