AI and Low-Code Platforms Are Redefining Business Efficiency
Artificial Intelligence and low-code platforms are changing the way companies operate, and this shift is happening right now — not in some distant future.
For a long time, business automation meant following scripts, repeating tasks, and optimizing workflows that already existed. Functional, but limited.
What is emerging now is something quite different: systems that interpret context, make decisions in real time, and evolve as they are used. This new paradigm is being led by companies like Indra Group, which combines advanced AI with low-code platforms to create a layer of autonomous automation that goes far beyond what traditional RPA ever delivered.
But with more autonomy comes more responsibility. And that is exactly where cybersecurity enters the picture — not as a technical afterthought, but as a central part of the entire operation.
Below, you will learn how this convergence is reshaping business efficiency across sectors like banking, energy, and transportation, what real risks come along with this evolution, and what companies need to consider to navigate this landscape with security and intelligence.
When AI Stops Executing and Starts Thinking
The difference between traditional automation and what modern Artificial Intelligence platforms deliver today is enormous, and it goes well beyond speed or processing volume. Classic RPA, for example, worked well for repetitive and predictable tasks: copying data from one system to another, filling out forms, following a fixed approval workflow. It was efficient within a very controlled scope. The problem is that the business world rarely operates within controlled scopes, and that is precisely where AI changes the game in a radical way.
Jose Angel Tinoco, Director of Operations Tech at Indra Group, sums up this transition well: traditional automation solved for efficiency, but what we are seeing now are systems that understand context and act accordingly. This changes the logic of operations, not just optimizes them. It is a paradigm shift that affects how organizations think about their internal processes, from design all the way through execution.
What Artificial Intelligence brings to the table is the ability to handle variables, exceptions, and contexts that previously required human intervention. An intelligent system can analyze a transaction history, identify an unusual pattern, cross-reference that information with external data, and make a well-founded decision — all in milliseconds and without needing an analyst on the other end. This level of operational autonomy is what Indra Group is deploying in production across critical sectors like banking, energy, and transportation, where every second of a wrong decision carries a real, measurable cost.
Indra Group categorizes this automation evolution into distinct technical stages. The first was Robotic Process Automation (RPA), focused on productivity for predefined, linear tasks. Now, organizations are transitioning to Intelligent Process Automation (IPA) and Agentic Process Automation (APA). In these models, systems do not follow instructions rigidly — they interpret information, prioritize actions, and operate with a degree of autonomy that was unthinkable just a few years ago. According to Indra Group, this advancement is possible thanks to the maturity of large language models and their integration into platforms that manage entire processes.
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The qualitative leap here is that these systems do not just execute — they learn from execution. Every interaction feeds the model, every handled exception becomes training data, every piece of feedback from a human operator refines future behavior. This creates a virtuous cycle where automation becomes progressively more accurate and better adapted to the specific context of each company. And when you combine that capability with low-code platforms, the result is an implementation speed that would have been unthinkable five years ago.
The Role of Low-Code in This Equation
Low-code platforms have been around for a few years, but what is happening now is different from what we saw before. Initially, these tools were seen as a solution for companies that did not have robust development teams — a way to build simple applications without needing experienced programmers. That perception has changed significantly, and anyone still viewing low-code that way is missing a huge slice of what the technology actually offers today, especially when integrated with Artificial Intelligence capabilities.
The combination of AI with low-code platforms allows business teams — not just IT teams — to build sophisticated automation workflows with far less technical friction. An operations analyst can configure an intelligent approval process that uses AI to analyze documents, cross-reference data, and recommend decisions, all without writing a single line of code. This does not eliminate developers, but it frees up their time to work on more complex architectures while the rest of the organization moves forward in parallel.
Tinoco emphasizes that this approach delivers a responsiveness that simply did not exist before, since organizations can adjust processes without depending on long development cycles. And when these systems are integrated with AI, they evolve through their own use after implementation.
In practice, what Indra Group is deploying for its clients across Latin America is exactly this hybrid layer: a sophisticated technology foundation with embedded AI, delivered through low-code interfaces that allow for customization and continuous evolution. This translates into real business efficiency — processes that adapt faster to market changes, fewer operational bottlenecks, and a responsiveness that traditional organizations simply cannot match when running on legacy systems and conventional methods.
Indra Group Expansion in Mexico and Latin America
The deployment of these technologies in the United States and Latin America coincides with Indra Group‘s operational expansion in Mexico. On April 9, 2026, the company inaugurated new corporate offices at Parque Toreo in the State of Mexico. This new facility expands the corporation’s capacity to support digital transformation, AI, and cybersecurity projects across sectors like banking, energy, and transportation.
The expansion is aligned with the company’s goal of achieving double-digit sales growth and creating new jobs over the next three years. The corporation’s regional strategy also includes the NovaIA Center of Excellence, located in Tunja, Colombia. This center accelerates the adoption of applied AI across the Caribbean and Latin America.
NovaIA operates under a governance model that involves academia, government, and the private sector. The center is organized into five technical modules:
- Ideation and design
- Technical lab for generative models
- Visualization and demonstration
- Data and governance
- Infrastructure and security
This multidisciplinary structure shows that Indra Group’s approach goes beyond the technology itself — it involves governance, education, and collaboration across different sectors of society to ensure that AI adoption is carried out responsibly and at scale.
Cybersecurity: The Side Nobody Can Afford to Ignore
The more autonomous and connected an operation becomes, the larger the attack surface available to malicious actors. This is a basic principle of cybersecurity, and it applies with full force to the intelligent automation landscape we are describing. Systems that make real-time decisions, that access sensitive customer data, that integrate with critical energy or transportation infrastructure — these systems need layers of protection that match the sophistication of the technology itself.
Tinoco warns that when a system starts making decisions on its own, the focus needs to shift from functionality to auditability and alignment with business objectives. Cybersecurity stops being a peripheral mechanism and becomes an integral part of operations.
Minsait Cyber, Indra Group’s technology subsidiary, identifies the global cybersecurity landscape in 2026 as being defined by geopolitical tensions and the unregulated adoption of AI. Erik Moreno, Director of Minsait Cyber at Indra Group, states that organizations need to transition from reactive measures to operational resilience. The environment demands the adoption of Security by Design and Zero Trust Architecture (ZTA) as minimum standards.
These architectures operate under the principle of never trust, always verify, which limits the lateral movement of attackers within distributed networks. Additionally, Cybersecurity Mesh Architecture (CSMA) offers a modular approach to unifying dispersed security controls.
Technical risks mapped for 2026
Among the technical risks identified for 2026 are attacks on code repositories and public libraries, which have become primary risk vectors for the software supply chain. To mitigate these threats, the corporation recommends the systematic use of Software Bill of Materials (SBOM) to inventory all software components in use.
Additionally, Cloud Native Application Protection (CNAPP) platforms allow organizations to monitor the entire development lifecycle. Security operations are also evolving with the use of AI-assisted SIEM (Security Information and Event Management), which helps reduce so-called alert fatigue — that avalanche of notifications that ends up causing analysts to miss truly critical events.
Data management represents another critical priority for B2B companies. Technologies such as Data Security Posture Management (DSPM) and Data Loss Prevention (DLP) are necessary to control so-called dark data — unclassified information with no visibility within the organization.
Moreno also emphasizes the protection of biometric data and the need for governance policies addressing Shadow AI — the unauthorized use of AI agents by employees within companies. Digital hygiene remains a significant weakness, as insecure configurations and delayed patches continue to facilitate unauthorized access.
The regulatory landscape in Mexico
In Mexico, the absence of a comprehensive cybersecurity law complicates the creation of national strategies based on concrete data. Moreno points out that the pace of legislation is reactive and lags behind technological advancements, creating regulatory gaps that need to be addressed urgently, especially considering the large-scale events on the horizon.
2026 World Cup and the Pressure to Scale
The 2026 World Cup represents a concrete and very near-term use case for everything we have been discussing here. An event of this magnitude, with games spread across the United States, Mexico, and Canada, mobilizes a massive volume of financial transactions, logistics operations, energy flows, and transportation demands within a compressed timeframe and under extremely high global visibility. For host countries and cities, this is both an opportunity and a stress test for all available technological and operational infrastructure.
In Mexico, where Indra Group has a significant presence, preparation for this scenario involves precisely the implementation of intelligent automation systems capable of scaling quickly, absorbing demand spikes without performance degradation, and maintaining business efficiency even under atypical conditions. Banking and payment systems need to process volumes well above average without failures. Public transportation and logistics need to coordinate mass movements with precision. Energy needs to guarantee stable supply even with elevated consumption during game times.
And all of this needs to be protected. Large-scale events are preferred targets for cyberattacks, whether financially motivated or driven by political visibility. A massive increase in digital and electronic banking operations is expected during the tournament, which will likely trigger hyper-personalized attacks and fraud through AI-powered social engineering. The combination of Artificial Intelligence, low-code platforms, and a solid cybersecurity posture is not just a matter of operational efficiency in this context — it is a matter of resilience and national reputation.
Governance and the Human Factor in Intelligent Automation
One point that many companies underestimate is the human side of this transition. Implementing intelligent automation is not just a technology decision — it is an organizational decision. Teams need to be trained to work with Artificial Intelligence tools and low-code platforms, managers need to understand what the systems are deciding and why, and the company culture needs to absorb this new dynamic where humans and systems collaborate rather than one simply replacing the other.
Rigorous governance is essential when increasing system autonomy. It is not enough for an AI model to work — it needs to be auditable, transparent, and aligned with the strategic objectives of the business. Companies that ignore this dimension tend to underutilize the tools they implement and achieve results well below their potential.
The governance model adopted by the NovaIA Center in Colombia is an interesting example of this approach. By involving academia, government, and the private sector within the same structure, Indra Group creates an ecosystem where technological innovation is anchored in principles of responsibility, transparency, and collaboration. This type of model is increasingly necessary as autonomous systems gain ground in operations that directly impact people’s lives.
What Companies Need to Consider Today
The convergence of Artificial Intelligence, low-code, and cybersecurity is not a future trend to place on a 2027 roadmap. It is happening now, and companies that are moving in this direction are already reaping concrete competitive advantages in terms of operational speed, cost reduction, and responsiveness to market changes. The gap between organizations that have adopted this approach and those still operating with conventional models is growing at an accelerating pace.
The business efficiency that emerges from this new model is not about doing more with less in the sense of cutting resources. It is about creating capabilities that simply did not exist before — making better decisions faster, identifying opportunities and risks before they become problems or losses, and building operations that dynamically adapt to the environment instead of reacting after the fact.
That is the real value that the combination of Artificial Intelligence, low-code platforms, and a serious cybersecurity strategy delivers for organizations willing to take this step. And with Indra Group’s expansion in Mexico and Latin America, this ecosystem gains even more traction in a region that sits at the center of global events and a growing demand for intelligent, secure digital transformation. 🚀
