General details
EDIHs involved
Challenges
Progradum S.L. is a Spanish company dedicated to the research, development, and implementation of innovative solutions in human resources and education. Its core focus lies in optimizing recruitment, training, and internal mobility processes through a digital platform that integrates modules for CV management, hard and soft skills assessment, and video interviews. With a strong foundation in social sciences and education, Progradum supports organizations in streamlining talent management while promoting data-driven decision-making.
Its main challenge was to incorporate disruptive technologies into its business model, particularly in its human resources management platform, by leveraging artificial intelligence to optimise candidate profile analysis and job offer evaluation.
Solutions
Thanks to its participation in DIGIS3, PROGRADUM initially accessed the services Mentoring for digital transformation of enterprises and Basic advice on DETs within Service Group 2 (SG2: Testing Before Investing). During this phase, a company diagnosis was conducted, identifying its digitalisation needs and establishing an action plan for the adoption of AI in its processes. Work sessions were held to evaluate different technological models, prioritizing those that best aligned with its automation and process optimisation goals.
After this initial phase, PROGRADUM moved on to the testing stage with a focus on experimentation, also within SG2, where the technical feasibility of the solution was validated. During this stage, an AI-based prototype was developed to assist humans in making objective decision-making by analysing résumés and supporting the classification of candidates based on their suitability for different positions. Iterative testing was conducted to ensure the system's effectiveness, and a REST API was implemented to facilitate integration with the company’s digital platform.
Results and Benefits
The result of this process has been the creation and validation of an innovative solution that enhances efficiency in human resources management. PROGRADUM has successfully advanced in its digital transformation and strengthened its position as a more competitive company, with a system that reduces recruitment time and improves the accuracy of candidate evaluations.
Qualitatively, this solution has strengthened PROGRADUM’s position as a competitive and technologically advanced company in the consulting sector. The integration of AI into its business model has not only streamlined internal operations but also provided a scalable and adaptable tool that can be further developed to meet evolving market needs.
This success story demonstrates how access to expert guidance and technological experimentation within DIGIS3 can help SMEs adopt emerging technologies, positioning themselves better in the market and optimising their key processes.
Perceived social/economic impact
The implementation of this AI-based human resources management solution could generate a significant social and economic impact, both within the company and across the broader labour market. By streamlining recruitment processes and enhancing candidate evaluation accuracy, the solution could contribute to a more efficient and fairer job market, reducing biases and improving employment opportunities for a wider range of applicants.
One of the most noticeable economic benefits might be an increase in hiring efficiency, leading to lower recruitment costs for businesses. Companies using PROGRADUM’s platform could potentially fill vacancies faster, reducing the financial burden associated with lengthy recruitment processes. Additionally, by ensuring better candidate-job matches, employee retention rates could improve.
On a social level, the AI-driven evaluation system could help mitigate unconscious bias in recruitment by focusing on objective criteria rather than subjective human judgement. This would be particularly beneficial for underrepresented groups in the workforce, promoting a more inclusive hiring approach. The increased transparency in candidate selection could foster greater trust in recruitment processes, benefiting both job seekers and employers.
In the long term, this case could exemplify how digital transformation in human resources might lead to wider societal benefits, including a more dynamic labour market, enhanced employability for job seekers, and increased business resilience in adapting to changing workforce demands.
Measurable data
As a result of the support provided to PROGRADUM, the following actions have been undertaken with various companies:
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Training delivery: Conducted training sessions for the HR teams of four IBEX 35 companies on how to effectively use the PROGRADUM tool.
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Pilot project support: Provided training and ongoing support for a three-month pilot with a company specializing in candidate selection.
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Collaboration agreements: Initiated verbal collaboration proposals with two IBEX 35 companies and a Spanish technology consultancy, to be formalised upon the first signed contract, aimed at supporting the implementation of the tool.
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Product improvements: Integrated functionalities and enhancements based on feedback and requests from three IBEX 35 companies, reflecting a responsive and user-driven development approach.
DMA score and results - Stage 0
The average DMA questionnaire results, obtained at T0, show that PROGRADUM was at the early stages of its digital transformation (11%), with significant potential for improvement through targeted investments in digital technologies and skills. Then, digital investments focused primarily on administrative tasks, and while they used mainstream technologies, they could benefit from more advanced solutions such as AI. Additionally, adopting ICT technologies could make operations more sustainable. There was considerable untapped potential, and embracing more digital technologies could immediately boost productivity. This result was obtained at T0 and reflected the organisation's performance in six areas: Digital Business Strategy, Digital Readiness, Human-centric Digitalisation, Data Governance, Automation and Artificial Intelligence, and Green Digitalisation.
DMA score and results – Stage 1
The average DMA questionnaire results, obtained at T1, show that PROGRADUM’s digital maturity has increased to 25% thanks to the services provided by DIGIS3. While the organisation was initially at the early stages of its digital transformation, it has made notable progress by focusing on strategic investments in digital technologies and skills, including experimenting with AI through a prototype developed with DIGIS3. Digital investments are still primarily concentrated on administrative tasks, but PROGRADUM is now in a position to explore more advanced solutions, including newer internet-based technologies (e-commerce, B2B, B2C, etc.). Regarding data management, the SME has a concrete policy in place for the storage, organisation, access, utilisation, and security of data, with data stored in a structured format and a degree of integration between IT systems. The company values data analysis for informed decision-making, operational efficiency, and customer service improvement. Additionally, it has a cybersecurity plan, measures for emergency situations, data backup provisions, and staff training on cyber threats. Nevertheless, there remains considerable untapped potential, and experimenting with and adopting further digital technologies could offer an immediate boost to the company’s productivity and overall outlook. Nonetheless, the SME has made significant progress, with its digital maturity increasing substantially, reflecting a much stronger foundation for future growth and innovation.
Lessons learned
Throughout the digital transformation process, several key lessons emerged that can serve as guidance for other SMEs and EDIHs undertaking similar initiatives.
What worked well:
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Step-by-step adoption of AI: breaking down the implementation into well-defined phases—diagnosis, experimentation, validation—allowed for a smooth transition and minimized operational disruptions.
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Iterative testing and user feedback: conducting multiple testing rounds ensured that the AI model was aligned with real HR management needs, leading to better performance and higher adoption rates.
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Seamless API integration: implementing a REST API facilitated a smooth connection with existing digital platforms, maximising the solution’s usability.
Challenges and what could be improved:
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Initial data preparation: to maximise the effectiveness of the AI model, it is crucial to ensure high-quality, well-structured data from the outset. Focusing on data cleaning and standardisation at the start of the project will further optimise the results as the project progresses.
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Managing expectations around AI capabilities: AI can significantly transform HR processes, complementing human decision-making. Setting clear expectations from the beginning ensures that all stakeholders understand the role of AI and its benefits in the process.
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Scalability considerations: while the AI prototype performed well in initial testing, further adjustments are required to scale it effectively across larger datasets and multiple clients.
For future implementations, it is advisable for SMEs to prioritize data quality early in the process. By doing so, companies can maximize the benefits of digital transformation while minimizing risks and implementation challenges.
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