The future of tech roles is multidisciplinary: How AI augmentation blurs the boundaries

There's a quiet shift happening in tech organisations. The rigid boundaries between disciplines (QA, development, DevOps, product) are becoming increasingly porous. Not because of some management fad, but because AI is changing how we allocate our time, and that's forcing us to rethink what our roles actually are.

I've spent my career in QA, gradually expanding into quality engineering and technical analysis. But lately, I've noticed something: the time I used to spend on repetitive documentation, test case generation, or hunting through Jira and ADO backlogs is shrinking. AI tools are handling the grunt work faster than I ever could. AI isn’t taking my job; it's giving me back my time.

AI isn't replacing you - it's freeing you

Let's be honest about what AI is actually doing in our day-to-day work. For intelligent AI users, it's compressing the time spent on the tedious and repetitive tasks that we've always known were necessary but time consuming.

Need to convert a product requirement document into structured QA notes? AI can scaffold that in seconds. Want to generate baseline test scenarios from technical specifications? Done. Researching API documentation to verify exam materials? What used to take hours and days now takes minutes.

This isn't replacing expertise; it's augmenting execution speed. The critical thinking, the understanding of why something works (or doesn't), the ability to spot the edge case that breaks everything, that still requires human intelligence and always will. But the tedious admin overheads? That's being compressed.

The result is genuinely more free time in your workday. Not "free time" as in scrolling through Teams and Outlook, but productive capacity that can be redirected. And here's where the multidisciplinary future starts to emerge.

When you have time, you can expand your domain

Traditionally, specialisation made sense because there simply wasn't enough time to do anything else. If you were a QA analyst, your day was consumed with testing, documentation, bug triage, and regression cycles. There wasn't bandwidth to learn the deployment pipelines or dig into how your CMS structures content models.

But when AI compresses your manual overhead, you suddenly have capacity. And if you're technically minded, that capacity naturally gets directed towards understanding the broader system.

I've found myself diving deeper into Optimizely's technical architecture, not because it's strictly "my job", but because understanding how Visual Builder compositions work, how Opti ID handles SSO flows, or how GraphQL queries are structured makes me better at QA. When you understand the full stack, you write and execute better tests. You catch architectural issues before they become production incidents. You ask better questions during sprint planning.

This isn't scope creep; it's natural domain expansion enabled by having the time to learn.

Multidisciplinary understanding delivers better products

Here's the uncomfortable truth: highly specialised roles can produce highly siloed thinking. When QA only understands testing, developers only understand code, and product only understands features, you get organisational blindspots.

Multidisciplinary roles break down these silos. When your QA analyst understands deployment pipelines, they can identify integration test failures before code hits staging. When your developer understands content modelling, they build better CMS architectures. When your technical analyst understands both quality engineering and platform capabilities, they can verify not just that something works, but that it's built sustainably and following best practices.

The products that emerge from teams with broader domain understanding are fundamentally better. They're more coherent, more maintainable, and more resilient to edge cases.

The skills that matter now

If AI is augmenting on the repetitive tasks and freeing up time for domain expansion, what skills actually matter?

Technical breadth over narrow depth

You don't need to be an expert in everything, but you need to be conversational across multiple domains. Understand enough about DevOps to read a pipeline configuration. Know enough about frontend to spot that a div is missing a class.

Systems thinking

The ability to see how components interact, how decisions ripple through a system, and where the real complexity lives. This separates someone who runs tests from someone who helps engineer quality into the system.

Rapid learning capability

AI tools make information more accessible, but you still need the cognitive capacity to absorb new technical concepts quickly and integrate them into your mental models.

Cross-functional communication

When you're working across disciplines, you need to translate between technical contexts. Explaining deployment risks to product managers, or content modelling constraints to developers, becomes a core competency.

What multidisciplinary actually looks like in practice

This isn't just theoretical. My role as a Technical Analyst is essentially a lived example of this shift. On paper, it's just QA. In practice, it's QA plus business analysis plus product ownership plus technical leadership plus Scrum Master responsibilities when needed, the list goes on.

I'm still executing manual and automated testing, yes and I always will be. But I'm also involved in technical discovery phases, gathering business requirements for the team, providing Optimizely platform expertise for new and existing site builds, managing release pipelines, and coaching teams on Agile practices. I'm reviewing developers’ code to understand the possible impact for QA, implementing AI-driven process efficiencies, and acting as a "tech-detective" when complex issues emerge.

This isn't scope creep or role confusion. It's recognition that quality can't be bolted on at the end, living proof that you can shift all the way left. When you understand the business requirements, the platform capabilities, the deployment architecture, and the testing surface area, you deliver better outcomes. You catch issues earlier. You design better solutions. You ask the right questions during discovery instead of finding production P1s on go-live day.

The role exists because the boundaries between these disciplines are artificial. Quality assurance informs requirements gathering. Business analysis improves test coverage. Technical leadership shapes product decisions. They're not separate functions; they're interconnected perspectives on the same goal: delivering high-quality digital products.

This isn't optional anymore

The shift towards multidisciplinary roles isn't a trend you can opt out of. It's a natural consequence of AI augmentation giving us time, and technical curiosity directing that time towards broader understanding. Clients start to expect faster and leaner delivery teams based on the assumption that your teams are augmented with AI. Organisations that recognise this and actively encourage domain expansion will build better products with more resilient teams.

The future in an AI enabled world isn't just QA or development or DevOps or Product. It's QA and Development and DevOps and Product, with individuals who can think across disciplines and organisations that support them in doing so.

AI isn't replacing jobs; it's redefining what jobs can be. And if you've ever felt constrained by the narrow boundaries of your role, that's probably a good thing.