The Technology Map of 2025: Key Technological Trends
With 2025 on the horizon, businesses face a decisive moment to identify the technological trends of 2025 that will define the next stage of growth and competitiveness. Generative Artificial Intelligence (GenAI), automation, and cloud computing are revolutionizing how core processes are designed, optimized, and executed. These tools are not just trends; they are strategic resources capable of transforming operations and creating new opportunities.
GenAI: A Strategic Pillar for Innovation
According to IDC, leading companies that have already integrated generative artificial intelligence are achieving an average ROI of $10.3 for every $1 invested, significantly outperforming the general average of $3.7.
In 2025, Generative AI will continue to solidify its position as a key technology for innovation in software development and operational management. Its ability to generate multi-module systems optimized with advanced patterns like microservices and serverless architectures will reduce development time. This will enable faster integrations tailored to the evolving needs of organizations.
Additionally, Generative AI will play a central role in automating testing processes by generating scenarios based on real user patterns to detect errors and ensure high standards from the early stages of projects. These practices will not only enhance quality but also accelerate development cycles as environments become increasingly agile.
On the other hand, AI agents will represent a significant evolution, enabling systems to act autonomously to achieve specific objectives. Their ability to dynamically adapt to changing environments will allow for more efficient handling of complex tasks. Combining autonomy with adaptability, these agents will operate in real-time to address highly complex business challenges.
Integration with multimodal interactions—such as text, voice, images, and structured data—will enhance their ability to manage interconnected workflows. This will transform key processes, improve critical operations, and enable personalized experiences in an increasingly demanding business environment.
RAG: Real-Time Knowledge Retrieval and Personalization
Retrieval-Augmented Generation (RAG) technology is emerging as one of the most promising technological trends of 2025 for organizations managing large volumes of information. By combining content generation with real-time data retrieval, RAG optimizes response accuracy and the relevance of business applications.
In this context, language models optimized for RAG are pushing this technology to new levels of efficiency, enabling quick data retrieval from large information corpora and offering more accurate and relevant responses. These advancements will be further enhanced by agent-based systems that extend RAG’s capabilities by dynamically adapting to various contexts and scenarios.
In 2025, RAG will revolutionize areas such as knowledge management, workflow-specific AI assistants, and customer service. Its scalability and adaptability will make it easier to integrate into organizations of all sizes, expanding its impact across various business applications.
Advances in the Cloud: Prediction and Automation
Cloud computing will remain one of the most vital infrastructures in 2025, especially in technological environments such as distributed systems, hybrid architectures, and dynamic workloads that demand scalability and real-time adjustments.
Intelligent distributed computing will be crucial, balancing workloads across public, private, and edge cloud technologies to reduce costs, minimize latencies, and enhance performance and security. Platforms like Kubernetes and AWS Auto Scaling are already using historical and real-time data to anticipate demand spikes, optimizing operations and ensuring continuous performance. Additionally, the modular design of hybrid clouds will offer flexible scalability, enabling companies to grow and adapt rapidly without the risk of overprovisioning.
Moreover, these technologies, along with other technological trends of 2025, will allow companies to adopt more flexible and effective strategies for managing resources. While many organizations have already migrated to the cloud, some still face cultural or regulatory resistance. For these companies, 2025 will be a pivotal opportunity to embrace these models.
Security as a Modular Strategy
As business environments become more distributed, ensuring security from the foundation of software development will be critical. DevSecOps practices will enable the identification and resolution of vulnerabilities from the earliest stages, automating audits, code analysis, and security testing to strengthen trust in systems before deployment.
The Zero Trust model continues to gain traction as a key approach in distributed environments, continuously validating components such as source code and dependencies to prevent unauthorized access and protect critical systems.
Advanced governance will also be essential for organizations managing large volumes of data generated by GenAI and IoT, ensuring regulatory compliance and privacy from the code architecture level in an increasingly regulated environment.
Computer Vision: Contextual Intelligence for More Accurate Decisions
Computer Vision (CV) will continue to deliver significant advancements in monitoring and optimizing processes. In 2025, it will not just be about object detection but also about understanding relationships between objects and contextualizing visual data for more informed decisions.
Technologies like YOLOv7 and OpenCV stand out for their ability to provide deeper contextual analyses, while dynamic reconstruction from flat images opens new possibilities for optimizing operational workflows and validating complex interfaces.
The Strategy for 2025
The key technologies of 2025 will not only transform processes but also redefine how organizations plan and execute their strategies. Generative AI, the cloud, and automation are proving to be indispensable resources for those aiming to remain competitive.
Strategically integrating these technologies and aligning their implementation with business objectives will be crucial for building a sustainable advantage in an increasingly demanding technological landscape.
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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
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
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