Labor Market

Emerging Professions in the Age of Artificial Intelligence: A Current Overview

In this article, we will look at some professions that are gaining space as artificial intelligence advances, including technical roles, management positions, executive roles, and hybrid functions that connect technology and business.

Evandro Gayer Gomes
Evandro Gayer Gomes
Professionals across multiple industries connected by artificial intelligence, illustrating the transformation of the workforce and the emergence of new career paths.

Introduction

As artificial intelligence continues to be adopted across the market, the topic is no longer limited to the technology sector. It is becoming part of the daily work of professionals in many different industries.

There are still many questions about what the future of work will look like as the field advances. Much of the debate remains stuck between two simplified views: the idea that artificial intelligence will quickly eliminate jobs, or the assumption that new opportunities will be limited to highly technical roles, such as AI Engineer or Prompt Engineer.

The reality appears to be broader. Artificial intelligence is creating new roles, strengthening existing ones, and, most importantly, changing the routine of traditional professions. Instead of looking only at job titles, it is more useful to understand which skills are becoming more valuable and how the technology is entering the day-to-day operations of companies.

In this article, we will look at some professions that are gaining space as artificial intelligence advances, including technical roles, management positions, executive roles, and hybrid functions that connect technology and business.

Will artificial intelligence eliminate jobs?

The most recent data suggests a more balanced answer: artificial intelligence is likely to transform many professions, but that does not mean all of them will be fully automated.

According to the report by the International Labour Organization, Generative AI and Jobs: A Refined Global Index of Occupational Exposure, around one in four workers worldwide is in an occupation with some level of exposure to generative artificial intelligence. At the same time, only a small share of global employment falls into the highest exposure category. The ILO’s conclusion is that, in most cases, the most likely effect is the transformation of tasks, not the full elimination of occupations.

The OECD follows a similar line in the study Artificial intelligence and the changing demand for skills in the labour market. Instead of suggesting that everyone will need to become a machine learning specialist, the study points out that artificial intelligence changes the content of work. In many occupations, digital, cognitive, emotional, administrative, and management skills become more important.

In short: artificial intelligence tends to change the content of jobs before it changes the names of all jobs.

In practice, this means that someone may continue working as an analyst, manager, consultant, designer, marketing professional, lawyer, doctor, or teacher, but with new tools at the center of their routine. The expectation is that the progress of the technology will make these professionals more productive, allowing one person to achieve results that previously might have required a larger team.

What changed in the market outlook in 2026?

In 2025, much of the discussion about artificial intelligence and work was still dominated by projections. In 2026, more concrete signs of market reorganization are becoming visible.

An article from the World Economic Forum, Software developers are the vanguard of how AI is redefining work, notes that 37% of surveyed developers said that artificial intelligence has already expanded their career opportunities, while 65% expect their role to be redefined in 2026. The most interesting point is the direction of this change: less emphasis on routine coding and more emphasis on architecture, integration, and AI-assisted decision-making.

This interpretation is consistent with job openings already visible in the market, such as Senior AI Engineer (AI Automation), Senior AI Software Engineer, AI Solutions Architect, and AI Manager. These titles show that demand for software engineers has not disappeared. In many cases, it has been repositioned for a context in which artificial intelligence, automation, agents, and integration with corporate systems have become part of daily work.

The same movement is also visible outside the technical core. The World Economic Forum article These 3 charts show how AI is affecting wages, job quality and hiring decisions points to growing demand for artificial intelligence skills in sectors such as finance, manufacturing, healthcare, logistics, and public services. Lightcast reinforces this trend in the report Beyond The Buzz: Developing the AI Skills Employers Actually Need, which indicates that 51% of job postings requiring artificial intelligence skills in 2024 were outside IT and computer science occupations.

What to watch in 2026

  • Growth in roles for AI Engineers and AI Software Engineers.
  • The emergence of management positions focused on artificial intelligence.
  • Expansion of hybrid roles across technology, product, operations, and business.
  • Greater demand for governance, compliance, and risk management.
  • Increasing use of artificial intelligence agents in corporate operations.

Before looking at each role in more detail, it is worth noting an important pattern: not all of these professions require a computer science degree or deep experience in machine learning. Many of them emerged to connect technology, business, operations, governance, and strategy.

Role Main area Profile
AI Engineer Technology Development and deployment of artificial intelligence solutions
AI Software Engineer Technology Software engineering with integrated artificial intelligence
AI Solutions Architect Technology / Business Architecture and design of enterprise solutions
Forward Deployed Engineer Technology / Business / Operations Deployment of solutions alongside customers and adaptation to real-world operating contexts
AI Manager Management Coordination of artificial intelligence initiatives and teams
Chief AI Officer (CAIO) Executive Corporate artificial intelligence strategy
AI Consultant Consulting Identification and implementation of use cases
Pre-Sales AI Consultant Sales / Technical Technical support for sales and proofs of concept
AI Product Specialist Product Evolution and quality of products that use artificial intelligence
Applied AI Operations Lead Operations Deployment and operation of artificial intelligence solutions
AI Corporate Trainer Corporate education Training teams to use artificial intelligence
AI Governance & Compliance Manager Governance Risk, compliance, and regulation

AI Engineer

Description: a professional responsible for developing, integrating, and deploying artificial intelligence systems in production environments.

In practice, this role may involve using language model APIs, building AI agents, automating processes, working with vector databases, implementing RAG (Retrieval-Augmented Generation), integrating with corporate systems, and monitoring solutions after deployment.

It is a strongly technical role, but increasingly connected to product delivery. In many companies, the AI Engineer needs to understand software, data, infrastructure, security, and quality evaluation.

AI Software Engineer

Description: an evolution of the traditional software engineer role in a context where artificial intelligence has become part of the application’s core stack.

In addition to classic software engineering skills, this professional works with agents, agentic workflows, LLM integration, response evaluation, observability, security, and the design of applications that use artificial intelligence as a central part of the user experience.

This role is particularly relevant because it helps correct a common market misconception: artificial intelligence does not automatically make software engineers irrelevant. In many cases, it increases the need for strong engineers who can turn models into reliable, testable systems that are properly integrated into the business.

AI Solutions Architect

Description: a professional responsible for turning a business need into a viable, secure, and scalable artificial intelligence architecture.

This role defines how models, data, integrations, infrastructure, security, and governance work together within an organization. In an enterprise artificial intelligence initiative, the AI Solutions Architect often helps answer questions such as:

  • Which data can be used?
  • Which model or provider makes sense for the current use case?
  • How will the solution integrate with existing systems?
  • How should quality, cost, risk, and return be measured?
  • Which controls must exist before the solution goes into production?

It is a role that requires strong systems thinking, enterprise experience, and the ability to communicate with both technical and executive audiences.

Forward Deployed Engineer

Description: a professional who works close to the customer to turn real business problems into functional artificial intelligence solutions that are integrated and adapted to the company’s operating context.

The Forward Deployed Engineer usually works at the intersection of engineering, product, consulting, and implementation. Instead of only developing a solution in isolation, this professional takes part in understanding the customer’s process, identifying practical constraints, integrating existing systems, adjusting workflows, and helping bring the solution into real use.

In artificial intelligence projects, this role becomes especially relevant because many solutions depend on context, internal data, corporate system integrations, continuous evaluation, and adaptation to how teams actually work. For that reason, it is particularly useful in companies that sell platforms, agents, automations, or artificial intelligence products to other organizations.

AI Manager

Description: a professional who coordinates teams, vendors, priorities, and projects related to the adoption of artificial intelligence within a company.

Depending on the organization, the focus may be digital transformation, innovation, automation, governance, or the development of products based on artificial intelligence. The AI Manager does not necessarily need to be the deepest technical specialist on the team, but they do need enough understanding to make decisions, assess risks, and guide execution.

This role tends to become more common as companies stop treating artificial intelligence as an isolated experiment and start organizing portfolios of initiatives, performance metrics, and governance models.

Chief AI Officer (CAIO)

Description: an executive responsible for defining and coordinating the corporate strategy for artificial intelligence.

The Chief AI Officer role gained more visibility in 2026. According to IBM, in the article The rise and ROI of the chief AI officer, 76% of surveyed organizations reported having a Chief AI Officer in 2026, compared with 26% in 2025. The same article includes a useful statement from Jacob Dencik, Research Director at the IBM Institute for Business Value: “AI is no longer just a technical discussion.”

In practice, the Chief AI Officer works on topics such as:

  • defining investment priorities;
  • governance and risk management;
  • coordination between business and technology areas;
  • selection of use cases with return potential;
  • creation of internal standards for the responsible use of artificial intelligence.

This role should not be seen merely as a new executive title. Its real value lies in guiding the adoption of artificial intelligence solutions, preventing fragmented initiatives, and connecting artificial intelligence to measurable business outcomes.

AI Consultant

Description: a professional who helps companies identify opportunities to use artificial intelligence, assess feasibility, and support implementation.

This is a hybrid role between technology and business. The AI Consultant usually participates in diagnostics, use case design, ROI analysis, tool selection, governance, and pilot projects.

This function is especially important for startups and companies that still need to separate useful initiatives with potential financial return from experiments driven only by market hype.

Pre-Sales AI Consultant

Description: a professional who supports the commercial process for artificial intelligence solutions.

This role commonly appears in companies that sell platforms, agents, automations, or artificial intelligence services to other organizations. The work involves demonstrations, proofs of concept, technical meetings with customers, requirements gathering, and initial solution design.

This role shows that artificial intelligence is already creating demand in sales as well, not only in technology.

AI Product Specialist

Description: a professional focused on improving products that use artificial intelligence.

This role may involve testing, evaluating response quality, analyzing user feedback, defining requirements, structuring prompts, tracking metrics, and collaborating with engineering, design, and support teams.

It combines product, operations, and user experience. In products with agents or chat interfaces, this professional helps turn model behavior into a reliable experience for the end user.

Applied AI Operations Lead

Description: a professional responsible for ensuring that artificial intelligence solutions work properly within real company operations.

In sectors such as healthcare, insurance, logistics, customer service, and financial services, deploying artificial intelligence requires process adaptation, team training, usage monitoring, workflow review, and tracking of performance indicators.

AI Corporate Trainer

Description: a professional specialized in training employees and leaders to use artificial intelligence tools productively and responsibly.

With the rapid spread of generative artificial intelligence, many organizations have started investing in internal training programs. The role of the AI Corporate Trainer is to help teams understand what artificial intelligence can do, where it fails, which data can be used, and how to apply the technology to real tasks without losing quality or security.

AI Governance & Compliance Manager

Description: a role focused on governance, privacy, regulatory compliance, and risk management.

As companies adopt artificial intelligence in critical processes, the need grows for internal policies, usage controls, impact assessments, vendor reviews, documentation, and compliance with regulations. This role is likely to become especially relevant in regulated sectors such as healthcare, finance, insurance, education, and government.

What changes for professionals outside the technical field?

For most people, the most relevant change is the need to update their skill set within their own area of work.

In marketing, for example, artificial intelligence is already being used in content creation, data analysis, SEO, audience segmentation, market research, and campaign ideation. In addition to creativity and strategic thinking, it becomes important to use artificial intelligence tools with judgment, review outputs carefully, and connect production to business goals.

In human resources, artificial intelligence can support resume screening, job description creation, skills analysis, internal communication, and training materials. In this context, the differentiator is using the technology without losing human judgment, context, and responsibility.

In administrative and operational areas, artificial intelligence can help summarize documents, organize information, answer internal questions, automate repetitive workflows, and support decision-making. In healthcare, education, legal, and finance, the same movement is also visible, usually with more attention to governance, human review, and information security.

A simple comparison helps illustrate the point. Consider someone who works in an administrative role and deals every day with emails, spreadsheets, reports, and documents. In the past, much of their time might have been spent reading repetitive material, copying information, summarizing data, and preparing internal communications. With artificial intelligence, part of that work can be accelerated. But someone still needs to define what should be done, validate the result, correct misinterpretations, make decisions, and, above all, be accountable for the consequences.

This is where new skills become important. A professional does not necessarily need to become an AI Engineer, but they do need to learn how to ask better questions, review outputs, protect sensitive data, identify errors, and understand where the tool helps or gets in the way.

What do the data suggest about opportunities?

Recent reports do not support a simple thesis of widespread job destruction. The picture is more mixed.

PwC, in its Global AI Jobs Barometer 2025, analyzed nearly one billion job postings and found that the skills required in occupations exposed to artificial intelligence are changing 66% faster than in other occupations. The study also points to a wage premium for professionals with skills related to artificial intelligence.

The World Economic Forum, in the Future of Jobs Report 2025, projects that technology, artificial intelligence, big data, and cybersecurity will be among the important drivers of change in the labor market. At the same time, human skills such as analytical thinking, creativity, resilience, leadership, and continuous learning continue to appear as important.

In Brazil, Brasscom projected growth in formal employment in the ICT macro-sector in 2025. According to the report Perspectivas do Mercado de Trabalho do Macrossetor TIC, the baseline scenario indicated an increase of 88,000 formal jobs, potentially reaching 147,000 in the optimistic scenario. This shows that the Brazilian market also feels pressure for professionals capable of combining technology, data, security, and business perspective.

These data do not mean there will be no negative impacts. Some entry-level roles, repetitive tasks, and activities based only on simple text production, basic analysis, customer service, and call centers may face more pressure. Even so, the main trend appears to be the recomposition of work: fewer mechanical tasks, more use of tools, more integration between areas, and greater focus on practical results.

Which skills are likely to gain value?

The first skill is understanding what artificial intelligence can do well and where it still fails. This includes knowing how to use generative tools, but also recognizing limitations, hallucinations, bias, privacy issues, and risks of misuse.

There is also an important point here: not everything should be solved with generative artificial intelligence. Artificial intelligence is a broad field, and different problems require different approaches. If the goal is to predict a numerical value, such as estimating the price of a property based on characteristics like location, size, number of bedrooms, and market history, supervised regression models may be more appropriate than a generative tool. For optimization problems, such as delivery routing, workforce allocation, scheduling, or finding the best combination of resources under constraints, optimization methods and metaheuristics may make more sense.

The second skill is translating business problems into processes that can be supported by artificial intelligence. Many companies do not only need someone who knows the latest tool. They need people who can look at a routine, identify bottlenecks, propose automations, measure impact, and adjust the process.

The third skill is combining domain expertise with digital fluency. A good marketing professional using artificial intelligence, for example, is not simply someone who can generate text quickly. It is someone who understands brand, audience, channel, metrics, and positioning, and uses artificial intelligence to accelerate parts of the work without losing quality. The same reasoning applies to HR, legal, finance, healthcare, education, operations, and customer service.

The fourth skill is continuous learning. The technology changes quickly, tool names change, and job titles are still being consolidated. For that reason, the ability to adapt may be more important than memorizing a fixed list of emerging professions.

Main point: the competitive advantage is likely to come from the combination of domain knowledge, critical thinking, and the practical use of artificial intelligence.

Conclusion

Artificial intelligence is creating new professions, but its influence goes beyond roles with “AI” in the title. The most important movement is the transformation of familiar functions, which are beginning to incorporate intelligent tools, automation, and new ways to produce, analyze, and make decisions.

For technical profiles, there are clear opportunities in AI engineering, solution architecture, data, security, agents, integration, and governance. For professionals in other fields, the most realistic path may be learning how to apply artificial intelligence within their own area of expertise.

The opportunity, therefore, is not always in abandoning one’s current career. In many cases, it is in repositioning it. Professionals who combine business knowledge, domain expertise, and the careful use of artificial intelligence tend to be better prepared for this new cycle in the labor market.

Sources

Read More

Link copied