Category: Market Insights

The Storytelling Engine Behind Digital Media Startups

Why video editors and scriptwriters are becoming strategic hires in high-performing content teams.

A digital media company can have a compelling subject, hundreds of hours of footage and a sophisticated distribution strategy. None of those elements guarantees a story worth watching.

The decisive work often happens between the raw material and the published piece: in the script that gives the story its structure, and in the edit that transforms disconnected fragments into clarity, tension and emotion.

This is changing the role of scriptwriters and video editors inside digital media startups. Once treated primarily as production specialists, they are increasingly becoming strategic members of the content team. Their decisions influence whether viewers keep watching, whether a format can be repeated and whether the audience recognises a consistent editorial identity.

For companies building video-first products, recruiting editors and scriptwriters is no longer simply a creative requirement. It is an organisational decision.

These are storytelling roles, not executional roles

A scriptwriter does more than prepare a voice-over. A video editor does more than assemble footage.

A scriptwriter does more than prepare a voice-over. The role involves turning complex subjects into narratives that are clear, memorable and capable of sustaining attention across an entire production.

“As a scriptwriter, my work is to transform complex subjects into powerful, memorable stories that elevate the audiovisual product and capture the attention of millions of viewers. In a startup like this, the scriptwriter is also a key part of defining the company’s voice and the way it connects with its audience.”

Tessi Rovira, Scriptwriter at Q for Media

This makes the scriptwriter responsible for more than narrative structure. Their decisions also influence the editorial voice of the company: how it explains ideas, creates emotional tension and builds a recognisable relationship with its audience.

Both roles make continuous decisions about what the audience needs to understand, when information should be revealed, how tension should develop and which moments deserve attention. Their work shapes the viewer’s experience long before a video reaches a platform.

“At Q for Media, writing and editing are not executional roles. They are strategic storytelling roles. We are not looking for people who simply write a voice-over or assemble images.”

Pau Belana, Head of Content at Q for Media

The complexity becomes especially visible in documentary production. The team may begin with hundreds of hours of real, fragmented material rather than a controlled set of scripted scenes. The story is not waiting inside the footage in a finished form. It has to be found, tested and constructed.

As Pau Belana explains, the objective is to transform that material into documentaries capable of competing with premium formats in clarity, tension and emotion. Technical skill is essential, but it is not enough. The real differentiator is editorial judgement: knowing what the story is, what does not belong in it and how every production decision supports its direction.

Writing and editing cannot work in isolation

In a fragmented production model, the script is delivered to the editor as a finished instruction. The editor produces a version, sends it for review and waits for feedback.

High-performing teams work differently. The script continues to evolve through the edit, while possibilities discovered in the footage can reshape the writing. Both professionals need to understand not only their individual task, but also the intention behind the other person’s decisions.

“The best work happens when the scriptwriter and the video editor understand each other and work hand in hand. It helps us avoid unnecessary revisions because we are aligned on the story and on what each person needs from the other.”

Daniel Martin, Video Editor at Q for Media

This alignment is particularly important in documentary formats, where the same material can support several legitimate narratives. The editor must understand the approach established by the script and translate it into pacing, sound, visual structure and emotional rhythm.

From raw footage to a coherent story

1. Narrative direction. The scriptwriter identifies the central tension, the point of view and the information the audience needs.
2. Editorial translation. The editor finds the images, rhythm, sound and transitions that can sustain that direction.
3. Shared refinement. Both profiles review the result together, improving the story rather than protecting individual decisions.

When the two roles are disconnected, revisions become the moment when fundamental disagreements are discovered. When they share a narrative framework from the beginning, revisions become refinement.

What this changes in the hiring process

Recruiting creative professionals requires a different evaluation model from hiring for many conventional corporate positions.

A CV can explain where someone has worked and which tools they know. A portfolio can show whether they understand structure, pacing and visual language. But neither necessarily reveals how that person will perform inside a demanding content squad.

Companies need to assess how candidates think, collaborate and respond when an idea does not work. Useful questions include why they structured a story in a particular way, what they removed from the final version and how they handled feedback that challenged their original approach.

• Craft: command of narrative structure, editing, research, rhythm, sound and platform-specific formats.
• Audience intelligence: understanding how creative decisions influence attention, retention and comprehension.
• Collaboration: the ability to communicate decisions, receive feedback and improve the work without becoming attached to personal ownership.
• Critical thinking: the judgement to challenge a weak idea, even when it appears correct on paper.

According to Belana, the cultural fit required for this environment is precise: hunger for excellence, critical thinking, no attachment to ego and a team mentality in which feedback and continuous improvement are treated as part of the work rather than as external pressure.

Creative hires also help build the system

In an established media company, production workflows, visual conventions and technical standards may already be defined. In a startup, early creative hires often have to help create them.

Daniel Martin points out that an editor can contribute far beyond an individual video. The role can help identify the right formats for each platform, develop a recognisable visual identity and establish technical standards the company may not yet have fully defined.

The same applies to scriptwriters. Beyond producing individual scripts, they can help establish recurring structures, research processes, editorial principles and narrative formats that make production more consistent.

This is where creative talent begins to influence scalability. Publishing more content does not automatically create a scalable operation. Without shared criteria, additional output usually produces more revisions, greater inconsistency and more dependence on individual contributors.

A production team becomes scalable when its knowledge starts to live inside the system: in common standards, repeatable workflows and a shared definition of what good content looks like.

The real competitive advantage is a shared narrative language

As more companies treat content as a long-term business asset, the demand for editors and scriptwriters will not be driven only by the volume of content they can produce.

The more important question is what kind of production system they can help build.

A strong scriptwriter gives an idea direction. A strong editor gives it form, rhythm and emotional weight. Their greatest contribution appears when those disciplines stop behaving like sequential tasks and begin operating as a shared storytelling function.

For digital media startups, that integration produces better stories, but also clearer workflows, more useful feedback and a content identity capable of growing without losing coherence.

The strategic hire is not simply the person who can write or edit. It is the person who can help the team understand what the story is; and build the system required to tell it well.

Hiring creative talent requires more than reviewing a portfolio

The strongest content teams combine technical excellence, narrative judgement and a culture designed around collaboration. Identifying that combination requires a hiring process capable of evaluating how candidates think, not only what they have already produced.

IKIGAI Talent Group helps growing companies identify and attract specialised professionals capable of strengthening both the creative output and the systems behind it.

Editorial note: the quotes have been translated into English and lightly edited for length and clarity.

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Frequently Asked Questions

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Transparencia salarial en España: cómo las empresas deberían prepararse antes de 2027

By Cristina Quiñones
Digital Senior Consultant at IKIGAI Talent Group

La transparencia salarial va a dejar de ser una buena práctica voluntaria para convertirse en una exigencia estructural en la gestión de personas. La Directiva Europea 2023/970 refuerza el principio de igualdad retributiva entre mujeres y hombres por un mismo trabajo o por un trabajo de igual valor, y obliga a las empresas a justificar con criterios objetivos, neutros y trazables cómo se definen sus salarios.

Aunque España ya cuenta con obligaciones en materia de registro retributivo, auditoría salarial y planes de igualdad, el nuevo marco europeo nos pide elevar eleva el nivel de exigencia. El cambio no va solo de publicar sueldos. Va de poder entender por qué una persona cobra lo que cobra, cómo definir una banda salarial, qué criterios se deben utilizar para progresar en nuestro plan de carrera y establecer mecanismos para detectar si existen diferencias retributivas que no puedan justificarse de forma objetiva.

Qué es realmente la transparencia salarial

Hablar de transparencia salarial no significa necesariamente hacer públicos todos los salarios individuales de una compañía. Significa construir un sistema retributivo más claro, más defendible y menos dependiente, estableciendo medidas para evitar decisiones opacas o discrecionales.

En la práctica, implica que la empresa debe ser capaz de responder con consistencia a preguntas como estas:

• Qué banda salarial corresponde a un puesto.
• Qué criterios explican que una persona esté en un tramo u otro.
• Cómo se comparan roles de igual valor dentro de la organización.
• Qué variables justifican una diferencia salarial.
• Cómo se comunican las oportunidades de progresión.
• Qué información se facilita a candidatos y empleados.

El punto crítico no es solo tener datos. Es construir un criterio retributivo que pueda sostenerse ante candidatos, empleados, representación laboral, inspección o, llegado el caso, un procedimiento administrativo o judicial.

Qué cambia con la Directiva Europea de Transparencia Salarial

Uno de los cambios más visibles afectará al proceso de selección. La directiva reconoce el derecho de los candidatos a recibir información sobre la retribución inicial o la banda salarial inicial del puesto, basada en criterios objetivos y neutros respecto al género. Esa información podrá facilitarse, por ejemplo, en la oferta de empleo, antes de la entrevista o antes de la firma del contrato. Además, la empresa no podrá preguntar al candidato por su historial salarial previo.

Este punto cambia la lógica de muchas conversaciones de hiring. Hasta ahora, muchas empresas han construido la negociación salarial desde la expectativa del candidato o desde su salario anterior. Con la nueva norma, el foco se desplaza hacia el valor del puesto, la estructura interna y los criterios objetivos de compensación.

También se refuerza el derecho de los trabajadores a solicitar información sobre su nivel retributivo individual y sobre los niveles retributivos medios, desglosados por sexo, de las categorías de trabajadores que realicen el mismo trabajo o un trabajo de igual valor.

En paralelo, las empresas deberán reportar información sobre brecha retributiva. Según la directiva, las organizaciones de 250 o más personas trabajadoras deberán facilitar esta información por primera vez, como tarde, el 7 de junio de 2027 y después cada año. Las empresas de entre 150 y 249 personas trabajadoras también deberán hacerlo por primera vez el 7 de junio de 2027, pero posteriormente cada tres años. Las de entre 100 y 149 personas trabajadoras tendrán como fecha inicial el 7 de junio de 2031.

Debemos entender que 2027 será una fecha clave, porque para entonces, las empresas tienen la obligación de tener datos, criterios y procesos claros y ordenados.

España no parte de cero, pero el estándar sube

En España ya existe un marco de igualdad retributiva relevante. El Real Decreto 902/2020 obliga a todas las empresas a tener un registro retributivo de toda la plantilla, incluido personal directivo y altos cargos, con valores medios de salarios, complementos y percepciones extrasalariales desagregados por sexo.

Además, las empresas que elaboren un plan de igualdad deben incluir una auditoría retributiva. Esta auditoría tiene como objetivo comprobar si el sistema salarial de la empresa aplica de forma efectiva el principio de igualdad entre mujeres y hombres, y debe ayudar a definir medidas para evitar, corregir o prevenir desigualdades.

La diferencia es que la directiva europea aumenta la presión sobre la trazabilidad. Ya no bastará con tener un registro actualizado o con cumplir formalmente una obligación documental. Las empresas tendrán que demostrar que sus criterios salariales son comprensibles, comparables y objetivos.

Este matiz es importante. Una organización puede tener datos salariales, pero no tener una arquitectura salarial clara. Puede tener rangos, pero no saber explicar por qué se asigna un tramo u otro. Puede tener evaluaciones de desempeño, pero apoyadas en criterios demasiado ambiguos. Y ahí es donde la transparencia salarial empieza a convertirse en un proyecto de transformación interna.

El impacto en selección, promoción y liderazgo

La transparencia salarial ya afecta directamente a los equipos de Talent Acquisition, People, HR Business Partners, Managers y Dirección.

En selección, obligará a definir mejor las bandas antes de salir al mercado. Publicar una posición sin tener claro el rango, el nivel del rol o el margen real de negociación será cada vez más difícil de sostener. También cambiará la conversación con candidatos, especialmente en perfiles tecnológicos, digitales, sales senior y executive, donde la compensación suele estar condicionada por variables como seniority, impacto, bonus, equity, responsabilidad internacional o escasez de mercado.

En promoción interna, la empresa tendrá que justificar mejor por qué una persona progresa, por qué cambia de tramo salarial y qué diferencia un nivel profesional de otro. Esto obligará a revisar modelos de evaluación del desempeño, sistemas de leveling, criterios de carrera y procesos de revisión salarial.

En liderazgo, el reto será especialmente cultural. Los managers necesitarán formación para hablar de compensación con rigor, evitar respuestas improvisadas y entender cómo conectar salario, desempeño, nivel de responsabilidad y progresión profesional sin generar ruido interno.

La transparencia salarial no solo expone diferencias. Expone la calidad del sistema que las construye.

¿Qué riesgos aparecen si la empresa no se prepara?

El riesgo más evidente es legal. La directiva prevé un refuerzo de los mecanismos de reclamación, reparación y sanción. Además, cuando una empresa no cumpla determinadas obligaciones de transparencia, puede trasladarse al empleador la carga de demostrar que no ha existido discriminación retributiva.

Pero más allá del riesgo jurídico. También puede tener consecuencias a nivel reputacional, organizativo y de competitividad en el mercado.

Una empresa que no pueda explicar sus salarios puede perder confianza interna. Puede generar fricción entre equipos. Puede dificultar la retención de talento. Puede hacer menos atractivas sus ofertas en un mercado donde los candidatos valoran cada vez más la claridad, la coherencia y la equidad.

La transparencia salarial mal preparada puede convertirse en conflicto. Bien trabajada, puede convertirse en una ventaja: ayuda a profesionalizar la gestión del talento, mejora la calidad de las conversaciones salariales y reduce la improvisación en decisiones críticas.

Cómo deberían empezar a prepararse las empresas

Prepararse para la transparencia salarial no debería plantearse como un proyecto puramente legal. Debería abordarse como una revisión integral del sistema de compensación y comunicación interna.

El primer paso es mapear los puestos y niveles reales de la organización. No solo los títulos formales, sino las responsabilidades, el impacto, la autonomía, la complejidad y los criterios que hacen que dos puestos puedan considerarse comparables o de igual valor.

El segundo paso es revisar las bandas salariales. Una banda útil no es una cifra amplia para cubrir cualquier negociación. Debe estar conectada con el nivel del rol, el mercado, la estructura interna y los criterios de progresión.

El tercer paso es auditar posibles brechas. Si aparecen diferencias, la empresa debe poder distinguir entre diferencias justificadas y diferencias que requieren corrección. La directiva prevé una evaluación retributiva conjunta cuando exista una diferencia media de al menos el 5% en una categoría de trabajadores, no esté justificada con criterios objetivos y neutros respecto al género, y no se haya corregido en el plazo previsto.

El cuarto paso es formar a quienes van a tener que sostener estas conversaciones: People, Talent Acquisition, managers y líderes de negocio. La transparencia salarial no se implementa solo con documentos. Se implementa también con criterio compartido.

Transparencia salarial: de obligación normativa a estrategia de talento

La transparencia salarial va a obligar a muchas empresas a ordenar decisiones que hasta ahora podían permanecer en zonas grises. Esto no significa eliminar toda flexibilidad. Significa que la flexibilidad tendrá que estar mejor diseñada, aplicada y sustentada.

Para las empresas que se adelanten, el cambio puede ser una diferencia competitiva clave ante el mercado laboral que se está definiendo en la actualidead. Una política salarial clara mejora la experiencia del candidato, refuerza la confianza interna, facilita conversaciones de desarrollo más maduras y reduce la dependencia de negociaciones poco estructuradas.

La pregunta no es si las empresas tendrán que adaptarse. La pregunta es si llegarán a 2027 con un sistema preparado o con una serie de documentos construidos con urgencia.

Prepara tu empresa para la transparencia salarial

Fuentes consultadas:

• Directiva Europea 2023/970 sobre transparencia retributiva
• Real Decreto 902/2020 de igualdad retributiva entre mujeres y hombres

The Problem Is Not That AI Replaces Juniors: Is That Companies Stop Building Future Seniors.

By Cristina Quiñones
Digital Senior Consultant at IKIGAI Talent Group

AI can automate many tasks that used to sit at the beginning of a professional career. That does not mean companies can remove junior talent from their workforce architecture without consequences.

The real risk is not only that fewer entry-level roles exist. The deeper risk is that companies weaken the mechanism through which professionals learn, mature and eventually become senior talent.

For years, junior roles have not only been a source of execution. They have been a training ground. They have allowed people to understand how decisions are made, how standards are applied, how errors are corrected and how business context turns technical or operational work into real impact.

If that layer disappears too quickly, companies may gain short-term efficiency and lose something much harder to rebuild: their own future senior pipeline.

AI Is Changing Junior Work, But It Should Not Eliminate Junior Learning

There is no point denying the obvious. Many junior tasks are being transformed by AI. Research, documentation, first drafts, reporting, data preparation, basic analysis, sourcing support and administrative coordination can now be accelerated or partially automated.

That shift can be positive. It can remove repetitive work, reduce operational friction and allow teams to focus on higher-value activities earlier.

But there is a mistake I see companies beginning to make: they treat the task as if it were the whole role.

Many junior tasks had two functions. They produced output, but they also created learning. A junior professional who prepares a market map, reviews a technical requirement, supports a sales pipeline or documents a hiring process is not only completing a task. They are learning how the company thinks, prioritizes, decides and evaluates quality.

AI can accelerate execution. It cannot, by itself, replace the human process of building judgment through exposure, feedback and responsibility.

The New Contradiction: Companies Want Junior Profiles With Senior-Level Skills

One of the clearest signals in the market is that entry-level expectations are rising. A recent analysis of PwC’s 2026 AI Jobs Barometer, reported by Business Insider, points to a growing demand for advanced skills in entry-level roles exposed to AI, including leadership, emotional intelligence, team building and strategic decision-making.

This creates a structural contradiction.

Companies reduce the spaces where junior professionals can learn, but still expect young talent to arrive with autonomy, strategic thinking, business maturity and strong decision-making capabilities.

That is not a sustainable talent model. Seniority does not appear suddenly in the market. It is built through accumulated context, mistakes, mentoring, exposure to complexity and progressive responsibility.

If companies remove the early stages of professional development, they should not be surprised when the market later lacks prepared senior talent.

The problem is not that junior professionals need to evolve. They do. The problem is expecting them to become senior without giving them the conditions to develop senior judgment.

From Talent Pyramid to Talent Diamond: Why Hiring Only Mid-Level Profiles Is Not Enough

Another market signal points in the same direction. Reports on CEO hiring intentions suggest that some organizations are moving away from traditional talent pyramids and toward more diamond-shaped structures, with a stronger concentration of mid-level profiles and fewer junior roles.

At first glance, this can look efficient. Mid-level professionals require less training, are closer to productivity and can often operate with more autonomy from day one.

But the model has an important limit: mid-level talent has to come from somewhere.

A company can buy seniority from the market for a while. It can attract people trained elsewhere, hire professionals who have already gone through their learning curve and compensate for a weak internal pipeline with external recruitment.

But if many companies make the same decision at the same time, the market eventually becomes thinner, more expensive and more competitive.

In that scenario, the companies that kept investing in structured junior development will have an advantage. Not because they resisted AI, but because they understood that automation and talent development are not opposite strategies.

What This Means For Technology, Digital And Sales Teams

This issue matters especially in specialized functions. In technology, digital and sales, junior roles are often the first place where professionals learn the difference between completing a task and creating business value.

IN TECHNOLOGY

Junior engineers do not only learn how to write code. They learn architecture, technical debt, product context, delivery standards, ownership and collaboration with business teams.

If AI absorbs basic coding or documentation tasks without redesigning the learning path, companies may produce faster outputs but weaker technical maturity.

IN DIGITAL

Junior digital profiles do not only execute campaigns or update dashboards. They learn how performance, attribution, CRM, lifecycle marketing, product positioning and customer acquisition connect to growth.

If those first layers disappear, companies risk losing the training ground where analytical and commercial judgment are built.

IN SALES

Junior sales profiles do not only support prospecting or pipeline administration. They learn market reading, client timing, discovery, objection handling, qualification and the difference between activity and opportunity quality.

AI can help sales teams work faster. But it cannot replace the lived learning that turns a junior commercial profile into a consultative seller.

The Real Question Is Not Whether To Hire Juniors. It Is How To Redesign Junior Roles

The answer is not to protect junior roles exactly as they existed before AI. That would be too simplistic.

The answer is to redesign them with more precision.

Companies need to separate what can be automated from what must still be learned. They need to identify which tasks were low-value repetition and which tasks were actually building context, discipline and judgment.

In my view, a stronger junior talent strategy in the age of AI should include five decisions.

1. Separate automatable tasks from essential learning

Not every junior task needs to survive. But the learning behind some of those tasks does.

If AI prepares a first draft, the junior still needs to learn how to evaluate it. If AI summarizes a market, the junior still needs to learn what the summary misses. If AI supports sourcing, the junior still needs to learn what makes a profile relevant beyond keyword matching.

2. Build AI-augmented junior roles

The junior profile should not compete against AI. They should learn to work with it.

That means training juniors to ask better questions, challenge outputs, validate sources, identify weak assumptions and transform AI assistance into better professional judgment.

3. Make mentoring more intentional

If there are fewer repetitive tasks, companies need more deliberate learning moments.

Junior professionals need structured feedback, exposure to decision-making, access to senior reasoning and visibility over why certain decisions are made. Without that, AI may increase speed but reduce professional development.

4. Measure potential, not only immediate productivity

A junior hire should not be evaluated only by how quickly they produce output.

Companies should look at learning speed, curiosity, rigor, adaptability, analytical discipline and ability to improve through feedback. Those are the signals that often predict future seniority better than immediate execution alone.

5. Protect the pipeline in critical functions

In areas such as AI, data, cloud, cybersecurity, growth, product and enterprise sales, the cost of not developing junior talent may become visible too late.

By the time a company realizes it lacks senior talent, the development gap may already be several years old.

The Strategic Question For CEOs, CTOs And Talent Leaders

The question companies should ask is not only: “Can AI perform this junior task?”

The more important question is: “If we remove this task, where will the learning happen?”

Because a company that stops hiring junior profiles does not only reduce headcount. It may also reduce its ability to produce seniority internally.

That is a strategic workforce issue, not just a recruitment issue.

The strongest companies will not be those that replace junior talent as quickly as possible. They will be those that redesign early-career roles so that juniors become more capable, more analytical and more AI-fluent from the beginning.

AI Should Raise The Standard Of Junior Talent, Not Remove The Stage Completely

AI gives companies an opportunity to rethink junior hiring with more precision.

It can remove low-value work. It can accelerate research. It can improve preparation. It can help junior professionals access context faster and produce better first versions of their work.

But it should not become an excuse to stop developing people.

“The future of senior talent depends on what companies decide to teach, expose and delegate to junior professionals today.“

The real challenge is not deciding whether AI will replace juniors. The real challenge is designing the kind of junior talent worth developing in an AI-augmented organization.

Rethinking Your Hiring Strategy In The Age Of AI?

Article by Cristina Quiñones, IKIGAI Talent Group.

From Fortresses to Connections:Why modern leadership depends on ecosystem value

In the classical business paradigm – the Taylorist factory where the objective was to assemble parts into products – organizations were often understood as fortresses: closed structures competing for limited resources. That view is no longer enough.

Modern companies operate as open systems. Their success no longer depends only on internal efficiency, but on their ability to orchestrate a sustainable network of relationships, dependencies and shared value.

From closed structures to open systems

Under this model, interdependence emerges: a condition in which the performance of each system is intrinsically linked to the performance of the others.

In business, this means that a product may be excellent, but if the payment gateway fails or the distribution channel is not aligned, the system starts to break. The value created by one part of the organization depends on the reliability, coherence and alignment of the wider system around it.

The Partnership Manager as infrastructure designer

The Partnership Manager is the person who manages these dependencies. Their role is not simply to “make friends”, but to design shared-value infrastructures that prevent entropy.

Without this flow of alliances, the company consumes itself trying to solve external problems with limited internal resources. Partnerships are no longer a peripheral activity. They are one of the ways an organization protects its capacity to scale without isolating itself from the ecosystem that sustains it.

When the partnership becomes part of the product

This strategic vision becomes more meaningful when we understand that an alliance is not something external to the solution. In many cases, it becomes part of the product’s own DNA.

I recently discussed this idea with Estefanía Cela, a Digital Product Manager recently graduated from Nuclio Digital School. Her MVP, The Wellness Lab – recognized as the best project of her cohort – explores how to turn wellbeing into an informed decision through collective experience. She defines it with striking clarity:

“A partnership is not a channel; it is a structural layer of the product. When it is well designed, it does not only amplify reach. It reduces friction in the experience and multiplies value for the user. The best alliances are not closed; they are integrated to the point where the user does not perceive where one company ends and the other begins. That is where real competitive advantage happens.”

Reading the ecosystem from the inside out

That same logic also appears in the perspective of Léa Blanchard, Partnership Manager and, as she defines herself, “enthusiast of everything”. Her view moves the conversation away from the external search for partners and back into the company itself:

“For me, developing partnerships does not begin by talking to potential partners. It begins internally: understanding who we are, what we want to build and where we operate from.

From there, I read the ecosystem and detect opportunities as systems with a ricochet effect, where value is amplified and connects multiple actors.

A partnership stops being an agreement and becomes architecture: a system with front-end and back-end that removes friction, not just a “generate leads for me” mechanism.

That is where the shift happens: when you align distribution, experience and execution to create living systems that generate revenue, but also meaning, coherence and real growth. And then, yes, the game begins.”

Competitive advantage now depends on how you connect

Ultimately, in a connected ecosystem, the unit of survival is no longer the individual company. It is the network of which that company is part.

The end of business autarky means that, in a saturated and technologically fragmented market, competitive advantage no longer resides only in what you own. It resides in how you connect, how you integrate and how you make value circulate across the system.

The Partnership Manager is, in that sense, responsible for helping the organization stop behaving like an isolated object and start operating as a vital node in a value network.

Leadership as connection, not fortification

That is why the central idea remains so relevant: leadership today is not about building higher fortresses. It is about creating deeper connections.

For organizations scaling in fragmented markets, this shift is not theoretical. It changes how products are built, how go-to-market systems operate and how strategic roles create value. The companies that learn to connect better are not only more open to collaboration. They are better prepared to build relevance inside the ecosystems they depend on.

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Frequently Asked Questions

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From Innovation to Adoption: What HRC 2026 Reveals About Catalonia’s Digital Health Scale-Up Challenge

Catalonia’s digital health ecosystem has entered a new stage of maturity. The next challenge is no longer only building better health technology, but scaling the teams, trust and go-to-market capabilities that can bring innovation into real healthcare environments.

By Charlotte Rovito
VP Growth, IKIGAI Talent Group

The strongest signal from Health Revolution Congress 2026 was not simply the number of digital health solutions on display. It was the maturity of the conversation around them.

The sector no longer feels like an ecosystem trying to justify why digital health matters. That stage is increasingly behind us. The more relevant question now is different: how does innovation become adoption, how does adoption become trust, and how does trust become scalable growth?

For Catalonia, that question arrives at a decisive moment.

According to ACCIÓ’s Digital Health in Catalonia technology snapshot, the Catalan digital health ecosystem now includes 419 companies, generates €652 million in revenue and supports more than 5,300 jobs. More than half of these companies are startups, and 65% already incorporate artificial intelligence into their products or services.

Presented at HRC 2026 by Inma Rodriguez, Market Intelligence Manager at ACCIÓ, the study offered a clear snapshot of a maturing ecosystem: more companies, stronger technological adoption and a growing role for artificial intelligence across Catalonia’s digital health sector.

Those figures are important. But the deeper reading is not only that the sector is growing. It is that Catalonia’s digital health market is moving from promise to execution.

HRC 2026 was a market signal, not just an industry event

The Health Revolution Congress has become one of Europe’s key meeting points for digital health. Its value is not only in gathering startups, hospitals, investors, technology providers and institutions. Its value is in making visible the pressure points of the market.

From IKIGAI Talent Group’s perspective at HRC 2026, three signals stood out.

First, artificial intelligence is no longer a peripheral topic in health innovation. It is becoming part of the operating infrastructure of the sector. Second, the conversation around digital health is increasingly linked to implementation: clinical integration, data security, adoption, reimbursement, patient experience and go-to-market. Third, the companies that will lead the next phase will not be those with technology alone, but those able to build the teams that can take that technology into complex healthcare environments.

This distinction matters. A strong product can open attention. A strong team converts that attention into market access, trust and growth.

Catalonia is no longer an emerging digital health ecosystem

The Catalan market has the characteristics of an ecosystem entering a scale-up phase.

It has a growing base of companies. It has research centres, hospitals, accelerators, universities and public initiatives. It has international attention. It has foreign investment. It has a strong entrepreneurial layer. And, increasingly, it has companies whose ambition is not only local validation, but international expansion.

ACCIÓ identifies Catalonia as the leading region in Europe for foreign technology investment in the healthcare sector by number of projects and invested capital between 2021 and 2025. The same report places Catalonia among the leading global regions for healthcare technology foreign investment.

This context changes the question for HealthTech and MedTech companies operating from Barcelona and the wider Catalan ecosystem. The challenge is no longer only to prove that the region can produce innovation. The challenge is to scale that innovation with enough precision to compete in healthcare markets where trust, evidence and commercial execution are difficult to separate.

That is where talent becomes strategic.

In an early-stage market, hiring often follows urgency: build the product, validate the technology, get the first clients, close the first pilots. In a maturing market, the hiring problem becomes more structural. Companies need leadership, product maturity, AI and data capability, regulatory understanding, clinical credibility and go-to-market teams able to work across long sales cycles and complex stakeholders.

Digital health does not scale like generic software. It scales through healthcare systems, physicians, procurement teams, payers, patients, data governance frameworks and clinical workflows. That requires a different talent architecture.

AI in health is moving from narrative to operating requirement

One of the clearest market shifts is the acceleration of artificial intelligence in health.

In Catalonia, 271 digital health companies already incorporate AI into their products or services, representing 65% of the sector. The applications range from clinical tools and diagnostics to patient monitoring, medical decision support, digital therapies, simulation, robotics, prevention, marketplaces and technology consulting.

This is not a minor change. When AI adoption becomes widespread, it stops being a differentiator by itself. The differentiator moves elsewhere: into the quality of the data, the clinical relevance of the use case, the capacity to validate the model, the ability to integrate it into existing workflows and the credibility of the team bringing it to market.

Recent public examples from Barcelona’s ecosystem show this clearly.

Biorce, a Barcelona-based HealthTech company applying AI to clinical trials, has attracted significant Series A funding and positioned its platform around the optimisation of trial design and execution. Sycai Medical, founded in Barcelona, uses AI to support early detection of abdominal cancers and premalignant lesions, with a device designed to integrate into medical imaging workflows.

These companies represent different parts of the same movement: AI is not only being discussed as a technological trend; it is being built into clinical, operational and commercial models.

The implications for hiring are considerable.

AI-driven HealthTech companies need more than AI engineers. They need product leaders who understand clinical environments, data specialists who understand healthcare constraints, regulatory profiles who can navigate medical device pathways, commercial leaders who can sell into complex healthcare systems, and customer-facing teams who can translate technical value into trust.

In this context, the talent question becomes more precise: not “do we need AI profiles?”, but “what combination of technical, clinical, regulatory and go-to-market capabilities will allow this AI solution to be adopted?”

The market is rewarding solutions that can move closer to adoption

One of the strongest impressions from HRC 2026 was that visibility alone is no longer enough.

The companies that attracted attention were not simply presenting technology. They were trying to prove readiness: readiness for pilots, partnerships, funding, clinical validation, commercial expansion or integration into existing healthcare structures.

The recognition of Doole Health during the Digital Health Innovation Competition at HRC 2026 captured that transition well. The moment was not only a celebration of one company. It reflected the broader role of the congress as a place where startups are tested against market expectations: investors, hospitals, corporates, technology partners and innovation teams are all looking for signals of viability.

Those signals are increasingly practical. Can the solution be implemented? Can it be trusted? Can it fit into clinical operations? Can it move beyond a pilot? Can the company build the commercial and technical structure required to scale?

That is also why companies such as Top Doctors remain relevant reference points in the Catalan digital health conversation. They illustrate a more mature layer of the ecosystem, where the challenge is not only digital access to healthcare, but depth of service, patient trust, operational reliability and sustained market presence.

A maturing ecosystem needs both types of companies: the emerging startups proving new categories, and the more consolidated players showing what scale, trust and market continuity can look like.

In HealthTech, go-to-market is also a trust-building discipline

HealthTech and MedTech companies often operate with an understandable bias towards evidence. Clinical data, safety, ROI, compliance and performance matter. They should matter. In healthcare, weak claims can have consequences far beyond commercial inefficiency.

But HRC also reminded us of something that technical markets sometimes underestimate: trust is not built only through data. It is also built through experience, clarity, memory and human connection.

“We obsess over clinical data, QALY, ROI. Rightly so. But our physicians, payers, and procurement directors? They’re human too.”

Myriam Bougo,
Principal Consultant MedTech | Go-To-Market & Growth Strategist at InnHealthium

That distinction matters for HealthTech and MedTech companies entering a scale-up phase. Evidence remains essential, but adoption also depends on how clearly a solution is explained, how much risk it reduces for each stakeholder and how much trust the team behind it is able to build.

In MedTech and HealthTech, go-to-market does not work only by presenting a solution. It works by helping different stakeholders believe that a new technology can be understood, trusted, implemented and defended internally.

That requires a specific kind of commercial talent.

The strongest sales and growth profiles in this market are not simply sellers. They are translators. They understand technology, but also clinical pressure. They understand product value, but also procurement friction. They understand evidence, but also timing, stakeholder mapping and organisational adoption.

As the digital health market matures, this ability to translate innovation into confidence will become one of the most valuable go-to-market capabilities.

The next bottleneck will be talent

ACCIÓ’s report identifies several challenges for digital health in Catalonia: scaling technology providers, enabling secondary use of healthcare data, ensuring accessibility, managing talent and adapting to change, and expanding the availability of notified bodies.

These are not isolated challenges. They are connected by a common thread: execution capacity.

A company can have strong technology and still struggle to scale if it lacks the right leadership. It can have promising AI and still fail to gain adoption if it lacks clinical credibility. It can have investor interest and still lose momentum if it does not build a go-to-market team that understands healthcare buying processes. It can have a validated solution and still move too slowly if product, regulatory, data and commercial functions are not aligned.

For HealthTech and MedTech companies, talent strategy is therefore not a support function. It is part of the scale-up strategy.

The most critical profiles will not be limited to one function. The next stage of digital health will require:

• AI, data and engineering profiles able to build robust, secure and clinically relevant products.
• Product leaders who understand healthcare workflows, user adoption and regulatory complexity.
• Regulatory and quality specialists capable of helping companies move through medical device and compliance requirements.
• Go-to-market and sales leaders with experience in long-cycle, trust-based healthcare environments.
• Customer success and implementation profiles able to support adoption after the first contract or pilot.
• Executive leaders who can connect technology, clinical value, financing and international expansion.

The companies that build these capabilities early will have an advantage. Not because they will hire more people, but because they will hire with a clearer understanding of what the next phase demands.

What HRC 2026 ultimately revealed

HRC 2026 showed an ecosystem with ambition, capital, technology and international visibility. But it also showed that the next chapter will be harder than the previous one.

Building a digital health product is difficult. Scaling one into healthcare systems is harder.

The second phase requires evidence, but also narrative. Technology, but also trust. Funding, but also execution. AI, but also governance. Commercial ambition, but also clinical sensitivity. Above all, it requires teams capable of operating at the intersection of all these forces.

That is the market signal HealthTech and MedTech companies should take seriously.

Catalonia has already proved that it can generate digital health innovation. The next question is which companies will be able to turn that innovation into adoption, market access and sustainable growth.

For companies preparing for the next stage

The next phase of digital health will not be defined only by stronger technology. It will be defined by the teams capable of bringing that technology into real healthcare environments, earning trust and scaling with precision.

For HealthTech and MedTech companies navigating that transition, talent strategy becomes part of the growth strategy.

If your company is preparing for its next stage of scale, you can book a strategic conversation with Charlotte Rovito, VP Growth at IKIGAI Talent Group, to assess which leadership, technical and go-to-market profiles will be critical to turn innovation into adoption and growth.

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Sources consulted

• ACCIÓ, Generalitat de Catalunya: Digital Health in Catalonia. May 2026 Technology Snapshot.
• Health Revolution Congress 2026 materials and IKIGAI Talent Group observations from the event.
• Public reporting on Biorce’s Series A funding and AI platform for clinical trials.
• Public reporting on Sycai Medical’s AI-based cancer detection technology and funding round.
• IKIGAI Talent Group’s previous analysis of relevant companies in the Catalan digital health ecosystem, including Top Doctors.

HealthTech in motion: which booths to follow closely at the Health Revolution Congress 2026

The Health Revolution Congress 2026 arrives in Barcelona at a particularly relevant moment for the HealthTech ecosystem. The conversation is no longer only about new digital solutions, but about something more complex: how to scale healthcare technology with teams capable of combining product, regulation, clinical experience, data, artificial intelligence and business development.

In that context, the Innovation Plaza will be one of the most relevant spaces of the congress. Not only because it will bring together companies, hospitals, projects and healthcare providers, but because it offers a clear view of which types of innovation are gaining traction and which talent capabilities are becoming increasingly critical.

The congress will take place on May 27 and 28 at the Recinte Modernista de Sant Pau, Barcelona, and IKIGAI Talent Group will also be present at the Innovation Plaza.

For those who visit our booth, we have prepared a special HealthTech salary guide, together with other surprises designed to help understand how talent is moving in this sector.

The Innovation Plaza as a map of the HealthTech market

The Innovation Plaza can be read as a market map. In the same space, attendees will find artificial intelligence solutions, digital care platforms, patient experience providers, data tools, medical devices, hospitals, training projects, specialized communication companies and players connecting innovation with real-world adoption.

This mix matters because the future of HealthTech does not depend on a single category. It depends on the ability to connect technology, clinical evidence, regulation, healthcare operations, user experience and talent.

For a company growing in HealthTech, the challenge is rarely just developing a good solution. The real challenge is building the right team to take that solution to market, integrate it into real healthcare environments and sustain its evolution with rigor.

HealthTech companies worth following at HRC 2026

Not every booth tells the same story about the market. Some companies are especially useful to understand where HealthTech is moving: clinical AI, interoperable data infrastructure, patient engagement, remote care, workflow efficiency and digital patient experience.

For that reason, this selection focuses on nine companies that reflect some of the clearest movements shaping the sector. The goal is not to build a full directory of exhibitors, but to identify the signals that help explain what kind of innovation, and what kind of talent, will matter most in HealthTech.

1. Heidi: clinical AI moving into the daily workflow

Heidi is one of the clearest examples of how AI in healthcare is moving from experimentation into daily clinical work. Its technology focuses on clinical documentation, helping healthcare professionals turn patient consultations into medical notes and other clinical documents while reducing the administrative load around care.

The business relevance is not simply that Heidi uses AI. It is where that AI is being applied. Clinical documentation sits directly inside the professional workflow, where time, trust, accuracy and adoption matter as much as the model itself. That makes it a stronger signal than many generic AI healthcare propositions.

It also opens one of the most important debates in the next stage of digital health: how to scale AI tools in clinical environments without treating consent, privacy, data governance and professional trust as secondary issues. In this category, growth will not depend only on model performance. It will depend on whether the product can earn a legitimate place inside the clinician-patient relationship.

For companies operating in this space, the talent challenge is highly specific: AI product, machine learning, health data security, clinical workflow design, integrations, compliance and go-to-market teams able to sell into healthcare without treating it like a standard SaaS market.

2. Vitagroup HIP: the infrastructure layer behind digital health

Vitagroup HIP represents one of the most structural conversations in European HealthTech: the move toward open, interoperable and vendor-independent healthcare data platforms. Its Health Intelligence Platform is not positioned as another application sitting on top of the system, but as a data infrastructure layer for hospitals, regions and healthcare systems.

That distinction matters. Many digital health solutions promise better care, better access or better patient experience, but they still depend on fragmented data environments. Without infrastructure that connects clinical information securely and consistently, the market risks building innovation on top of disconnected systems.

The relevance of HIP is that it brings the conversation back to foundations: data architecture, interoperability, open standards, governance and long-term institutional adoption. In HealthTech, the most visible product is not always the most strategically important one. Sometimes the decisive layer is the one that allows every other solution to work at scale.

From a talent perspective, this type of platform demands profiles capable of operating in complexity: data architects, interoperability specialists, cloud engineers, cybersecurity experts, backend teams, product leaders and project managers used to working with public administrations, hospitals or large healthcare operators.

3. Serena Labs: patient engagement becoming a data intelligence problem

Serena Labs sits at the intersection of engagement, structured data and AI-driven analytics for insurers, pharmaceutical companies and healthcare providers. Its platform connects digital interfaces, data capture and predictive intelligence across different stages of the customer and patient journey.

What makes this relevant is the shift it represents. In healthcare, engagement is no longer just a marketing layer or a retention initiative. It is becoming a data intelligence problem. Every interaction can generate signals about intent, risk, conversion, adherence, churn or care pathway behavior.

For insurers, pharma companies and healthcare providers, that changes the kind of capability required internally. It is not enough to have digital acquisition or CRM in isolation. The differentiating capability is the ability to connect engagement, clinical or behavioral data, analytics and action in a way that improves both business outcomes and patient experience.

This is where the talent conversation becomes more sophisticated. Platforms like Serena require teams that can move across data engineering, AI analytics, product, cloud architecture, privacy, CRM, growth and consultative sales in healthcare. The value is not in one function alone, but in how those functions are connected.

4. Horus ML: AI that has to earn clinical relevance

Horus ML works on artificial intelligence and machine learning projects for healthcare, with a focus on clinical-care environments and a strong R&D component. Its solutions cover areas such as medical imaging, precision medicine and remote monitoring.

The interesting point is that Horus ML does not represent AI as a generic productivity tool. It sits closer to clinical decision-making, where the standard is much higher. In this context, an AI model cannot be evaluated only by technical performance. It has to be interpretable, clinically relevant, validated and usable by professionals who operate under real constraints.

That is why companies like Horus ML are useful signals for the market. They show that the next wave of healthcare AI will not be won by teams that only understand algorithms. It will be won by teams that understand how clinical problems are defined, how evidence is built and how technology is translated into workflows that professionals can actually trust.

The talent profile behind this type of company is necessarily hybrid: AI engineers, deep learning specialists, data scientists, clinicians with analytical capabilities, health product managers and validation profiles capable of connecting research, product and adoption.

Waitlist to IKIGAI’s HealthTech Salary Guide 26′

HealthTech is moving fast, and salary benchmarks are becoming harder to read without context. Join the waitlist to receive our special HealthTech Salary Guide 2026 and understand how talent, compensation and hiring priorities are evolving across the sector.

5. Doole Health: remote care beyond the video consultation

Doole Health develops an interoperable digital health platform for telemedicine, remote patient management and clinical communication. Its proposition connects patients, caregivers and healthcare professionals through a digital environment designed for continuity, follow-up and coordination.

The important reading is that remote care is maturing. The first version of telemedicine was often reduced to video consultation. The next stage is broader: clinical data management, smart forms, personalized education, treatment follow-up, adherence, appointment management and shared care plans.

That evolution changes the operational meaning of telemedicine. It becomes less about replacing a visit and more about extending care beyond the visit. For healthcare organizations, this creates value only if the platform can integrate with existing systems, support professional workflows and remain intuitive enough for patients and caregivers.

This is also where the talent needs become broader. A remote care platform requires product, mobile, backend, interoperability, UX, data, AI, clinical operations, compliance and customer success. The companies that scale in this category will not be the ones that simply digitize contact. They will be the ones that help coordinate care more intelligently.

6. Capillary.io: vertical AI applied to a specific clinical practice

Capillary.io is a specialized platform for AI-powered videocapillaroscopy. It allows professionals to use existing capillaroscopes or microscopes, import images, analyze them with algorithms, generate reports and collaborate with other professionals in clinical or research environments.

Its value lies in its specificity. While much of the market talks about AI in healthcare in broad terms, Capillary.io focuses on a concrete clinical practice where objectivity, consistency and standardization matter. That makes it a useful example of a more mature kind of AI adoption: not AI as a general promise, but AI applied to a clearly defined diagnostic workflow.

In areas such as capillaroscopy, the opportunity is not only speed. It is the possibility of reducing observer variability, turning images into more structured data and supporting research or follow-up with more consistent evidence. This is where AI can become operationally meaningful for specialists.

Companies in this category need talent that understands both the technical and clinical sides of the problem: computer vision, machine learning, clinical UX, security, regulatory readiness, cloud product and collaboration with medical experts. The competitive edge is not the algorithm alone. It is the ability to fit the algorithm into a real clinical practice.

7. Top Doctors: trust as a digital health asset

Top Doctors represents one of the more mature layers of digital health: connecting patients, medical specialists and private healthcare services. Its relevance goes beyond appointment booking or marketplace dynamics. It sits in a more delicate space: trust, reputation, access and patient choice.

In healthcare, marketplace logic behaves differently than in other sectors. The user is not only looking for availability or convenience. They are looking for confidence in a high-stakes decision. That means the product has to manage information, credibility, user experience and medical reputation with much more care than a standard consumer platform.

For IKIGAI, Top Doctors is interesting because it shows how digital health businesses can scale around trust infrastructure. The company sits at the intersection of product, growth, data, partnerships, healthcare sales and patient experience.

The talent implication is clear: these models require teams that can combine consumer-grade digital execution with healthcare-specific sensitivity. Growth, CRM, performance, product, engineering and partnerships all matter, but they need to operate inside a market where trust is not a brand claim. It is the product itself.

8. TuoTempo: the patient journey as an operational advantage

TuoTempo fits into one of the most important shifts in healthcare operations: the optimization of the patient journey. In digital health, the experience does not end when a patient books an appointment. It includes reminders, scheduling, communication, continuity, follow-up and the reduction of friction across the entire care process.

This matters because many healthcare organizations do not lose efficiency at one single point. They lose it across transitions: between appointment and attendance, between diagnosis and follow-up, between clinical recommendation and patient adherence. Patient journey technology becomes valuable when it reduces those gaps without adding complexity to professionals or patients.

For hospitals, clinics and healthcare groups, this type of solution is increasingly strategic. It can improve operational efficiency, reduce no-shows, support communication and create a more coherent experience without necessarily changing the clinical core of the organization.

The talent behind this category is not only technical. It requires product, UX, integration, customer success, enterprise sales, data and digital operations profiles that understand healthcare as a system of interactions, not as a sequence of isolated transactions.

9. Blue Route: early intervention as a digital health opportunity

Blue Route focuses on early detection, professional assessment and personalized guidance in child development, with special attention to possible signs of ASD. Its model combines questionnaires, observation videos, specialist review and personalized routes with activities and guidance for families at home.

The reason this booth is relevant is not only the technology itself, but the problem space it addresses. Neurodevelopment support is often marked by uncertainty, waiting times and emotional complexity for families. A digital tool in this context has to do more than collect data. It has to guide, structure and support a process that is deeply human.

Blue Route also reflects a broader HealthTech direction: solutions that connect AI, clinical validation, regulation and user experience around early intervention. This is a different kind of digital health proposition, because the user is not only a patient or a professional. The family becomes part of the care environment.

From a talent perspective, this kind of company requires a particularly careful mix: AI applied to healthcare, mobile product, UX for families, clinical validation, regulatory affairs, growth and partnerships with healthcare professionals or institutions. In this type of product, credibility depends as much on clinical seriousness as on digital usability.

The complete map of booths at the Innovation Plaza

In addition to the booths highlighted above, the Innovation Plaza will feature a broad map of companies and organizations that reflect the diversity of the ecosystem:

If the alphabetical ordering criterion is maintained in the physical layout, IKIGAI would likely be located in a particularly interesting area of the route: close to Horus ML, ME Barcelona and other players connected to technology, digital health, hospitality, AI and patient-oriented solutions.

What this map reveals about HealthTech talent

The list of booths shows several clear trends. The most important one is that HealthTech can no longer be read as a single category. It is a market where technology, product, regulation, data, user experience, healthcare operations and business development coexist.

AI applied closer to the real workflow

The presence of companies such as Heidi, Horus ML, Capillary.io, Serena Labs and Scicake points to a more mature phase of AI in healthcare. The conversation is no longer limited to models or promises. It is moving toward clinical documentation, image analysis, prediction, engagement, monitoring, prevention and decision support.

This increases demand for profiles capable of building products with rigor: AI Engineers, Machine Learning Engineers, Data Engineers, technical Product Managers, security specialists, integration profiles and leaders capable of translating technology into adoption.

Interoperability as a condition for scale

Doole Health and vitagroup HIP show that the sector needs solutions capable of integrating with existing systems, connecting data and sustaining continuity of care. Digital health cannot scale if each solution operates in isolation.

This raises the importance of profiles in cloud architecture, interoperability, open standards, backend, cybersecurity, data governance, healthcare integrations and complex project management.

Patient experience as a competitive advantage

Top Doctors, TuoTempo, Serena Labs, Doole Health and Blue Route show that patient experience has become a strategic layer. Access, scheduling, communication, onboarding, follow-up, trust and adherence are now part of the healthcare product.

Here, profiles in product, UX, CRM, lifecycle, growth, customer success and consultative healthcare sales become increasingly important.

Regulation and quality as part of growth

CMG MedDev, ASPHALION MedTech and the wider context of medical software remind us that in healthcare, speed without control is not an advantage. Regulatory quality, evidence, technical documentation and security condition both market access and the ability to scale.

This reinforces demand for regulatory affairs, quality assurance, clinical operations, compliance, cybersecurity and technical leadership with experience in regulated environments.

Hospitals and clinical centers as market validators

The presence of HM Hospitales, Fundació Puigvert and other clinical actors introduces a key reading: the HealthTech that matters is the HealthTech that can enter real environments. Healthcare adoption requires integration, trust, interoperability, evidence and the ability to coexist with complex clinical processes.

Skills as invisible infrastructure

BRIGHTskills reminds us that talent does not appear automatically when technology advances. The sector needs to train, reskill and specialize its workforce so that AI, digital health, data and regulation do not remain disconnected from everyday work.

IKIGAI Talent Group at HRC 2026

IKIGAI Talent Group will be present at the Health Revolution Congress 2026 with a very specific lens: understanding what talent the HealthTech ecosystem needs to turn innovation into sustainable growth.

From our experience across Technology, Digital, Executive and IT Sales profiles, we see that the HealthTech sector is entering a stage where attracting technical talent is no longer enough. Companies need to build teams capable of moving between technology, healthcare, regulation, business and real-world adoption.

That is why, during the congress, we will share a special HealthTech salary guide with those who visit our booth. A resource designed to provide context on how compensation is evolving, which profiles are gaining weight and how to read the market with more judgment.

We will also have some surprises prepared for those who want to talk about talent, hiring and growth in HealthTech.

The question behind every HealthTech solution

The Health Revolution Congress 2026 will be a valuable opportunity to discover solutions. But it will also be an opportunity to read the market with more depth.

Because behind every platform, every device, every AI tool and every innovation proposal, there is an increasingly important question: which teams will make it possible for all of this to scale?

At IKIGAI, we will be there to talk precisely about that.

Come and meet us at the Health Revolution Congress 2026!

If you are attending the Health Revolution Congress 2026, come and visit the IKIGAI Talent Group booth. We have prepared a special HealthTech salary guide and other surprises for those who want to better understand how talent is moving in one of the most promising sectors of the digital ecosystem.

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Network & Security Engineer: a key role for continuity, security and cloud evolution

A profile built for organizations that need resilient, secure and cloud-ready infrastructure

The Network & Security Engineer has evolved from a purely technical role into a strategic profile that connects business continuity, cybersecurity and cloud infrastructure.

In many organizations, there is a technological layer that simply has to work: the network. It is not always visible, not always understood and rarely becomes the center of the conversation until it fails. But when it does, the impact is immediate: operations stop, teams lose connectivity, services are affected and security risks become more visible.

Behind that stability is the Network & Security Engineer, a profile that has moved beyond a purely technical function to become an increasingly strategic role within technology teams.

In a business context shaped by hybrid models, cloud growth, distributed offices and higher cybersecurity demands, this role is key to ensuring three critical layers: operational continuity, system protection and the evolution of technological infrastructure.

Although traditionally associated with network management, its impact goes much further. A Network & Security Engineer does not only maintain connectivity. This professional also helps the organization grow, adapt, work securely and sustain increasingly complex architectures.

The work of these profiles has a real and direct impact on the day-to-day operations of any company. From ensuring that an office can operate normally to making sure that data travels securely across continents, their contribution connects technology, business and security.

Their contribution can be summarized in three main areas:

• Business continuity: they ensure that the network is available, optimized and prepared to minimize downtime, interruptions or performance issues.
• Security: they protect systems against external threats by controlling access, traffic, vulnerabilities and security policies.
• Technological evolution: they participate in transformation projects such as cloud migrations, SD-WAN deployments, network renewals or the implementation of new architectures.

In global companies, they also often act as a bridge between local teams and international organizations, participating in projects that affect multiple regions, offices and technological environments.

Their day-to-day work

The work of a Network & Security Engineer combines operations, advanced support and strategic projects. It is not only about resolving incidents, but about keeping an infrastructure ready to support the growth of the organization.

Some of their most common responsibilities include:

• Network infrastructure management: configuring and maintaining switching, routing, WiFi networks and network hardware, often using technologies such as Cisco, Meraki or Aruba.
• Firewall and security administration: analyzing access requests, opening ports and applying security policies in coordination with specialized teams such as the SOC.
• Complex Level 3 incident resolution: intervening in critical network or security issues that require a high level of technical expertise and diagnostic capability.
• Architecture design and implementation: from corporate networks to SD-WAN solutions, cloud environments or hybrid connectivity models.
• Automation and documentation: standardizing configurations, creating procedures and continuously improving processes to reduce errors and increase efficiency.
• Participation in global projects: office deployments, WiFi network renewals, technology migrations, cloud integrations or the evolution of international architectures.

In many cases, these professionals also evolve into more strategic positions, such as network architects, security specialists or infrastructure leaders.

From on-premise to cloud

One of the most relevant changes in this profile is the transition from mainly on-premise infrastructures to cloud and hybrid models.

For years, many enterprise networks were built on physical infrastructure, MPLS connections and more centralized models. That approach still exists in many organizations, but it no longer fully explains the current reality.

Today, companies need networks capable of supporting:

• secure cloud migrations;
• remote access across global environments;
• stable connectivity for distributed teams;
• optimal performance for critical applications;
• data protection across increasingly decentralized architectures.

This shift means that the Network & Security Engineer no longer manages only hardware or local connectivity. This profile also needs to understand cloud environments, distributed architectures, access security, segmentation, traffic policies and new ways of protecting an organization that no longer operates from a single perimeter.

The evolution of the role does not remove the classic networking foundation. It expands it. A strong profile still needs solid knowledge of networks, routing, switching, firewalls and troubleshooting. But companies increasingly value the ability to connect that technical foundation with cloud, security, automation and a global view of infrastructure.

Demand for this type of profile

The combination of networking, security and cloud knowledge makes the Network & Security Engineer an especially relevant profile within the IT market.

Companies are looking for professionals who can operate in complex environments, understand the operational impact of an incident, coordinate with international teams and participate in projects that directly affect business continuity.

Demand is not driven only by the fact that networks are important. It is driven by the growing dependency organizations have on connected, secure and flexible infrastructures. In this context, profiles capable of combining connectivity, security and cloud evolution offer clear differential value.

Ultimately, this is not only about keeping a network active. It is about ensuring that the company can operate, protect itself and grow on top of an infrastructure prepared for the present and for what comes next.

Why demand is rising

• Hybrid and distributed work models
• Cloud and hybrid infrastructure growth
• Greater cybersecurity exposure
• Need for stable global connectivity
• Business dependence on resilient infrastructure

A Network & Security Engineer is no longer only the person who keeps the network running. In many organizations, this profile is becoming one of the key links between operational stability, security maturity and technological transformation.

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Frequently Asked Questions

What makes an AI profile truly valuable in 2026

What the AI hiring market is really rewarding in 2026

In 2026, the market is not simply rewarding AI knowledge.

It is rewarding a far more demanding combination: strong software fundamentals, the ability to work with ambiguity, experience taking models into production, enough product judgment to connect technical decisions to business outcomes, and enough autonomy to build without excessive structure around the role.

That is the more useful way to read the AI hiring market right now. The value of an AI profile is not being defined by “AI” alone. It is being defined by what kind of AI capability the business actually needs, how mature that capability already is, and how much execution responsibility the company expects one person to carry.

The IKIGAI Talent Group Salary Guide 2026 makes one thing especially clear: AI / Machine Learning Engineer remains among the most in-demand technology roles, within a tech market where overall demand for technology roles grew by approximately 34% in 2025 compared to 2024. But that signal is easy to misread if it is reduced to salary or demand alone. What matters more is what the market is actually rewarding underneath that demand.

And right now, it is rewarding profiles that can do much more than “work in AI.”

What the market is really rewarding

There is still a tendency to read AI hiring too superficially. Companies say they want AI talent. Candidates see strong demand and strong compensation signals. But underneath that surface, the market is becoming much more selective about what kind of value it is willing to pay for.

That value usually comes from a specific mix of capabilities rather than from one label.

At the top end of the market, the profiles attracting the strongest interest are rarely those who only understand models in isolation. They are the ones who combine several high-value traits at once:

• solid software engineering foundations
• strong command of Python
• experience moving models into production
• comfort operating with limited structure
• enough product understanding to connect technical choices to business outcomes
• the autonomy to build, prioritise and execute without waiting for a fully mature environment

This matters because it changes the conversation. The market is no longer rewarding AI expertise as a narrow specialty alone. It is rewarding the ability to turn AI capability into something operational, usable and commercially relevant.

What companies say they want	What the market is often really rewarding
“AI experience”	AI capability connected to real execution
“Strong technical background”	Software depth plus AI implementation maturity
“Someone autonomous”	A profile able to build with limited structure and high ambiguity
“Someone who can deploy models”	A candidate who can move from prototype to production-grade business value
“A senior AI profile”	A candidate with the right mix of seniority, context fit and execution range

Why Founding AI Engineer has become such a magnetic role in startups

One of the clearest signals in the market right now is the rise of the Founding AI Engineer, especially in startups.

This is not a role companies open because they want a conventional specialist. They open it because they want someone who can help build the AI layer almost from zero. That usually means much more than writing models or fine-tuning pipelines. It often means helping define the technical direction, making early product choices, deciding what should be built first, and operating with a level of autonomy that would feel unusual in more structured engineering environments.

That is why the role is attractive, but also difficult to define well.

When a startup says it wants a Founding AI Engineer, what it often really wants is a profile with unusually broad range: someone technical enough to build, senior enough to make judgment calls, pragmatic enough to ship, and product-aware enough to understand where AI actually creates business value.

In other words, it is not just looking for AI skill. It is looking for early-stage leverage.

That is also why these roles become expensive and hard to close. The number of profiles that can genuinely operate with that mix of software depth, production readiness, autonomy and startup context is far smaller than the label itself suggests.

Why seniority pressure is moving upward

Another important signal in the market is that pressure is increasingly concentrated in higher seniority bands.

The most demanded AI profiles are not only “AI profiles” in the generic sense. They are often senior, staff or principal-level profiles with enough depth to own difficult decisions in ambiguous environments.

That tells us something important about where the market is. Many businesses are no longer simply experimenting with AI as a future capability. They are trying to operationalise it, integrate it into product or workflow, and make it defensible inside the business. That kind of move usually requires more than a capable mid-level technical hire. It requires senior profiles who can make architecture, production and implementation decisions with less supervision.

That is where the market starts paying more attention to profiles with a software-first background. Strong AI candidates are increasingly valued not just because they understand models, but because they can build reliably, think in systems and carry production responsibility.

This is one of the reasons Python-heavy profiles with real production experience are gaining so much traction. They sit at the point where software engineering discipline and AI capability begin to reinforce each other.

Why software plus AI is becoming one of the strongest combinations in the market

If one capability mix is standing out particularly strongly, it is the combination of software engineering and AI.

That does not mean every AI profile needs to come from the same background. But it does mean that the market is increasingly rewarding candidates who can do more than model work in an isolated environment. Businesses want people who can build around the model, not only people who can improve the model itself.

That distinction matters.

An AI capability becomes much more valuable when it can move through real engineering constraints: APIs, infrastructure, integration layers, versioning, deployment logic, reliability and maintainability. That is why a profile with strong software fundamentals and enough AI depth can often create more business value than a narrower specialist profile, even if both technically “work in AI.”

The market is effectively rewarding translation capacity: the ability to turn AI from an idea or prototype into something that lives inside the product or the business.

Where companies are misreading the market

The most common misread is also one of the most expensive: assuming that because AI is in demand, the market will tolerate an underdefined role.

It usually will not.

Many companies are opening AI searches with a level of scope that does not match the seniority, structure or compensation logic behind the role. They want a principal or founding-level profile to build the AI platform, work deeply in Python, understand data, handle elements of DevOps, bring product judgment and operate with near-total autonomy. In effect, they are combining several difficult mandates into one search.

The issue here is not ambition. The issue is calibration.

When the mandate becomes too broad, two things usually happen. First, the pool of genuinely relevant candidates shrinks very quickly. Second, the role becomes harder to explain and defend in the market. Candidates sense when the brief is trying to absorb too many unresolved needs at once.

This is where the market starts to punish weak role design.

The role may sound exciting internally. But externally, it can read as a search that has not yet separated platform ownership, product direction, production responsibility and technical depth into a coherent ask.

Common AI hiring mistake	What it usually reveals
Asking for a Founding AI Engineer with full platform ownership, Python, Data and DevOps in one role	The mandate is carrying too many unresolved needs at once
Targeting principal-level capability with mid-level calibration	Seniority and scope are misaligned
Using “AI profile” as a broad category	The company has not clarified the actual capability gap
Assuming demand alone will explain hiring difficulty	The business is underestimating the importance of role design

Why AI roles are still being confused

Another important source of friction is that companies still confuse several adjacent AI roles far too easily.

The two confusions that appear most often are:

• AI Engineer vs Data Scientist
• Machine Learning Engineer vs Software Engineer with AI

These are not small distinctions. They change how the role should be scoped, how it should be evaluated, and what kind of value the company should expect from the hire.

An AI Engineer is often expected to sit closer to implementation, integration and production use of AI systems. A Data Scientist may be stronger in modelling, experimentation and analytical depth, but not necessarily best positioned to carry engineering ownership in production. A Machine Learning Engineer usually brings stronger model deployment and infrastructure logic than a Software Engineer with AI exposure, even if both can operate around similar tools or workflows.

When those differences are flattened, the company often thinks it is simplifying the search. In reality, it is making the search harder to calibrate. The title looks clear, but the role underneath it is still mixed.

This matters because compensation, seniority and candidate fit all begin to drift when the role family itself is not well separated.

Why AI Architect and Principal AI Engineer are especially hard to hire for

At the top of the difficulty curve, two profiles stand out especially strongly: AI Architect and Principal AI Engineer.

These roles are difficult not only because they are senior, but because they are genuinely rare. There are very few professionals who combine deep enough technical capability, enough implementation maturity and enough strategic judgment to operate at that level. And the ones who can often are not actively looking.

That scarcity is made more complicated by context.

Many of these profiles are shaped in startup or innovation-heavy environments, where they are used to speed, ambiguity, broad ownership and less formal organisational structure. That can make them a very strong fit for some companies and a difficult fit for others, especially where the organisation expects enterprise-style process around a role that has grown in more fluid conditions.

That contrast matters. A Python backend profile coming from an enterprise environment may bring strong engineering discipline, but not necessarily the same kind of exposure to open-ended AI architecture decisions in early-stage or innovation-driven contexts. The market does not reward those backgrounds equally for the same mandate, because the operating environment behind the role is different.

This is one of the reasons these searches often feel so slow. The difficulty is not only in accessing the talent. It is in finding the right intersection between capability, context and expected ownership.

What this says about how AI value is being priced

If we step back, a broader pattern becomes visible.

The market is not pricing AI profiles according to one simple variable. It is not just paying more for “AI knowledge,” and it is not simply paying more for “scarcity.” It is pricing a more complex equation:

• technical depth
• software credibility
• production readiness
• autonomy
• product understanding
• environment fit

That is why two profiles who both look “senior in AI” on paper can carry very different market value. The one who can operate in production, make better technical trade-offs, work with ambiguity and build in a less structured environment will often be much more valuable than the one whose experience remains narrower or more isolated.

This is also why reading AI compensation without reading AI context leads to weak conclusions. The market is not rewarding the label. It is rewarding the operating value behind the label.

What companies should take from this

The practical implication is quite clear.

If a business wants to hire well in AI, it needs to define the role through capability and context, not through trend language.

That means asking better questions before the search goes live:

• Do we need experimentation, implementation, production ownership or architecture?
• How much autonomy will this person really need?
• Are we hiring into a structured environment or expecting the person to build structure?
• Do we need deep model expertise, or stronger software-first AI capability?
• Are we calibrating for a senior, staff or principal-level mandate in a realistic way?

Stronger searches usually start with sharper internal definition. Not because definition makes the market easier, but because it makes the mandate more defensible.

Final reflection

What makes an AI profile truly valuable in 2026 is not AI knowledge in isolation.

It is the ability to connect AI capability to real execution: software depth, production maturity, autonomy, product judgment and enough range to operate in environments where the problem is still not fully structured.

That is why the most valuable profiles are rarely the simplest to describe. And it is why so many AI searches become difficult when the company tries to compress too many expectations into one role without clarifying what kind of value it actually needs.

The market is still rewarding AI talent strongly. But increasingly, it is rewarding the profiles that can make AI usable, scalable and commercially relevant inside the business.

If you want the broader market context behind these signals, including role demand and salary benchmarks across technology, download the IKIGAI Talent Group Salary Guide 2026.

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Frequently Asked Questions

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The hiring bottlenecks that will define tech recruitment in 2026

The market signals behind 2026 hiring bottlenecks

In 2026, tech hiring will not get harder in the same way across the market. The biggest bottlenecks will not appear in every search. They will concentrate in the roles where business urgency, functional scarcity and internal ambiguity are already colliding.

That is the most useful way to read the market right now. The problem for many companies is not that hiring has become universally harder. The problem is that some searches are already harder for structural reasons, and those reasons are not always visible when the role is first opened.

The IKIGAI Talent Group Salary Guide 2026 makes one thing especially clear: demand is still clustering around a specific set of technology capabilities. AI / Machine Learning Engineer, Software / Fullstack Engineer, Cloud / Network Engineer, Cybersecurity Specialist, and Data Engineer / Data Analyst remain among the roles where market pressure is most visible.

That does not simply mean these profiles are popular. It means they are becoming the clearest test of whether companies know how to define, calibrate and run a search in a market that is no longer forgiving vague hiring logic.

Where the real hiring pressure will build in 2026

The market is not rewarding only what is new. It is rewarding what is necessary.

That distinction matters. Many companies still read the tech market as if pressure were concentrated only in the most visible AI roles. It is not. Pressure is spreading across the functions that build, secure, scale and operationalise digital products.

This is why the most important hiring bottlenecks of 2026 will not be random. They will form around roles that sit close to business transformation, technical maturity and execution risk.

Role family	Why pressure is building	Why searches get harder to close
AI / Machine Learning	Strong demand linked to productisation, automation and enterprise adoption	Mandates are often still too broad, mixed or underdefined
Fullstack	Still central to delivery speed and product execution	Role breadth often hides ambiguity rather than clarity
Cloud / Infrastructure	Scalability, resilience and platform maturity matter more than before	Many businesses have outgrown old infrastructure logic without redefining the role properly
Cybersecurity	Security is moving closer to the operational core of the business	The cost of hiring imprecision is much higher
Data	Data is expected to support reporting, product, forecasting and AI readiness at once	The function keeps expanding faster than many companies can structure it

This is not only a demand story

It is tempting to explain every difficult search with the same argument: demand is high, talent is scarce, so hiring gets harder. There is some truth in that. But it is still too shallow to be useful.

A difficult search is usually created by several pressures at once. Market demand matters. Functional scarcity matters. But so do unclear ownership, unrealistic scope, slow decisions, weak compensation logic and internal misalignment on what the business is actually trying to hire for.

That is why some companies struggle far more than others to close the same kind of role. The market is only part of the problem. The rest sits inside the mandate itself.

That is also why the same role family can behave very differently from one company to the next. A hard search does not always mean the market is empty. Sometimes it means the brief is still carrying too much ambiguity for the market to trust it.

AI and Machine Learning: the most visible bottleneck, but not the simplest one

AI and Machine Learning will remain one of the clearest pressure points in 2026. The reason is obvious on the surface: the demand is real, the capability is scarce, and many businesses still need to strengthen their AI layer quickly.

But the deeper problem is usually not only scarcity. It is definition.

Many companies open AI roles as if the market would return clarity automatically. It does not. In many cases, the mandate combines too many different expectations: experimentation, data maturity, model deployment, product integration, business translation and technical leadership.

That creates a fragile kind of search. The company thinks it is hiring a clear profile. The market often reads an ambition that is still too open, too aspirational or too compressed for one role.

That is why AI bottlenecks do not begin only when a company starts sourcing. They often begin earlier, when internal definition is still weaker than the strategic urgency behind the hire.

If you want to connect this pressure to live market opportunities, you can also explore current AI roles on the IKIGAI offers portal.

Fullstack: when role breadth stops helping

Fullstack will remain one of the most active labels in the market. That makes sense. It is still a highly useful function for teams that need versatility, speed and the ability to build across the product stack.

But it is also one of the easiest categories to misuse.

Sometimes “fullstack” describes real front-end and back-end depth. In other cases, it becomes a catch-all label used to cover delivery gaps, architectural exposure, product sensitivity and execution pressure at the same time.

When that happens, role breadth stops being an advantage and becomes ambiguity.

This is one of the most silent hiring bottlenecks of 2026: searches that look reasonable on paper but stall because the market no longer accepts vague mandates hidden behind broad titles.

Strong fullstack engineers are not only evaluating the stack. They are evaluating code quality, team maturity, technical leadership and whether the business really understands what it needs.

Cloud and infrastructure: where maturity gaps become visible

Cloud and infrastructure roles will continue to generate pressure because they sit at the point where growth, reliability and technical maturity meet.

As businesses scale, the challenge is no longer only to build product. It is to sustain product with resilience, security, performance and room to evolve. That increases the value of cloud and infrastructure capability, but it also makes these searches more demanding.

The bottleneck appears when the company has outgrown its previous infrastructure logic but has not yet translated that shift into a properly calibrated search.

That is usually where the process begins to wobble. The role sounds important, but key questions remain unresolved: is the need architectural or executional, platform-focused or reliability-led, migration-heavy or debt-driven?

Without that clarity, the market does not only become more competitive. It becomes harder to persuade.

Relevant opportunities are also visible across current cloud roles on the IKIGAI site.

Search type	What companies often say	What the market often hears
AI / ML	We need an AI engineer	We are still mixing experimentation, deployment and product expectations
Fullstack	We need someone versatile	We have not defined where specialisation is actually needed
Cloud	We need stronger infrastructure	We have not clarified whether this is architecture, DevOps, platform or reliability
Cybersecurity	We need to strengthen security	We may still be reacting to accumulated risk without a clear long-term scope
Data	We need stronger data capability	We have not yet structured what the function is really supposed to carry

Cybersecurity: pressure rises because the margin for error is smaller

Cybersecurity is no longer a side concern. It is increasingly part of the operational core.

That changes the hiring equation. A weak fit in product or engineering may damage speed. A weak fit in cybersecurity can damage governance, risk posture and leadership confidence.

That is why these searches tend to become slower, more selective and more politically sensitive inside the company. More stakeholders enter the process. The tolerance for compromise drops. The cost of a weak mandate becomes much more visible.

At the same time, stronger candidates in security tend to assess companies with more caution. They want to understand whether the function has real backing, whether the scope is strategic or reactive, and whether the role exists to build maturity or simply absorb accumulated problems.

That is what makes cybersecurity one of the clearest bottlenecks of 2026: not just strong demand, but lower tolerance for hiring error on both sides of the market.

Data: a function expanding faster than many companies can structure it

Data remains one of the most pressured areas in the market not only because it is in demand, but because the function itself continues to expand.

In many companies, data is now expected to support reporting, product intelligence, forecasting, operational efficiency and AI readiness at once. That makes the category look strong from the outside, but it also makes searches more sensitive to weak internal design.

This is where many businesses get caught. They open a Data Engineer search when the deeper need is architectural. They search for an analyst when the real issue is decision discipline. They want senior data capability without having yet built the ownership structure the function requires.

That is why data bottlenecks in 2026 will not come only from scarcity. They will come from expansion without enough internal ordering.

That is also why live data roles are useful as a market signal, not only as job listings.

What makes the 2026 bottlenecks different

The difference is not only that demand remains high. The difference is that the market is getting less forgiving.

A year ago, some businesses could still explain a difficult search as a general market issue. In 2026, that explanation will become less persuasive. Pressure remains real, but it is becoming increasingly obvious that some searches fail less because of pure scarcity and more because of the gap between business ambition and role definition.

That changes the standard. It will no longer be enough to say that strong candidates are hard to find. Companies will need to ask whether the mandate is well built, whether the seniority fits the company stage, whether compensation is calibrated against real market logic, and whether the process signals enough seriousness to earn trust from the market.

The uncomfortable truth: some searches will become difficult before they ever reach the market

There is one uncomfortable idea running through all these role families.

Many of the searches that will get harder in 2026 will not become difficult only once they meet the market. They will arrive there already complicated.

A company may open an AI, fullstack, cloud, security or data role assuming the main problem will be competition for scarce talent. In many cases, the real problem appears earlier: in a mandate that is too broad, in a role that mixes too many expectations, in compensation that is poorly calibrated, or in internal alignment that is still immature.

This is expensive precisely because it often becomes visible late. In an active market, weak internal definition does not always stop the process from starting. What it does is degrade the process. It makes it slower, less precise and harder to close well.

What stronger hiring teams will do differently

The companies that handle these bottlenecks better will not necessarily be the ones with the biggest brand or the highest salary flexibility. They will be the ones that reduce ambiguity before they create demand.

That means several concrete things:

• define the problem behind the role, not only the title
• separate operational urgency from structural need
• calibrate seniority and compensation against real market logic
• understand whether the business has reached the maturity the role requires
• align leadership, hiring manager and talent team before opening the process

Put differently: in 2026, hiring well in these functions will be less about reacting faster and more about reading the market better.

Final reflection

The hiring bottlenecks that will define tech recruitment in 2026 will not be random. They will concentrate where the market is already telling us something very clear: some capabilities are no longer optional, and some searches can no longer be run through generic hiring logic.

AI and Machine Learning, Fullstack, Cloud, Cybersecurity and Data are not just hot categories. They are pressure points. And pressure points reveal more than demand. They reveal where a company is ready to scale with precision, and where it is still trying to hire before fully understanding what it needs.

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If you want the broader map behind these signals, salary ranges and role families, the IKIGAI Talent Group Salary Guide 2026 is the next natural step. That is where these pressure points become visible with more context and more operational detail.

If you want to see what offers we have nowadays aviable, see our lastests offers here.

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Frequently Asked Questions

The Future of Tech and Its Impact on the Job Market

How is the Job Market going to evolve with Cutting-Edge Tech Trends?

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In today’s rapidly evolving world, technology plays a pivotal role in shaping various aspects of our lives.

From communication and transportation to healthcare and entertainment, technological advancements have revolutionized industries and transformed the way we work. As technology continues to advance at an unprecedented pace, it inevitably impacts the job market, creating new opportunities and challenges for workers across different sectors.

In this article, we will explore the future of tech and its profound impact on the job market.

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Introduction

In recent years, breakthroughs in Artificial Intelligence, Robotics, Big Data, and the Internet of Things (IoT) have transformed the way we live and work.

These technological advancements have the potential to reshape the job market, leading to both job creation and displacement.

To navigate the future of tech, it is crucial to understand the implications it holds for the workforce.

Technological Advancements and Automation

With the rapid advancement of technology, automation is becoming increasingly prevalent in various industries.

One of the key advantages of automation is the significant increase in efficiency and productivity. Machines and technology can perform tasks with greater speed, accuracy, and consistency compared to human workers.

This allows companies to streamline their operations, reduce errors, and optimize their overall output. Automated systems can work around the clock, eliminating the limitations of human labor in terms of working hours and fatigue.

However, the rise of automation also raises concerns about job security for certain individuals. Jobs that primarily involve repetitive tasks, such as assembly line work, data entry, or customer service roles, are more susceptible to being automated.

This can lead to potential job displacement and unemployment for those whose roles can be easily replaced by machines.

Job Displacement and Evolving Skill Sets

As automation replaces certain job functions, there is a need for individuals to adapt and acquire new skills to remain employable.

In the future, the job market will place a strong emphasis on a highly skilled workforce capable of navigating complex technologies and leveraging them for innovation.

While automation may eliminate some jobs, it also creates new opportunities that require specialized knowledge and expertise. To thrive in this tech-driven landscape, individuals must actively engage in lifelong learning and upskilling.

Lifelong learning is the continuous pursuit of knowledge and skills throughout one’s career. It involves actively seeking out opportunities for personal and professional growth, whether through formal education, online courses, workshops, or self-study.

By staying updated with the latest advancements and trends in technology, individuals can adapt and evolve their skill sets to meet the demands of the evolving job market.

If you want to learn more, you can read the following article: https://ikigaitalentgroup.com/upskilling-and-reskilling/

The Importance of Lifelong Learning

The pace of technological innovation is accelerating, and the skills that are in demand today may be obsolete tomorrow.

This means that individuals need to be constantly learning and adapting in order to stay ahead of the curve.

There are many benefits to lifelong learning

• Stay relevant in the job market
• Increase your earning potential
• Gain new skills and knowledge
• Expand your network
• Improve your problem-solving and critical thinking skills
• Boost your confidence and self-esteem
• Stay mentally and intellectually engaged

There are many different ways to engage in lifelong learning.

You can take online courses, attend workshops, participate in professional development programs, or simply read books and articles about new technologies and trends.

The important thing is to find a method that works for you and to make a commitment to lifelong learning.

Here are some additional benefits

• It can help you achieve your personal and professional goals.
• It can make you a more well-rounded individual.
• It can help you stay motivated and engaged in life.
• It can give you a sense of purpose and satisfaction.

If you are interested in lifelong learning, there are many resources available to help you get started.

Your local library, community college, or university may offer a variety of courses and programs.

There are also many online resources, such as Coursera, EdX, and Udemy.

Emerging Tech-Driven Industries

The future of technology is full of promise, with new industries and job opportunities emerging all the time. Fields such as Artificial Intelligence (AI), Data Science, Cybersecurity, and Renewable Energy are all expected to witness significant growth in the coming years.

These emerging sectors require specialized knowledge and expertise, providing avenues for individuals to explore new career paths and contribute to groundbreaking innovations.

For example, AI engineers are in high demand, as businesses are increasingly looking to automate tasks and make better decisions using AI-powered insights. Data scientists are also in high demand, as businesses are increasingly looking to make sense of the vast amounts of data they collect.

In addition to the technical skills required for these jobs, individuals also need to have strong problem-solving and critical thinking skills.

They need to be able to think outside the box and come up with creative solutions to complex problems.

They also need to be able to work effectively in teams and communicate their ideas clearly.

Collaboration between Humans and Machines

The rise of Artificial Intelligence (AI) and other technologies has led to some people fearing that machines will eventually replace human workers. However, there is a growing body of evidence that suggests that AI and humans can work together in a complementary way, leading to increased productivity and efficiency.

For example, AI can be used to automate tasks that are repetitive or dangerous, freeing up human workers to focus on more creative or strategic work. AI can also be used to provide real-time insights and analysis, helping human workers make better decisions.

In addition, AI can be used to augment human capabilities. For example, AI-powered tools can help humans to see better, hear better, and think more creatively. This can lead to new and innovative ways of working that would not be possible without AI.

Of course, there are some jobs that are likely to be automated in the future. However, these jobs are likely to be the ones that are repetitive, dangerous, or require little creativity or critical thinking. Jobs that require creativity, emotional intelligence, critical thinking, and complex problem-solving are likely to rely on human skills, complemented by the capabilities of technology.

In the future, we are likely to see a world where humans and machines work together in a more integrated way. This will lead to a more productive and efficient workforce, as well as new and innovative ways of working.

Remote Work and Digital Nomadism

Remote work and digital nomadism are two terms that are often used interchangeably, but they actually have different meanings.

It refers to the ability to work from anywhere, while digital nomadism refers to a lifestyle of traveling and working from different locations.

There are many benefits to remote work, including:

• Increased flexibility and freedom
• Reduced commuting costs
• Improved work-life balance
• Increased productivity

Digital nomadism is a more extreme form of remote work. Digital nomads typically travel the world, working from different locations. This lifestyle can be very rewarding, but it also presents some challenges, such as:

• Finding reliable internet connections
• Dealing with time zone differences
• Maintaining a social life

If you are considering a career in remote work or digital nomadism, there are a few things you should keep in mind. First, you need to make sure that you have the skills and experience necessary to be successful in a remote work environment. Second, you need to be self-motivated and disciplined. Third, you need to be able to manage your time effectively.

If you are willing to put in the effort, remote work and digital nomadism can be very rewarding. You can have a more flexible and fulfilling work life, while also seeing the world and experiencing new cultures.

Ethical Considerations in the Future of Tech

As technology becomes more pervasive, ethical considerations are becoming increasingly important. Issues such as Data Privacy, Algorithmic Bias, and the Ethical use of Artificial Intelligence (AI) need to be addressed in order to ensure that technology is used responsibly for the benefit of society.

• Data privacy is one of the most important ethical considerations in tech. As more and more of our personal data is collected and stored online, it is essential that this data is kept safe and secure. We need to be aware of how our data is being used and have the ability to control how it is shared.

• Algorithmic bias is another important ethical consideration in tech. Algorithms are used to make decisions in a wide variety of contexts, from lending decisions to hiring decisions. However, algorithms can be biased, which can lead to unfair treatment of certain groups of people. It is important to ensure that algorithms are designed and used in a way that is fair and equitable.

• The ethical use of AI is also a critical ethical consideration in tech. AI has the potential to be used for good or for evil. It is important to ensure that AI is used in a way that benefits society and does not harm individuals or groups. For example, AI could be used to develop new medical treatments or to improve the efficiency of transportation systems. However, AI could also be used to develop autonomous weapons or to create surveillance systems that violate people’s privacy.

The future of tech and the job market will demand professionals who can navigate these ethical complexities and ensure that technology is used responsibly for the benefit of society.

These professionals will need to have a strong understanding of ethical principles and be able to apply them to the development and use of technology.

They will also need to be able to communicate effectively with stakeholders and build consensus on ethical issues.

Entrepreneurship in the Digital Age & the Future of Tech

The digital age has made it easier than ever for aspiring entrepreneurs to start their own businesses. Technological advancements have lowered the barriers to entry, making it possible to launch a business with a relatively small investment.

In the past, entrepreneurs needed to have a lot of capital in order to start a business. They also needed to have access to a physical location, such as a store or office. However, the digital age has made it possible to start a business with very little capital and from anywhere in the world.

There are many online platforms that allow entrepreneurs to launch and manage their businesses. These platforms provide entrepreneurs with access to a global market, as well as tools and resources that can help them to succeed.

In addition to online platforms, the digital age has also made it easier for entrepreneurs to market their businesses. Social media platforms, such as Facebook and Twitter, can be used to reach a large audience with relatively little effort.

As a result of these technological advancements, entrepreneurship skills and the ability to adapt to the digital landscape are becoming increasingly valuable in the job market. Employers are looking for candidates who can think creatively and solve problems, as well as those who are comfortable with technology.

If you are interested in entrepreneurship, the digital age is an excellent time to start your own business. With the right skills and resources, you can succeed in the digital economy.

Closing the Digital Divide

Technology has the potential to transform lives and communities. However, not everyone has equal access to technology. This is known as the digital divide. The digital divide can have a significant impact on people’s lives, limiting their opportunities for education, employment, and civic engagement.

There are many factors that contribute to the digital divide. These include income, education, location, and race. People who live in rural areas, for example, are more likely to be disconnected from the internet than people who live in urban areas. People with lower incomes are also more likely to be disconnected.

The digital divide can have a number of negative consequences. For example, it can limit people’s access to education and job opportunities. It can also make it difficult for people to participate in civic life.

There are a number of things that can be done to close the digital divide. These include providing affordable internet access, offering digital literacy training, and making sure that digital devices are accessible to people with disabilities.

Closing the digital divide is essential to ensuring that everyone has the opportunity to benefit from technology. It is also important for promoting social justice and equality.

Conclusion

The future of tech holds both promise and challenges for the job market.

While automation and technological advancements may lead to job displacement, they also create new opportunities in emerging industries.

To thrive in this rapidly evolving landscape, individuals must embrace lifelong learning, adapt to changing skill requirements, and collaborate effectively with technology.

As we navigate the future, it is crucial to prioritize ethical considerations, foster entrepreneurship, and work towards bridging the digital divide.

In conclusion, the future of tech holds immense potential to shape the job market.

While automation and emerging technologies may bring about job displacement, they also create new avenues for innovation and growth.

By embracing lifelong learning, adapting to changing skill requirements, and prioritizing ethical considerations, individuals can navigate the tech-driven job market and seize the opportunities it presents, adapting to the constanly evolving changes that await us in the near Future of Tech.

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FAQs

1. How will automation affect job opportunities? Automation may replace certain job functions, but it also creates new opportunities in emerging industries. Adaptation and upskilling are key to navigating the evolving job market.
2. What skills will be in high demand in the tech-driven job market? Skills such as artificial intelligence, data science, cybersecurity, and renewable energy will be in high demand. Additionally, soft skills like critical thinking and creativity will remain valuable.
3. How can individuals prepare for the future of tech in the job market? Lifelong learning, staying updated with technological advancements, and embracing new skills through training and professional development are essential to prepare for the future job market.
4. What role does ethics play in the tech industry? Ethical considerations are crucial in ensuring responsible use of technology. Professionals need to navigate issues such as data privacy, algorithmic bias, and the ethical use of AI.
5. How can we bridge the digital divide in accessing tech-driven job opportunities? Bridging the digital divide requires efforts to provide training and resources to underserved communities, ensuring equal access and opportunities for all.

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And that’s all for this article!

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Thanks for reading & learning about “The Future of Tech and Its Impact on the Job Market“.

If you want to discover the roles & salaries for Executive, Technology & Digital departments, you can download our Salary Guide for 2023!