Artificial intelligence is not learning to think but to work, replicating how humans apply knowledge. This advancement threatens to devalue professional services that were once costly, shifting value toward what cannot be automated. New professional profiles are emerging that ensure a job future based on a new type of interaction with AI.
Artificial intelligence is not learning to think like humans, but it is learning to work like them. And this changes (even more) the labor market.
Mario Garcés, founder of The MindKind, a Spanish neuroscientist and entrepreneur competing with OpenAI, Meta, and Google in the race to develop general artificial intelligence, claims that “large language models (LLMs) have accumulated humanity's knowledge, but now what is being captured in new metamodels—systems that learn how to use other models and knowledge by observing how humans do it—is the use we are making of that knowledge. In other words, it’s capturing the way humans interact with language models.” The new frontier of AI is no longer accumulating knowledge, but capturing how humans apply it in their work.
The systems are starting to learn work methods, not just data, and no longer learn exclusively from human knowledge, but from human professional behavior.
Previously, value was placed on knowing; knowledge was scarce, and the expert was the one who accumulated information and experience. Now, knowledge is abundant; value shifts to how it is applied, and that "how" is becoming capturable.
Mario Garcés’s idea explains why skills are changing: it’s no longer enough to know. One must formulate problems, evaluate results, assume responsibility, and work with automated systems.
A New Value
The work of knowledge is not ending, but the work of repeatable knowledge is becoming cheaper, and the premium grows for what cannot be easily standardized (responsibility, negotiation, context, client relationships, decisions under uncertainty, risk management).
It’s not about making prompts, but about competence in productive interaction: formulating well, iterating methodically, verifying, documenting decisions, and managing risks. This redesigns tasks and elevates professional roles like AI strategy specialist, interaction trainer, augmented creator, validator or supervisor, and expert in governance, ethics, and security.
What used to be differentiated and expensive is now starting to resemble a commodity: it becomes abundant, standardized, easy to compare among providers, and, therefore, loses margin and price.
The key is understanding that capturing the use of knowledge is, in practice, about capturing work methods (how we ask, evaluate, or decide) and turning them into product capabilities. And when an activity becomes "product," the knowledge that was once sold as a service becomes commoditized.
The disruption isn’t that AI knows—it’s that it starts to do. And when doing becomes standardized, the market pays less for the basic service and more for supervision and decision-making.
The International Labour Organization (ILO) warns that AI does not directly eliminate jobs, but exposes entire tasks to automation, especially in text-intensive and digital-process occupations. The World Economic Forum anticipates a massive reconfiguration of skills by 2030.
The message is clear: the value of standard cognitive work is rapidly decreasing.
In just one weekend, using commercial language models, a professional can draft three complex legal documents—nullity, opposition, and appeal—with professional-level detail and correct citations of case law. Without patent agents, without specialized law firms, they can accomplish work that used to take days or weeks by experts.
This example can be applied to other sectors such as graphic design, strategic marketing, preliminary research, artistic creation, financial analysis, or basic consulting.
The example of a recruiter and selection processes also helps understand all of this: filtering is made cheaper, while true evaluation becomes more expensive. Previously, much of the value of a recruiter lay in screening resumes, drafting job offers, doing first filters, summarizing interviews, and coordinating processes. Later, AI achieved better automation in job descriptions, candidate screening by criteria, initial scoring, interview questions, summaries, and candidate comparisons... What remains valuable is defining the real profile: what the business really needs versus what’s just posturing from the hiring manager. It’s about detecting human signals (motivation, cultural fit, learning capacity, integrity) and avoiding biases and risks.
The conclusion is that the “mechanical filter” is becoming commoditized, while good selection becomes more strategic and demanding.
Winners in 2026
The initial idea from The Mind Kind founder leads us to think that those who will win are not the most technical, but those who learn to interact with artificial intelligence in a new way. The core skill is not prompting but orchestrating with criteria, and so some winning profiles for 2026 are:
One of them is the AI Work Orchestrator. It can be said that this role already exists in practice, though some companies call it GenAI Solutions Architect, AI Product/Delivery Lead, LLMOps/AI Platform Engineer, or Applied GenAI Lead. What defines this role is not the title, but the mission: turning a business objective into an operable, measurable, and secure AI system (not a demo). These professionals are in demand in sectors such as banking and insurance, where they deal with much documentation, repeatable processes, and a high cost per error.
They are also valued in retail, ecommerce, telecommunications and contact centers, as well as in pharma and life sciences—in regulated environments with a lot of internal knowledge and a need for traceability—or in industry, technology, and cloud.
Morgan Stanley is a concrete example of orchestration in companies. The company didn’t just deploy an LLM to answer questions. They launched internal assistants to surfacing—make relevant information come to the surface: if an analyst or advisor asks something, the system brings up the exact reports and paragraphs that answer it—and distills knowledge from its own research body. Instead of inventing, the system retrieves relevant pieces of internal knowledge (retrieval), drafts a usable summary for the advisor or analyst (generation), and relies on an evaluation framework to ensure the AI performs with the reliability and consistency required in a financial environment.
The orchestrator defines permissions (who can see what), selects good internal sources (current versions, approved documents), sets quality criteria, and builds continuous tests.
Many companies are already explicitly hiring AI Interaction Trainers known as Enablement Leads or AI Learning & Enablement Managers, with tasks like designing corporate programs or training pathways.
These professionals ensure that an organization works with AI in a secure, measurable, and repeatable way. They don’t teach tricks; they design work protocols (not prompts) and turn them into team habits.
They build "internal capacity": AI champions within organizations, launch kits (communication, use cases, guides), or scenario libraries.
They also measure the degree of AI adoption (active users, recurrence), productivity (time saved, delivery cycles), quality, and risks (incidents, data leaks, and non-compliant outputs).
Among the sectors demanding these professionals are health and pharma, with high data sensitivity and strict regulation; legal; media and content—USA Today has included an AI Learning & Enablement Manager role focused on enterprise programs and incorporating ethics, governance, and responsible use in training; consulting—Capgemini has a GenAI Adoption & Enablement Specialist, and Accenture has launched massive training programs in generative AI; and also finance, insurance, corporate retail, and administration.
An augmented content professional is a creative (copywriter, designer, video editor, UX writer, branded content creator) who no longer adds value primarily by producing from scratch, but by directing, editing, and differentiating AI-generated content to make it useful for the business, consistent with the brand, and safe to publish. This profile should be seen as a blend of creative director, chief editor, and generative tools operator.
AI can generate one hundred versions, and the augmented content professional defines which version works (and why); what cannot be said (legal claims, promises, regulatory terms); or how to maintain the brand voice (tone, style, or consistency).
Their superpower isn’t prompting but professional editing: verifying coherence and accuracy, detecting hallucinations, reviewing biases, and ensuring the content respects internal policies.
Basically, the sectors that demand these professionals are advertising agencies; consumer and big brands; retail; B2B software and performance marketing; ecommerce; and media and entertainment.
If AI is involved in critical decisions, a new quality control of the 21st century emerges: professionals who validate models, document decisions, monitor biases, and ensure traceability to defend—before a regulator or judge—why the system made a certain decision.
AI Decision Supervisors ensure that an AI system (or "model") influencing important decisions works as intended, can be explained and audited, doesn’t discriminate, and has a clear accountability. Basically, they make sure that AI in production becomes defendable AI.
They decide whether the case is "critical" and what level of control is required; they validate before deployment and conduct quality, bias, robustness, explainability, and limitation tests: they define “where not to use it” and when it must pass to a human.
These professionals are mainly demanded in finance and banking sectors, where automated decisions (credit, fraud, risks, or trading) are made.
In insurance, they are needed for models in pricing, fraud, claims management, and customer service; in health for managing clinical risk; and in HR for screening, evaluation, and promotions.
The AI Ethics, Compliance, and Risk Manager is the person (or team) that ensures AI in a company is legal, safe, auditable, and "defendable."
Although the title may vary, you can identify this role when someone is responsible for inventorying use cases and models, approval processes, vendor clauses and retention, data, prompts, and logs policies, evaluation and monitoring, and coordination between legal, compliance, data, and product teams.
The job of these professionals is to prevent AI from becoming what is called "shadow IT" (where each person uses tools their own way), and instead, they aim to make it something that can be managed like any other corporate risk. In practice, AI ethics, compliance, and risk managers select and control vendors (LLMs, platforms, integrators); define criteria for purchasing AI (security, privacy, support, certifications, jurisdiction, subprocessors); negotiate clauses (data retention, training use or non-use, audits); and assess third-party risks.
The sectors demanding these roles include banking and financial services. AI is involved in high-impact decisions (risk, fraud, credit, advisory), and the sector already has a culture of model risk. JPMorgan explicitly describes its Model Risk Governance function to assess machine learning and AI risks; and Lloyds Banking Group has AI Risk Oversight roles requiring experience in model risk, AI governance, and AI risks.
Other sectors that demand these professionals are insurance, health (companies like Roche have roles focused on AI Governance & Ethics, signaling that this is already industrializing in life sciences); payments and platforms with cross-cutting regulatory risks; enterprise technology and large organizations. Microsoft has created an Office of Responsible AI, and IBM refers to its experience with the AI Ethics Board and its holistic approach to AI governance (people, processes, technology).
Skills in Demand
Knowing how to interact with AI translates into a set of very specific professional skills, and also functions as a "meta-skill" that helps anticipate what professional capabilities will be valuable in the future. Among these key skills for those who really know how to work with AI are:
- Problem formulation and contextual thinking: Turning a vague objective (“improve customer service”) into tasks, constraints, and success criteria. This connects with market trends: the World Economic Forum ranks analytical thinking as a rising skill, along with technological literacy.
- Operational communication with intelligent systems: It's not about making “nice prompts,” but directing: giving phased instructions, asking for verifiable outputs, or ensuring the model asks before inventing when information is lacking.
- Workflow design and light automation: This aligns with the "human-agent teams" vision and the role of the "agent boss" described by Microsoft: humans who direct agents and redesign workflows.
- Evaluation and verification: This is the skill that differentiates professionals from casual users. It allows detecting hallucinations and subtle errors, measuring quality with criteria (accuracy, consistency, risk), and creating recurring tests when the system is used at scale.
- Applied data literacy: The OECD has observed that generative AI is making certain skills, like “data analysis & interpretation,” more important for SMEs. Literacy allows understanding where the data comes from, its quality, biases, and relevance; and helps interpret results, not just generate them.
- Security, privacy, and usage hygiene: This is about not putting sensitive data where it doesn't belong; understanding risks like data leaks, permissions, retention, and prompt-injection attacks; and knowing how to work with a “minimum necessary,” anonymization, and access control.
