Managing Technical Debt: Preparing for the Adoption of Emerging Technologies
Technical debt is a reality for any tech company, but in today’s fast-evolving landscape—where technologies like artificial intelligence (AI) are reshaping industries—managing it has become more critical than ever. Small technical compromises made to meet deadlines can accumulate, eventually becoming significant barriers to leveraging the opportunities that AI and other emerging technologies offer. If left unchecked, technical debt increases costs, slows down the delivery of new features, and stifles innovation—precisely when scalability and agility are key in a highly competitive market.
The Impact on Scalability
Technical debt not only raises costs but also threatens a company’s ability to grow and adapt quickly. As demand for technologies like AI increases, inefficient infrastructure and suboptimal technical decisions can hinder the successful implementation of these solutions. This creates roadblocks that slow technological progress, often requiring expensive redesigns and consuming critical resources in a competitive environment.
Inefficient code and poorly designed architectures complicate the integration of new technologies such as AI, limiting an organization’s ability to remain agile and scale its systems effectively. In many cases, technical debt can even degrade the performance of AI models, reducing their capacity to handle large datasets or deliver real-time insights—significantly diminishing their business impact.
Technical Debt in Cloud Migration
Migrating to the cloud provides an opportunity to optimize systems and reduce technical debt. However, if legacy applications and outdated systems are not refactored before migration, moving these flawed structures to the cloud will only amplify the problems. This is particularly relevant as cloud infrastructure is essential for harnessing the full potential of AI and other scalable solutions that the market demands.
A strategic approach, where code is evaluated and refactored prior to migration, ensures that systems can scale effectively in the cloud, facilitating the use of advanced technologies like AI. This proactive approach reduces risks, enhances operational flexibility, and allows companies to compete in a constantly evolving market.
Strategies for Addressing Technical Debt
To tackle these challenges, companies must prioritize the most critical areas of their systems—those that directly impact their ability to innovate and scale, especially when adopting emerging technologies like AI. Continuous refactoring is an effective strategy: making small, regular adjustments prevents issues from piling up and helps keep maintenance costs under control.
Incorporating technical debt management into the agile development cycle ensures that necessary fixes aren’t deferred. This is crucial for companies looking to quickly adopt technologies like AI, where maintaining efficient systems can make the difference between thriving or falling behind in a highly competitive market.
Moreover, improving communication between technical teams and business leaders is essential. CTOs and IT leaders must justify investments in reducing technical debt by demonstrating how it affects the company’s competitiveness in an environment where AI and scalability are key drivers of growth. Using clear metrics—such as reduced code duplication or improved test coverage—is critical to making a compelling case for this investment.
Tools for Detecting and Reducing Technical Debt
Keeping systems competitive requires advanced tools that optimize maintenance and streamline fixes. Solutions like SonarQube and CodeClimate not only help identify problems quickly but also quantify the impact of technical debt, allowing teams to prioritize their efforts accordingly.
AI-powered tools like DeepSource can automate the correction of common issues, freeing teams to focus on more strategic improvements. Cast Highlight complements these tools by offering a broader business perspective, helping to plan refactoring initiatives based on risk and sustainability—especially important in environments that demand high scalability.
Securing the Future of Development in a Competitive Landscape
As AI continues to transform industries and the need for scalability becomes increasingly urgent, proactively managing technical debt is vital. Failing to do so limits a company’s ability to innovate and scale, ultimately reducing its competitiveness in a fast-moving market.
By incorporating continuous refactoring strategies, prioritizing critical system areas, and leveraging advanced tools, businesses can ensure their systems are prepared to adopt new technologies and remain agile. In a world where artificial intelligence and scalability are key growth drivers, effectively managing technical debt is an essential investment for long-term success. Ready to address your technical debt and ensure your systems are built to scale? Let’s talk.
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Isabel Rivas
Business Development Representative irivas@huenei.com
The true value of generative AI lies not just in adopting off-the-shelf solutions but in leveraging data-driven AI to tailor these technologies to meet specific company needs. This data-centered approach enhances results and creates a sustainable, differentiated competitive advantage.
Three Levels of Generative AI Adoption
Companies’ use of generative AI can be classified into three levels: Taker, Shaper, and Maker.
At the first level, Takers implement ready-made AI solutions. This allows them to achieve quick results at a low cost, but without deep adaptation to their processes. While this facilitates initial adoption, its long-term impact is limited.
At the second level, Shapers modify data-driven AI models using their own data, improving accuracy and control over the outcomes. This enables them to better address specific business challenges.
Finally, at the Maker level, companies build or fully customize AI models from scratch. This approach provides total control over the technology, shaping it entirely to business needs, offering unparalleled flexibility and mastery.
Strategy for Advancing Personalization
Personalizing generative AI solutions allows companies to align outcomes with their specific goals by leveraging data-driven AI models that reflect their unique processes. This enhances decision-making accuracy and optimizes key operations, leading to a competitive advantage that is difficult to replicate.
To move toward this level of personalization, ensuring the quality of internal data used to train the models is essential. The training phase is critical in guaranteeing the accuracy and effectiveness of the results, as the models rely on relevant and representative data from the business processes.
Data preprocessing is a crucial step at this stage. Processes like data cleaning, normalization, and dimensionality reduction (using techniques such as PCA or t-SNE) enhance the quality of the dataset and maximize the model’s ability to identify accurate patterns. Tools like Snowflake and Databricks help manage large datasets and prepare them for training.
Platforms such as OpenAI, Google Vertex AI, and Azure Machine Learning offer the capabilities to fine-tune and train generative AI models with proprietary data, ensuring the solutions are tailored to the specific challenges each organization faces.
Challenges of AI Personalization
Transitioning to a more advanced use of AI comes with several challenges. One of the main hurdles is the initial investment required to establish the necessary data infrastructure and strengthen the technical team. While the upfront costs may seem high, the long-term gains in competitiveness and efficiency often justify the investment.
Another challenge is managing the technical aspects of personalized models. Continuous training and periodic updates are essential to maintain their relevance and effectiveness, as the business environment constantly evolves. Models trained with outdated or incomplete data will see their accuracy and usefulness decline.
To mitigate this risk, companies must implement recurring training cycles and automatic update mechanisms. Transfer learning, a technique that enables the reuse of pre-trained models to adapt to new datasets, can speed up this process and lower training-related costs.
For companies facing resource limitations or skill gaps, working with Agile Dedicated Teams can be a solution. These specialized teams bring the necessary expertise and flexibility to train, update, and optimize AI models, ensuring they perform efficiently in a rapidly changing market.
In addition, MLOps (Machine Learning Operations) practices automate the monitoring and updating of models, ensuring that training and optimization cycles remain uninterrupted. This not only reduces operational burdens but also ensures that models respond quickly to changing market conditions.
Lastly, data security and intellectual property protection are critical when using internal information to train AI models. Encryption and anonymization techniques must be applied to minimize risks and ensure compliance with regulations.
Building Custom Models: Full Control over AI
Some companies choose to go beyond superficial personalization and develop fully customized AI solutions. Creating models from scratch or with a high degree of customization gives them complete control over their function and evolution.
However, this doesn’t mean all development must be done internally. Many organizations partner with technology experts who bring specialized knowledge and resources, combining deep business insights with the technical expertise of their partners. This collaboration ensures that AI solutions are optimized and aligned with strategic objectives.
Maximizing Generative AI through Personalization
Personalizing generative AI is crucial for companies looking to stand out and fully capitalize on this technology. A strategic approach that prioritizes training models with high-quality data is key to ensuring the accuracy and effectiveness of results.
Fine-tuning models with internal data not only boosts accuracy but also ensures that solutions align with the business’s specific needs, providing a lasting competitive edge. To advance toward a personalized strategy, companies need to evaluate data quality, strengthen technical teams, and carefully select the most appropriate use cases.
In doing so, businesses will not only leverage generative AI but also lead innovation in their sectors with custom-built technological solutions.
Interested in personalizing your generative AI solutions? Contact us and let’s discuss how we can help your business achieve its goals.
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Francisco Ferrando
Business Development Representative fferrando@huenei.com
Training artificial intelligence (AI) models requires vast amounts of data to achieve accurate results. However, using real data poses significant risks to privacy and regulatory compliance. To address these challenges, synthetic data has emerged as a viable alternative.
These are artificially generated datasets that mimic the statistical characteristics of real data, allowing organizations to train their AI models without compromising individual privacy or violating regulations.
Regulatory Compliance, Privacy, and Data Scarcity
Regulations around the use of personal data have become increasingly strict, with laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States.
This approach to data provides a solution for training AI models without putting personal information at risk, as it does not contain identifiable data, yet remains representative enough to ensure accurate outcomes.
Use Cases for Synthetic Data
The impact of this technology extends across multiple industries where privacy protection and a lack of real-world data present common challenges. Here’s how this technology is transforming key sectors:
Financial
In the financial sector, the ability to generate artificial datasets allows institutions to improve fraud detection and combat illicit activities. By generating fictitious transactions that mirror real ones, AI models can be trained to identify suspicious patterns without sharing sensitive customer data, ensuring compliance with strict privacy regulations.
For instance, JPMorgan Chase employs synthetic data to bypass internal data-sharing restrictions. This enables the bank to train AI models more efficiently while maintaining customer privacy and complying with financial regulations.
Healthcare
In the healthcare sector, this approach is crucial for medical research and the training of predictive models. By generating simulated patient data, researchers can develop algorithms to predict diagnoses or treatments without compromising individuals’ privacy. Synthetic data replicates the necessary characteristics for medical analyses without the risk of privacy breaches.
For instance, tools like Synthea have generated realistic synthetic clinical data, such as SyntheticMass, which contains information on one million fictional residents of Massachusetts, replicating real disease rates and medical visits.
Automotive
Synthetic data is playing a crucial role in the development of autonomous vehicles by creating virtual driving environments. These datasets allow AI models to be trained in scenarios that would be difficult or dangerous to replicate in the real world, such as extreme weather conditions or unexpected pedestrian behavior.
A leading example is Waymo, which uses this method to simulate complex traffic scenarios. This allows them to test and train their autonomous systems safely and efficiently, reducing the need for costly and time-consuming physical trials.
Generating and Using Synthetic Data
The generation of synthetic data relies on advanced techniques such as generative adversarial networks (GANs), machine learning algorithms, and computer simulations. These methods allow organizations to create datasets that mirror real-world scenarios while preserving privacy and reducing the dependence on sensitive or scarce data sources.
Synthetic data can also be scaled efficiently to meet the needs of large AI models, enabling quick and cost-effective data generation for diverse use cases.
For example, platforms like NVIDIA DRIVE Sim utilize these techniques to create detailed virtual environments for autonomous vehicle training. By simulating everything from adverse weather conditions to complex urban traffic scenarios, NVIDIA enables the development and optimization of AI technologies without relying on costly physical testing.
Challenges and Limitations of Synthetic Data
One of the main challenges is ensuring that synthetic data accurately represents the characteristics of real-world data. If the data is not sufficiently representative, the trained models may fail when applied to real-world scenarios. Moreover, biases present in the original data can be replicated in synthetic data, affecting the accuracy of automated decisions.
Constant monitoring is required to detect and correct these biases. While useful in controlled environments, synthetic data may not always capture the full complexity of the real world, limiting its effectiveness in dynamic or complex situations.
Synthetic data is just one of the tools available to protect privacy while training AI. Other approaches include data anonymization techniques, where personal details are removed without losing relevant information for analysis. Federated learning, which enables AI models to be trained using decentralized data without moving it to a central location, is also gaining traction.
The potential for synthetic data extends beyond training models. These data can be used to enhance software validation and testing, simulate markets and user behavior, or even develop explainable AI applications, where models can justify their decisions based on artificially generated scenarios.
As techniques for generating and managing synthetic data continue to evolve, this data will play an even more crucial role in the development of safer and more effective AI solutions.
The ability to train models without compromising privacy, along with new applications that leverage artificially generated data, will allow businesses to explore new opportunities without the risks associated with real-world data.
Are you ready to explore how we can help you safeguard privacy and optimize AI implementation in your organization? Let’s talk.
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Isabel Rivas
Business Development Representative irivas@huenei.com
Rethinking AI Talent Recruiting for Competitive Advantage
The demand for highly specialized talent in artificial intelligence (AI) is growing rapidly, becoming a critical priority for companies aiming to implement AI-based solutions. The labor market is increasingly complex, with businesses facing challenges in identifying, attracting, and retaining the right professionals.
In this article, with insights from Javier Pérez Lindo, Professional Services Manager at Huenei, we explore the hurdles in finding and keeping AI talent, the key profiles needed in this field, and strategies to remain competitive.
Evaluating Specialized Talent: An Ongoing Challenge for Businesses
The fast pace of technological change means companies not only need to find skilled professionals but also ensure these individuals are capable of continuous learning. Tools and technologies that are relevant today may quickly become obsolete.
As Javier Pérez Lindo points out, “It’s crucial that AI professionals not only master current solutions but also possess the ability to adapt and continuously learn, as this field evolves at an extraordinary pace.”
In addition to technical expertise, companies need qualified internal evaluators who are up to date with the latest trends and advancements in AI. These evaluators play a vital role in identifying promising candidates and accurately assessing their abilities in a rapidly shifting landscape.
“At Huenei, we place great emphasis on keeping our internal evaluators informed about industry advancements, ensuring our hiring process accurately reflects the potential and capabilities of the candidates we bring in,” says Pérez Lindo.
Beyond Compensation: Strategies for Retaining Top AI Talent
The AI job market is fiercely competitive, with experienced professionals often receiving multiple offers. In this context, companies need to offer more than just competitive salaries.
Opportunities for career development, access to cutting-edge projects, and exposure to the latest technologies are key factors that can make a significant difference in attracting and retaining top talent. “Today, offering a good salary is not enough. Professionals want to work in environments where they can grow, innovate, and face new challenges constantly,” Pérez Lindo emphasizes.
To retain talent, it’s also crucial for leaders to stay informed about the latest AI technologies. Fostering a collaborative environment where professionals can work alongside equally knowledgeable peers, and promoting innovation within the organization, helps keep top talent engaged.
Continuous training programs, which ensure employees stay updated on the latest trends, are also essential to ensuring long-term commitment and retention.
Key Profiles and Technologies Driving AI Development
The most sought-after AI roles combine advanced technical expertise with proficiency in key technologies. Machine learning engineers typically use tools like TensorFlow and PyTorch to build models, while data scientists work with large datasets using Python and Apache Spark.
AI developers fine-tune code generated by AI systems, and infrastructure specialists ensure efficient deployment on cloud platforms like AWS and Azure. In natural language processing (NLP), technologies such as GPT and BERT are foundational, while AutoML automates model development. These roles are essential for scaling and optimizing AI solutions effectively.
Agility and Flexibility with Dedicated Development Teams
Many companies are opting to work with Agile Dedicated Teams to tackle recruitment challenges. These teams provide flexible scaling based on project needs, allowing businesses to avoid lengthy hiring processes and focus on strategic decisions.
This approach promotes agility, enabling businesses to quickly respond to market changes or new opportunities without sacrificing the quality of work or overburdening internal resources. As Pérez Lindo explains, “Dedicated teams provide the agility essential in AI projects, enabling you to adapt quickly while staying focused on key strategic priorities.”
Turnkey Projects: The Advantages of Outsourcing AI Development
Outsourcing AI projects offers an efficient solution for companies lacking specialized internal resources. Turnkey projects provide the advantage of deploying AI solutions quickly, with reduced risk and better cost control. This approach allows businesses to tap into external expertise without overwhelming internal teams.
By outsourcing, organizations can concentrate on their core business areas while ensuring high-quality AI development and minimizing the risk of errors.
Looking Ahead: The Future of AI Talent Acquisition
Finding and retaining specialized AI talent requires a strategic and flexible approach that adapts to rapid technological advancements. The ability to learn and work with new technologies will be crucial for companies looking to maximize the potential of artificial intelligence. “The businesses that will succeed in attracting top AI talent are those that foster dynamic, innovative environments,” Pérez Lindo concludes.
Companies that offer challenging projects and adopt cutting-edge technologies will attract the best professionals and drive the development of their teams. By combining flexibility, dedicated teams, and project outsourcing, organizations can remain competitive and agile in a constantly evolving landscape.
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Francisco Ferrando
Business Development Representative fferrando@huenei.com
Shadow AI: The Hidden Challenge Facing Modern Businesses
Today’s businesses are immersed in a constant cycle of innovation, where artificial intelligence (AI) has become a crucial ally. However, as the excitement to implement AI to solve daily problems and enhance efficiency grows, a new challenge has emerged: Shadow AI. This phenomenon, though less visible, can seriously compromise the security and efficiency of organizations if not managed properly.
In this article, we will explore Shadow AI with key insights from Lucas Llarul, Infrastructure & Technology Head at Huenei, who shares his perspective on how to tackle this challenge.
“Shadow AI is a threat that can turn into an opportunity if managed strategically,” asserts Lucas Llarul.
The Nature of Shadow AI: Beyond Unauthorized Tools
Shadow AI reflects a trend where employees, in an effort to streamline their tasks or meet specific needs, resort to AI tools without the knowledge or approval of the IT team. Llarul explains: “Using unauthorized solutions, even with the intention of boosting efficiency, entails significant risks.” These unmonitored tools can process sensitive information without adequate security measures, exposing the organization to critical vulnerabilities.
A clear example is the case of Samsung, where employees leaked confidential information to OpenAI’s servers by using ChatGPT without authorization. “This incident illustrates how unregulated AI usage can compromise information security in any organization, even those with strict security policies,” adds Lucas.
The issue is not only technical, but also strategic: when each department selects its own AI solutions, information silos are created, disrupting workflows and data sharing across departments. This creates a technological disarray that’s difficult and costly to fix.
Solution Fragmentation: A Barrier to Scaling
Technological fragmentation is one of the biggest challenges growing companies face. Lucas emphasizes, “When AI tools aren’t integrated and each team adopts its own solutions, the company can’t operate smoothly.
This directly impacts the ability to make fast, data-driven decisions. Moreover, the costs associated with maintaining disconnected or redundant technologies can escalate rapidly, jeopardizing sustainable growth.”
The lack of technological cohesion not only hampers innovation but also creates barriers to internal collaboration, compromising a company’s competitiveness.
Avoiding the Chaos of Shadow AI: A Proactive Strategy
Llarul suggests that the key to avoiding the risks of Shadow AI lies in adopting a proactive strategy that prioritizes visibility and control over the tools used within the company. “The first step is to create a detailed inventory of all the AI tools in use.
This not only helps identify which technologies are active but also clarifies their purpose, which is crucial for managing security risks and ensuring that the chosen tools truly meet operational needs,” he explains.
From a technical standpoint, IT team involvement is essential to ensure that AI solutions are properly integrated into the company’s infrastructure and meet security and compliance standards.
Furthermore, it’s not about banning unauthorized tools but understanding why employees turn to them. “If the organization provides approved and customized solutions that address teams’ real needs, it can foster an environment where innovation occurs in a controlled, risk-free manner,” adds Lucas. By involving IT teams from the outset and aligning solutions with the company’s strategic goals, it’s possible to centralize control without stifling dynamism and efficiency.
The Value of Customized Solutions in the AI Era
Llarul emphasizes that the answer is not only to centralize control but also to offer tailored alternatives: “By developing AI solutions tailored to each department’s specific needs allows technology optimization without compromising security or operational efficiency.” This also helps avoid problems arising from tool fragmentation and redundancy, fostering technological cohesion.
“Companies that implement tailored solutions aligned with their objectives can scale without facing the challenges imposed by technological fragmentation. A personalized approach fosters innovation and enhances competitiveness,” he adds.
Turning Shadow AI into a Growth Opportunity
Shadow AI is a growing challenge, but not an insurmountable one. Lucas concludes, “Companies that proactively manage AI implementation can turn this challenge into a chance for expansion.” By centralizing tool adoption, encouraging customization, and promoting a culture of responsible innovation, organizations will be better positioned to harness the full potential of artificial intelligence.
Are you interested in exploring how we can help you manage Shadow AI and improve AI adoption in your company? Let’s talk.
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Francisco Ferrando
Business Development Representative fferrando@huenei.com
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