AI and skills to be renewed: the new digital work gap

It’s a paradox: the Italian digital market is about to open the door to 137,000 new job opportunities in the coming months alone, but more than 40% of these positions are likely to remain unfilled. The reason? A lack of candidates with the right skills, in a country where less than half the population has basic digital skills.

The opportunities exist, but they are concentrated in specific profiles. The demand no longer comes only from purely technological companies, but also from “traditional” sectors such as healthcare, manufacturing, and consumer goods. Artificial intelligence is accelerating the transformation and redefining priorities: the International Monetary Fund estimates that 60% of jobs in advanced economies are exposed to these changes.

In short, it is not so much your job that is at stake, but the relevance of your skills: today’s market requires hybrid figures who combine solid technical mastery with strategic vision to overcome the GenAI Divide, the gap between basic use of AI tools and their application as a competitive advantage.

The new gravity center of the tech market: demand shifts to the real economy

Today, “tech work” goes beyond traditional IT. In Italy, the spread of basic digital skills remains limited and many companies struggle to find suitable profiles, but in Europe, ICT specialists number more than ten million, around 5% of the workforce, and their influence extends across the board.

Every productive sector uses digital solutions to improve processes, efficiency, and decision-making, and demand is shifting toward those who know how to transform data into results.

As a result, companies are not just looking for professionals who can program: they are looking for people who can read metrics, ensure IT security, manage cloud infrastructures, and integrate analytical and automation tools into marketing and sales flows. And, for some time now, they have been looking for people who can integrate AI into business processes.

The market rewards those who combine specialization and a broad overview: in Italy, 63.4% of planned hires require cross-functional digital skills, while 37.1% require skills related to innovative solutions such as robotics, IoT, or big data analytics.

Where the opportunities really are

It is therefore clear that job roles are evolving and that the spectrum of opportunities is wide, if you have the right skills.

In 2024, the Italian digital market grew almost six times faster than the national GDP, reaching a value of €81.6 billion; this expansion was fueled above all by the widespread economic fabric that is undergoing its own digital transition. This translates into concrete needs in the sectors that drive demand, such as healthcare, consumer goods, and industry, with record difficulties in finding personnel. This is followed closely by finance, automotive, and construction, where the modernization of processes and platforms is opening up opportunities for profiles with a solid technical background and an aptitude for collaborating with non-IT functions.

Hybrid roles are emerging in job advertisements: data analysts for finance, security experts in public administration, and digital specialists for retail and supply chains. A Unioncamere analysis shows that over 50% of Italian companies require mathematical and IT skills in their selection processes, regardless of the sector of application. The picture is clear: the most stable opportunities are shifting to the real economy, within companies that need to build—often from scratch—digital infrastructure, flows, and skills.

The mismatch and difficulty in finding candidates, with peaks of 76% in some areas, stem from the fact that this growth is no longer driven by the big names in technology, but by companies in the midst of digital transition that are grappling with a shortage of qualified personnel.

AI as an accelerating factor

Artificial intelligence is accelerating this evolution and has entered production and organizational processes. In advanced economies, about 60% of jobs are exposed to AI-related transformations.

In many contexts, productivity is growing and new professions are emerging; in others, repetitive tasks and the demand for purely executive roles are decreasing.

Profiles in expansion include Big Data Specialists, AI/Machine Learning Specialists, Cybersecurity Experts, and engineers in fintech and cloud fields. These figures respond to specific needs: governing complex systems, protecting information assets, and scaling solutions in competitive markets. By automating a growing portion of executive tasks, this technology reduces the value of purely technical work and increases the value of professionals who know how to govern tools for strategic purposes. Strategists are not those who risk being replaced by AI, but those who use it to improve analysis and devote time to activities with higher added value.

Beyond the hype: the gap between technology adoption and results

The adoption of AI presents complexities. On the one hand, the AI Index 2025 and McKinsey reports indicate that investments in generative artificial intelligence already exceed $30 billion annually and could lead to a potential productivity growth of $4.4 trillion per year. On the other hand, without widespread upskilling programs, this value remains theoretical. MIT’s report The GenAI Divide found that 95% of corporate projects based on generative AI do not produce measurable returns, confirming the fragility of the link between hype and concrete results.

One of the causes is the phenomenon of “Shadow AI,” or the unofficial use of AI tools by employees outside of structured business processes. An effective response combines three levers: clear policies on the use of tools, processes for validating/moderating results, and targeted training on prompts, risk assessment, and impact metrics. Experimentation becomes valuable when it is traceable, repeatable, and aligned with objectives.

This transition also generates new balances. Research by MIT has shown that in companies that implement AI solutions, the proportion of hires for junior positions tends to decrease. There is a polarization that favors roles with greater strategic responsibilities. Entering the workforce today without targeted training exposes you to the risk of being left on the sidelines as automation absorbs the simplest tasks.

How the ideal profile in companies is changing

The digital divide translates into a constant mismatch: too few qualified candidates compared to actual demand. In Italy, the situation is worse in areas with less industrial tradition, while in the north the gap is less marked but still significant.

However, the underlying problem is not a numerical shortage, but the clash between two professional models.

For years, the technology industry has rewarded the hyper-specialist: a profile with vertical and very deep skills, perfect for optimizing a small cog in a huge and already well-established production machine. Today, companies in traditional sectors that drive demand do not need to optimize a cog: they need to build the entire machine. This has generated demand for a different profile, the strategist-specialist, who combines technical expertise with business vision and is capable of translating actions into measurable economic results.

The clash between professional models: the hyper-specialist vs. the specialist-strategist

The era of explosive growth of Big Tech has favored the creation of huge, ultra-specialized teams, where a professional could devote their career to a single, specific problem. This approach ensured maximum efficiency and depth on micro-tasks, but often at the expense of an overall vision.

Progressive automation, accelerated by artificial intelligence, is making many of these purely executive tasks less central, shifting value towards strategy and coordination. As a result, a hyper-vertical profile, if lacking an understanding of the business context, is less attractive to companies in the real economy that need flexibility and a direct impact on growth.

This is where the new figure emerges, capable of navigating the complexity of digital transformation. Value is created by knowing how to link technical activities, such as SEO or data analysis, to an increase in turnover or the acquisition of new customers. The MIT report on the “GenAI Divide” highlights that successful companies choose their technology partners based on a “deep understanding of our workflow” rather than on individual features. This confirms the demand for a specialist-strategist who speaks the language of business and knows how to use technology as a lever to achieve business objectives.

How to become a specialist-strategist

Automation reduces the value of fragmented and purely executive tasks and requires a focus on the role of the specialist-strategist, who maintains depth in a discipline but knows how to read context, priorities, and constraints, and translates technical activities into measurable effects on revenue, margins, cycle times, and service quality.

Becoming the specialist-strategist that the market is looking for requires the construction of a “T-shaped” profile, in which solid vertical expertise (technical depth) serves as the basis for a horizontal vision (business strategy) that connects processes, metrics, and cross-functional collaboration. It is not about knowing a little about everything, but about mastering a discipline and knowing how to apply it in a broader business context.

Priority technical skills

A strategist’s credibility is based on deep technical expertise: without mastery of a discipline, strategy remains a theoretical exercise. Industry analyses converge in highlighting demand concentrated in specific areas:

• Data Science and AI, from data cleaning to applied models, to transform data into decisions.
• Cybersecurity, identity, posture, and governance to protect the business.
• Cloud Engineering, architecture, costs, and reliability to manage infrastructure.
• High-performance Digital Marketing, SEO, and Performance Advertising for customer acquisition.

Added to these is AI Literacy, the ability to use Artificial Intelligence tools in an informed and productive way.

Mastering one of these areas is a prerequisite for having a real impact. Strategic credibility starts here: a technical foundation that produces concrete value.

The business skills that make the difference

The real difference, what transforms a technician into a strategist, lies in cross-functional skills: understanding an income statement, communicating with other business lines, translating a technical activity into a performance indicator relevant to management (KPI). Skills such as problem solving, effective communication, and project management are no longer “soft” but become strategic skills.

This means knowing how to present the ROI of an SEO campaign in language that the CEO can understand, or explaining to the sales department how data collected online can generate new leads. Presenting an initiative as a cause-and-effect flow helps in decision-making: objective → technical levers → intermediate metrics → economic impact.

Translating technique into KPIs is what separates a good technician from a decisive professional. Some examples:

• SEO → Revenue: from share of voice to organic sessions, then qualified leads, closing rate, and attributed revenue.
• Data → Margins: from customer acquisition cost (CAC) to lifetime value (LTV), to the LTV/CAC ratio and payback period.
• Ops → Efficiency: from time-to-market to defect rate, to the impact on operating costs and customer satisfaction.

Continuous training: the only sustainable strategy

Tech work is no longer defined solely by what you know how to do today, but by how quickly you update your skills, whose expiration date is getting closer and closer.

Leading research converges on two findings: half of basic skills will need to be updated within a few years, and the most exposed domains (AI, data, security, cloud) require frequent learning cycles. According to the World Economic Forum, a technical skill today has a useful life of less than five years, which in areas such as AI falls to less than 18 months. In Italy, the Osservatorio del Politecnico di Milano translates this challenge into a need for retraining involving over 12 million workers, which is essential to manage the digital transition.

LinkedIn has found that over 40% of the skills most sought after by companies change on average every five years, and the OECD (Organisation for Economic Co-operation and Development) confirms that growing exposure to AI is shifting demand towards more complex skills, both technical and managerial. In Italy, Unioncamere-Excelsior data show that more than 63% of hires require widespread digital skills and 37% already involve innovative technologies.

What does this mean? It means that almost half of basic professional skills will need to be updated by 2027, and that the only real strategy for remaining competitive is a constant commitment to updating. The speed of change has made the concept of “one-off” training obsolete.

From “benefit” to driver of competitiveness

Every statistic on tech jobs tells the same story: skills quickly become obsolete and the gap between supply and demand remains wide. This means that a degree is no longer enough to guarantee you a career: the required standards change every 12-24 months; what makes the difference is your ability to continuously update your skills, with courses that combine practice, community, and metrics to assess progress.

Leading international institutions agree with this analysis. The OECD describes a “skills-first” paradigm, where in mature markets the value of formal qualifications is increasingly complemented by that of demonstrable skills in the field. In other words, it is not enough to say “I have a degree in computer engineering”; you have to show that you know how to use AI, cloud, and collaborative tools in real projects.

This perception is also shared by workers: a study by TalentLMS indicates that 61% consider upskilling and reskilling essential for their careers. Companies that invest in development programs, as LinkedIn notes, are able to adapt more quickly and retain talent. Gallup adds that over 70% of HR managers in large companies expect job replacements due to AI, but many employees say they still do not have access to structured training programs. It is a paradox that opens up enormous opportunities for those who decide to take action on their own: if you train before certain skills become standard, you gain a direct advantage in terms of employability.

We are moving towards a skills-based economy, where training is an integral part of a professional’s career path: the goal is not to accumulate certificates, but to build a coherent and applicable portfolio of skills to meet the needs of an ever-changing market.

What you know today will not be enough tomorrow: a practical model for your training

Investing in training generates a measurable return. Deloitte research shows that organizations that promote continuous learning not only innovate more (+92%), but also retain their talent better, with retention rates up to 50% higher. From the professional’s perspective, an analysis by McKinsey highlights how dedicating a few hours a week to training significantly increases the likelihood of promotions and salary increases. The study openly refers to the “imperative of upskilling,” emphasizing how companies that train their staff on a continuous cycle are better able to adapt to change and remain competitive.

Digital requirements are updated every quarter. The AI literacy that makes a difference today will become a basic requirement tomorrow, just as the use of the internet or Office software did in the past. This is why companies are looking for people who have internalized a mindset of continuous learning, not just specific skills.

A simple model to apply includes:

• 70-20-10: percentage of time to be devoted to field experience, discussion/mentoring, and formal training, respectively.
• Personal metrics: at least 12 hours/month devoted to study; 1 application project per quarter; 1 assessment/certification per semester.
• Portfolio: document cases, metrics, and impact; transform each course into a visible output (dashboard, playbook, demo).

The real difference is made by communities of practice, shared projects, and certifications that demonstrate the level achieved. The result is a consistent, measurable path that can be used in selection processes and internal evaluations. This is how updating becomes part of the job, not an isolated event.