Regina Llopis participates in the event «The sum of Intelligences» organized by the UdG

Last May 26th, Regina Llopis, President of Grupo AIA, took part in the online event coordinated by the Universidad de Girona titled «The sum of Intelligences».

More information at and hashtag Twitter  #congrésdigitalSI.

ASISA and Grupo AIA agree to develop jointly AI-based software

Grupo AIA and ASISA have signed an agreement to develop jointly artificial intelligence-based software aiming at improving efficiency and dynamism of the insurance company´s management processes and ultimately to meet its customers and insurers´ needs.

The project´s kick-off between Grupo AIA and ASISA, a leading health insurance company with the best Net Promoter Score, NPS[i], is aimed at developing innovative software based on basic science (Physics and Mathematics) and Data Science to meet their business needs. This knowledge transfer will enable Grupo AIA to provide intelligence to ASISA´s business processes through advanced analytics methodologies such as Machine Learning.

Grupo AIA focused on solving management efficiency of medical actions at this phase of the collaboration project, will deploy a Machine Learning-based tool that will make medical actions management more efficient and ASISA will be able to make wiser decisions that will benefit its customers. Grupo AIA´ s contribution based on the state-of-the-art Artificial Intelligence technology will help to improve the insurance company´s management and control processes in this business area.

This collaboration project is framed within ASISA´s technological transformation across business processes impacting on both, welfare and management activities´ efficiency and effectivity improvement as well as customers´ satisfaction. The ultimate goal of this process for the company is to provide customers a total digital service including the whole process from services contracts to their management.

[i] Braintrust ´s Observatory of Health Competence.

Regina Llopis participates at the Climate Change World Summit

What can women contribute to climate change prevention?  Based on this premise, a round table was held at the green zone of the Climate Change World Summit in Madrid, COP25, with the special participation of influential women from different fields to discuss Climate Change problems and how they could help to combat it.

Regina Llopis, president of Grupo AIA, Andrea Barber, cofounder and CEP of Rated Power; Mari Luz Cádiz, researcher at Universidad de Oporto); Patricia Fernández, researcher at Centro de Biotecnología y Genómica de Plantas, (UPM-INIA); Cristina Romera, researcher at the Instituto de Ciencias del Mar (CESIC) and moderated by Ángeles Heras, Secretary of State for Universities, Research, Development, and Innovation have participated at this round table and was promoted by the Women Observatory, Science and Innovation.

A variety of problems and challenges facing the planet were exposed at this Climate Summit.  Among the solutions proposed, Cristina Romera has mentioned the research done on plastic recycling and new materials to replace the oil by-product. Patricia Fernández explained how the research on plants is combating pests and allowing the formulation of new pesticides based on plants´ natural defense mechanisms, so efforts have been made towards intelligent and sustainable agriculture.

However, implementing these ideas would entail moving from the research area to the business one, and knowledge transfer to the industry to implement the changes required against climate change. On this issue, Regina Llopis, president of Grupo AIA, reminded the slim participation of women in entrepreneurship.

Regina Llopis highlighted that only 7% of the capital risk in Europe goes to businesses created and managed by women and in Spain, only 8% of women are business angels, in other words, investors at the first stages of the business development.

These figures show the low participation of women in the business world and the also president of WA4STEAM has urged women to be involved in climate change combat through the creation of technological-based businesses. Areas such as artificial intelligence, Physics, Mathematics, or Engineering allow promoting a more sustainable world.

Artificial Intelligence for new drugs discovery

Biomedical science innovation based on AI technology is the long-awaited opportunity for achieving higher effectivity in this industry. New drug development through R&D innovation in a shorter time and at a lower cost is the Holy Grail of the biopharmaceutical industry.

Scientific innovation not only involves finding the molecular mechanism of a disease but also the development of new drugs for the cure, palliation or prevention of diseases.

Innovation in the pharmaceutical industry costs over 2.400,00 million euros, according to Farmaindustria. On the other hand, R&D global investment in the pharmaceutical sector accounts for 30.000,00 million, only in Europe and these figures hikes to 142.000,00 euros worldwide.

From this amount, 57% goes to design, development and clinical tests evaluation phases. The remaining 40% goes to basic research, approval processes and pharmacovigilance.

According to data provided by Biopharmaceutical representatives, developing a new medicine takes about 12 to 13 years from its discovery to its clinical use in patients. However, only a few molecules reach the commercialization phase. Many are left behind along the phases of the drug development process.

It´s precisely in drug targeting discovery and designs that AI-based techniques have cut downtime by half and costs by 25% in the production of new drugs.

Currently, the Spanish biopharmaceutical, Sylentis, has implemented a software based on Neural Networks, SVM and Machine Learning to gather, filter and reinterpret experimental data generated by the pharmaceutical industry. This allows them to enhance and develop the drugs thanks to a  software that trains to generate thousands of specific compounds to deal with a disease in a matter of a few days. The pharmaceutical company reduces the expensive and time- consuming task of candidate´s selection from years to only a few days.

AI for personalized drugs.

A survey conducted by Deloitte and MIT Sloan Management Review last June found out that only 20 % of biopharmaceutical companies are digitally mature enough, and the lack of a clear vision, leadership and financing are slowing down companies´ growth.

According to MarketsandMarkets, AI´s demand in the biopharmaceutical industry is expected to grow from US$ 198.3 million in 2018 to US$ 3.88 billion in 2025.

The four projected areas to drive most of the AI market forward in biopharmacy between 2018 and 2025: drugs discovery, precision medicine, diagnostic imaging and medical diagnosis and research. The report says drugs´ discovery reached a larger market share during the survey period.

These areas that go from the target candidate molecules selection to the production of the new drug provides a unique opportunity to speed up drugs development. The potential improvement of the process includes:

  • Process redesign to speed up new molecules discovery time and is based on expert knowledge.
  • Digitalization of repetitive processes automation and generation of new content and data.
  • Advanced analytics incorporating internal and external sources. Here new predictive model would be included.

A key role for AI algorithms is molecules interaction forecasting to find the disease mechanisms. In turn, these mechanisms could help setting new biomarkers to identify, design, validate and optimize new drugs candidate target and identify existing drugs that could be reused for other indications


Vicens Gaitán, CDS in Grupo AIA: “AI allows us to exploit information better than ever before”

What are AI key points? Are AI-driven companies getting a competitive advantage? At the conference round table held last September at La Salle, “Artificial Intelligence. Why & How to keep your Company alive” were raised these questions and sparked lively discussions thanks to speakers´ participation: Vicens Gaitán, Chief Data Science in Grupo AIA, Pier Paolo Rossi, Advanced Customer Marketing & Analytics Director in Banc Sabadell and Daniel Marco, Department for Digital Policy and Public Administration of Catalonia.

Digital Transformation has become a turning point in companies´ work culture, particularly in data management and AI-based technology to achieve their competitive advantage in their industrial sectors.

However, during the speakers´ interventions at the conference, it was made clear implementing AI was not setting an “automatic pilot”, but instead it involved having essential key elements.

Above all, being able to implement AI to the business to meet specific needs leading to more intelligent decision-making. Vicens Gaitán, Chief Data Science in Grupo AIA, pointed out that “thanks to the implementation of artificial intelligence techniques in companies, these companies can be trained to exploit data as never before”.

In other words, data-driven companies striving for the best and more efficient decision-making must work on these four big areas:

  • Knowing the dataset location and capture it. Data management time must be adequate for accurate and suitable subsequent decision-making fitted to each business case.
  • Dataset creation and assembly
  • Apply the algorithm that will extract information from the data.
  • Make decisions based on results provided by the algorithms.

One of the most sensitive aspects at the conference was the need for customer-centric decision- making for more effective results. Consequently, technology should go hand by hand with the business to lead customer-centric projects.

Artificial intelligence techniques enhance businesses’ capacities to adapt and anticipate strategic and tactic decision making to their real customers´ demand. Therefore, it is essential that the business and Big Data teams work together to achieve business intelligence.

The degree of maturity of Artificial intelligence-driven businesses is based on their capacity to integrate AI as their main structure. The greater artificial intelligence, the greater the corporative intelligence.


The hidden figure behind a successful AI implementation in the organizations.

The Artificial Intelligence implementation in companies is cross functional: Marketing, Finance, Operations… they all have benefited from the emergence of data driven across business processes in their organizations.

In a recent study published by Fujitsu and Pier Audoin Consultants, shows that the benefits companies have gained through Artificial Intelligence implementation are starting to pay off. This is not a matter of five years´ time. The AI´ s time has come. However, the figures are still low: only 11% of the surveyed companies are implementing AI strategies, 29% have AI projects in progress and 35% expect to do it in the next two years.

Under this classification, they would be defined as innovators, early adopters, followers. In other words, based on the company´s maturity and data adaptation, they will belong to one of the groups before mentioned.

Accordingly, 53% of the companies that have implemented AI or have in mind doing it believe improvement of automation processes depends on it, whereas almost 75% are creating business units for AI´s implementation take-off. The main areas this technology is implemented on is higher production efficiency, maintenance forecasting and above all, in customers´ behavior forecasting for appropriate business actions.

Nevertheless, a survey delivered by MIT Sloan Management Review and Boston Consulting Group a few weeks ago highlighted different data.

Although it claims AI´s rewards promise, these are not risk-free for example, a competitor taking the risk and going a step ahead. These are the innovators that use AI for the company´s across business processes alignment, investment and integration.

Many leading companies see AI not as an opportunity but as a risk strategy.  And this perception has gone up from 37% to 45% from 2017 to 2019 respectively.

Concerning risk management, many AI based initiatives have failed. Seven out of ten of the surveyed companies claimed they have hardly benefited from this technology. And it´s not a trivial matter when almost 90% of companies have invested in AI.

Thus, even if some companies have found out success with AI, most struggle to add value based on it. As a result, many executives face challenges associated with AI: It´s a source of non- exploited opportunities, an inherent risk. But, above all, it´s an urgent issue to tackle. How can executives exploit the opportunities, manage risks and minimize AI associated problems?

Data translator: the hidden figure

Professionals training, not only in technical and scientific areas but also in communication and interpretation, becomes essential for the differential AI value generation. Deep understanding of the business needs and knowing how to convey that to the technical teams in charge of implementing AI is the Holy Grail of all the companies and providers of this service.

On the other hand, Mckinsey says that success results based on AI and data analytics do not depend only on data scientists, data engineers or data analytics teams. A transversal figure is required: a data translator.

Mckinsey believes this figure can ensure the organizations achieve real impact from their analytical initiatives as it can help to understand correctly the business needs and translate them into a scientific -technical language and vice versa.

Data translation experience allows this figure to get deep knowledge of the core business and its value chain in diverse areas: distribution, health, marketing, manufacturing or any other environment.

As the consulting company defines it, in their role, translators help to guarantee deep knowledge generated through sophisticated analytics is translated into impact at every level of the organization. By 2026, The Global McKinsey Institute estimates translators demand will reach two or four million only in the U.S.

Thus, translators take advantage of their insights in AI and analytics to convey these commercial objectives to data professionals who will create the models and the solutions. Finally, the translators ensure the solution produce the insights the company can interpret and execute and ultimately, communicates the benefits of these insights to the businessmen to boost adoption.

One way to reduce the risk strategy companies have taken when they decide to be ahead of their competitors in their sector, is without any doubt, the capacity to interpret the data and offer insights based on them.



Artificial Intelligence for Customer’s identity

When customers know what they want, companies must figure out what it is. However, according to Gartner, CMOs only invested 29% of their budgets in new technologies to meet their needs.

Probably marketing specialists do not have the appropriate technology or the technology they have isn´t enough. However, often marketing departments do not have full knowledge of the capabilities of the technologies they have paid for and consequently, do not take advantage of them.

To guarantee a high return of the investment for the business (ROI) the marketing specialists should start by auditing their technological ecosystem in their organizations to finally set the best ROI strategy.

However, the assumption that having a specific technology will guarantee to make money, is a common mistake. According to a survey conducted by the consultant company McKinsey, the technological gap between leading companies and others staying behind is growing. This means companies that make data-driven decisions will make the difference as compared to the rest of companies that are still struggling for basic data analysis and technology.

For both, innovating companies and the ones lagging behind, data analytics emerges as an opportunity to offer better insights, in other words, having a healthy data culture. Some will implement it and others will not. Therefore, some will obtain a higher ROI at the expense of companies that will lag behind as they will not implement a data-driven culture.

Once a healthy data-driven culture and appropriate technology are implemented, the time to listen to customers has come. As said before, the more consumer’s attention is captured, the more customer’s personalization will grow. The marketing departments invest around 14% of their budget to achieve the so long waited for personalization, very little in return for what it offers.

AI-based Customer identity

The need to know customers opens the adoption and use of AI not only at marketing scale but also at a broader and general level in the organization. The survey conducted by McKinsey Global Institute containing more than 3000 companies called “Artificial Intelligence: The next digital frontier?” shows that the first AI users are closer to the digital frontier. It´s precisely at this frontier where the leading companies in each sector are found. AI is present in all the groups, at the core business of the value chain, it is used to boost revenues, reduce costs, and has total executive management support. The companies that have not adopted AI technology at a certain scale or at the core business, are not sure of the revenues they could expect with such an investment.

Innovative companies know that existing and future customer personalization is achieved by customer’s knowledge. Here is where AI is introduced completely, and marketing specialists are trained to identify each one of them and personalize their actions.

One of the key elements of the customer’s identity techniques is that it allows us to characterize actionable concepts. These actionable concepts are the result of incorporating the business expert knowledge to the AI, also called Intelligent Observation Systems. The result is a set of concepts providing a broader customers perspective.

Therefore, customer’s identity techniques enable companies to obtain, for instance, better insights into consumer’s habits. Once they are identified, the different areas of the organization have enhanced the ability to make personalized offers leading to a greater benefit while delivering greater levels of customer satisfaction. For example, churn may be avoided once the customer’s satisfaction is analyzed. Additionally, price predictive models for certain products can be set.

Innovative companies are the ones able to respond to the classic questions: How do my customers behave? What value of information can I obtain from their habits? How can I improve the companies´ revenues? Partly, it´s due to their capacity to be always at the state of the art.


Regina Llopis, FEDEPE 2019 Businesswomen Leadership Award

Regina Llopis, president and CEO of Grupo AIA (Aplicaciones en Informática Avanzada, S.L.), has been awarded “The Businesswomen Leadership Award” granted by the Spanish Federation of Female Directors, Executives, Professionals and Entrepreneurs, FEDEPE. This distinction is a recognition to the efforts, creativity, and motivation in business management best practices for success.

During her speech, Regina Llopis highlighted the impact of the awards and urged the rest of the awardees, many technology leaders among them, to continue as a model for other scientific and technological women. She reminded the reward was a recognition to a company´s steady work on Artificial Intelligence for thirty years. Grupo AIA has developed new methods and algorithms based on Artificial Intelligence and other disciplines for solving complex problems in different industrial sectors and services. Moreover, Grupo AIA ´s corporative strategy is based on investment in R&D as it allows it to ensure its technological leadership and comparative advantage. Likewise, Regina Llopis has set new business partnership models with customers.

On the other hand, the Vice-President of the Acting Government of Spain, Carmen Calvo, recognized the role of Spain as a benchmark for gender equality, however, she admitted many remains to be done by the institutions. During Ana Bujaldón´s speech, FEDEPE´s president, she urged to make progress towards establishing binding rules for equality once for all.

FEDEPE award honors Regina Llopis ´management success in achieving R&D leading position through advanced technological research opportunities with leading prestigious institutions as NASA in the U.S.A and others in the EU. And all this is due to a highly creative and qualified team which has allowed AIA to lead Artificial Intelligence in the last thirty years.

The ceremony was held at the headquarters of the Higher Council for Scientific Research (CSIC) in Madrid, Spain. The awards granted were: the Leadership Women’s Directive Award to Pilar López, president of Microsoft Spain; Professional Woman Leadership Award to Silvia Gil, captain of the Spanish Guardia Civil ,Woman Innovation and Entrepreneurship Award to Arantxa Unda, CEO of Sigesa, a medical software company; Communication Committed to Women Award to the EFE Agency; Boosting the Promotion of Women Award to the sports corporation Adidas; and the International Award to Bisila Bokoko, Founder and CEO of BBES, a business development agency in New York. Susana Griso and Juan Luis Cano in charge of conducting the ceremony created a cheerful and professional atmosphere.

A Special Award was granted for the Promotion of Professional Development of Women with Disabilities to Rosa García, Member of the Board of Directors of Tubacex and Sener.

FEDEPE awards held its 28th annual edition and once again counted on her Majesty the Queen attendance to the ceremony as President of Honor and the support of the Ministry of the Presidency. The ceremony was presided by her Excellency Vice President of the Acting Government of SPAIN Carmen Calvo, and the FEDEPE ´s President and Vice President, Ana Bujaldón and Francisca García Vizcaíno respectively,

Adaptive Machine learning or how to analyze environment´s volatility

The environment is constantly changing. Although we may think the environment is immutable, that the data collection on people and the environment hardly changes, there is nothing further from the truth. Data changes so rapidly that predictions based on Machine learning techniques become soon obsolete. Why?

According to Gartner´s latest trends curve, one of the technologies that will flourish in the next years will be the Adaptive machine learning, also known as Adaptive automatic learning. This technology allows for continuous learning with online machine learning models and in real-time. This capacity allows machine learning models to adapt themselves to the constantly changing world.

Due to this high adaptation capacity, this technology is particularly useful for autonomous cars training. These cars must be able to incorporate new data in real-time, analyze it and make decisions based on this data.

However, the use of this technology goes beyond autonomous cars (which Gartner gives over 10 year-period+ to see one). Adaptive autonomous learning in real-time requires efficient reinforcement learning, in other words, how an algorithm must continually interact with its environment to maximize its reward. These algorithms would work for agriculture, online marketing, smart cities, and even financial institutions or any other industry using IoT.

In these changing environments is not possible to collect all the generated data, organize and quantify it and train a “traditional “machine learning model as it must be retrained and make decisions in real-time.

The idea that emerged in the 50s

Although it may sound very innovative, the truth is back in the 50s B.F. Skinner developed a training machine focused on skills development. The idea was to monitor students´ progress in programmed learning. The machine adapted the questions based on the students´ previous correct answers and provided them the capacity to learn at their own pace.

It was not until 1970 that this idea was applied to artificial intelligence. However, as happened with many other things back then, the development of the adaptive automatic learning was slowed down due to computing capacity, capture and data storing. Simply, they were not ready yet.

Despite all this, Adaptive automatic learning did not die there. In the last decades, these technologies became more agile, scalable and easier to use although not widely used.

Nowadays, there are a myriad of fields for using adaptive machine learning, besides autonomous cars or robots. In the financial field, this technology allows for online data incorporation, for example, to monitor the stock market. It is also being used for location prediction (very useful for autonomous cars), advertising and in insurance field.

According to Gartner, adaptive machine learning is still a challenge as systems based on self-learning and autonomy will require considering aspects such as privacy, ethical aspects, and security. On the other hand, companies will apply continuous learning for autonomous and smart decision-making.

This doesn´t mean current methodologies based on machine learning must be changed as once the off-line algorithms are trained, the adaptive learning allows to improve, contextualize and personalize even more existing machine learning models.

Behavioral Marketing, towards product´s customization

Today we are used to receiving products and services recommendations from certain companies and we usually think these recommendations are made specially for of us. We believe products ads sent to us are personalized, targeted to each person, however, it isn´t quite true.

For most people, personalization and recommendation are the same thing. When a company adapts its services to meet customers´ needs, it can use recommendations to personalize their purchases, although these words may sound like synonyms, they aren´t.

The technological firm´s first approach to what constitutes a personalization engine is defined in its Gartner´s guide for Digital Personalization Engines in 2015 as:

A process that creates a relevant and individualized interaction between two parties designed to improve the customer´s experience. It uses information based on the recipient´s personal and behavioral data as well as behavioral data of similar people to offer an experience to meet specific needs and preferences.

Therefore, personalization engines are solution providers for the personalization process. They determine and provide the customer experience to deliver personalized messages through digital channels (emails, text, automatic notification) and provide analysis and reports to the business user.

Whereas product or content recommendations, involve a small selection of items in the catalogue sent to the user in a variety of ways (banners, messages, recommendation tables), in other words, a recommendation is more like a presentation of items.

However, a recommendation is a form of personalization, but a personalization is not a form of recommendation. Let´s take YouTube for example, Google video platform can suggest related videos based on users previous viewing habits. A recommendation would also be based on what other YouTubers watched. Hence, Facebook personalizes the news feed of your profile based on the activity of your friends but, does not recommend one or the other. This is personalization, as it is based on the individual´s specific habits rather than a general algorithm. The more you know about a person, the better, not only about videos habits. In other words, a recommendation is usually based on elements whereas a personalization is based on individuals.

Therefore, how can we talk about personalized recommendation systems when one refers to a product and the other to a person? Simple: incorporating the behavioral habits of the person in the recommendation engine. The more you know about a customer, the more personalized the products recommendation will be.

Today, there are only a few companies that can really personalize their behavioral marketing strategies as it is necessary to implement not only an algorithm for the recommendation, but also to incorporate all the customers and non-customers data. This is the challenge. Let´s give an example to explain how to overcome this.


Behavioral Marketing in banking

We know how the banking sector is moving ahead at a staggering speed to meet customers´ needs. Gone are the days when financial products ads were targeted to everybody regardless of their personal conditions causing many bad headaches.

Today, the sales and marketing departments of banks need to know better their customers to be able to offer them what they really need at the right time. Banking institutions are using a strategy consisting of incorporating the user´s browsing data.

Behavioral Marketing involves the sales, commercial and ads activities based on the customer´s behavior patterns obtained through cookies, social networks, web analytics, browsing and purchasing history and IPs. In this case, cookies collect anonymous information of browsing habits aiming at targeting ads according to their interests.

Therefore, Behavioral Marketing allows for ads customization based users profile and avoid sending ads to customers which will end up as spammers.

Consequently, when a customer accesses a bank web page it leaves a trace like a “bread trail” showing the yellow brick road of what, how long and what part of the web it has seen, whether it has taken any action like checking its credit. At the same time, if we incorporate all the available information of this customer (browsing activity, purchasing history, IP address, social networks, app data, etc.) we get knowledge of its propensity to certain banking products.

Keeping in mind the user identity will allow for customers characterization and their relationship with the institution through a series of variables that will describe more precisely each user. Using browsing habits as part of the user’s characterization will allow for a broader vision and therefore improve personalization of products recommendation.

Deep Learning and Machine Learning techniques allow to incorporate user´s data which could enhance the personalization model and would allow us to know the propensity of a specific person to buy a certain product. In other words, better conversion rates of their sales departments in their products offers.