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Challenges and Opportunities in the AI Era

AI challenges and opportunities in the workplace
Reading Time: 3 minutes
Although the rapid development of AI, which has already been extensively analyzed and discussed, is not the only driver of change and disruption that companies will face in the future, it is undoubtedly proving to be a powerful accelerator in all digital spheres. The challenges and opportunities that an organization must address in the AI Era have increased swiftly, particularly in the realm of digital talent.
As artificial intelligence (AI) rapidly transforms the workplace, talent development leaders face unprecedented challenges. According to a LinkedIn study, it is estimated that by 2030, the skills required for many jobs will have evolved by 50% compared to 2016. This transformation, accelerated by generative AI, could push that figure up to 68%.
In this fast-paced environment, the question is not whether companies should adapt, but how to do so effectively.

The role of AI in the evolution of skills

The rise of AI in the workplace is changing not only the nature of tasks but also the skills that people need to develop. While technical skills used to have longer lifecycles, AI is shortening these cycles, forcing organizations to adopt a continuous learning approach. According to a McKinsey study, more than 70% of employees believe they will need to acquire new skills due to automation. Companies that fail to foster agile learning risk falling behind in this new era of competitiveness.

Building skills more rapidly

One of the main focuses is aligning personal ambition with career-driven learning. Employees want to learn, but it is crucial for organizations to help chart clear pathways where individual aspirations are linked to professional growth. This not only increases motivation but also drives business results. Deloitte highlights, in one of its analyses, that companies with strong learning cultures are better able to retain talent and improve productivity, two critical factors in times of rapid change.

Facilitating internal mobility

The concept of internal mobility continues to gain traction globally. Encouraging employees to move horizontally within an organization is key to retaining talent and preventing the loss of human capital. A LinkedIn study suggests that employees who experience internal mobility are 41% more likely to stay with the company. Organizations that create platforms where their employees can apply for internal roles, rather than solely looking for external talent, achieve better results and greater team engagement.

Measuring the success of talent development

A major challenge for talent development leaders is demonstrating the tangible value of learning. This is where the “proof of impact” comes into play. Establishing clear metrics, such as talent retention or response time to adapt to new technologies, allows companies to see how their efforts are contributing to strategic goals. According to the World Economic Forum, companies that measure and optimize their talent development programs see more significant improvements in employee satisfaction and retention.

Empowering leaders and promoting individual growth

Leadership also plays a key role in this process. Managers must champion talent development within the organization, creating spaces where teams feel supported and motivated. A Gallup study reveals that employees with managers who actively encourage their continuous development are twice as likely to feel engaged in their work. Additionally, aligning each employee’s personal growth goals with business opportunities is essential. When employees feel empowered, not only does their productivity increase, but they also contribute to innovation.
Many of these reflections were already critically important before the waves of continuous change we have been experiencing in recent years, but with the advent of AI in the workplace, their significance has only grown rapidly. This has forced organizations to delve deeper into the need to embrace these concepts, not just to grow, but in many cases to survive.
References:
 
Antonio Castaño

Autor Antonio Castaño Romero

Are You Worried about the Average Vacancy Cost? And the Rising Fixed Costs?

Average Vacancy Cost in IT hiring
Reading Time: 3 minutes

Discover six key points on how to control the Average Vacancy Cost in IT hiring talent without overloading your fixed cost structure.

At some point, we’ve all likely felt that our HR department isn’t as proactive as it should be when it comes to attracting and hiring the talent we need. Maybe we think they take too long and ask for too many confirmations. Why don’t they help us more instead of making things more complicated?
One of the answers surely lies in the minds of recruiters: the acronym AVC (Average Vacancy Cost). The term is self-explanatory, but to define it properly: it’s the indicator that measures the cost associated with filling a vacancy within an organization, including recruitment, selection, training, and more. If I were one of the HR leaders in my company, I would certainly be paying attention to it.
In the pharmaceutical sector, where innovation and efficiency are key to maintaining competitiveness, talent management is crucial. However, one challenge many companies face is this Average Vacancy Cost, particularly for hiring IT professionals.
If we add to this the approvals and processes required to increase fixed cost structures in any organization, attracting the talent we need when we need it becomes highly complicated, especially in the tech field.

How Does AVC Impact My Company?

Every time an IT position remains unfilled, not only are strategic projects delayed, but a series of indirect costs are also incurred:
  • Recruitment costs: Job postings on platforms, selection agencies, and time spent by HR teams.
  • Loss of productivity: While the vacancy remains unfilled, projects can be delayed or managed inefficiently.
  • Training costs: Every new employee requires time for onboarding and training.
In a context where technology evolves rapidly and time is critical, quickly and efficiently filling a vacancy is essential.

What Benefits Do You Gain from Working with Specialized IT Partners?

  1. Reduction of AVC: By partnering with specialized IT staffing agencies, it’s possible to significantly reduce the Average Vacancy Cost. These partners take on the responsibility for recruitment, training, and onboarding, freeing you from those costs and delays. With a network of pre-screened professionals ready to step in, you can access quality talent without long selection processes.
  2. Hiring agility: In an environment where IT projects often require highly specialized skills, partners with expertise in the Pharma and IT sectors can provide exactly the talent you need, when you need it. This reduces the time spent filling vacancies and ensures that projects don’t stall due to lack of personnel.
  3. Variable costs vs. fixed costs: Outsourcing the recruitment of IT professionals allows you to convert a fixed cost (salaries, benefits, etc.) into a variable cost that only arises when a vacancy needs to be filled. This is particularly useful for temporary projects or times when demand for personnel fluctuates. This way, you avoid burdening the company’s structure with permanent costs, improving financial flexibility.
  4. Access to specialized talent: Specialized partners have access to a pool of IT professionals with experience in the pharmaceutical sector, ensuring that candidates not only master the required technologies but also understand the nuances of the industry, such as managing sensitive data, regulatory compliance, and Pharma-specific software.
  5. Scalability and flexibility: As your company or projects grow, the need for more specialized profiles may vary. By working with a partner, you have the flexibility to scale your teams as needed, avoiding the rigidity of maintaining an oversized in-house team during low-demand periods.
  6. Risk mitigation: By delegating AVC to a partner, you minimize the risk of hiring the wrong candidate. Partners often offer guarantees if the profile doesn’t fully meet the vacancy’s needs, reducing the likelihood of repeating costly selection processes.
Outsourcing IT roles to specialized partners is a smart strategy to reduce the Average Vacancy Cost in IT hiring, streamline processes, and avoid overburdening your structure with fixed costs. In such a dynamic industry like pharmaceuticals, where the swift adoption of technology and flexibility are critical, this approach allows you to focus on what truly matters: innovation and growing your business.

References

  • Harvard Business Review – Many articles discuss the impact of outsourcing on organizational efficiency and the reduction of fixed costs.
  • Society for Human Resource Management (SHRM) – This organization frequently publishes studies on cost management in Human Resources, including recruitment and outsourcing.
  • Gartner – Reports from this consultancy often address cost optimization in IT and the benefits of working with external partners.
  • McKinsey & Company – Publishes studies on digital transformation and how to optimize organizational structures in industries such as pharmaceuticals.
 
Antonio Castaño

Autor Antonio Castaño Romero

Trends in attracting and retaining talent in IT/Pharma

Flexible compensation in IT for Pharma: A key strategy to attract and retain talent
Reading Time: 2 minutes
The importance of flexible compensation as a key tool for retaining and attracting talent is fundamental in the IT area, but especially important if we talk about IT within Pharma. Both within our organization and if we have an expert partner in talent, we must be aware of this fact.
The pharmaceutical sector, highly regulated and in constant technological evolution, demands IT talent that is capable of implementing, maintaining and improving critical systems for the research, development, production and distribution of pharmaceutical products. In this context, flexible compensation is a key tool to attract and retain the best IT professionals.
  1. Flexibility and customization: Flexible pay allows IT employees to tailor part of their compensation to their personal needs, thus improving their job satisfaction. For example, in an environment where continuous training is crucial, employees can allocate a portion of their gross salary to professional refresher courses at no additional cost to the company. This is especially relevant in the pharmaceutical industry, where technical knowledge and the ability to adapt to new technologies are essential.
  2. Tax benefits: By allowing certain expenses, such as transportation or health insurance, to be covered from gross salary, employees’ taxable income is reduced. This results in a lower tax burden and higher net disposable income, which is highly valued by IT staff, who often seek to maximize their total compensation in a competitive labor market…
  3. Impact on employer branding: Implementing a flexible compensation plan reinforces the company’s image as an employer that cares about the well-being of its employees. In the pharmaceutical sector, where competition for IT talent is intense, offering compensation that is tailored to employees’ personal needs can be instrumental in attracting the best candidates.
  4. Adapting to new realities: The pandemic accelerated digitization in the pharmaceutical industry, increasing the demand for specialized IT professionals. Flexible compensation allows companies to adapt to these new realities, offering benefits that fit the current circumstances, such as teleworking, which can be supported with technology and connectivity benefits.
  5. Reduced turnover and improved work climate: The use of flexible compensation can help reduce turnover in critical areas such as IT, where the loss of a key employee can have a significant impact on strategic projects. In addition, by improving the perception of the company as a place that cares about the well-being of its employees, a positive work climate is generated, which in turn improves productivity and innovation.
  6. Examples of successful implementation: Leading pharmaceutical companies (e.g. Novartis, Pfizer, Roche, Sanofi) have started to integrate flexible compensation plans as part of their strategy to attract IT talent. Others have also offered ongoing training programs funded from the employee’s gross salary, which not only reduces the tax burden, but also improves staff skills and knowledge, aligning with the latest technological innovations in the field.
  7. Future outlook: With increasing digitization and the adoption of key technologies such as artificial intelligence and big data, the demand for IT talent in the pharmaceutical industry will continue to rise. Companies that take a flexible and personalized approach to compensation will be better positioned to attract and retain the professionals they need to lead in this new technological era.
In summary, flexible compensation is not only an effective tool for improving IT employee compensation in the pharmaceutical industry, but also strengthens the company’s ability to attract and retain talent in a highly competitive and technologically advanced environment.

Referencias

  • Definitive guide to flexible remuneration .- Cobee (2024)
  • LinkedIn Talent Solutions Reports .- LinkedIn (2024)
  • Stack Overflow Developer Survey .- Stack Overflow (2024)
Antonio Castaño

Autor Antonio Castaño Romero

How to maintain a high-performing IT team within the Pharma sector, without dying in the attempt…

How to Manage IT Team Turnover in the Pharma Sector Effectively
Reading Time: 4 minutes

Maintaining a high-performing IT team within the pharmaceutical sector is a crucial challenge for organizations looking to innovate and stay competitive. How to achieve this, or at least how to be prepared to meet it?

Surely this is not the best time to ask questions that can destabilize us emotionally: enjoying the summer, rest and a well-deserved vacation is now the priority. But while we are immersed in it, having closed and reviewed the planning of the projects we are developing, and that we hope to resume on our return, the truth is that the world keeps turning.
And while it turns, new circumstances may appear that we will have to manage on our return: and surely among these possible surprises when we return to our current projects, one of the most feared is the one that has to do with the cursed word: job rotation.
If our area of work is related to the latest technologies such as Data Science, Big Data, Artificial Intelligence or the digitalization and IT environment in general, it is quite possible that we will find some surprises of this type after the first days of our return; but if we are also in the Pharma sector, it is almost likely that this could happen.
Is there a pattern to turnover in our area or sector? What factors make it more likely? How can I minimize the risk? Isn’t it true that turnover is actually healthy for high-performing teams, or at least a certain level of turnover?

Annual patterns

There is no study that uniformly identifies the specific months of the year when labor turnover is highest for all industries. However, there are some general trends that can be observed in both the IT and pharmaceutical industries.
General trends in IT: Turnover tends to increase at certain times of the year, particularly at the beginning of the year (January – March) and in the summer (June – August). This can be due to several factors, such as project completion, company budget restructuring at the beginning of the year, and staff turnover after receiving annual bonuses or meeting certain tenure periods.
Pharmaceuticals: In the pharmaceutical industry, variations in turnover can be influenced by new product development phases or regulatory processes. In this sector, turnover can also be influenced by major organizational changes, such as mergers or acquisitions, which tend to occur in specific cycles during the year, often related to financial and regulatory calendars.

Patterns linked to the evolution of the sector

IT turnover trends within Pharma: The introduction of new technologies and accelerated digitalization, such as the implementation of artificial intelligence platforms and the use of big data for research and development, have increased the demand for specialized IT professionals. This has generated fierce competition for qualified talent, which increases turnover, especially after the completion of key projects or when there are opportunities in other sectors that offer better returns.
Impact of mergers and acquisitions: In the pharmaceutical industry in particular, M&A moves have a significant impact on labor turnover, particularly in IT departments. During and after these actions, IT employees can be affected by restructurings, which can lead to increased turnover at certain times of the year, generally aligned with the company’s financial cycles.
In general, in both sectors, spikes in turnover are often observed during periods of accelerated innovation or significant economic change, which create opportunities in new technology areas or drive internal restructuring.

Others

Impact of the economic climate: Economic uncertainty also plays a significant role. During periods of economic stability, workers tend to change jobs more frequently in search of better opportunities. However, in times of uncertainty, turnover may decrease, as employees prefer the security of their current job.
Talent retention and specific challenges: Despite high demand, IT talent retention within the pharmaceutical industry can be affected by the perception of limited career development opportunities compared to other more dynamic sectors, such as pure technology. Pharmaceutical companies that fail to provide a work environment that integrates technological advances with clear development opportunities may experience higher turnover rates.

How to minimize risk

While for some of the patterns we have analyzed, we can manage some foresight and leverage some levers to minimize risk, many others are clearly beyond fully effective management control. 
Therefore, seeking as much flexibility as possible is perhaps the best prevention. If a changing environment cannot be modified, the best option is to be flexible and adapt quickly at the lowest possible cost.
Collaboration with technology partners that allow us to quickly incorporate talent trained and updated in the technology needed in each phase of the project, without burdening the fixed structure of the organization, is a good solution to obtain the necessary flexibility and minimize the impact of labor turnover that will inevitably come to a greater or lesser degree.
As is well known, turnover at controlled levels is healthy and we must live with it, but if it is too high or we are not able to react to it with the necessary speed, the results will be far less satisfactory than we expect.

References:

  • Turnover Trends So Far in 2024 (and What Recruiters Should Know) .- interviewstream (2024)
  • What’s behind industry employee turnover rates? .- Reward Gateway (2024)
  • Employee Turnover Rate: Definition, Formula & 2024 Trends .- Toggl Track (2024)
  • Trends And Estimates For The Pharmaceutical Industry In 2023 .- World Pharma Today (2023)
  • Across 25 industries, pharma staffers most satisfied with compensation: analysis .- FiercePharma (2024)
Antonio Castaño

Autor Antonio Castaño Romero

How to implement a Data innovation strategy for the healthcare sector

Reading Time: 3 minutes
The evolution of Data Science applicable to healthcare has been undergoing a major acceleration in recent years. Data has been applied robustly in this field for some time now, but with the development of increasingly advanced techniques in machine and deep learning and others more related to AI, such as natural language processing (NPL), the evolution of the value obtained from data is becoming exponential.
It is complicated to have all the necessary profiles in each phase of a project of this type since they have, by their very nature, a clear multidisciplinary component. In this sense, the support of Talent As a Service models to have the right talent at the right time is key.
Another fundamental element is to know which phases must be undertaken so that innovation in the healthcare data strategy delivers the necessary value in a safe way; all of them are important and necessary.

Define Clear Objectives and Goals

  • Establish what you want to achieve with your analytics strategy: improve decision making, enhance customer experience, or develop new products.
  • Align these objectives with the overall business strategy to ensure cohesion and relevance.

Build a Qualified Team

  • Hire the Right Talent: Form a multidisciplinary team with the key profiles identified above: Data Scientists, Data Engineers, Data Analysts, NLP Specialists, Machine Learning Engineers, Explainable AI (XAI) Specialists, Federated Learning Experts and a Data Science Project Manager.
  • Continuous Training: Invest in continuous training and development to keep the team updated with the latest trends and technologies (Analytics Insight)​ (MyGreatLearning).
  • In this phase, having a talent partner (TaaS) that reduces the difficulties and risks to a minimum may be the best option if you do not have all the necessary talent in the organization. In addition, continuous training and qualification will be guaranteed.

Invest in the Right Tools and Technologies

  • Data Infrastructure: Establish a robust data infrastructure using tools such as Hadoop, Spark and cloud storage solutions to handle large volumes of data efficiently.
  • Advanced Analytics and Machine Learning Platforms: Use platforms such as Azure ML, TensorFlow, PyTorch and Scikit-learn to build and deploy machine learning models (Yale School of Medicine)​ (McKinsey & Company).
  • Data Visualization Tools: Implement tools such as Tableau and Power BI for effective data visualization and reporting.

Develop a Data Governance Framework

  • Establish policies and procedures for data management, including data quality, security and privacy.
  • Implement compliance measures to adhere to relevant regulations and standards, such as the GDPR for data protection and the National Security Scheme (mandatory in the Spanish public administration).
  • This phase is absolutely fundamental in a sector such as the pharmaceutical industry, where the high sensitivity of data is an element to be taken into account (SpringerLink).

Implement Agile Methodologies

  • Use agile project management techniques to ensure flexibility and iterative progress. This allows for continuous improvement and rapid adaptation to changing requirements ​ (McKinsey & Company)..
  • Regularly review and adjust strategy based on performance metrics and feedback.

Leverage Advanced Analytics and AI

  • Integrate machine learning and AI to gain deeper insights and automate decision-making processes. Focus on Explainable AI to ensure transparency and confidence in your models (Yale School of Medicine)​ (SpringerLink).
  • Explore federated learning to improve privacy and security while effectively utilizing distributed data sources (McKinsey & Company). When developing a data strategy in an industry as heavily regulated as healthcare we must assess and manage any potential risks.

Foster a Data-Driven Culture

  • Promote a data-driven mindset throughout the organization by fostering data literacy and making data accessible to all relevant stakeholders.
  • Use storytelling with data to effectively communicate insights and promote informed decision making (Yale School of Medicine).

Monitor and Evaluate

  • Continuously monitor the performance of the data strategy against defined objectives and KPIs.
  • Use feedback loops to refine models and processes, ensuring continuous improvements and innovation  (McKinsey & Company).

Scale and Innovate

  • As the data strategy matures, explore new areas of innovation such as Edge Computing, real-time analytics and Data-as-a-Service (DaaS) models.
  • Keep the company abreast of emerging trends and technologies to remain competitive and forward-looking  (MyGreatLearning)​ (McKinsey & Company).
 
This sequence of phases will allow us to innovate and maximize the value of our data with an adequate risk control, obtaining key support in decision making.
 

References

10 Data Science Papers for Academic Research in 2024 .- Analytics Insight (2023)
Latest Trends in Data Science 2024 .- GreatLearning (2024)
What Does Natural Language Processing Mean for Biomedicine? .- Yale School of Medicine (2023)
Natural language processing in healthcare .- McKinsey & Company (2018)
Natural Language Processing for Health-Related Texts .- Sprinkler (2021)
Natural Language Processing in Health Care and Biomedicine .- Sprinkler (2013)
Antonio Castaño

Autor Antonio Castaño Romero

AI revolution, redefining and reinventing tech talent (part 3)

Reading Time: 3 minutes
We conclude the series of articles on how the AI revolution has modified and continues to modify the IT Talent environment, today moving from the realm of professional talent to the realm of companies.
Just as important as a certified professional who conveys the necessary confidence in their skills, is to be able to be sure that the organization or company developing products and services associated with AI can demonstrate a reasonable level of auditing and certification.
In this environment, the investment and return are not yet so clear, especially because many of the certifications or audits that are launched do not yet have the certainty of their continuity in the medium term. In the changing environment and exponential development of AI it is quite logical that this is the case.
A good basis to start with is to be already certified or “have the seal”, as they say, of standards already well tested and widespread in information security, process management and quality (ISO 27001, ISO 9001, National Security Scheme, etc.). If our organization has already passed through here, facing the more IA and Data oriented certifications will be much simpler, and above all much more accessible.
Let’s not forget that some of the aspects that most concern large companies when adopting AI, in addition to the ethical implications, are those aspects related to compliance, data security and location, privacy, etc.

Standards and Certification Organizations

ISO (International Organization for Standardization)

  • Description: This ISO committee develops international standards for artificial intelligence, including aspects of security, reliability and ethics.

IEEE (Institute of Electrical and Electronics Engineers)

  • Description: IEEE offers a number of courses and certifications focused on artificial intelligence and AI ethics.
We focus on the ISO because of its international diffusion mainly.
ISO/IEC JTC 1/SC 42 is a joint subcommittee of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) dedicated to standardization in the field of artificial intelligence. More details on its functions, focus areas and the standards it develops are presented below:

ISO/IEC JTC 1/SC 42: General Overview

Functions and Objectives

  • Establishment of Standards: Develop international standards that address artificial intelligence technologies and applications.
  • Coordination: Coordinate with other ISO and IEC technical committees and subcommittees, as well as other organizations, to ensure consistency and avoid duplication.
  • Evaluation and Audit: Evaluate the social, legal and ethical implications of artificial intelligence technologies.
  • Adoption Facilitation: Facilitate the adoption of AI standards by industry, governments and other bodies.

Areas of Focus

  • Big Data: Standards related to the management and analysis of large volumes of data.
  • Machine Learning: Standards for the development, training, evaluation and application of machine learning models.
  • Governance and Ethics: Guidelines and standards on the responsible and ethical use of AI.
  • Trusted AI: Standards that ensure transparency, explainability, security and privacy in AI systems.

Main Standards Developed

  • ISO/IEC 22989:2022 – AI Concepts and Terminology: Establishes common terminology and fundamental concepts in the field of artificial intelligence, providing a uniform basis for the development of other AI standards.
  • ISO/IEC 23053:2022 – AI Framework: Provides a general framework for the development and implementation of artificial intelligence systems, covering aspects such as architecture, life cycle and best practices.
  • ISO/IEC 24027:2020 – Data Quality Assessment for Machine Learning: Standards for assessing the quality of data used in the training and validation of machine learning models.
  • ISO/IEC 20546:2019 – Big Data Overview and Vocabulary: Provides an overview and standard vocabulary for key terms and concepts related to Big Data.
  • ISO/IEC TR 24028:2020 – Assessment of Machine Learning Classification Performance: Guidelines for the assessment of the performance of machine learning classification models, including metrics and evaluation methods.
  • ISO/IEC TR 24030:2021 – Implementation of AI: Provides guidelines for the implementation of AI systems, covering technical, organizational and ethical aspects.
  • ISO/IEC TR 24028:2021 – Guidelines on AI Ethical and Societal Considerations: Guidelines on ethical and societal considerations in AI development and implementation, including issues such as transparency, accountability and inclusiveness.

Relevance and Application

  • Industry: The standards developed by ISO/IEC JTC 1/SC 42 are crucial for industry, as they provide a structured and recognized framework for developing and evaluating AI technologies.
  • Governments: Governments can use these standards to formulate policies and regulations to ensure the ethical and responsible development of AI.
  • Academia and Research: Academic and research institutions can adopt these standards to guide their projects and ensure interoperability and ethics in their work.
  • Society: By addressing ethical and governance issues, these standards help mitigate risks and ensure that AI technologies benefit society as a whole.

Participation and Continuous Development

The ISO/IEC JTC 1/SC 42 subcommittee works continuously to develop new standards and update existing ones, based on evolving technology and market needs. Members include experts from various countries and organizations, ensuring a global and multidisciplinary representation in the standardization process.
In this context, with the speed of development of AI and Data Science and their interrelationship, standards already in use continue to be refined and new ones developed to meet the challenges that AI is generating. 
When it comes to overcoming possible barriers to AI adoption, the certification and auditing framework is a fundamental element, and it is also the one that is developing more slowly, but it is also advancing continuously to provide a secure framework for AI application and to avoid problems that may undoubtedly arise in the future.
Antonio Castaño

Autor Antonio Castaño Romero

AI revolution, redefining and reinventing tech talent (part 2)

IA Revolution
Reading Time: 5 minutes
Today we continue with the analysis of how the AI revolution is redefining the Talent framework in the IT environment, if in the first article we focused on the new professional profiles, in this second part we will emphasize how internationally recognized certifications are being created that support the knowledge of professionals and what regulated training is accessible to future AI and Data professionals.  In the third, and last chapter of this series, we will review the certifications oriented to organizations and companies, which validate their expertise, security and ethics when applying AI and which are also having an exponential growth within the AI revolution.

Professional certifications

In everything related to AI and its applications, the European environment is a step behind the USA, in the aspect of professional certification as well, the most globally recognized professional certifications are born from many of the big players that are driving from minute 1 this revolution and that, unfortunately, are not European.  We expose certifications with an important path and a globally recognized prestige, not all of them are here, but this would be our first selection. Some of them complement in an ideal way the formal training that we will analyze later, on the other hand, as is well known: there are already many options for training in all fields of AI and Data that can be a good previous and more self-taught step before investing time and money in some of the options presented here, in the main cloud training platforms the impact of AI and Data is, today, simply brutal:

Certifications in Artificial Intelligence and Data Science

Certified Artificial Intelligence Practitioner (CAIP)

  1. Organization: CertNexus
  2. Designed for professionals who wish to demonstrate their skills in the design, development and management of artificial intelligence solutions.

Google Professional Machine Learning Engineer

  1. Organization: Google Cloud
  2. Validates a professional’s ability to design, build and manage machine learning models on Google Cloud Platform.

Microsoft Certified: Azure AI Engineer Associate

  1. Organization: Microsoft
  2. It is aimed at AI engineers who use Azure Cognitive Services, Azure Machine Learning and Knowledge Mining to design and implement AI solutions in Microsoft Azure.

IBM AI Enterprise Workflow Certification

  1. Organization: IBM
  2. Offered in partnership with Coursera, this certification covers the full cycle of artificial intelligence application development, from data preparation to implementation.

TensorFlow Developer Certificate

  1. Organization: TensorFlow (Google)
  2. Aimed at developers who wish to demonstrate their competence in the use of TensorFlow for the development of machine learning and deep learning models.

Data Science Certifications

Certified Analytics Professional (CAP)

  • Organization: INFORMS
  • Validates the knowledge and ability to apply advanced analytical principles and solve complex business problems.

SAS Certified Data Scientist

  • Organization: SAS
  • Designed for professionals who want to demonstrate their skills in data manipulation, advanced analytics and implementation of predictive models.

Cloudera Certified Professional Data Engineer (CCP Data Engineer)

  • Organization: Cloudera
  • Validates skills to develop data processing solutions and create data workflows using Cloudera technologies.

AI Ethics Certifications

AI Ethics and Governance Certification

  • Organization: The Alan Turing Institute
  • It covers ethical and governance issues in the development and implementation of AI systems.

Regulated training

We analyze here only the available in the European environment, the available in the USA would give us for another article. The power of this training at international level is very important due to the fact that the educational institutions that promote it have a very high prestige. It should be noted that, unlike the professional certifications we analyzed at the beginning of this article, they have a more transversal approach, ranging from technical aspects to AI application ethics. It is foreseeable that this offer will increase exponentially in the coming years, but today we already have high training possibilities that guarantee knowledge of AI and Data Science. From a professional point of view, the employability of a professional with this training is practically immediate. We highlight here some of the most outstanding options:

Regulated Training in Spain

University degrees

Degree in Artificial Intelligencel
  • Polytechnic University of Madrid (UPM): Offers a degree in Artificial Intelligence with a multidisciplinary approach, ranging from programming to AI ethics.
Degree in Data Science and Artificial Intelligence
  • Carlos III University of Madrid (UC3M): Combines data science with artificial intelligence, providing a solid foundation in mathematics, statistics and programming.

Master’s degrees

Master’s Degree in Artificial Intelligence
  • Polytechnic University of Catalonia (UPC): This program focuses on advanced AI techniques, machine learning and natural language processing.
Master’s Degree in Data Science and Computer Engineering
  • University of Granada (UGR): It offers training in data science, big data and artificial intelligence.
Master in Artificial Intelligence
  • National University of Distance Education (UNED): Distance learning program that covers from theoretical fundamentals to practical applications of AI.

PhD´s

PhD in Artificial Intelligence
  • Polytechnic University of Madrid (UPM): Focused on advanced research in AI, covering areas such as deep learning, computer vision and robotics.

Regulated Training in the European Union and UK

University degrees

BSc in Artificial Intelligence
  • University of Amsterdam (The Netherlands): English-language program that provides a solid foundation in algorithms, machine learning and AI ethics.
BSc in Data Science and Artificial Intelligence
  • Maastricht University (The Netherlands): It combines data science and AI, with a focus on practical applications and interdisciplinary projects.

Master’s degrees

Master in Artificial Intelligence
  • KU Leuven (Belgium): This master’s degree covers a wide range of topics in AI, including machine learning, robotics and natural language processing.
MSc in Artificial Intelligence
  • University of Edinburgh (United Kingdom): One of the most recognized programs in AI, with a focus on research and practical applications.
EIT Digital Master School: MSc in Data Science
  • European Institute of Innovation and Technology (various European universities): It offers a combination of data science and AI, with cross-university mobility and a focus on innovation and entrepreneurship.

PhD´s

PhD in Artificial Intelligence
  • University of Cambridge (United Kingdom): Focused on cutting-edge AI research, with projects in areas such as computer vision, natural language processing and AI ethics.
PhD in Machine Learning
  • ETH Zurich (Switzerland): This program focuses on advanced research in machine learning, with applications in various scientific and technological areas.

Specialized Courses and Certifications

European Association for AI (EurAI)
  • It offers certifications and specialized courses in AI, including continuing education programs for professionals.
Coursera & edX
  • Platforms that collaborate with European universities to offer courses in AI, data science and machine learning, many of which are accredited.
AI4EU Academy
  • European Union initiative to provide AI training through online courses and educational resources.

European Union Initiatives and Programs

AI4EU (Artificial Intelligence for Europe)
  • European platform for AI collaboration, including training, research and policy development. AI4EU Academy offers educational resources and training programs in AI.
Horizon Europe
  • EU research and innovation framework program that funds AI projects and training, promoting collaboration between academic, industrial and governmental institutions.
As we have been able to analyze throughout the last two articles, the impact of AI, more than demonstrated in all the aspects we know within the field of Information Technologies, is spreading increasingly in all the fields that affect the talent of professionals. From the creation of new profiles and the redefinition of many of the existing ones to the formal training they need for their success in the professional market, we have covered other important aspects that make it very clear that this revolution is no longer just a future, it is present and has come to stay.
Antonio Castaño

Autor Antonio Castaño Romero

Learn at least 5 of the key IT profiles for Innovation and Efficiency in the Pharmaceutical Sector

Reading Time: 2 minutes

The pharmaceutical sector faces constant challenges that require innovative and efficient solutions. In this context, Information Technology (IT) profiles have become essential to driving digital transformation, improving operational efficiency, and accelerating innovation. All of these profiles are among the most in-demand in the market; being able to invest in them while minimizing risks and adaptation periods is one of the keys to success, along with flexibility and adaptation to the needs of each moment.

Below, we will explore some of the most sought-after IT profiles and their impact on the pharmaceutical industry.

1. Data Scientists:

Data scientists are crucial in analyzing and interpreting large volumes of data. In the pharmaceutical sector, their ability to handle and analyze clinical, genomic, and market data allows companies to identify patterns, optimize research and development (R&D) processes, and predict patient behavior trends. Thanks to their work, companies can make more informed decisions and develop more effective and personalized medications.

2. Software Developers:

Software developers play a vital role in creating applications and platforms that facilitate data management, process automation, and internal and external communication. In the pharmaceutical industry, these professionals develop web content applications, supply chain tracking platforms, and mobile applications that improve patient adherence to treatments. Their work not only enhances operational efficiency but also contributes to the safety and quality of products.

3. Cybersecurity Experts:

Cybersecurity is a priority in the pharmaceutical sector, given the handling of sensitive and confidential data. Cybersecurity experts protect systems and information against threats and cyberattacks. They implement defense strategies, conduct security audits, and ensure compliance with regulations and standards. Their work ensures that critical information is protected, which is vital for maintaining the trust of patients and business partners.

4. Cloud Engineers:

The adoption of cloud solutions has transformed how pharmaceutical companies manage their data and applications. Cloud engineers are responsible for designing, implementing, and maintaining secure, scalable, and efficient cloud infrastructures. This enables companies to store large volumes of data, facilitate global collaboration, and reduce operational costs. Additionally, the flexibility of the cloud accelerates the development of new medications and treatments.

5. Artificial Intelligence (AI) Specialists:

Artificial intelligence is revolutionizing the pharmaceutical sector. AI specialists implement algorithms and models that can analyze large data sets, identify new drug candidates, optimize supply chain logistics, and personalize patient treatments. AI not only speeds up the drug discovery process but also improves the precision and efficacy of treatments, leading to better patient outcomes.

The Importance of Experience in the Pharmaceutical Sector:

Specific experience in the pharmaceutical sector is crucial for IT professionals, as the industry is highly regulated, and medical-legal validation processes are complex and rigorous. Having a deep understanding of these regulations and processes allows IT experts to ensure that technological solutions meet the required quality and safety standards. This experience provides significant advantages, such as the ability to anticipate and mitigate regulatory risks, ensure data integrity and traceability, and facilitate audits and approvals by health authorities. Ultimately, having IT professionals with pharmaceutical sector experience enhances the efficiency and reliability of processes, translating into faster and safer market introduction of new treatments.

IT profiles are playing an increasingly crucial role in the pharmaceutical sector. Their ability to innovate, protect, and optimize processes is essential to meeting the industry’s current and future challenges. The integration of these technical skills with pharmaceutical knowledge is leading to significant advances that benefit both companies and patients. In a world where technology and health are increasingly intertwined, having the right IT talent is essential for the sector’s success and sustainability.

#Quodem #TaaS #Pharma #Innovation #Talent #FutureOfWork

Antonio Castaño

Autor Antonio Castaño Romero

AI revolution, redefining and reinventing tech talent (part 1)

Revolución IA / IA Revolution
Reading Time: 4 minutes
According to the most serious market studies, a new professional framework for the development of AI is already being defined, although this process is far from complete and we need to be very vigilant, as the point of stabilization is only just on the horizon. This fact, in a sector such as pharmaceuticals, which is one of the fastest developing sectors for AI, has created a pressing need for a better understanding of the context surrounding the impact on the talent required for its implementation.
The rise of artificial intelligence (AI) has led to the creation and redefinition of several specialized professional profiles within the IT field, each with specific roles in the market.
Some are newly created, the most specific to AI, and others are redefined and deepened within existing profiles, almost always linked to an element that is very close to AI: the processing and analysis of data in all its aspects.
The symbiosis between AI and Data Science is such that it is sometimes difficult to draw a clear boundary; analyzing the skills of each profile involved is a good way to start.

Data Scientist

Data Scientists are experts in analyzing and processing large volumes of data to extract valuable information to guide strategic decision making.They use machine learning techniques and statistical analysis to build predictive models and present understandable results to business stakeholders.
Functions:
  • Analysis and processing of large volumes of data.
  • Creation of predictive and machine learning models.
  • Data interpretation for strategic decision making.
  • Data visualization and presentation of results to stakeholders.

Data Engineer

Data Engineers are responsible for designing and building systems that facilitate the efficient processing and storage of large amounts of data. Their work is crucial to ensure that data is available and of high quality for analysis and modeling.
Functions:
  • Designing and building data processing systems.
  •  Integration and management of databases and data lakes.
  • Creation of data pipelines for analysis and modeling.
  • Data quality assurance and data availability.

Data Analyst

Data Analysts are in charge of extracting and analyzing data to obtain insights to support decision making. They create reports and dashboards that help identify trends and patterns, providing key information for business strategies.
Functions:
  • Extracting and analyzing data for actionable insights.
  • Creation of reports and dashboards.
  • Supporting data-driven decision making.
  • Identification of trends and patterns in data.

Machine Learning Engineer

Machine Learning Engineers specialize in the development and implementation of algorithms and models that allow machines to learn from data. Their focus is on optimizing and deploying these models in production environments to solve complex problems.
Functions:
  • Development and implementation of machine learning algorithms and models.
  • Optimisation of models for performance and scalability.
  •  Implementation of machine learning solutions in production.
  •  Maintenance and continuous improvement of AI models.

AI Engineer

AI Engineers are dedicated to developing artificial intelligence systems that emulate human behavior. They use advanced technologies such as natural language processing and computer vision to create innovative solutions that are integrated into products and services.
Functions:
  • Development of AI systems that mimic human behavior.
  •  Implementation of technologies such as natural language processing (NLP) and computer vision.
  • Integration of AI into products and services.
  • Collaboration with other technical and business teams for the implementation of AI solutions.

AI Researcher

AI Researchers focus on exploring new techniques and algorithms in the field of artificial intelligence. Their work includes publishing scientific papers and collaborating with academic institutions to advance the understanding and application of AI.
Functions:
  • Conducting advanced research in new AI techniques and algorithms.
  •  Publication of scientific papers and presentation of findings at conferences.
  • Collaboration with academic and research institutions.
  •  Exploration of new applications and emerging technologies in AI.

AI Ethics Specialist

AI Ethics Specialists assess the ethical implications of artificial intelligence systems. They develop policies and guidelines to ensure the responsible use of AI, promoting transparency and fairness in its application.
Functions:
  • Assessment of ethical implications of AI systems.
  • Developing policies and guidelines for the responsible use of AI.
  • Monitoring compliance with ethical standards in AI projects.
  • Promoting transparency and fairness in the use of AI technologies.

Chatbots and Virtual Assistants Developer

They create programs that interact with users using natural language.They implement natural language processing techniques to improve language understanding and generation, integrating these systems with various platforms and services.
Functions:
  • Design and programming of chatbots and virtual assistants.
  • Implementation of NLP techniques for language understanding and generation.
  • Integration of chatbots with existing platforms and services.
  • Continuous improvement of interaction and user experience.
 

AI Robotics Engineer

AI Robotics Engineers develop intelligent robots with autonomous capabilities.They integrate computer vision and machine learning systems into robots, programming autonomous behaviors and validating their performance in real environments.
Functions:
  •  Development of intelligent robots with autonomous capabilities.
  •  Integration of computer vision and machine learning systems in robots.
  • Programming of autonomous behaviors and decisions in robots.
  • Testing and validation of robots in real environments.
The change we are experiencing in the world of data thanks to AI, and the need to rethink or reinvent many of the technical profiles needed in the Pharma sector to implement technological innovation. is forcing us to adapt to our organizations.
In this context of change and growth, the needs for new professionals who know how to adapt and surf in change convert companies dedicated to attracting talent in the ideal partner to get flexibility and adaptability necessary, minimizing the risk of creating internal structures in our organization that may become obsolete before their time.
In a second article we will take a closer look at how, in the context of formal training, AI is redefining the training of specialized talent and in which certification or auditing framework we can start moving to make sure we are moving in the right direction.

Bibliography

Antonio Castaño

Autor Antonio Castaño Romero