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
Serverless: The New Paradigm for Agile and Competitive Companies
Far from being just a trend, serverless architecture is driving a fundamental shift in how businesses approach cost optimization and innovation. This technology is redefining how organizations design, develop, and scale their applications, freeing up valuable resources to focus on their core business.
Alejandra Ochoa, Service Delivery Manager of Huenei, states: “Today, serverless encompasses a complete ecosystem including cloud storage, APIs, and managed databases. This allows teams to focus on writing code that truly adds value to the business, reducing operational overhead and increasing agility. The ability to scale automatically and respond quickly to market changes is essential to stay competitive in an environment where speed and flexibility are crucial.”
Competitive Advantage and ROI
Alejandra Ochoa emphasizes the importance of the serverless cost model: “The accuracy in billing introduced by serverless is revolutionary. By charging only for actual execution time in milliseconds, this ‘pay-per-use’ approach aligns expenses directly with value generated, drastically optimizing TCO (Total Cost of Ownership). This not only impacts operational costs but also transforms financial planning, allowing for greater flexibility and precision in resource allocation.”
This model enables companies to automatically scale during demand spikes without incurring fixed costs during low activity periods, significantly improving their operating margins. This effortless scaling capability is a differentiator in terms of agility, allowing companies to stay competitive in highly dynamic markets.
Challenges and Strategic Considerations
While serverless offers transformative benefits, it’s crucial to address challenges such as cold start latency, potential vendor lock-in, and monitoring complexity. Alejandra Ochoa notes: “These challenges require a strategic approach, particularly regarding the choice of programming languages and platforms.”
For example, cold start times for Java functions in AWS Lambda are nearly three times longer than for Python or Node.js, which is an important factor when choosing a programming language for critical workloads. Similarly, in Google Cloud Functions, cold start times for functions written in Go are considerably longer than for functions in Node.js or Python, which can affect performance in time-sensitive applications.
“Beyond technical challenges,” Ochoa adds, “it’s important to consider the impact on the IT operating model. Transitioning to serverless requires a shift in skills and roles within IT teams. It’s crucial to invest in staff training and process adaptation to maximize the benefits of this technology.”
Synergy with Emerging Technologies
The convergence of serverless with AI and edge computing is opening new frontiers in innovation. This synergy enables real-time data processing and the deployment of more agile and cost-effective AI solutions, accelerating the time-to-market of innovative products. Additionally, the emergence of serverless platforms specialized in frontend development is democratizing full-stack development and enabling faster, more personalized user experiences.
Ochoa provides a more specific perspective on this trend: “In the AI space, we’re seeing how serverless is transforming the deployment of machine learning models. For instance, it’s now possible to deploy natural language processing models that automatically scale based on demand, reducing costs and improving efficiency. Regarding edge computing, serverless is enabling real-time IoT data processing, crucial for applications like monitoring critical infrastructure or managing autonomous vehicle fleets.”
Strategic Impact and Use Cases
Serverless excels in scenarios where agility and scalability are crucial. It facilitates the transformation of monolithic applications into more manageable microservices, improving development speed and market responsiveness. In the realm of IoT and AI, it allows for efficient processing of large data volumes and more agile deployment of machine learning models.
Ochoa shares her perspective on the strategic impact: “In the financial industry, serverless is revolutionizing transaction processing and real-time risk analysis. In healthcare, there’s enormous potential for large-scale medical data analysis, which could accelerate research and improve diagnostics. Furthermore, serverless is redefining how companies approach innovation and time-to-market. The ability to quickly deploy new features without worrying about infrastructure is enabling shorter development cycles and more agile responses to market demands.”
Conclusion
Adopting serverless architectures represents a strategic opportunity for companies seeking to maintain a competitive edge in the digital age. By freeing teams from the complexities of infrastructure management, serverless allows organizations to focus on innovation and delivering real value to their customers.
“For tech leaders, the question is no longer whether to consider serverless but how to implement it strategically,” concludes Ochoa. “This involves not only technical evaluation but also careful consideration of available vendors and technologies, as well as planning for the future evolution of architecture. At Huenei, we are committed to helping our clients navigate this transition and make the most of the opportunities offered by serverless, including its integration with emerging technologies like AI and edge computing.”
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Francisco Ferrando
Business Development Representative fferrando@huenei.com
Generative AI is no longer in the experiment stage. Chief Information Officers (CIOs) are now looking to ramp up these solutions and gain a real edge in the market. However many companies are hitting roadblocks that prevent them from maximizing the potential of Generative AI.
While the challenges organizations face often fall into common categories, the solutions must be tailored to each company’s unique needs.
Choosing the Right Path
The first step is deciding how your company will integrate these new tools. There are three main options: pre-built tools, custom models with your own data, and building your own large language models (LLMs).
Here are some key factors to consider when making this choice:
Resources and budget: Pre-built tools are the most cost-effective option but offer less control. Integrating models with your data requires investment in infrastructure and talent. Building LLMs from scratch is the most expensive option, requiring significant resources and cutting-edge expertise.
Specific needs and use cases: If you only need Generative AI for basic tasks, pre-built tools might suffice. However, if you require highly specialized AI for your core products or services, building custom solutions will provide a greater long-term advantage.
Data ownership and regulations: In some industries, regulations or data privacy concerns might necessitate integrating models with your data or building solutions in-house.
Long-term AI strategy: If AI is simply another tool in your toolbox, pre-built solutions might work. But to gain a competitive advantage through AI, you’ll need to develop unique in-house capabilities.
For example, FinanceCorp initially used pre-built Generative AI tools for tasks like writing and summarizing reports. However, these tools proved inadequate for complex financial tasks like risk analysis and contract reviews. To achieve the performance they needed, they had to switch to a custom model solution with their own data.
Taming the Generative AI Beast
One key lesson learned from pilot projects is the importance of avoiding a sprawl of platforms and tools. A recent McKinsey survey found that “too many platforms” was a major obstacle for companies trying to implement Generative AI at scale. The more complex the infrastructure, the higher the cost and difficulty of managing large-scale deployments. To achieve scale, companies need a manageable set of tools and infrastructure.
One solution is to establish a centralized, single-source enterprise Generative AI platform. While this requires initial standardization efforts, it can significantly reduce operational complexity, ongoing maintenance costs, and associated risks in the long run. It also facilitates consistent and scalable deployment of Generative AI across the organization.
A hybrid approach that combines internal and external expertise might be the most effective strategy. Partnering with a leading technology provider can provide a solid foundation for a robust Generative AI platform. However, you’ll also need to build an internal team with expertise in data science, AI engineering, and other relevant fields. This team can then customize, expand, and manage the platform to meet your specific business needs.
For instance, HSBC, after piloting solutions with seven different Generative AI vendors, faced challenges with high maintenance costs, governance issues, and integration complexities. They decided to consolidate everything on Microsoft’s platform and standardize APIs, data flows, monitoring, and other aspects. This approach helped them reduce their AI operating costs by over 60%.
Conquering the Learning Curve
Finally, there’s the ever-present learning curve. CIOs understand the technical skills needed for Generative AI, such as model fine-tuning, vector database management, and application and context engineering. However, acquiring this knowledge can be a daunting process. Building all the specialized skills in-house can be extremely slow and challenging. Even with an accelerated learning curve, it could take months for an internal team to reach the required level of expertise.
Retail giant GiganteCorp allocated a significant budget of $15 million to assemble an elite team of 50 data scientists and engineers with experience in fine-tuning cutting-edge language models, application engineering, and vector knowledge bases. However, due to the high demand for these specialists in the market, they were only able to fill 40% of the positions after a year.
The lack of prior experience and the need to master new technologies can make implementing Generative AI seem like a formidable task. However, by partnering with an experienced technology partner, companies can overcome these challenges and unlock the full potential of Generative AI to transform their operations.
After several failed attempts to develop their own Generative AI models, the legal firm BigLaw partnered with experts from Anthropic. Their guidance in best practices, benchmarking, iterative refinement, and thorough testing enabled their contract review system to achieve over 95% accuracy in less than six months, a 30% improvement over previous attempts.
A specialized Generative AI partner can and should continue to provide ongoing consulting and support services, even after initial capabilities have been implemented within the organization. Inevitably, challenges, bottlenecks, or highly specific requirements will arise as Generative AI usage is deployed and scaled. Accessing the deep expertise of these consultants can be key to resolving them effectively.
The Generative AI models deployed by the fintech company Novo initially yielded excellent results in tasks such as fraud detection and customer support. However, after eight months, performance degradations began to be observed as data patterns shifted. They had to implement continuous data retraining and recycling pipelines to maintain accuracy levels.
In conclusion, Generative AI systems are not one-time projects; they require continuous refinement and updating. Adopting a mindset of constant testing, learning, and improvement based on feedback and empirical data is crucial for maximizing the long-term value of Generative AI.
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Francisco Ferrando
Business Development Representative fferrando@huenei.com
Progressive Web Apps (PWAs) are revolutionizing the way businesses deliver web experiences. By merging the best aspects of traditional websites and native mobile apps, PWAs offer a seamless, app-like user experience accessible through any web browser.
The best of two worlds
PWAs work for every user, regardless of the browser they’re using. They provide an enhanced experience for modern browsers that support the latest web standards, while still functioning as a traditional website on older browsers. Can work offline or with poor network connectivity by leveraging service workers, a script that acts as a client-side proxy, and caching app resources and data for offline use.
Also, they are searchable and discoverable through search engines, just like regular websites, providing a wider reach compared to native apps in app stores.
Progressive Web Apps eliminate the need to develop and maintain separate native apps for different platforms (iOS, Android, etc.). A single codebase can target multiple platforms, reducing development and maintenance costs. Unlike native apps, PWAs do not require installation from app stores, making them accessible to anyone with a web browser. They can be updated seamlessly without user intervention, ensuring users always have access to the latest version. This eliminates the need for manual app updates, reducing overhead and ensuring a consistent experience across users.
PWAs can leverage existing web infrastructure and APIs, making it easier to integrate with existing systems and processes within the organization. This can reduce the need for extensive refactoring or migration efforts. Additionally, they can be built using a modular architecture, allowing different components or features to be developed and deployed independently. This can aid in scalability and enable large businesses to incrementally roll out new features or updates.
Overall, PWAs offer businesses a cost-effective, scalable, and user-friendly solution for delivering engaging web experiences across multiple platforms, while leveraging existing web infrastructure and technologies. This can lead to improved user engagement, reduced development and maintenance costs, and better compliance with security and privacy standards.
The AI obsession
Progressive Web Apps can integrate AI technology to provide enhanced functionality and user experiences.
These new applications can leverage NLP to enable voice commands, chatbots, or virtual assistants. This allows users to interact with the app using natural language, enhancing accessibility, and providing a more intuitive user experience.
Also, machine learning algorithms can be integrated into PWAs for various purposes, such as:
Personalization: Analyzing user behavior and preferences to provide personalized recommendations, content, or experiences.
Predictive analytics: Predicting user actions, needs, or preferences based on historical data and patterns.
Image/object recognition: Identifying objects, faces, or features in images or videos within the PWA.
AI can be used to assist users in filling out forms by automatically populating fields based on user inputs or previous data, reducing friction and improving the user experience. Can also analyze user behavior, preferences, and context to deliver highly relevant and personalized notifications at the right time, improving engagement with the PWA.
With the help of technologies like TensorFlow.js, AI models can be integrated into PWAs and run directly in the user’s browser, enabling intelligent features even when the device is offline.
What about data privacy?
Developing an AI-powered Progressive Web App (PWA) that meets stringent privacy standards and complies with certifications like ISO 27001 demands a comprehensive approach. Companies must embrace a “Privacy by Design” mindset from the outset, weaving data protection principles into every phase of development.
Data minimization is key, collecting only essential user information for the AI functionality while providing transparent communication about data usage. Robust data handling measures, including encryption, secure protocols, and fortified storage, safeguard user privacy.
Empowering users with clear consent mechanisms and control over their data fosters trust. Rigorous auditing, logging, and periodic risk assessments maintain accountability and enable swift identification of potential issues.
Adhering to privacy regulations like GDPR and implementing secure AI model training processes further reinforce compliance. Ethical AI principles, such as transparency, fairness, and explainability, underpin the system’s responsible operation.
By integrating privacy and security measures holistically throughout the lifecycle, companies can deliver innovative AI-powered PWAs that prioritize user trust and data protection, setting new standards for responsible technology.
Too good to be true?
Considering the breadth of skills required, it may be challenging for a single team or organization to possess all the necessary expertise. In such cases, finding an experienced partner or consulting firm that specializes in AI-powered development can be a viable option.
An experienced partner can provide:
Proven expertise and a skilled team with the required technical capabilities
Established best practices, methodologies, and tools for PWA and AI development
Experience in navigating regulatory and compliance requirements
Access to specialized resources and infrastructure
Ability to scale resources as needed and provide ongoing support and maintenance
PWAs equipped with AI capabilities represent a powerful tool for businesses seeking to deliver a superior user experience, reduce costs, and gain a competitive edge. By partnering with an experienced software development firm, you can leverage this technology while ensuring the highest security and privacy standards are met.
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Isabel Rivas
Business Development Representative irivas@huenei.com
Development teams face ever-increasing pressure to deliver high-quality solutions at breakneck speed. Traditional testing methods, however, can be slow and cumbersome, acting as a bottleneck in your agile workflow.
At Huenei, we understand this challenge. That’s why we’ve harnessed the power of Artificial Intelligence (AI) to revolutionize our software testing processes. We can help you do the same.
“AI-powered testing tools have been a game-changer for our teams,” says Alejandra Ochoa, Service Delivery Manager at Huenei. “By automating and optimizing various aspects of the testing lifecycle, we’ve been able to significantly reduce the time and effort required to ensure the quality of our software solutions.”
Accelerating Test Automation with AI
One of the key benefits we’ve experienced is the ability to create airtight automated tests up to five times faster than traditional methods. We leverage advanced algorithms and natural language processing (NLP) capabilities, allowing our developers and testers to write automated tests in plain English. This NLP-based scripting approach simplifies the test authoring process and promotes collaboration among team members with varying technical backgrounds.
“The NLP-based scripting feature has been a game-changer for our agile teams,” Ochoa explains. “Our testers can now focus on testing scenarios and leave the technical implementation details to the AI-powered tools, resulting in more efficient and effective test coverage.”
With this remarkable effort decrease in automatic test case generation, we’re empowered to seamlessly integrate automated testing right from Sprint #1 for all new application developments. Plus, as the Sprints unfold, we can effortlessly expand test coverage, ensuring superior quality and coverage every step of the way. And even more benefits, once the software application has been ended, we have full automated testing coverage!
Streamlining Test Execution and Error Detection
But the benefits don’t stop there. AI-powered testing tools have also optimized test execution, reducing build times from hours to minutes. This efficiency translates into faster feedback loops, allowing our development teams to identify and address issues more promptly, ultimately speeding up the delivery of high-quality software solutions to our clients.
Moreover, these tools excel at identifying and diagnosing functional and visual errors that may have gone unnoticed by human testers. By analyzing vast amounts of data and recognizing patterns, AI-powered testing solutions can catch even the most subtle defects, ensuring a more robust and reliable software product for our clients.
Embracing AI in Agile Testing
At Huenei, we’ve fully embraced the integration of AI in our software testing processes, recognizing its potential to enhance our agile development methodologies. From data-driven testing to cross-platform compatibility testing, AI-powered tools provide comprehensive capabilities that enable our teams to work more efficiently and deliver superior results.
“One of the key advantages of AI in our agile testing processes is the ability to continuously learn and adapt,” Ochoa notes. “As AI-powered tools process more data and encounter new scenarios, they refine their algorithms and improve their testing strategies, resulting in more effective and efficient testing over time.”
Empowering Teams with Intelligent Solutions
At the core of our approach is the belief that AI should empower our teams, not replace them. While AI-powered testing tools automate and optimize many aspects of the testing process, human expertise and oversight remain crucial. We strike a balance between leveraging AI capabilities and ensuring that our developers and testers are actively involved, providing valuable insights and making critical decisions.
“We prioritize responsible and trustworthy AI practices,” Ochoa emphasizes. “Our ethical guidelines and principles ensure that our AI-powered testing tools are developed and deployed with fairness, accountability, and transparency in mind.”
By harnessing the power of AI in our software testing processes, we unlock new levels of efficiency, quality, and agility.
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