In enterprise projects where agentic AI is already in production, the breaking point is rarely the model. Far more often it is the boundary: no one has defined precisely which tasks the agent may carry out on its own, which it may only propose, who reviews the output, and against what criteria. The result is work that looks finished, fluent, delivered on time, but that nobody has actually checked.

The direction of travel is well known: before long, managers will coordinate not only people but also sets of specialized agents operating at varying degrees of autonomy. What is becoming strategically and operationally relevant is no longer simply who knows how to use this technology, but who knows how to define, delimit and verify the work the technology performs.

What a hybrid team actually is

In companies already working this way, activity tends to be distributed across three parallel layers:

• work that remains entirely human: relationships, negotiations, decisions on priorities;
  
  
• work delegated to agents and subject to human oversight;
• work carried out as a person–agent pair, where the two contributions are so closely intertwined that they become indistinguishable in the final output.

This configuration gives rise to roles that did not exist a few years ago: the person who supervises a fleet of agents across a process, the person who maintains the quality of the knowledge bases those agents draw on, the person who translates business objectives into executable operating instructions. These are hybrid roles by definition, because they require technical competence to understand what the system can do and functional competence to know what, in that specific context, it makes sense to have it do.

The technology is available to every company. The ability to give it the right boundaries is not.

How to set the level of autonomy assigned to an agent

Delegating to a colleague is a relational act, and a developmental one. Delegating to an agent is a design act: you delegate for speed and scale, and the level of autonomy has to be calibrated explicitly, task by task. The working criterion is usually graduated delegation, built on three variables:

1. Criticality: how much an error on that task costs;
2. Reversibility: how easily it can be put right;
3. Verifiability: how readily the quality of the output can be checked.

A task that is low-criticality and reversible can be entrusted with broad autonomy. A task that is critical, irreversible or hard to verify calls for close supervision, or should not be delegated at all.

This is where the question bears directly on how profiles are selected: calibrating these three variables is less a technology decision than a process decision. Establishing what an error costs in an accounting reconciliation, how reversible a change to a billing system is, or how verifiable a credit risk analysis is, all require knowledge of that process, that industry, that regulatory framework. A professional who has mastered the framework but not the application domain will tend to delegate either too much or too little, and in both cases the project suffers.

Why accountability is not delegated along with execution

With AI agents, one principle becomes more binding than it is with people: delegating execution does not transfer accountability. The EU AI Act, for high-risk systems, and Italian Law 132/2025 converge on the same safeguard of meaningful human oversight, and professional project management standards place governance and oversight among the founding principles of AI adoption.

The operational implication is concrete: whoever answers for the outcome is still a person, and that person must be able to reconstruct which data and which instructions produced it. Traceability, mandatory for high-risk systems, is sound management practice even where no formal obligation applies.

Calibrated trust and validation judgment

With people, trust is built over time and is, in part, an extension of credit. With an agent, it has to be proportionate to verified performance, domain by domain, and updated whenever the context changes.

The two failure modes are well documented and mirror each other. On one side, over-reliance: monitoring tapers off and outputs are accepted without checks, a risk that generative AI amplifies because it produces fluent, persuasive text by design. On the other, algorithm aversion: after a single visible error the tool is abandoned, work returns to manual, and tangible benefits are given up. Organizations tend to fall into both at the same time, on different processes.

Correcting this asymmetry calls for what might be termed validation judgment: the ability to reliably assess an output one did not produce oneself. It is a harder operation than producing the work directly, and it has one prerequisite that cannot be circumvented. You cannot validate a financial analysis without being able to read a balance sheet, and you cannot correct code without being able to program. Supervision requires subject-matter competence; it does not replace it.

For this reason, the choice of profiles in a hybrid team carries more weight than before, not less. Genuine seniority in the domain is what makes supervision possible. Without it, supervision becomes a signature on a document nobody has really read.

The skills required

At ProActivity we work with a smart team augmentation methodology: experienced professionals who govern and orchestrate intelligent agents on specific processes.

In selection, alongside technical depth, we therefore also assess a set of factors that includes:

• Contextualized functional experience. A Data Engineer with ten years’ experience is an excellent profile on paper, but in a financial services firm or a high-compliance environment the value comes from someone who has already worked under those constraints, and who therefore knows which activities, within that perimeter, should not be handed over to an automated system.
• The ability to break an objective down into verifiable instructions: the operational version of the same competence that makes a person effective at directing the work of others.
• The ability to spot an error inside a plausible output. This is the hardest quality to detect, and it is the reason our selection process brings together specialized recruiters and senior technical experts from the relevant field: on this point, peer assessment works better than any checklist.

Conclusions

In hybrid teams the differentiator shifts. Technical skills remain the prerequisite, but the value is generated by functional and transversal ones: knowing the client’s processes, understanding the regulatory constraints of the sector, communicating with structure and clarity, exercising critical judgment on a result produced by someone else, whether human or artificial. These are the capabilities that make it possible to set the boundaries, not merely to execute a task inside boundaries someone else has already drawn.

This reading is what ProActivity has built its approach on. What sets it apart from traditional staff augmentation models is understanding the client’s strategic objective before the search begins. The matching process therefore weighs technical skills, functional experience, organizational integration and an understanding of the specific context in which the professional will be called on to operate, all at the same time. Profiles are not simply supplied: they are managed throughout the entire engagement, with active oversight of the quality of delivery.

To this is added the variable that rarely appears in business cases and that often decides the outcome of a program: time. The strategic indicator today is not only the cost of skills, but the speed at which a company can access them. In this sense ProActivity acts as a multiplier of organizational capacity, reducing the time required to move from planning to execution.

The principle is the same one that guides the whole Fortitude Group: digital sovereignty is not about controlling the infrastructure, but about the ability to govern your own innovation path independently. At the point where a growing share of the work is performed by agents, that ability comes down to the competence of the people who decide what the agents should and should not do.

For companies building teams of this kind, the ProActivity team is available to assess together which skills are needed, and on what timeline. Get in touch!

L'articolo Hybrid Teams: The Technical and Functional Skills Needed to Govern the Work of AI Agents<br/><br/> proviene da ProActivity.

A flexible staffing model allows organizations to overcome typical healthcare IT bottlenecks, ensuring operational agility capable of contracting development and integration timelines. On the financial plane, as highlighted by analyses from MBO Partners, the targeted engagement of contractors generates an optimization of fixed costs between 20% and 30% compared to the direct hiring of equivalent internal roles. Finally, embedding freelancers to support internal staff does not outsource innovation capacity; rather, it allows technological knowledge to be retained and structured directly within the enterprise.

Many Deadlines, Few Available Skills

Hospitals, local health authorities (ASLs), private clinics, and biomedical companies are simultaneously tackling the digitization of clinical processes, the opening of new service channels for patients, and the modernization of infrastructure. These projects vary in scope, but share a common thread: a firm delivery date and a highly specific technical perimeter.

In Italy, digital healthcare spending reached 2.7 billion euros in 2025, representing a 9% year-over-year growth (Digital Health Observatory, Politecnico di Milano).

With the completion of the PNRR infrastructure targets in August 2026 — which saw the adaptation of the Health Data Ecosystem (EDS) for the Electronic Health Record (FSE 2.0) and the full operation of the National Telemedicine Platform (PNT) — the challenge for the healthcare sector shifts to consolidating the results achieved and evolving toward European regulatory scenarios.

The strategic objective for the second half of 2026 and the coming years requires a triple leap in quality:

1. Full semantic interoperability: moving from simple document compliance to a true integration of native clinical data based on the Implementation Guidelines for the FSE 2.0 (Department for Digital Transformation / Ministry of Health)
2. Data governance and reuse for research: structuring databases in compliance with the new European regulatory framework of Regulation (EU) 2025/327 on the European Health Data Space (EHDS)
3. Full deployment of telemonitoring: ensuring operational stability, clinical integration, and the sustainability of community services activated according to the guidelines and specifications of the digital health agency on the National Telemedicine Platform Portal (AGENAS)

What is missing is neither funds nor deadlines, written down in black and white. A shortage of skills is reported by 32% of healthcare organizations as a barrier to digital innovation, while 73% of general directors note insufficient internal digital skills to sustainably support the adoption of new technologies (Digital Health Observatory, Politecnico di Milano). A facility can have an approved budget and a scheduled deadline, yet have no one on staff who has ever read a FHIR Implementation Guide.

Application Case: Clinical Interoperability and Telemedicine in a Healthcare Facility

Consider a hospital enterprise or an accredited group of private facilities that must align its systems with EHR 2.0 (FSE 2.0) and activate telemedicine services integrated with regional infrastructure.

Clinical interoperability represents the most demanding frontier. EHR 2.0 marks the transition from a document-driven logic to a data-driven logic, adopting the HL7-FHIR standard as the national benchmark to guarantee technical, syntactic, semantic, and organizational interoperability (Advenias). In practice, this means mapping existing Electronic Health Record data—produced by different vendors with proprietary data models stratified over fifteen or twenty years—onto the FHIR resources specified by national guides. The standard can be studied; the difficult part is understanding why two reports that appear identical on screen cannot be mapped in the exact same way.

Compliance and security are not a separate chapter of the project: they are what make it testable and deployable. Adaptation to sensitive data regulations, end-to-end encryption, access profiles, and consent traceability are paramount. Added to this are the NIS2 directive—which includes healthcare facilities among essential entities—and the Medical Device Regulation (MDR) when software falls within the definition of a medical device.

Telemedicine further requires dedicated patient portals and integration with IoT medical devices for monitoring chronic patients. The requirement that makes these implementations complex is not building them, but enabling them to communicate with regional infrastructure and the EHR instead of allowing them to function as isolated islands—which, historically, has been the most frequent outcome.

Cloud e data management, infine, completano il quadro: migrazione delle infrastrutture ospedaliere legacy verso ambienti cloud ibridi, con i vincoli di localizzazione e qualificazione previsti per i dati sanitari.

A medium-sized facility can rarely maintain all these competencies internally on a permanent basis: many are required intensively during the project lifecycle, and only residually once fully operational.

Performance Benefits of the Freelance Model in Healthcare

A developer who knows FHIR on paper but has never worked with real clinical data can potentially spend several months building context. Conversely, a professional who has already completed an EHR integration in another Region arrives with the right questions already formulated, recognizes mapping errors that would otherwise emerge only during integration testing with the national gateway, and knows in advance where the existing vendor’s data model will require negotiation rather than a technical modification.

Activation times also change by orders of magnitude: engaging a qualified freelance professional takes days, compared to months for a traditional hiring process. When a milestone is dictated by a PNRR timeline, this differential is not merely an optimization: it is the difference between meeting a deadline and missing it.

There is also a less visible benefit: IT professionals working across multiple clients bring with them integration patterns, reference architectures, and lessons learned from errors made elsewhere. In a domain where Implementation Guides evolve and regional practices diverge, that asset mitigates execution risk and elevates internal quality standards—from documentation structure to compliance testing procedures. The internal team retains ownership of the roadmap, while external professionals multiply its execution capacity.

Cost Savings for Healthcare Facilities

• Reduction of fixed costs: Market analyses indicate typical savings of 20-30% compared to the total corporate cost of an equivalent full-time employee, factoring in payroll taxes, benefits, recruiting, and training (MBO Partners). For private clinics, nursing homes (RSAs), and biomedical companies where the IT function is structurally undersized, targeted consulting replaces permanent headcount that would otherwise be unsustainable.
• Cost structure flexibility: Each professional is compensated for the actual duration of the need, with no downsizing costs at project completion.
• Avoidance of regulatory risk: This is financially the heaviest item and almost always absent from business cases. Failing to meet PNRR milestones jeopardizes funding, while non-compliant management of health data exposes organizations to GDPR penalties and reputational damage that, in healthcare, directly translates into a loss of patient trust.

Who Retains Knowledge When the Project Ends?

In the healthcare sector, the distinction between smart staff augmentation and IT outsourcing is clearer than elsewhere, because the consequences are long-lasting. Entrusting FHIR mapping and clinical data architecture entirely to an external supplier means placing the knowledge of how company data is structured, translated, and exposed outside the organization. Every subsequent evolution—a new telemedicine service, integration with a disease registry, or a request for secondary data use in research—will depend on whoever holds that knowledge.

The freelance model yields a different outcome, but only if engagement terms explicitly provide for it: structured knowledge transfer, pair working with internal IT staff, documentation delivered as a formal deliverable, and training for personnel who will maintain the system. These are clauses that must be written into the contract, not merely hoped for. When present, the organization reaches the deadline with compliant systems and an internal team fully capable of understanding, governing, and evolving them: knowledge enters the organization rather than leaving it.

This is also how a healthcare facility practically builds its digital sovereignty. This does not mean possessing every single skill in-house, but rather being able to decide its own technological path without structurally depending on those who built it.

The ProActivity Approach

ProActivity, a Fortitude group company, has built its model on this very logic. The difference compared to traditional staff augmentation lies not in the ability to source qualified professionals, but in understanding the client’s strategic goal even before initiating the search. The true operational need rarely coincides with the explicit request in a job description.

The matching process evaluates four dimensions simultaneously: technical skills, functional domain experience, organizational integration capacity, and understanding of the operational context. In healthcare, the latter is decisive. Someone who understands clinical workflows, the operational constraints of a ward, and the language of medical staff generates value long before someone who possesses only technical skills (and makes far fewer errors in requirement interpretation).

Finally, there remains the factor that most often determines the outcome of a transformation program, yet rarely appears in business cases: time. The strategic indicator is not merely the cost of skills, but the speed with which the organization gains access to them.

Conclusions

Speed of execution, economic sustainability, and system control are not conflicting objectives: with the correct engagement model, freelance professionals act as enablers and multipliers of internal skills, enabling healthcare facilities to meet regulatory deadlines without draining internal knowledge. The decisive variable is not which technology is chosen, but under what conditions the experts who know how to use it are brought on board.

Contact us for an assessment of specialized skills requirements for your digital health project.

L'articolo Healthcare Sector: The Impact of IT Freelancers on Timelines, Compliance, and Data Governance proviene da ProActivity.

What the Energy & Utilities Sector Requires

The energy transition is, to all intents and purposes, a digital transition.

The data outlines the context. The European Commission estimates that €584 billion in investment will be needed in electricity grids by 2030, of which around €170 billion is earmarked for the digitalization of distribution; the same Commission puts the direct annual savings for consumers generated by effective digitalization of the energy system at €71 billion (EU Action Plan “Digitizing the energy system,” European Commission).

The main constraint is neither technological nor financial, but one of human capital: the International Energy Agency documents that the availability of digital profiles within utilities remains largely insufficient, to the point of holding back digitalization investments themselves (IEA, 2024). Meanwhile, Pelion–ABI Research finds that 68% of energy sector leaders consider the lack of skills — both internal and external — the main obstacle to deploying digital technologies (Energy Live News, 2026).

Use Case: Digital Platforms for Renewable Energy Communities

Renewable energy communities are one of the fastest-growing segments of the Italian market: as of January 31, 2026, 1,839 configurations were active for 178 MW installed, with projections exceeding 3,500 RECs by year-end; businesses already account for around 35% of connected users (Osservatorio CER Italia).

Every energy community relies on a software infrastructure: metering and incentive-allocation platforms, integrations with GSE systems, and monitoring and member-management tools. A multi-utility company aiming to establish itself in this market with a proprietary platform must quickly assemble a defined set of skills, including: cloud architecture, data engineering for metering data flows and GSE interfaces, application development, and cybersecurity — all needed to meet the requirements of the recent NIS2 directive, which classifies utilities as essential entities.

Several sourcing options are available, each with different implications:

1. Direct hiring: does internalize skills, but search times are measured in months and are incompatible with the market window.
2. Full outsourcing to a system integrator: reduces time-to-market, but once the contract ends, knowledge of the architecture, data, and business logic remains outside the company; every subsequent evolution also creates renewed dependency on the vendor.
3. Smart team augmentation based on freelancers with IT and functional skills: professionals selected for their mix of technical skills and domain experience, integrated into the client’s internal team.

What Benefits Do Freelance IT Professionals Generate?

The differential value lies in the contextualization of skills. A data engineer with prior experience in metering data and GSE practices will most likely be productive from the very first sprint, without the months typically needed to understand the domain; a product owner familiar with ARERA resolutions prevents regulatory rework that would otherwise affect the release plan.

Activation times, moreover, shift by an order of magnitude: engaging a qualified freelance professional takes days, compared to the months often required by an internal hiring process.

Team composition also becomes modular: skills can be scaled up or down depending on the project phase, without tying planning to the availability of internal headcount.

Alongside these direct benefits, there is an indirect one that is often underestimated: professionals working across multiple projects and clients bring with them reference practices and architectures already proven elsewhere. This experiential asset reduces execution risk — technology choices are validated against real-world cases, not just vendor documentation — and raises the standard of internal practices, from how data pipelines are set up to the quality of documentation. The internal team retains ownership of the roadmap and architectural decisions; external professionals multiply its execution capacity and accelerate its technical maturity.

The economic benefit, finally, operates on three main levels:

• Direct cost: market analyses indicate typical savings of 20–30% compared to the total company cost of an equivalent employee, factoring in social contributions, benefits, recruiting, and training (MBO Partners).
• Cost structure flexibility: skills required only during specific project phases — initial architecture, pre-release security hardening — are compensated only for the actual duration of need, with no downsizing costs at project close.
• Avoided opportunity cost: in a market where active RECs could nearly double within twelve months, every month of delay in go-live translates into market share ceded to competitors. This is the most significant economic factor — and the one least present in traditional business cases.

The decisive distinction from outsourcing lies in the engagement conditions. When the contract includes structured knowledge transfer, pair working with the internal team, documentation as a deliverable, and training for the people who will take ownership of the platform, the freelance professional acts as an enabler and multiplier of internal capabilities.

The outcome, in the case analyzed, is twofold: an REC platform operational within the planned timeline, and an internal team capable of understanding, governing, and evolving it independently.

The ProActivity Approach

ProActivity, a company of the Fortitude Group, has built its model precisely on this logic. The difference compared to traditional staff augmentation lies not in the ability to identify qualified professionals, but in understanding the client’s strategic objective before the search even begins: when a company launches a transformation project, the real need rarely coincides with what’s explicitly stated in the job description. Behind the search for a cloud architect there may be an infrastructure migration to accelerate; behind the search for a product owner, the goal of reducing time-to-market for new digital services.

For this reason, the matching process evaluates four dimensions simultaneously: technical skills, functional experience in the sector, organizational integration capability, and understanding of the specific context in which the professional will operate. Added to these is the factor that most often determines the success of a transformation program and rarely appears in business cases: time. The strategic indicator is not just the cost of the skills, but the speed with which the company can access them.

The result is what is described in the utilities case: a reduction in the time needed to move from planning to execution, and a higher probability of turning technology investments into results, with professionals acting as multipliers of the client’s organizational capability.

The Energy & Utilities sector case shows that performance and economic efficiency are not conflicting goals: with the right engagement model, they reinforce each other. Project success does not depend solely on the technology chosen, but on the ability to put the right people in a position to generate value from day one.

Contact us for an assessment of the vertical tech skills your specific digital project needs.

L'articolo Digital Technology and the Energy Sector: The Impact of Freelance IT Professionals on Time, Costs, and ROI proviene da ProActivity.

In such a heavily regulated ecosystem, we can identify what might be called the paradox of digital transformation: increased budget allocation does not guarantee project success. The bottleneck is almost never the technology chosen, but rather the availability of qualified human capital capable of operating and overseeing the tools implemented.

The data confirms this. According to the EU Joint Research Centre, over 50% of European companies struggle to find senior-level ICT specialists. In the financial sector specifically, a Gartner study identifies the shortage of qualified personnel as one of the industry’s main strategic challenges: more than half of financial leaders must urgently plan to bring in new IT and functional skills to close productivity gaps.

Even in Fintech, then, knowing how to write code is not enough: success requires mastery of the regulatory context and the dynamics of a high-compliance market, achieved through the field expertise that qualified professionals bring.

From Data Oversight to Innovation Control

This scenario touches directly on the core of digital sovereignty, understood today not merely as the geographic location of data, but as a company’s ability to autonomously govern its innovation path, without passively depending on third-party technologies and skills.

A financial institution may implement the most advanced cloud architectures or launch the integration of Artificial Intelligence solutions, but if it lacks, within its own ecosystem, professionals capable of understanding, adapting, and protecting these technologies in compliance with regulatory constraints, the investment risks remaining partially unused — with direct consequences for operational autonomy and data security.

Consider a banking institution that needs to update its core infrastructure to integrate new payment modules and Open Banking services, ensuring adherence to the technical requirements of PSD3 and the resilience requirements of the DORA regulation mentioned above.

The time window granted by supervisory authorities and stakeholders is narrow: a delay in time-to-market translates into regulatory penalties, higher operating costs, and lost market share.

The complexity of an initiative of this kind is twofold:

• On the technical side: vertical IT skills are needed in Cloud Architecture, API Management, and Data Engineering.
• On the organizational side: the initiative must cut across long-standing silos and rigid financial audit criteria.

A purely technical team is not enough: what’s needed are professionals with strong functional and cross-disciplinary skills, capable of engaging with the legal department, risk managers, and security officers to translate legal constraints into agile, compliant software architectures.

Beyond Traditional Staff Augmentation: Context-Oriented Matching

Traditional models of IT resource integration follow a quantitative, standardized logic: sourcing based on keywords and years of theoretical experience. The typical result is the placement of profiles that are technically flawless on paper, but often lack knowledge of the regulated banking context.

The gap becomes costly once the project is underway: communication friction, vetoes from control functions, and code rewrites for compliance issues not anticipated during the design phase.

The market, meanwhile, is moving in this direction: global Staff Augmentation will reach $857.2 billion by 2031 (CAGR of 13.2% — details here), with a shift toward skills-first strategies and contextualized functional expertise.

Engaging senior freelancers selected on functional and contextual grounds addresses these limitations directly:

• Contextual and compliance experience: the professional has already faced and resolved similar critical issues in comparable contexts and understands the operational dynamics and typical constraints of the domain in which they will operate.
• Enabling cross-functional skills: the resource has the negotiation and interfunctional communication capabilities needed to act as an operational bridge between technical teams and business and control functions.

When flexible staff augmentation is guided by this vision, the impact on project KPIs is measurable:

• Time-to-Market: bringing in profiles with specific functional skills reduces digital platform release times by up to 40% compared to initial estimates, defusing regulatory bottlenecks.
• Immediate productivity (zero ramp-up): against a traditional time-to-hire for IT roles that often stretches over several weeks, onboarding specialized freelancers eliminates internal regulatory training time, ensuring operational readiness from day one.
• TCO (Total Cost of Ownership): an agile model based on targeted skills reduces the total operating cost of IT projects by between 40% and 60% compared to large-scale consulting structures or permanent hiring — whose replacement cost for a senior tech profile can reach 6–9 months’ salary, according to SHRM.

The ProActivity Approach

In this scenario, ProActivity does more than simply supply talent: it works as an extension of the client’s organizational capability.

The starting point is a simple observation: often, the strategic indicator for a company is not the nominal cost of the resource, but the speed of access to the right functional expertise.
Reducing the time between strategic planning and operational execution is precisely what turns technology investments into real value.

We work to translate our clients’ needs into concrete results through a synergistic approach that begins with a deep understanding of the business. We do not limit ourselves to a simple reading of the job description: we closely analyze the operational context, regulatory constraints, and the real strategic purpose of each project, with the goal of identifying the ideal lever to help achieve business objectives in the shortest possible time and with maximum efficiency.

This vision is paired with precision matching, which identifies the most suitable professionals by cross-referencing their vertical technical skills with sector-specific functional experience, the ability to integrate quickly into the client’s organizational culture, and solid command of compliance regulations.

Conclusions

Technologies are increasingly becoming commodities, while contextualized functional skills are not. This is precisely where competitive advantage is won: in the ability to rapidly assemble the right teams around business challenges, ahead of regulatory deadlines.

ProActivity stands alongside companies as a partner able to amplify internal capabilities, accelerate project execution, and support growth objectives in the era of Open Finance.

To learn more about our approach based on flexibility and functional skills, read the article “Fortitude Group’s Vision ” on our Blog, or contact our experts for dedicated consulting.

L'articolo Freelance IT Professionals in the Fintech Industry: The Value of Functional Skills Beyond Technical Expertise proviene da ProActivity.

An organization may own the most advanced cloud platform on the market, invest millions of euros in cybersecurity tools or implement latest-generation artificial intelligence solutions. However, without people capable of understanding, governing and extracting value from these technologies, the risk is that part of that investment remains unused or delivers results below expectations.

Technology, today, is increasingly accessible. The skills that make it competitive, on the other hand, are becoming the scarcest resource.

Recent research by the EU’s Joint Research Centre on the digital labour market shows that more than half of European companies struggle to find ICT specialists, while achieving the Digital Decade objectives will require significant growth in the number of qualified professionals in the coming years.

At the same time, projections indicate that Europe risks missing its 2030 targets for digital skills and ICT specialists, highlighting an increasingly marked mismatch between talent demand and supply.

This is not an issue that concerns HR departments alone. It is a problem that directly impacts companies’ ability to innovate, compete and grow.

The Digital Transformation Paradox: The Budget Is There, the Skills Are Not

Over the past five years we have witnessed a genuine acceleration of investment in digitalization. Cloud, artificial intelligence, advanced data analytics, cybersecurity and automation have become central elements in the industrial plans of organizations across every sector. Yet, despite growing budgets, many projects continue to experience delays, scope revisions or results below expectations — and the cause is not necessarily technological.

Imagine a large manufacturing company deciding to implement a generative AI platform to improve its document management and knowledge management processes. The technology is selected, the business case approved and the budget allocated. A few months later, however, an unexpected problem emerges: there is a lack of professionals able to properly design the data architecture, integrate the solution with existing company systems and govern its adoption by users.

The technology is available. The investment is approved.But the project slows down because the human capital needed to turn that technology into value is missing.

The same happens when an organization decides to migrate to the cloud, strengthen its cybersecurity posture or implement new ERP platforms. Very often the real bottleneck is not the chosen technology, but the difficulty of quickly finding professionals who simultaneously possess advanced technical skills, project experience and knowledge of the business context.

Companies Are Looking for Acceleration

In a scenario marked by a growing shortage of specialized talent, the traditional concept of staff augmentation is increasingly showing its limits.

For years, the market reasoned in terms of “finding the resource”. A position was identified, a set of technical skills defined and a selection process launched to identify the most suitable candidate.

Today this approach is no longer sufficient: the organizations leading digital transformation are not simply looking for competent professionals — they are looking for the ability to accelerate their strategic programs. The difference may seem semantic, but it is in fact profoundly operational.
A Data Engineer with ten years of experience may be an excellent profile on paper. However, without prior experience in regulated environments, they will struggle to generate immediate value within a financial institution or an organization operating in high-compliance sectors.

Technical skills are essential, but on their own they are not enough.

The Real Value: A Mix of Technical and Functional Skills

One of the most frequent mistakes in selecting digital talent is focusing exclusively on certifications, known technologies or years of experience when, in reality, in enterprise projects success is often determined by less visible factors.

A professional who knows a technology perfectly but does not understand the client’s business processes will inevitably need more time to become truly effective; by contrast, a professional who combines technical experience, industry knowledge and interpersonal skills can contribute to the project from the very first days.

Think, for example, of an energy company developing a new digital platform for the management of energy communities. In a scenario of this kind, a software developer alone is not enough. What is needed are professionals who simultaneously understand the technological aspects, the regulatory dynamics of the energy sector, the operational processes and the needs of the stakeholders involved.

This is precisely why the most advanced organizations are progressively moving from a logic of searching for resources to a logic of searching for contextualized skills.

From Talent Provider to Skills Accelerator

It is in this context that ProActivity has built its approach: the difference from traditional models does not lie simply in the ability to identify qualified professionals, but in the commitment to deeply understand the client’s strategic objective before the search even begins.

When a company launches a digital transformation project, the real need rarely coincides with the explicit request in the job description.

Behind the search for a Cloud Architect there may be the need to accelerate an infrastructure migration. Behind the search for a Product Owner there may be the goal of reducing the time-to-market of new digital services. Behind the search for an AI specialist there may be the ambition to build new competitive capabilities using artificial intelligence.

Understanding this strategic dimension makes it possible to identify not simply the available professional, but the professional truly able to contribute to the result. For this reason, the matching process adopted by ProActivity simultaneously considers technical skills, functional experience, organizational integration capabilities and understanding of the specific context in which the professional will be called upon to operate.

There is one element that rarely appears in business cases but often determines the success or failure of a transformation program: time.

This is why the real strategic indicator today is not just the cost of skills, but the speed with which a company can access the skills it needs.

In this sense, ProActivity acts as a true multiplier of organizational capability. Not because it replaces internal skills, but because it enables companies to integrate and strengthen them rapidly through professionals selected for a specific business context.

The result is a reduction in the time needed to move from planning to execution and a greater likelihood of turning technology investments into tangible results.

Conclusion

In a market where technologies are increasingly accessible but skills increasingly hard to find, the real competitive advantage lies in the ability to quickly build the right teams around business challenges.

This is where ProActivity generates value. We do not limit ourselves to identifying professionals with solid technical skills: we select talent able to integrate into the client’s specific context, understand its operational dynamics and contribute concretely to the achievement of project objectives.

Because today the success of a project does not depend solely on the technology chosen, but on the ability to put the right people in the position to generate value from day one. Along this path, ProActivity positions itself as a strategic partner, able to accelerate execution, amplify internal skills and support organizations in achieving their growth and innovation goals.

Contact us to learn more about our approach and discover how we can help you.

L'articolo Accelerating Innovation Through Functional Expertise proviene da ProActivity.

And it is, unfortunately, precisely on the skills front that companies today face their most tangible obstacles: senior profiles that are hard to find, selection timelines that keep stretching, technologies that evolve faster than traditional hiring processes can keep pace with.

On top of this, there are often structural risks: rigid teams that cannot absorb workload spikes; fragmented vendors that multiply management costs and dilute accountability; regulatory frameworks that impose compliance standards difficult to uphold with traditional staffing processes; dependencies on key individuals who, when they leave, put entire programmes at risk.

Without a solid model for governing human capital, innovation stays on paper.

In this article we set out our perspective, starting from the vision of Fortitude Group, which — through ProActivity, its sister company Bitrock, and its portfolio of proprietary products — brings talent management within the perimeter of digital sovereignty and sustainable innovation.

Digital Sovereignty as a Competitive Advantage

Fortitude Group works with companies that want to govern today’s technological complexity and convert innovation into a strategic asset under their own control. The working horizon is clear: to reach a model in which technological innovation and business move together under a single principle of digital sovereignty.

The Group’s offering rests on three pillars:

• Applied AI Services that combine agentic AI, technical and functional expertise, and a delivery architecture that governs the entire stack. The premise is that AI can automate complex end-to-end processes only when the underlying infrastructure — and the teams that govern it — are agile, secure, and scalable.
• AI Ecosystem Governance is the level at which the Group orchestrates the full spectrum of Artificial Intelligence technologies through models that enable their integration into business processes in a secure, scalable, and transparent way. Proprietary technologies such as Radicalbit and Waterstream combine with certified enterprise-grade expertise to cover the entire stack.
• Leadership in technological sovereignty is the principle that guides the first two. Ensuring that intellectual property, data, and infrastructure remain under the client’s control is not an ideological choice: in a market where digital autonomy has become a competitive asset, it represents a condition of business resilience and success.

AI-ready Data Ecosystem: the Operational Response

These pillars take concrete form in what we define as the AI-ready Data Ecosystem: an integrated end-to-end chain that accompanies data from its origin to the moment it becomes a decision, through all the intermediate layers — ingestion, transformation, modelling, serving, observability, interface — without any governance gaps.

The operating model is born from the complementarity between the Group’s different souls. Bitrock brings high-end consulting and the ability to design mission-critical enterprise architectures. Waterstream, an MQTT broker, simplifies large-scale IoT data integration — a layer that is typically overlooked yet decisive in industrial projects. The Radicalbit platform governs the lifecycle of LLM and ML models in production, while the AI Gateway that sits between applications and models is capable of ensuring data masking, guardrails, and a complete audit trail.

Within this framework sits ProActivity, which enhances the entire stack through the smart team augmentation methodology, whereby expert professionals govern and orchestrate intelligent agents to optimise specific processes.

ProActivity: the Human Capital that Delivers Fortitude’s Vision

This is where human capital stops being a separate layer from technology and becomes the condition that makes its industrialisation possible.

Today’s challenges cannot be solved by a single skill set, however specialised. What is needed is proprietary products, strategic consulting, teams capable of scaling quickly, and a vision that holds all these components together coherently.

The point is to give companies back the ability to decide what to invest in, where to allocate data, how to measure AI ROI and — an aspect that tends to be underestimated — which skills to bring on board and on what timeline. To innovate without surrendering ownership of one’s decisions, even when those decisions concern people.

In this context, ProActivity has a precise role within Fortitude Group: to provide enterprise companies with the human capital needed to turn the Group’s vision into complete projects.

As a staff augmentation company focused on vertical IT freelancers, ProActivity delivers the expertise required by the enterprise context with the flexibility that a constantly evolving market demands. We work alongside companies where scalability, time-to-market, and mission criticality are not aspirations but constraints, supporting clients from covering temporary peaks to delivering turnkey projects.

A number of distinctive features allow us to translate the Group’s vision into tangible value for the enterprise context.

The first is the vertical quality of profiles. We draw on a database of verified freelancers, continuously expanded through a selection process conducted by specialist recruiters working alongside senior technicians from each area of expertise. We cover the entire technology spectrum required by an AI-ready Data Ecosystem — back-end, front-end and mobile, UX/UI, Data & AI, application maintenance, legacy technology support — with an average time of 12 days from opening a position to signing the contract. Compliance with reference frameworks is embedded in the process, not added as an afterthought.

A second strategic element is our industrial management approach: we do not simply supply profiles but manage them throughout the entire duration of the engagement, overseeing performance quality and reducing client-side management costs through integrated processes.

The principle remains the one that guides the entire Group: technology must integrate with the business, not the other way around. And, in our field experience, expert professionals — their quality, their onboarding at the right time, the way they collaborate with agents — are what separates declared innovation from innovation that is actually delivered.

Contact us for an initial session with our specialists.

L'articolo The Human Capital of Innovation: Fortitude Group’s Vision and the Role of ProActivity proviene da ProActivity.

In most cases, the breaking point isn’t the technology. The models are powerful, the platforms are mature, the software offering is broad and accessible. The breaking point is functional, and it’s almost always the same: the tool gets adopted before the problem, the priorities, and the real implementation context have been defined. In other words, AI gets treated as a product to plug in, rather than as a capability to build. The result, predictably, is a proliferation of technologically advanced tools and initiatives that remain organizationally sterile.

In this article, we want to look at why conscious management and control of AI are non-negotiable conditions for successful adoption, and how ProActivity — through its highly qualified freelance professionals with both technical and cross-functional skills — can support you in managing and optimizing your AI infrastructure.

How to Govern AI and Make It Efficient

There’s a substantial difference between getting a tool to work and optimizing a business process, and it’s one of the main factors that separates organizations that generate returns from AI from those that bear it as a cost center.

When this distinction isn’t actively managed, AI tends to produce a paradoxical effect. The company spends more without working better — in fact, the opposite happens: licenses pile up, subscriptions grow, inference costs become a significant line item on the balance sheet. And operational improvement stays marginal.

To produce value, the AI question needs to be flipped on its head: the starting point can’t be the model, it has to be the process. The real question to ask is: “which concrete business challenge can we tackle in a new way thanks to this tool?”

Without this shift in approach, the investment in AI remains a demonstrative exercise, disconnected from the P&L. This is where a company’s maturity gets measured: in the ability to read AI as a tool to be managed and leveraged for defined purposes, not as an end in itself.

Governing AI means treating it as an ecosystem, not as a series of isolated tools. It means defining who is responsible for what, where human oversight is needed and where it isn’t, which processes should be automated and which still require human judgment. It means building a clear perimeter within which AI operates reliably, monitorably, and — above all — in alignment with business objectives.

This is where the functional expertise comes in that ProActivity brings through the experienced professionals it places on client projects. These resources don’t just implement the technology and AI tools — they’re able to govern and manage them in a way that’s optimal and aligned with the value proposition of the business they’re working in. Through this methodological approach, AI shifts from being a cost to becoming a genuine strategic lever.

Translating the Algorithm into an Operational Tool

Within AI projects, there’s a role that’s still underrecognized but decisive: that of the translator — not linguistic, but conceptual. It’s the figure capable of mediating between the language of the model and the language of the business, between algorithmic complexity and the day-to-day operations of those who will use or manage that model.

Without this function, AI remains a black box. Functional leads find themselves facing outputs they don’t know how to evaluate: they either trust them blindly, or they don’t trust them at all. The human oversight that gets talked about so much today begins precisely here — not as manual control of every output, which would paralyze workflows, but as the ability to understand what’s being delegated to the machine, with what margin of error, and with what process implications.

Translating the algorithm means making technical metrics understandable, surfacing the model’s limits, building interfaces and flows in which the user knows when to trust and when to dig deeper. Translating the algorithm means making technical metrics understandable, surfacing the model’s limits, building interfaces and flows in which the user knows when to trust and when to dig deeper. It’s work made of questions as much as of code: what happens if the model gets it wrong? Who notices? Which decisions are reversible and which aren’t?

It’s work made of questions as much as of code: what happens if the model gets it wrong? Who notices? Which decisions are reversible and which aren’t?
These are questions of method, before they’re questions of technology — questions that, if ignored at the adoption stage, resurface later as operational crises.

The Role of the Freelancer in the AI Infrastructure

In this scenario, the freelance professional takes on a strategic role very different from the traditional one. They’re not a resource added to the team to cover a workload peak: they’re a competence that gets grafted into the organization to bring an approach that, internally, is often missing.

The reasons are structural. The pace at which AI evolves makes it inefficient, for many companies, to build complete internal teams across every technological vertical. The skills that are useful today may be obsolete in a few months, and the ones that will be needed tomorrow haven’t even been codified yet. In this context, the flexibility of on-demand talent isn’t a compromise relative to hiring — it’s a strategic choice to access up-to-date competencies, brought into the project at the exact moment they’re needed.

The high-level freelancer, within a well-governed AI infrastructure, doesn’t just write code. They act as an interpreter between algorithmic complexity and the operational tools the company will actually have to use. They bring an external perspective, free from internal cultural constraints, but remain fully accountable for the outcome.

It’s this balance between methodological autonomy and accountability for the deliverable that defines the ProActivity model: senior profiles, selected for verified expertise, capable of entering a project and contributing to its direction from day one.

It’s not about replacing the internal structure, but completing it. The company keeps the knowledge of its own business; the freelancer brings the knowledge of the state of the art. The two, combined, generate a level of execution that’s hard to reach natively — and that makes it possible to tackle the complexity of AI projects without weighing down the fixed-cost structure.

Conclusion

The debate on AI has so far been dominated by the technological dimension. We’ve discussed models, parameters, benchmarks, adoption speed. It’s a legitimate conversation, but an incomplete one. Because what separates the companies that draw value from AI from those that simply bear its costs isn’t the available technology — by now largely the same for everyone — but the ability to put it at the service of a clear strategic design.

From a consulting perspective, this is exactly the point of the AI-ready Data Ecosystem proposed by Fortitude Group: a technical, organizational, and cultural infrastructure designed so that the company can govern its own digital evolution autonomously. The stack, in this vision, is a resilient and scalable system, in which every layer has its own human oversight and in which the right resources, in the right places, orchestrate intelligent agents on specific processes.

Translating this design into practice requires competencies that aren’t found in manuals, nor formed entirely inside companies. You need people who have already seen what works and what doesn’t, who know how to distinguish sterile use cases from high-impact ones, who can turn a business need into a sustainable architecture. You need, in other words, applied experience and a consultative posture that integrates technical vision with process vision.

This is exactly the ground on which ProActivity was built, with its Smart Team Augmentation methodology: specialized minds that integrate into client contexts to govern and orchestrate intelligent agents, optimize specific processes, and maintain the coherence of the entire system. A way to move past the constraints of traditional structures and accelerate projects without weighing down their structural cost.

Innovation, today, is sustainable only when the right people are at the center of the transformation. Technology alone isn’t enough: it’s the method — and the quality of the people who apply it — that makes the difference.

Get in touch to learn more about our approach and book a tailored consultation.

L'articolo Beyond the Algorithm: Why True AI Success Also Depends on Functional Skills proviene da ProActivity.

On-demand talent is the key to scaling projects without the constraints of traditional structures. Providing highly qualified professionals means guaranteeing companies agility, vertical specialization, and a timely response to market evolutions.

AI: Several Skills, One Purpose

One of the primary challenges companies face today in the digital sphere is the integration of Artificial Intelligence (AI) tools and systems into their business processes.

AI integration is not a path without obstacles. Companies face crucial challenges that go beyond simple software installation. To optimize processes and mitigate risks, a mix of professionals is needed to handle the various aspects involved in AI adoption, including:

• Cost Management: Uncontrolled resource consumption can lead to runaway expenses. Specialized professionals are necessary to optimize model routing and implement intelligent caching systems, transforming AI into a measurable asset rather than a variable expense.

• Data and Security Risks: Every interaction with external providers can expose critical information outside the corporate perimeter. Security and governance experts are essential to establish dynamic guardrails and protect sensitive data.
• Regulatory Compliance: Regulations such as the EU AI Act and GDPR impose rigorous traceability and accountability on the models used. Professionals with a strategic vision ensure that every implementation is verifiable and compliant with standards.
• Technological Lock-in: Dependence on a single provider limits freedom of choice and long-term competitiveness. In this regard, AI Engineering experts help build solutions independent of big vendors, ensuring sovereignty over technological choices.

ProActivity serves as the strategic hub for companies requiring an impactful transition toward a data-driven economy, connecting the business landscape with an elite talent pool specialized in Data & AI Engineering.

Our value proposition lies in the functional ability to manage the entire technological value chain. Through a solid engineering foundation, the professionals in our network translate the abstraction of algorithms into advanced operational tools, capable of generating an immediate return on investment and stabilizing innovation within the company’s core processes.

The intervention of ProActivity thus allows Artificial Intelligence to evolve from a futuristic promise to a concrete pillar of corporate competitiveness. Through the synergistic integration of optimized Machine Learning models and scalable data architectures, companies acquire the functional capacity to process massive volumes of information to extract predictive insights and automate complex production flows. This transformation process is supported by a systemic vision that ensures not only the effectiveness of the single model but its perfect harmonization with business objectives and pre-existing infrastructures.
The depth of ProActivity’s freelance network ensures comprehensive coverage of the project lifecycle. Thanks to their mastery of MLOps frameworks and advanced cloud platforms, our experts build AI ecosystems that stand out for reliability, scalability, and long-term sustainability. This operational excellence guarantees effective governance and secure data management, transforming technological theory into a solid industrial reality capable of facing the market through results-driven innovation.

Human Excellence at the Heart of Fortitude’s AI Strategy

ProActivity’s commitment deeply reflects the approach of the Fortitude Group, which promotes a vision of Full Stack AI where innovation is real only when it is sustainable, reliable, and oriented toward business results. In this paradigm, technology alone is not enough: the functional efficiency of a tool depends intrinsically on the people who govern it.

ProActivity represents the strategic element that allows for the realization of this philosophy. We are convinced that cutting-edge tools require high-profile expertise to be truly effective. The mere adoption of AI is not sufficient; the goal is to master it and direct it toward business optimization.

In this framework, ProActivity acts as the bridge between technological innovation and practical execution. Through our network, clients do not just purchase a solution; they gain access to professionals capable of maximizing the potential of tools, guiding digital evolution with technical mastery that transforms theory into operational value.

Human intelligence and experience remain the beating heart of our model: by putting the right people at the center of the process, we enable companies to scale innovation with the security of being supported by a comprehensive strategic vision.

Strengthened by the stability of the Fortitude Group, ProActivity confirms itself as the point of reference for those who want to transform Artificial Intelligence into a real, solid, and lasting competitive advantage. Contact us for a personalized consultation.

L'articolo AI and Team Augmentation: Scaling IT Innovation proviene da ProActivity.

Valuable freelance IT professionals are often experts who have built their reputation on vertical and consolidated skills. Their survival in the market depends on continuous updating, results orientation, and the ability to bring the wealth of experience gained in previous contexts to each new one. This transfer of cross-sector know-how is one of the least measured but most real advantages of freelance hiring.

Yet many companies still approach freelance IT professionals reactively: they seek them out when an internal resource is lacking, hire them in emergencies, and treat them as a temporary fix. This is a mistake that not only fails to exploit the potential of the model but also risks generating inefficiencies and hidden costs greater than those of a traditional structure.

There is no single type of freelancing

It is essential to clarify one thing first: IT freelancing is not a monolith. In fact, there are profoundly different models, each with specific operational and cost implications.
Time & Material (T&M) is the most common model: the professional is hired at a daily or hourly rate, with maximum flexibility on volume. However, it requires solid internal governance to prevent it from becoming an uncontrolled cost.
Fixed Price guarantees predictable spending within a defined scope. It works well when requirements are stable (a condition that is not always verifiable in IT projects) and becomes critical in the event of scope creep.
The embedded contractor is the most sophisticated model: the freelance professional is operationally integrated into the team, often full-time for the duration of the project, while maintaining their contractual independence. This is often the best model for more senior roles, such as Tech Lead or Solution Architect, where in-depth knowledge of the business context is a critical success factor.

The Advantages of IT Freelancing

The most common narrative about IT freelancing focuses on flexibility: this is correct, but reductive. The advantages of adopting this model are in fact varied and cover several areas, including:

Scalability without organizational trauma. Software development cycles are not linear. A company that can scale its technical team smoothly (without initiating emergency hiring processes or managing redundancies) is, overall, a more resilient organization.

Immediate access to critical skills. The talent shortage is not cyclical: the EU estimates a deficit of 10 million digital experts by 2030. In the meantime, companies cannot afford the time required for traditional recruitment. At ProActivity, the average time between opening a position and signing a contract is only 12 days.

Reduction in structural costs. The daily rate of a senior freelance professional may seem high at first glance, but a fair comparison must include the costs that an employee generates beyond their salary (contributions, welfare, training, administrative management, periods of low productivity). When considering the total cost of ownership, the balance often tips in favor of freelancers.

The Governance Factor

There is one element that distinguishes companies that achieve excellent results from integrating freelancers from those that are disappointed: the governance of the relationship. A freelancer is not a “turnkey” employee. They require defined objectives, capable internal contacts, and real integration into the project context: treating them as an “outsider” (excluding them from strategic conversations and product knowledge) means wasting the contribution of a valuable professional.

In ProActivity selezioniamo freelance IT verificati non solo sulle competenze tecniche, ma anche sulle soft skill e At ProActivity, we select IT freelancers who are verified not only for their technical skills, but also for their soft skills and ability to integrate into our clients’ teams. Because the right profile is not just the most qualified one: it is the one that produces value in the specific context of your company.
Furthermore, our work does not end with a simple match. We support the freelancer throughout the collaboration (assisting them with operational, administrative, and relational issues) and, at the same time, we support the company in managing the relationship, acting as a point of reference for any critical issues that may arise during the project. This ongoing support transforms a one-off engagement into a structured and sustainable collaboration over time.

Want to build your IT team with the best specialized freelancers? Contact us and find out how ProActivity can support you in your next tech & digital project by providing you with the valuable resources you need.

L'articolo IT Freelancing: Models, Advantages, and Features for Companies That Want to Grow proviene da ProActivity.

Abroad, the market is dominated by large, established players that offer standardization and guarantees of scalability. In Italy, on the other hand, the landscape is still extremely fragmented, with dozens of small companies managing a limited number of collaborators. ProActivity aims to import the Anglo-Saxon platform model, focusing on greater service customization. The major players mainly offer a marketplace model, in which the customer chooses freelancers and then manages the relationship directly, with limited support from the platform. Our service model, on the other hand, requires the customer to deal with a single point of contact—ProActivity—which then manages all freelancers, simplifying the administrative complexity. Our goal is to overcome the current fragmentation of the market and become a major national player capable of offering companies the number of IT talents they need, quickly and in compliance with all regulations—labor, security, and other types.

In your opinion, which customers will be most interested in the ProActivity model?

Currently, our main contacts are system integrators and large companies, entities that manage huge volumes of projects and have a constant need for numerical and technical flexibility. These customers are finding it increasingly difficult to manage dozens of different micro-suppliers, so they are looking for a solid partner capable of providing large numbers of qualified freelancers within a reasonable time frame. Managing activities for specific projects also leads to situations where the number of employees is sometimes adequate, while at other times it is excessive. To avoid situations where the cost of staff not involved in projects is too high, it is therefore clear that having a certain proportion of your workforce flexible is advantageous. SMEs also represent an interesting market in which we want to expand more and more, facilitating access to high-profile skills and people who can help them in the technological transition processes they are facing.

What is the main problem in the Italian market that ProActivity wants to solve better than others?

I wouldn’t talk about a single problem, but rather a combination of factors that make it interesting and advantageous to use freelancers instead of hiring employees. Firstly, the instability caused by geopolitical or financial crises—which represent shocks to demand—forces companies to adapt to markets that contract or expand very quickly. In this context, freelancing is an excellent response, as it allows companies to shrink or expand their scope quickly and at low cost. Added to this is technological acceleration: experts on specific stacks are needed immediately, and internal reskilling is often too slow. Finally, companies often face temporary peaks in workload for which permanent hiring is not the right solution. ProActivity solves this complexity by offering immediate and scalable access to skills that companies do not want, cannot, or do not find convenient to internalize. Finally, let’s remember that more and more professionals (especially highly skilled ones) find it convenient, for the lifestyle they want to have, to work as freelancers rather than be subject to the rigid constraints of an employment contract.

L'articolo Interview with Leonardo Cullurà, General Director @ ProActivity proviene da ProActivity.