How to accelerate digital transformation processes with artificial intelligence
Artificial intelligence is a key driver for digital transformation, due to its almost irreplaceable role in boosting automation and enabling the advanced data analysis and real-time decision-making.
When integrated with other technologies, it enables the redesign of traditional processes, improving customer experience, and accelerating innovation.
This not only streamlines companies' internal operations, but also enables new, more flexible, personalized, and results-oriented business models.
Besides, AI drives a profound evolution in organizational culture, by promoting a more agile mindset, based on continuous learning and experimentation.
However, its incorporation forces us to rethink structures, roles, and competencies to generate a digital transformation that transcends technology and impacts the company's strategy and purpose.
In this context, organizations that integrate AI as a central part of their digital transformation are better positioned to lead in an increasingly dynamic and competitive environment.
To delve deeper into the analysis of the impact that artificial intelligence has on the digital transformation of businesses, we spoke with Diego Parrás, Director of the Center for Studies in Artificial Intelligence Models Applied to Economic Sciences (CeMIACE) at the University of Buenos Aires.
With over 10 years of professional experience in the application of information technologies in public and private organizations, his knowledge and expertise includes AI systems and models, with a focus on neural networks based on Transformer architectures.
Let's start by getting to know the protagonist and expert of this article, and then move on to an analysis of the impact of artificial intelligence on digital transformation processes, with a special focus on financial services.
A professional career committed to AI and digital transformation
While pursuing a Bachelor's degree in Business Administration, the field of artificial intelligence, as we conceive and apply it today, was not explicitly listed as a primary vocation in his immediate future.
“My initial foray into these fields came through solving linear and logistic regression problems. This is an area I explored with particular interest thanks to the encouragement and guidance of my then-professor. It was he who encouraged me to delve deeper into these statistical tools, unaware that, in retrospect, I was laying the groundwork for a fascination that would lead me, years later, to the exciting world of artificial intelligence,” Diego recalls.
“When I started out in artificial intelligence, I was driven by a great curiosity about its transformative potential, without a detailed plan, but with the certainty of its impact", he adds.
Looking back on his professional career, Diego maintains that he was fortunate to meet great minds in the field of AI.
In the academic field, he created the undergraduate course Artificial Intelligence Management and the Diploma in Artificial Intelligence at the University of Buenos Aires (UBA), along with a study center and a book dedicated to this discipline.
From the Center for Artificial Intelligence Model Studies, Diego's team is developing several verticals of work:
Implementation of artificial intelligence in university teaching processes in economic sciences.
Generation of training and dissemination documents on how organizations are applying artificial intelligence in their various production and management processes.
In-depth study of various theoretical methods of monetary compensation for knowledge generation, used for the training of large foundational models.
Looking ahead, her main and constant goal is to continue learning in this dynamic field.
This relentless pursuit of knowledge is what truly motivates me. Another of her goals is to complete her second book on artificial intelligence and create innovative spaces to more widely disseminate knowledge on this topic.
It also aims to contribute to developing new ways to capitalize on the valuable human knowledge contributed to training AI models.
"I consider it vitally important to explore mechanisms that recognize and reward this intellectual contribution. Ultimately, I seek to continue promoting both the democratization of knowledge in AI and a more fair, inclusive, and sustainable ecosystem," he emphasizes.
From his teaching role,The first thing he recommends to young people who are interested in artificial intelligence is that they should not be afraid of it. enter that fascinating universe.
For Diego, artificial intelligence opens up a vast horizon of possibilities, and being proactive is key to making the most of it. The key is to experiment and make mistakes, because that's an essential part of learning.
Impact of AI on digital transformation processes
Artificial intelligence, especially in its generative and agentic forms, acts as a fundamental catalyst in the digital transformation of organizations, as it allows significant changes to be accelerated and pre-existing barriers to be overcome.
While generative AI systems are capable of creating content and proposing solutions with a high degree of innovation, agentic AI systems operate autonomously to solve complex problems and efficiently optimize processes.
These systems also make it easier for companies to anticipate needs, such as optimizing inventory or providing customer service, through advanced chatbots.
All of this converges in artificial intelligence's remarkable ability to free up valuable time, empowering digital transformation and allowing teams to focus on higher-impact strategic initiatives.
Impact of AI on improving operational efficiency and decision-making
The inherent potential of AI to process and correlate thousands of variables simultaneously translates into a profound transformation of operational efficiency.
This is reflected in the automation of extensive and complex processes, such as advanced data management or logistics route optimization. This leads to a significant reduction in errors and considerable time savings.
In terms of decision making, AI's ability to perform predictive and real-time analysis enables Identify emerging trends and potential risks well in advance.
This streamlines the formulation of precise decisions and minimizes exposure to uncertainties.
Careful planning and a gradual, strategic approach can overcome the challenges of AI implementation.
What AI-complementary technologies are key to achieving a successful digital transformation?
Artificial intelligence should not be conceived as a standalone technology. Its full potential is achieved when integrated with other complementary technologies that are essential for the success of a digital transformation.
Let's look at some examples.
Cloud computing
The cloud is essential, providing the scalable and flexible infrastructure needed to store and process the vast volumes of data that artificial intelligence requires to function optimally.
Internet of Things
IoT technology plays a crucial role by generating a constant stream of real-time data from multiple connected devices, data that AI can analyze to provide immediate insights and solutions.
Barriers to implementing AI in an agile and effective manner
One of the main obstacles in the implementation of artificial intelligence in digital transformation processes lies in the availability of high-quality, correctly structured data.
As is well known, AI is intrinsically dependent on reliable information to generate accurate and valuable results.
Another significant challenge is resistance to change within organizations. Sometimes, both employees and leaders may perceive AI as a threat to established control or fail to fully understand its benefits. These situations hinder the adoption of technology.
Initial implementation costs and a shortage of specialized talent also represent significant obstacles. The same is true for strict regulations on privacy and data use, which may impose limitations on the scope of projects.
Although these challenges may seem considerable, careful planning and a gradual, strategic approach can effectively overcome them.
The role of organizational culture in the adoption of AI-based solutions
Organizational culture is a central pillar in the implementation of digital transformation projects and the successful adoption of any disruptive technology, especially for AI-based solutions.
If there is no organizational predisposition towards change and innovation, Even the most advanced technology can be underutilized or poorly implemented..
A culture that fosters innovation, digital transformation, continuous learning and interdepartmental collaboration is crucial for teams to perceive Artificial intelligence as an empowering tool and a strategic ally, rather than a threat to their roles.
It's critical that leadership foster an environment of trust and transparency, taking the lead role in clearly communicating how AI will be used and the tangible benefits it will bring to all levels of the organization.
Without this support and a receptive culture, AI projects run a significant risk of failing to achieve their goals. Not because of technical limitations, but because of a lack of alignment and cultural buy-in.
AI frees up valuable time that can be used to power digital transformation.
Most relevant trends in artificial intelligence
In a foresight exercise, the Center for Studies in Artificial Intelligence Models Applied to Economic Sciences at the University of Buenos Aires (UBA) identified several AI trends that companies should closely monitor over the next 12 to 24 months.
Especially considering its potential to redefine operating paradigms.
One of the most notable is the sustained rise of generative AI, which enables:
Automated content creation.
Computer-aided design.
Code development.
As a result, new frontiers are opening up in areas such as marketing and product development, as well as in all digital transformation projects.
Also, explainable AI (XAI) is of special relevance, the objective of which is to provide transparency and comprehensibility to algorithms. A crucial aspect to generate trust and ensure regulatory compliance.
The growth of autonomous AI agents, designed to perform specific tasks such as project management or advanced customer service, represents another promising development.
Like Small Language Models (SML), they enable data processing locally on devices and offer the potential to reduce latency in interactions and improve efficiency in real-time applications.
Evolution of foundational models and their potential in the corporate sphere
Foundational models, such as large language models (LLMs), have undergone extraordinary evolution in the last two years.
Its efficiency and adaptability to specific needs have increased significantly through techniques such as:
Fine-tuning
Recovery by augmented generation (RAG).
In the corporate sphere and within the framework of digital transformation projects, their potential is immense, as they can:
Automate the writing of complex reports.
Personalize customer interactions at scale.
Assist in the formulation of marketing strategies.
As Diego explains, the proliferation of increasingly powerful open source models is significantly facilitating the adoption of these technologies by companies and organizations of various sizes.
However, it is imperative that its use be framed within principles of responsibility and ethics to avoid the perpetuation of biases or the emergence of ethical problems.
Compliance with the technical requirements inherent to its adoption and compliance with the licensing of the models that are decided to be applied must be avoided.
Most promising advances in ethical and responsible AI
In the field of ethical and responsible AI, we are witnessing truly significant advances that reflect a growing commitment to human well-being and equity.
Among the developments with the greatest growth prospects, the following stand out.
Explainable AI (XAI)
Explainable AI (XAI) is emerging as one of the most promising advancements, enabling us to understand the mechanisms underlying a model's decisions. This is critical for building trust with end users and regulators.
Also noteworthy are tools designed to detect and mitigate bias in training data.
This addresses a critical problem that, if not managed properly, could perpetuate or even amplify existing inequalities.
Global regulatory frameworks
Global regulatory frameworks, such as the AI Act of the European Union, are driving companies to prioritize the ethical dimension in their AI developments.
Privacy-oriented techniques
Privacy-oriented techniques, such as federated learning (federated learning), allow models to be trained without the need to share sensitive data centrally.
In addition, they play a crucial role in protecting personal information while fostering innovation.
Recommendations for starting to explore AI solutions
For companies just starting out in the world of artificial intelligence, Diego suggests starting by identifying a specific, narrow business problem that can serve as an initial, tangible use case.
In this sense, it is crucial:
Prioritize data quality and governance, as they constitute the foundation upon which any effective AI solution is built.
Establish strategic alliances with external specialists or consultants, which can guide the initial strategy and help avoid common mistakes that can be costly.
Cross-functional integration with the various work teams involved is essential, from the early stages of the process.
Not only does it allow for clear communication of the expected benefits, it also fosters a sense of ownership and active participation among those involved in the transformation.
Transformative AI use cases in the financial sector
Artificial intelligence is having a profound impact on digital transformation projects in the financial sector, through use cases that are redefining the internal operations of entities and the way entities relate to their customers.
Fraud detection
Fraud detection is, without a doubt, one of the most impactful applications of artificial intelligence in the financial sector.
AI models can analyze anomalous transaction patterns in real time, identifying suspicious activity with speed and accuracy far beyond human capabilities, even before financial damage materializes.
Personalization of financial products and services
AI makes it possible to tailor offers, such as loans or investment portfolios, to each customer's unique profile.
As a result, their user experience and satisfaction are substantially improved.
Process automation
Automating processes, such as credit risk assessment, not only optimizes time.
It also increases the objectivity and accuracy of such assessments by being based on a comprehensive data analysis.
24/7 Attention
Intelligent chatbots and advanced virtual assistants offer 24/7 customer support, resolving common and complex queries.
This allows human teams to focus on tasks with greater added value and specialization.
What are AI agents?
AI agents are computational systems designed to perceive their environment, process information, and act autonomously or semi-autonomously to achieve specific goals.
To achieve this, they simulate intelligent behavior that can adapt to dynamic contexts.
Its main features include:
Machine learning capacity.
Interaction with users or other systems.
Decision making based on predefined rules or predictive models.
Adaptability to new situations or information.
While the benefits are considerable:
They automate repetitive or complex processes.
They optimize the use of time and resources.
They reduce operating costs.
They allow for a high degree of customization at scale.
Its applications are vast, ranging from real-time customer service and supply chain management to predictive analytics in finance and medical-assisted diagnosis.
In essence, AI agents allow transcend the sequentiality inherent in human multitasking, to address them from a perspective of simultaneity and parallel processing.
How can AI improve the personalization of financial services without compromising data security?
Artificial intelligence offers enormous potential to increase the personalization of financial services by analyzing customer behavior patterns to offer:
Tailor-made products and advice, such as pre-approved loans tailored to your needs.
Investment recommendations aligned with your risk profiles and personal goals.
However, it is imperative that this customization be implemented under a strict ethical and legal framework, which guarantees the protection of data privacy.
People and AI: key allies for digital transformation.
Techniques such as federated learning allow artificial intelligence models to be trained without the need to centralize or directly share sensitive information, as the data remains on users' local devices or in secure environments.
Additionally, data anonymization and pseudonymization, along with the use of robust encryption protocols, are fundamental strategies for ensuring that personal information remains protected at all stages of the data lifecycle, thus reconciling innovation with privacy.
Challenges for the financial system in scaling AI solutions in a regulated environment
Scaling AI solutions in the financial sector, an environment characterized by dense regulation, presents specific challenges that require a meticulous attention and strategy.
While the landscape may seem complex, with a strategic approach focused on compliance, data governance, and transparency, it is possible to overcome the challenges we share below to advance AI adoption.
Compliance with strict regulations
One of the biggest hurdles is complying with strict regulations such as the GDPR at the European level, or local data protection and cybersecurity regulations, which impose clear limitations on how customer data can be collected, processed, and used.
Transparencyand explainability of AI models
There is also an imperative need to ensure the transparency and explainability of artificial intelligence (XAI) models.
Algorithmic decisions, especially those that impact consumers, such as granting credit, must be auditable and understandable, both for regulators and for customers themselves.
Integrating AI with legacy systems
As mentioned above, integrating AI with legacy systems, which are often technologically rigid and feature information silos, can slow implementation and increase project costs.
Talent shortage
The lack of specialized talent with expertise in artificial intelligence and financial expertise remains a recurring problem that hampers the development and maintenance of these solutions on a large scale.
Role of AI in fraud detection and risk management
Artificial intelligence is poised to play an increasingly pivotal role in digital transformation projects, fraud detection, and risk management—two critical areas for the financial sector, where speed of response and analytical accuracy are essential.
In fraud detection, AI can analyze massive volumes of transactions in real time to identify anomalous patterns or atypical behavior. which could be indicative of suspicious activity.
It is a task that is impractical to perform manually, on the same scale and with the same efficiency.
In risk management, artificial intelligence uses historical and current data to build predictive models capable of anticipating potential problems, as:
The risk of default (credit risk).
Adverse market fluctuations (market risk).
Operational failures (operational risk)
This allows financial institutions to proactively adjust their strategies and mitigate losses. For example, they can assess an applicant's credit risk with a level of granularity and precision that significantly exceeds traditional methods.
Opportunities in the combination of AI and open finance
The synergistic combination of artificial intelligence and open finance is unlocking a vast array of opportunities for disruptive innovation in the financial sector.
With openfinance, consented access to customer financial data from various entities allows AI to develop hyper-personalized and contextual services.
For example, offering unique financial products dynamically tailored to the specific needs, behaviors, and circumstances of each individual or company.
It also facilitates the creation of more collaborative and inclusive financial ecosystems, connecting users with a broader range of service providers and democratizing access to sophisticated financial solutions.
For example, artificial intelligence could analyze aggregated data (always with proper consent) to help small and medium-sized businesses Obtain financing through alternative and more accurate credit scoring modelsIn this way, barriers that previously limited their access to credit could be overcome.
This collaborative, data-driven approach has the potential to radically transform the way people and businesses interact with financial services, making them more accessible, relevant, efficient, and user-centric.
