Five professions that will define the future of technology in 2030

• Talent evolution: 80% of engineering teams will transform into AI-powered "Tiny Teams" by 2030. 
• Autonomous Automation: Multi-Agent Systems (MAS) and Physical AI will be the drivers of operational efficiency. 
• Trust and Security: Digital Provenance and Preventive Cybersecurity become mandatory to mitigate risks. 

As it does every January, the prestigious technology consultancy Gartner has analysed in"Top 10 Strategic Technology Trends " which will be the main technological trends that will define the coming year, so that CIOs, CTOs, CISOS and those responsible for digital transformation can anticipate a future that, in this case, will continue to be dominated by the development of Artificial Intelligence and its implementation in all types of systems. 

From here, at UDIT we have analysed the way in which these trends will be projected into the future and the new job opportunities they create. Thus, in this article we propose what we consider to be the five professions that will define technology in 2030 and in which AI, full-stacksoftware development and robotics have a clear leading role. 

TinyTeams" leaderin native development and AI 

For some years now, the traditional model of large software engineering teams has been disappearing, giving way to much smaller and more agile teams (tiny teams), which will also gain in power thanks to the development of generative Artificial Intelligence. 

The role of the "Tiny Teams Leader" is to orchestrate AI-native development platforms where autonomous agents write code alongside human staff.  Their goal is to make a team of two or three people capable of delivering the volume of work previously required by entire departments, using tools ranging from single-prompt  code generation to what is already known as "vibe coding" (a set of tools that enable software development without requiring extensive technical expertise or deep programming knowledge on the part of the user) . 

Why will it be relevant in 2030? 

In its report, Gartner projects that by 2030,80% of organisations will downsize their software engineering divisions in favour of smaller teams augmented by AI. In addition, 40% of enterprise applications will be built on these platforms, up from just 2% in 2025.

 Key Skills

• Hard Skills: Proficiency in advanced Prompt Engineering, AI-native platforms, integration of AI Agents in DevOps flows. 
• Soft Skills: Agile leadership, change management, ethical oversight of machine-generated code and architectural synthesis skills. 

Multi-AgentSystems Orchestrator  (MAS ) 

Incipiently, today's generative AI, based on chatbots (it is the user who asks and gets answers), is evolving into a system in which multiple AIs "talk" to each other to solve problems, without the need for human intervention until the end of the process (validating or not the answer). 

As the American consulting firm points out, the logical evolution of this process crystallises in the development of figures such as the Multi-Agent Systems (MAS) orchestrator or designer. This professional coordinates the work of different specialised AI agents where each one manages a specific task and collaborates to complete workflows. In addition to designing the governance of the system, he or she is in charge of interoperability between agents from different providers and prevents them from acting according to their own interests or dishonestly ("rogue agents"). 

Why will it be relevant in 2030? 

MAS enquiries increased by 1,445% between 2024 and 2025, pointing to business interest. Gartner says that by 2027, 70% of enterprises will use specialised agents to improve the accuracy of critical processes and that by 2030, the ability to have agents from different vendors work together will be standard. 

 Key Skills

• Hard Skills: Modular system architecture, API governance, design of interoperability protocols between agents, observability tools for AI. 
• Soft Skills: Systems thinking (see the whole, not the parts), conflict resolution between agent logics, strategic vision of automation. 

AI Physics and Automation Engineer

The next great technological revolution will come when AI is reliably and massively able to "escape" from screens and control the physical world. It will come to robots yes, but also to most of the objects we interact with in our day-to-day lives. 

The AI Physics engineer will play a key role here, implementing systems that, by combining sensors, actuators and AI models, automate tasks in the real world through robots, drones and smart devices that sense, decide and act. 

Why will it be relevant in 2030? 

It is expected that by 2028, 80% of warehouses will use robotics or automation driven by these types of technologies. By that date, five of the top 10 AI vendors will offer specific physical AI products.

Key Skills 

• Hard Skills: Robotics and mechatronics, simulation and Digital Twins, Edge Computing, device fleet orchestration.
• Soft Skills: Interdisciplinary collaboration (bridging IT and Operations/OT),Safety first mentality, adaptability to industrial environments.

 Digital Provenance Architect

In a world flooded with synthetic content, certifying what is real and what has been generated by AI is already a real challenge. Furthermore, being able to verify the origin of the code of applications, websites, scripts, etc. is becoming a critical element for the security and compliance of organisations. 

The Digital Provenance Architect is the professional in charge of implementing technologies capable of verifying the integrity and origin of software, data and media, using tools such as attestation databases, digital watermarking and software bills of materials (SBOMs), with the aim of ensuring transparency and protecting against misinformation. 

Why will it be relevant in 2030? 

Gartner explains that regulations (such as the EU's AI Law) will require tracking the provenance of AI-generated content. On the other hand, this role will be essential to mitigate the legal risks companies are exposed to and their future reputational crises in an increasingly uncertain world. 

 Key Skills

•  Hard Skills: Cryptography and digital signatures, management of SBOMs and MLBOMs (Machine Learning BOMs), Watermarking technologies , Blockchain/Attestation Ledgers. 
• Soft Skills: Digital ethics, analytical rigour, legal-technical communication skills (compliance), reputational risk management. 

 Preventive Cybersecurity Strategist

Traditionally, cybersecurity has relied on a reactive strategy, based on identification, response to cyber-attacks and risk mitigation. With the development of AI, this is no longer sufficient and new threats will lead organisations to focus on anticipating and neutralising the attack before it occurs. 

In this new paradigm, the Preventive Cybersecurity Strategist plays a leading role, with a focus on three pillars: predicting threats through threat intelligence, cyber deception and preventing attacks through moving target defences.The company's posture shifts from "defend" to "hunt and neutralise" the attacker. 

Why will this be relevant in 2030 ? 

Leading research firms warn that documented vulnerabilities will exceed one million per year by 2030. Most shocking is the financial projection: by 2030, 50% of total security software spending will be on preventative solutions, as products lacking this capability will lose market relevance. 

 Key Skills

• Hard Skills: Threat Intelligence,Deception Tech, adversarial AI, predictive risk analysis. 
• Soft Skills: Adversarial mindset (think like the attacker), radical proactivity, crisis management under pressure, strategic thinking.