It also addresses the evolution of the QA role toward a more strategic profile, embedded within a quality model assisted by intelligent systems, where human intervention remains an essential factor for oversight, validation, and results control.

The Origins and Evolution of QA, and the Rise of AI

With the emergence of software and digital applications, quality control adopted a predominantly reactive approach focused almost exclusively on defect detection. However, the growing complexity of systems exposed the limitations of this model, driving a shift toward a more preventive and collaborative approach to quality assurance. This transition was supported by practices such as shift-left testing, test automation, and continuous testing within CI/CD environments — establishing QA as a core discipline within the software development lifecycle.

Against this backdrop, the rise of Artificial Intelligence introduced a new paradigm in how quality processes are conceived. This is not merely an incremental evolution, but a structural shift in the way validation processes are designed, prioritized, and executed.

The Impact of AI on the SDLC and QA

The impact of AI, however, has not been confined to QA alone. Its integration has unfolded progressively and transversally, affecting both development and validation phases — generating a direct impact on the final quality of software.

On one hand, development teams have incorporated generative AI tools for code generation, such as Copilot or Claude, significantly increasing delivery speed. Yet this advancement also introduces new risks related to the quality and maintainability of generated code, due to potential inconsistencies with the broader application context.

On the other hand, QA teams have integrated AI across multiple stages of the testing process, transforming the way quality assurance strategies are designed, executed, and maintained.

According to various industry reports — including QA and Software Testing in 2025 (based on over 100 development teams) and BrowserStack’s State of AI in Software Testing 2026 (based on over 250 technical leaders) — more than 60% of organizations have already incorporated AI into parts of their testing workflows, particularly in regression, smoke testing, and risk-based prioritization.

AI adoption is also extending to other areas of the SDLC, such as business analysis — where it supports requirements and feature definition — and design, facilitating the generation of interfaces and prototypes in tools like Figma. This reflects an increasingly transversal impact across the entire software development lifecycle.

As a result, the sense that AI has become a standard part of the toolstack for all stakeholders in the software development lifecycle is growing across the industry. This adoption is generating impact at both operational and strategic levels, redefining processes, roles, and quality metrics.

Benefits

Following several years of generative AI model adoption, the following key benefits can be identified within the QA domain:

• Test Case Generation: Automatic generation of test cases from code, functional requirements, or user stories.
  • Example: Given a user story such as “the user should be able to reset their password,” the system automatically generates cases covering valid/invalid passwords, expired sessions, multiple failed attempts, field format validations, and more.
• Test Prioritization: Intelligent test prioritization based on criticality, change impact, and risk analysis.
  • Example: Following a change to the checkout flow, the system automatically prioritizes tests related to tax calculations, discounts, and payment gateways.
• Log Analysis & Processing: Analysis, rewriting, and summarization of logs, along with detection of duplicate test cases or incidents.
  • Example: In an execution that has generated hundreds of log lines, the system groups repeated errors, summarizes the issue into a single incident, and reduces noise and manual analysis time.
• Self-Healing Tests: Automatic test maintenance, adapting to changes in interfaces or system flows.
  • Example: If a button changes from id="submit-btn" to id="submit-button", the system automatically updates the selector without requiring manual intervention.
• Root Cause Analysis: Automated failure analysis and support in identifying root causes.
  • Example: Faced with a login test failure, the system correlates backend logs, authentication changes, and database errors — suggesting a token service issue as the root cause.
• LLM-based Evaluation: Automated results evaluation using LLM models capable of analyzing test outputs, system responses, and logs to determine their validity or relevance based on defined criteria.
  • Example: Rather than validating only status codes, an LLM assesses whether an API error message is contextually coherent with the nature of the failure.
• Agentic Testing Systems: Autonomous agent-based systems capable of planning, exploring applications, generating scenarios, executing tests, and reporting results iteratively — adapting their behavior based on outcomes.
  • Example: An autonomous agent explores an application, identifies critical flows, dynamically generates tests, executes scenarios, and adjusts its strategy based on results.

Taken together, these advances accelerate the testing cycle across its various phases — analysis, design, execution, and reporting — particularly in well-structured environments with sufficient context available.

Risks

That said, AI integration also introduces significant new risks and limitations:

• Incomplete Test Cases: Generation of incomplete or incorrect test cases due to biases in training data. Some reports indicate that between 20% and 40% of automatically generated tests require manual review or correction.
  • Example: The system generates tests for a registration form but omits critical scenarios such as security validations, due to biases in the training data.
• Scenario Complexity: Difficulty modeling complex scenarios, particularly in critical systems.
  • Example: In a banking system, the model may fail to correctly represent flows that depend on multiple regulatory conditions, intermediate states, or external systems.
• Contextual Understanding Gaps: Difficulty detecting defects arising from business logic, system integration, or contextual coherence.
  • Example: A test passes at a technical level because the system fails to detect an incorrectly applied discount, not understanding the business logic associated with that promotion.
• False Positives/Negatives: Inaccurate defect detection — either reporting non-existent errors or failing to identify real failures under certain conditions.
  • Example: The system accepts an incorrect data result as valid because it is structurally and formally well-formed.
• Excessive Dependency: Potential erosion of technical knowledge within teams due to over-reliance on automated tooling.
• Automation Bias: A tendency to accept AI-generated results without sufficient validation. Research suggests that up to 30–40% of incorrect decisions made by AI systems go unchallenged.
• ROI: Difficulty objectively measuring the return on investment.
• Hallucinations: Model hallucinations — the generation of incorrect but apparently coherent results. Estimated rates range from 5% to 30% in complex tasks, depending on context.
• Non-Functional Testing: Limited capacity to deliver value in performance, scalability, security, or observability testing compared to functional testing.

These risks reflect a still-significant gap between the theoretical potential of AI and its actual performance in complex or critical contexts — where human oversight remains an essential element.

The Emergence of New Metrics

In this new landscape — where the integration of Large Language Models (LLMs) enables test case generation to be automated at scale — it becomes necessary to introduce new metrics capable of evaluating these non-deterministic systems through measurement approaches that go beyond simply quantifying how much is being tested, focusing instead on the real utility of that testing.

Unlike traditional testing, where outcomes are binary (pass/fail), AI-based systems require metrics that capture degrees of adequacy, coherence, and usefulness of the generated responses.

Some of the most relevant and emerging proposals include:

• Test Effectiveness Rate (TER): The proportion of tests that detect real defects relative to the total executed.
• Signal-to-Noise Ratio: The relationship between relevant results (valid defects) and generated noise (false positives or redundant tests).
• AI-generated Test Reliability: The degree of confidence in automatically generated test cases, assessed through cross-validation, golden datasets, or model-assisted review.
• Defect Detection Efficiency (DDE): The ability to detect defects in early stages of the development cycle.
• Actual Coverage vs. Generated Coverage: The difference between the theoretical coverage generated by AI and the effective coverage of critical functionalities.
• Test Maintenance Overhead: The effort required to maintain, correct, or filter automatically generated tests.
• LLM Evaluation Score: Assessment of the quality of generated responses using evaluator models (LLM-as-a-judge), based on criteria such as relevance, coherence, and correctness.
• Hallucination Rate: The proportion of AI-generated responses containing incorrect or unverifiable information.
• Task Success Rate: The percentage of tasks correctly completed by autonomous systems or AI-based assistants.
• Consistency Score: The degree of stability of generated responses when faced with equivalent or slightly modified inputs.

These metrics reflect a paradigm shift in quality evaluation — moving from a deterministic model based on coverage and execution, to a probabilistic model centered on the reliability, consistency, and utility of AI-assisted systems.

Adapting the QA Role in an AI-Assisted Environment

Beyond its impact on development and QA processes and on validation metrics, AI adoption is driving a significant transformation that directly affects the competencies and responsibilities of QA professionals.

Traditionally, the QA role focused on requirements analysis, test case design, test execution, and defect reporting. In the current context, this role is evolving toward a more strategic profile — oriented toward the oversight, validation, and governance of automated systems.

This consolidates the human-in-the-loop paradigm, in which the QA professional takes on supervisory, validation, and audit functions that may vary depending on the seniority of the profile.

Differential Impact by Experience Level

Junior profiles (testers) AI acts as an accelerator for learning and productivity, enabling:

• Assisted test case generation
• Standardization of defect reports
• Increased execution speed
• Reduced technical barrier to entry

Mid-level profiles (analysts) Value is centered on:

• Improved requirements analysis
• Supervision and validation of AI-generated scenarios
• Incorporation of business knowledge into models
• Identification of edge cases and complex dependencies

Senior profiles (leads) AI facilitates:

• Definition and optimization of quality strategies
• Advanced metrics analysis and new KPI development
• Filtering of noise generated by large-scale automation
• Alignment between technical quality and business objectives

Transversal capabilities Across all levels, a new key competency is emerging: the ability to craft effective prompts and provide adequate context to AI systems.

Knowledge of DevOps practices is also gaining relevance — enabling the integration of these systems into CI/CD pipelines and supporting selective test execution, where systems themselves determine which tests to run based on code changes, dependencies, and defect history, and prioritize them according to risk.

Feedback loops allow these systems to learn continuously from results, progressively optimizing coverage, prioritization, and testing effectiveness.

However, this advanced automation demands constant oversight to prevent biases, incorrect decisions, or loss of control over the quality process. As a result, the QA professional evolves into an orchestrator of quality in AI-assisted environments.

New Role: QA for AI Systems and Agents

Yet the transformation of QA from functional tester to quality orchestrator is not the only role-level shift the industry is experiencing.

The proliferation of AI-based systems introduces a new dimension in QA: the need to validate non-deterministic systems.

Unlike traditional software — where expected behavior is fixed and verifiable through deterministic assertions — AI systems generate probabilistic and variable outputs for the same input. As a result, QA must validate not so much the accuracy of a specific response, but the adequacy of behavior within an acceptable range. This involves assessing aspects such as:

• Coherence and relevance of responses
• Robustness against diverse or adversarial inputs
• Consistency of results when faced with equivalent inputs
• Presence of biases in generated responses
• Model degradation over time (model drift)

In this context, LLM evaluation frameworks become especially relevant — combining the use of golden datasets, automated evaluation through evaluator models (LLM-as-a-judge), and human validation.

In short, a new QA role is emerging — one in which the object of testing is no longer the various application types previously worked with, but rather the assurance of quality in non-deterministic models, where the validation focus shifts from expected outputs to the adequacy of behavior within a variable and acceptable range.

Costs and Challenges of AI Adoption in QA

All of this AI adoption and the transformation it drives across development and QA processes represents a significant investment — not only at the technological level, but also organizationally, operationally, and in terms of talent. This transformation, closely tied to the evolution of the QA role, introduces new demands that must be addressed from a strategic perspective.

Technical Costs

• Integration of AI tools into existing pipelines
• Architectural adaptation to support advanced automation
• Management of more complex infrastructures (processing, storage, observability)
• Need for additional tooling to monitor, audit, and validate AI systems

Operational Costs

• Increased process complexity
• Continuous oversight of automated systems
• Management of noise generated by large-scale automation
• Maintenance of models, prompts, and associated configurations

Organizational and Talent Costs

• Need for upskilling in new competencies (prompt engineering, AI literacy, DevOps)
• Greater demand for technically proficient profiles capable of validating AI-generated results
• Risk of technological dependency and loss of internal knowledge if not properly managed

Economic Costs

• Licensing fees for specialized AI-based tools
• Computational costs associated with advanced model usage
• Investment in team training and upskilling
• Potential increase in senior profiles required for oversight and validation

Various industry studies reflect that initial implementation costs can be significantly higher than those of traditional frameworks, particularly during integration phases. Furthermore, the lack of specialized talent and the difficulty of integrating with legacy systems rank among the main barriers to adoption — which ultimately depends on model maturation, organizational adaptation, and team learning curves.

Accordingly, AI adoption in QA must be approached as a medium-to-long-term strategic investment, not as an immediate cost optimization.

Substitution or Complementarity?

With all of the above in mind, let us address one of the most recurring debates in the industry: will Artificial Intelligence replace QA professionals?

Current evidence points clearly toward a scenario of complementarity. AI acts as a co-pilot that automates repetitive, low-value tasks — allowing professionals to focus on higher-complexity activities such as exploratory testing, complex scenario validation, user experience evaluation, and contextual analysis, playing a more strategic role centered on validation, oversight, and decision-making.

In fact, academic research indicates that AI adoption in testing still lags behind its use in development — evidencing a testing gap where human capabilities remain critical to guaranteeing the final quality of software.

Ultimately, far from disappearing, the role is evolving: the greater the automation, the greater the need for oversight, technical judgment, and business understanding.

As Margarita Simonova notes in the Forbes Technology Council piece The State of Testing in 2025: AI suggests, but the decision still belongs to humans.

Conclusion

Artificial Intelligence has established itself as a transformative force in QA, redefining both the processes and the roles associated with quality assurance.

Far from representing a threat, its adoption constitutes an opportunity to evolve toward a more efficient, strategic, and contextually aligned model — one suited to the growing complexity of modern software development.

In a context characterized by the acceleration of code generation and the mass production of software, QA takes on an even more critical role as a guarantor of quality. The effective integration of AI will enable professionals not only to increase their productivity, but also to reinforce their positioning as key actors within the SDLC.

Nevertheless, a realistic perspective is essential in the current climate of heightened expectations around AI. While its capabilities are significant, its implementation is far from fully autonomous or free of limitations. Issues such as inconsistent output generation, lack of business context, the presence of biases, and the need for constant oversight demonstrate that these technologies still require substantial human intervention.

In this sense, the value of AI lies not in replacing the QA professional, but in amplifying their capabilities. The gap between expected potential and current reality stems largely from the quality of integration, the adequacy of context provided, and the critical capacity of teams to interpret and validate AI-generated results.

In this new landscape, competitive advantage will not reside merely in adopting AI, but in the ability to integrate it critically, efficiently, and in alignment with product quality objectives. Because, ultimately, quality is not a property of software — it is the result of the decisions made by those who build and validate it.

References:

BrowserStack. (2026). State of AI in Software Testing 2026. Recuperado de https://www.browserstack.com/blog/inside-the-state-of-ai-in-software-testing-2026/

CopilotQA. (2025). QA and Software Testing in 2025: Trends, Challenges, and AI Adoption. Recuperado de https://copilotqa.com/qa-and-software-testing-in-2025/

Forbes Technology Council. (2025). The State of Testing in 2025: The AI Adoption Gap. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/12/15/the-state-of-testing-in-2025-the-ai-adoption-gap/

Forbes Technology Council. (2025). AI Is About to Reshape Millions of Software QA Jobs. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/10/06/ai-is-about-to-reshape-millions-of-software-qa-jobs/

Wifitalents. (2025). AI in Quality Assurance Testing: Statistics and Trends. Recuperado de https://wifitalents.com/ai-quality-assurance-testing-industry-statistics/

Anthropic. (2024). Understanding AI Hallucinations and Model Behavior. Recuperado de https://www.anthropic.com/research

Financial Times. (2025). AI hallucinations become a growing concern for enterprises. Recuperado de https://www.ft.com/content/e074d3a9-7fd8-447d-ac0a-e0de756ac5c5

arXiv. (2026). An Empirical Study on AI-Assisted Software Testing in Real-World Repositories. Recuperado de https://arxiv.org/abs/2603.13724

arXiv. (2026). The Testing Gap: Adoption of AI in Software Development vs Quality Assurance. Recuperado de https://arxiv.org/abs/2601.21305

arXiv. (2025). Challenges and Limitations of AI in Software Testing: A Systematic Review. Recuperado de https://arxiv.org/abs/2504.04921

The post QA in the Age of AI: Impact, Challenges and Evolution of the Role appeared first on Capitole.

AI has stopped being a futuristic debate and has become the new competitive battleground. It is no longer a nice-to-have; it is the accelerator that will determine who leads the market and who becomes obsolete. AI has evolved from something we could integrate into our business to something we must incorporate into our application stack if we want to stay competitive. Treating it as a passing trend is not miscalculation—it’s a sentence.

Assuming every company already has some level of AI experimentation underway, we can identify the following set of challenges as a thought framework for evolving AI within the organization. This is not truly a “best practices guide”—it is a strategic survival map.

1. The Foundational Challenge: Data and Process Governance

The first step is introspective: is our organization prepared to integrate AI into the core of the business, rather than as a peripheral assistant?

To implement models effectively, it is critical to identify what data can be used to feed and train them—whether deep learning, machine learning, or other AI approaches. We must also understand where in our value chain these models can be applied to improve performance, and how we will measure that impact—cost reduction, increased availability, risk control, shorter delivery times, and more. Strong data and process governance is the cornerstone of any initiative aimed at becoming a data-driven company.

2. The Strategic Challenge: The Deployment and Expansion Model

There is no single path to adopting AI. The approach depends on factors such as the end user, the technical team developing the solutions, and reliance on third-party services. This leads us to the second major challenge: defining the operating model.

Two main approaches—compatible, but ideally explored in sequence during early phases—tend to emerge:

• Business-Oriented Approach:
Deployment based on generalist tools (such as N8N) or more specialized solutions for specific use cases (such as Gumloop, Relay.app, Zapier). These are often cloud-based, pay-per-use, and rooted in RPA (Robotic Process Automation).

• Technical Approach (In-House Agents):
Direct implementation of AI agents within the enterprise environment using engines like GPT, Bedrock, or Gemini, trained privately or publicly depending on subscription and data sensitivity.

3. The Financial Challenge: Cost Control and Return on Investment (ROI)

The previous step leads directly to the third challenge: controlling operating costs. Before moving into production, it is essential to estimate the costs associated with the system’s usage under real-world conditions.

It is also considered best practice to implement tools that allow for cost monitoring—alerts, quotas, and thresholds—depending on the business criticality and continuity requirements of the process where AI has been integrated.

4. The Operational Challenge: Ensuring Accuracy and Consistency

The first three challenges focus on deploying AI, but the work doesn’t stop there. Once models are in production, we must ensure that their outputs remain accurate and reliable over time.

A widely known phenomenon, “hallucination,” occurs when a model deteriorates and begins to make irrational decisions. To prevent these hallucinations—which can pose serious business risks—we must incorporate validation and monitoring mechanisms tied to our AI agents. This is the first major post-deployment challenge, and its cost must be accounted for from the beginning.

5. The Future Challenge: Evolution and the Cost of Change

Finally, there is a more aspirational—but constant—challenge: ongoing evolution and the cost associated with it. The AI landscape is extraordinarily dynamic. Although this concept is broad and subjective, it must remain part of our mindset as a driver for continuous improvement. It should not paralyze initial deployment, but it must be integrated into long-term strategy to avoid technological obsolescence.

Conclusion: AI as a Strategic Necessity

In the end, the evolution of the market makes AI adoption not an option, but a short-term necessity. To navigate this journey successfully, the best strategy is to define a clear roadmap based on measurable, well-structured steps. Only then can we look toward the future with confidence, leveraging AI as a true engine of transformation.

The post The 5 Major Challenges of AI in Business: From Aspiration to Integration appeared first on Capitole.

The current LLM ecosystem showcases an extraordinary variety: from generalist models like GPT-4o and Claude 3.5 Sonnet, to technical specializations such as EXAONE 3.0 by LG AI (indeed, the television and appliance brand has established LG AI Research, which sets AI guidelines across all of the company’s product lines) for scientific research, as well as open-source solutions like LLaMA 3.3 that enable local, customized deployments (to provide greater assurance when working with sensitive or confidential data). This rapid growth has created a complex landscape where the question is no longer Which is the best model to use?, but rather Which is the right model for each specific use case?

On AI Appreciation Month, from Capitole we want to offer you a deep technical perspective on the current LLM ecosystem, evaluating not only the capabilities everyone is already familiar with, but also the persistent limitations (as with any technological solution) and the ethical challenges shaping the future of this transformative technology.

1. The Evolution of LLMs: From Black Boxes to Specialized Toolkits

Until recently, LLMs functioned as true black boxes, meaning that we understood they contained complex systems whose inner workings remained opaque even to their inventors. The transformer architecture, with its trillions of parameters trained on massive datasets, produced astonishing results without us being able to fully explain the “magic” behind these emergent capabilities. This context has drastically changed the rules of the game over the years 2024–2025. Today’s LLMs have evolved into specialized tools with well-documented competencies, clearly identified limitations, and concrete, precisely defined use cases. Industry, as well as the science and technology sectors, have established standardized norms, rigorous evaluation methods, and interpretability frameworks that allow us not only to understand the abilities of these models, but also to manage them and to clarify why they exist.

This evolution is evident in the current ecosystem: although models like GPT-4o maintain their universal versatility, we have seen the emergence of technical specializations such as EXAONE 3.0 for scientific research, Codex for programming, and BioGPT for biomedical applications. According to the 2024 Stanford AI Report, 67% of recent LLM deployments in enterprises have opted for specialized or fine-tuned models rather than general-purpose solutions, representing a fundamental shift in AI adoption strategies.

LLMs from 2022 through 2026 have shown us three clearly distinct eras:

The Era of Intelligent Chat (2022–2023) was characterized by the unforgettable arrival of ChatGPT and the first conversational models, followed by the emergence of open-source models such as LLaMA and Mistral.

The Era of Multimodality (2023–2024) introduced the first multimodal capabilities with GPT-4 and Claude, expanding context windows up to 200,000 tokens and creating efficient MoE (Mixture of Experts) architectures such as DeepSeek-R1.

Finally, the Era of Autonomy (2025–2026) marks the shift toward autonomous agents like Manus AI, with accelerating trends toward sophisticated personalization, domain-specific specialization, complete democratization, multi-LLM collaboration agents, and computational optimization.

2. Document Analysis Capabilities: The Case of Claude 3.5 and Extended Context

Document analysis represents one of the most significant challenges in business today. According to the McKinsey Global Institute, approximately 19% of the time knowledge workers spend is dedicated to searching for and gathering information, while reviewing complex documents can require between 40 and 60 hours per week in fields such as law and finance. In highly regulated sectors, such as energy or pharmaceuticals, detailed analysis of regulatory documentation can extend over months, requiring specialized teams and generating considerable operational costs. For example, Claude 3.5 Sonnet, from Anthropic, has transformed this landscape thanks to its vast context window of 200,000 tokens (equivalent to approximately 150,000 words), which enables the handling of complete documents without fragmentation.

Its advanced transformer-based architecture integrates sophisticated attention and memory methods that preserve semantic consistency across long texts, while its multimodal reasoning capabilities facilitate the combined exploration of text, tables, charts, and diagrams within complex documents. In real-world scenarios, Claude 3.5 Sonnet is able to process and analyze documents of up to 500 pages in about 3 minutes, extracting critical information, detecting patterns, and producing structured summaries with an accuracy between 85% and 92%, according to independent benchmarks. Companies such as Klarna have reported a 75% reduction in contract analysis time, while legal organizations indicate savings of 40 to 60 hours per case in regulatory document reviews, transforming workflows that previously required teams of analysts on a weekly basis.

These advances in intelligent document analysis represent a dramatic change in how organizations manage large volumes of information. For example, Claude 3.5 Sonnet is not only increasing operational efficiency but is also democratizing access to complex document analysis that previously required meticulous specialization, making it possible for smaller teams to handle information volumes typically reserved for large corporations. Nevertheless, it remains crucial to acknowledge current limitations such as:

• Accuracy fluctuates depending on the complexity of the domain.
• Processing conclusions may be more relevant for large volumes of data.
• Interpretation of results still requires human oversight to ensure correctness in critical moments.

3. Specialization vs. Versatility: How to Choose the Right LLM for Each Use Case

The arrival of specialized LLMs has fundamentally transformed the paradigm of AI model selection. Although during the 2022–2023 period the main question was Which is the best LLM?, by 2025 the ecosystem requires a more sophisticated perspective: Which is the perfect model for this specific use case? This evolution reflects a maturing market, where differentiation is no longer based solely on broad competencies, but on performance within specific areas, functions, and operational constraints.

Strategic selection of LLMs requires continuous evaluation based on three fundamental dimensions:

1. Technical Performance Requirements:
   • Precision in specific benchmarks (MMLU for general reasoning, HumanEval for code, GSM8K for mathematics).
   • Multimodal capabilities.
   • Required context window.
2. Operational Parameters:
   • Response latency (tokens per second).
   • Maximum transaction volume.
   • API availability and deployment options (cloud vs. on-premise).
3. Financial Criteria:
   • Cost per token.
   • Total cost of ownership.
   • Scalability of pricing.
   • Estimated ROI depending on usage volume.

When applying this framework to concrete use cases, clear optimization patterns emerge.

• GPT-4o stands out in multimodal customer interactions in reasoning tasks (MMLU: 87.2%) and visual capabilities, which supports its pricing of $5–9 per million tokens for high-value use cases.
• For document analysis, Claude 3.5 Sonnet optimizes the balance between cost and capability with its 200k-token context window and 89% accuracy in comprehension tasks, priced at $6–12 per million tokens.
• For deployments handling sensitive data, LLaMA 3.3 offers competitive performance (MMLU: 83.6%) with full control over data through local implementation, minimizing recurring expenses after the initial infrastructure investment.

This strategic diversification is clearly evident in the current ecosystem’s competitive positioning. In the previous matrix of specialization versus versatility (horizontal axis) and proprietary models versus open access (vertical axis), four distinctive quadrants emerge:

• The upper-right quadrant hosts unique generalist models such as GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash, which increase flexibility but require commercially licensed APIs.
• The lower-right quadrant offers versatile open-source alternatives like LLaMA 3.3 and Mistral Large, providing a broad functional spectrum with full control over implementation.
• The upper-left quadrant presents specialized proprietary solutions such as Manus AI for autonomous agents and Command R+ for document analysis, designed for very specific use cases.
• Finally, the lower-left quadrant contains specialized open-access models like EXAONE 3.0 for scientific research and DeepSeek for technical applications, combining specialization with complete transparency.

This segmentation reinforces that the ideal choice is determined both by the specific functional requirements and by the constraints around openness, security, and operational control within the corporate environment.

The implementation of this diversification has given rise to tactics involving multiple models that increase companies’ return on investment. Instead of relying on a single universal model, leading organizations are creating specialized ecosystems in which each model is optimized for specific usage scenarios.

For example, as shown in the previous diagram:

• Mistral Small 3 focuses on real-time analysis with computational efficiency, low latency, and immediate responses.
• GPT-4o handles customer interactions through content generation, contextual analysis, and multimodal adaptability.
• LLaMA 3.3 ensures the privacy of sensitive data with full control and on-premise execution.
• Command R+ enhances document analysis with factual accuracy, data extraction, and document handling capabilities.

This multi-model strategy yields 40% more return on investment compared to single-model implementations, demonstrating that strategic specialization surpasses universal versatility in corporate environments.

This evidence-based selection technique requires a structured evaluation process:

1. Precisely define the technical, operational, and financial requirements of the specific use case.
2. Establish measurable success indicators and minimum performance thresholds.
3. Conduct pilot trials with the shortlisted models using datasets that closely replicate the production environment.
4. Calculate the projected total cost of ownership over 12–24 months, including integration expenses, team training, and maintenance.

Therefore, the essential principle remains unchanged: strategic optimization outperforms the maximization of general capabilities, and the best choice is always anchored in data-driven analysis of each corporate context.

4. Ecosystem Mapping: Comparative Analysis of Leading LLMs in 2025

In the table below, we have attempted to bring order to the generative AI storm of 2025. You can see:

• The proprietary giants setting the pace in the race.
• The disruptors refining the balance between cost and performance variables.
• And finally, the open-source options that democratize access and data control.

For each model, we display:

• Its MMLU score (the benchmark metric measuring LLM comprehension).
• Price per million tokens.
• And the competitive advantage that makes it stand out for a specific use case.

As can be seen in the table, choosing the most suitable LLM is no longer about setting a Guinness record for the highest number of parameters, but about balancing three crucial aspects: actual task performance, operational cost, and business needs.

Therefore, the most effective strategy is usually a multimodal approach: assembling your optimal “battalion” for each specific task. In this way, you can increase ROI, resilience, and iteration speed.

5. Trends 2025–2026: Personalization, Open Source, and Autonomous Agents

Today, the landscape is much clearer, with three key trends, each carrying distinct consequences for business adoption.

Personalization through Fine-tuning and RAG has emerged as the primary driver of competitive differentiation. Companies such as Bloomberg (BloombergGPT), Morgan Stanley (GPT adapted for wealth management), and Salesforce (Einstein GPT) demonstrate that foundational models are only the starting point. The real value lies in adapting them to specific domains: fine-tuning for specialized behaviors and RAG for incorporating proprietary knowledge. According to Forrester 2024, 73% of successful enterprise implementations involve some level of personalization, delivering an average ROI 340% higher than generic deployments.

Vertical specialization is splitting the market into models optimized for particular domains. Qwen 2.5 dominates Asian markets with native cultural understanding, EXAONE 3.0 leads scientific research with 94% accuracy in technical tasks, and Harvey AI specializes in legal services, validated by over 200 companies worldwide. This trend suggests that the future lies in models that choose global versatility within specific areas, creating entry barriers both technical and data-driven.

The democratization of open source is driving convergence in capabilities. LLaMA 3.3 reaches 83.6% on MMLU (compared to 87.2% for GPT-4o), while Mixtral 8x22B rivals proprietary models in targeted tasks. Hugging Face reports over 500 million monthly downloads of open-source models, signaling widespread adoption. This convergence is reducing competitive advantages based solely on tangible technical capabilities and is shifting competition toward ecosystems, services, and horizontal specialization.

The alignment of these trends points to a future where business success in AI will depend less on access to sophisticated models (which are becoming increasingly commoditized) and more on the ability to personalize, specialize, and embed these technologies into concrete workflows. Organizations capable of tailoring base models to their unique contexts will retain enduring competitive advantages.

6. Conclusions: Strategic Implementation of LLMs in the Enterprise

The 2025 LLM landscape has evolved from simply searching for the most capable model to a paradigm of strategic optimization based on specific use cases. This progress demands a structured methodology for business selection and implementation:

Defined decision framework:
Structured analysis based on technical criteria (specific benchmarks), operational parameters (latency, throughput, deployment), and financial considerations (TCO, ROI, scalability) removes subjectivity in model selection. Organizations applying evidence-based techniques will consistently outperform those relying on intuition or market hype.

Specialization as a competitive advantage:
The merging of global capabilities among proprietary and open-source models shifts differentiation toward vertical specialization and personalization. The future belongs to organizations that master fine-tuning, RAG, and the adaptation of base models to singular corporate contexts, generating entry barriers built on data and domain expertise.

Democratization and execution:
Lower technical and financial barriers are making advanced AI capabilities more accessible but are also increasing the importance of implementation strategy. A company’s success will hinge on its ability to integrate LLMs into existing workflows, manage organizational transformation, and cultivate internal AI skills.

At Capitole, we support this transformation by translating technological advances into tangible business value. The LLM revolution is only just beginning, and organizations that adopt strategic, evidence-based approaches focused on specific use cases will lead the next decade of AI innovation.

The post From Turing to Autonomous Agents: Analysis of the 2025 LLM Ecosystem appeared first on Capitole.

Understanding the Synergy

Agile methodologies, with their iterative approach and focus on continuous improvement and customer feedback, align perfectly with the rapid evolution of AI. Here, it’s essential to clarify that we are primarily referring to Generative AI and Predictive AI. Generative AI, such as natural language processing and content generation models, enables the creation of new content, while Predictive AI uses Classical Machine Learning (ML) algorithms to analyse historical data and make predictions. These approaches allow AI to process vast amounts of data, augment human capabilities, automate repetitive tasks, and provide valuable insights to inform decision-making.

Key Areas Where Classical Machine Learning Can Enhance Agile Practices

Predictive Analytics for better planning: For accurate forecasting machine Learning algorithms can analyse historical data to predict future trends, aiding teams allocate resources correctly and estimate effort more accurately.

Risk mitigation: Because ML can identify potential bottlenecks early on teams can proactively adjust their plans and allocate resources effectively

 Self-Healing Tests: Machine Learning-powered testing frameworks can automatically adapt to code changes ensuring continuous quality and reducing time spent on regression testing.

Accelerated Development: ML models can generate entire functions based on natural language descriptions or code patterns which in turns speeds up development cycles.

Improved code quality: ML-driven refactoring tools can identify code smells, suggests improvements, and automatically apply refactorings, enhancing code readability and maintainability.

Intelligent code completion: ML-powered code completion tools can suggest necessary code snippets and functions based on context reducing typing effort and improving developer productivity.

If you are considering integrating Machine Learning to development teams, it is however important to take into consideration the following.

• Ensure that data is accurate, clean and complies with privacy regulations.
• Make ML models transparent and explainable to foster trust and accountability.
• Regularly update and retrain ML models to keep pace with evolving requirements and data.
• Finally foster an environment of collaboration between ML experts and software developers to ensure seamless integration.

While both Machine Learning (ML) and Artificial Intelligence (AI) are closely related and often used interchangeably, they have distinct characteristics and applications within Agile software development.  

Machine Learning is a subset of AI that focuses on algorithms that allow computers to learn from data without explicit programming. It involves training models on large datasets to recognize patterns, make predictions, and make decisions.  

AI, on the other hand, is a broader field that encompasses various techniques and technologies, including machine learning, to simulate human intelligence.  

Key Areas Where AI Can Enhance Agile Practices

Here are specific examples of how AI can be applied in Agile environments, along with the type of AI most relevant for each use case:

• Generating User Stories: AI can help generate initial drafts of user stories from business requirements, accelerating the creation of product backlogs.
• Automating Test Cases: AI models can automatically generate test cases based on code changes and requirements, significantly reducing the time spent on manual testing.
• Predicting Project Timelines: Predictive AI can analyse historical data from previous projects to predict delivery timelines and identify potential risks ahead of time.
• Improving Code Quality: AI-powered tools can detect defects in the code, suggest improvements, and automate code reviews, enhancing the overall quality of the software.
• Automated Documentation: Generative AI can help automatically generate accurate, up-to-date documentation, reducing manual effort and ensuring consistency. Models like GPT (Generative Pre-trained Transformers) can assist in creating technical documentation or progress reports from raw data, ensuring high coherence and accuracy.
• Improved Collaboration: AI-powered collaboration tools such as virtual assistants and recommendation systems can enhance communication and knowledge sharing among team members, even in remote settings. These tools help streamline problem-solving and knowledge transfer across distributed teams, Teams Copilot is an excellent and specific example we can use here, it is capable summarising meetings using recorded transcripts from concluded meetings.
• Enhanced Decision-Making: AI-driven insights can help Agile teams make better data-driven decisions regarding product backlogs, resource allocation, and risk mitigation. Combining Predictive AI with data analytics, teams can make more informed decisions based on real-time insights and historical data.

Let’s look at specific applications of AI in Agile that can drive efficiency and improve results:

Prompt Engineering: Optimizing AI Interaction

Prompt Engineering refers to the art of crafting clear and effective prompts to guide Generative AI models in producing the desired output. Below are key recommendations for getting the best results when working with AI in Agile projects:

• Be Specific: Clearly articulate the desired outcome of the AI-generated content.
• Provide Context: Background information is crucial for the AI model to understand the task.
• Define the AI’s Role: Indicate the specific role the AI should take when generating results (e.g., “Act as an expert scrum master with the objective of finding a permanent solution to the consistent problem of technical debt of a development team that is mature in agile methodologies give me a list of immediate actions to take, let your writing style be narrative and your tone persuasive”).
• Identify the Target Audience: Tailor the AI’s response to the needs of the end user, whether it’s a development team or a customer.
• Set a Clear Objective: Ensure the model understands the goal it needs to achieve.
• Establish the Tone and Style: Decide on the tone (formal, persuasive, cooperative) and writing style (narrative, descriptive, etc.).
• Experiment and Adjust: Continuously refine the prompts based on the results to improve the quality of the responses.

Conclusion: The Future of Agile with Generative AI

The combination of Agile and AI is transforming the way we work, unlocking new levels of innovation and continuous improvement. By adopting AI, Agile teams can deliver faster, more accurate results that are aligned with customer expectations.

At Capitole, we are at the forefront of digital transformation, helping our clients harness the power of Generative AI to optimize their Agile processes. If you want to maximize the value of your Agile teams with AI-driven solutions, reach out to us today. We’re here to guide you on this exciting journey toward the future of work.

Sources

• TensorFlow: https://www.tensorflow.org/
• Papers with Code: https://paperswithcode.com/
• Machine Learning is Fun: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471  
• https://github.com/mananahmed/sepoy-twitter-archive
• Agile Alliance: https://www.agilealliance.org/
• Scaled Agile Framework (SAFe): https://scaledagileframework.com/
• arXiv: https://arxiv.org/ , Scikit-learn: https://scikit-learn.org/
• Google AI Blog: https://ai.google/latest-news/
• PyTorch: https://pytorch.org/

The post AI-Powered Agile: The Future of Work appeared first on Capitole.

Strategic Adoption of GenAI

One of the common challenges faced by product managers and product owners is being unable to fully engage in their roles and instead becoming mere intermediaries between business requirements and the development team. This often happens because they lack the time, authority, or tools to perform their duties comprehensively. Moreover, technical debt and bugs frequently siphon team capacity when planning hasn’t accounted for these appropriately.

For product managers and product owners, GenAI is a game-changing tool to:

• Identify complex patterns: Analyze vast amounts of data and market trends.
• Generate structured information: Compile detailed materials from various sources in less time.
• Focus on active listening: Free up time for high-value activities like iteration and user feedback.

By leveraging GenAI, you can take charge and provide stakeholders with actionable insights, enabling the creation of new features and functionalities that deliver true value to users. Moreover, these tools help uncover new use cases or automations that improve product quality and prevent disruptions impacting users.

Efficient adoption of GenAI starts with mastering prompt engineering. The quality of the outcomes depends on how clearly we communicate with the tools. Models like Sara Tamsin’s (Context – Task – Instruction – Clarification – Refinement) or Kyle Barner’s RISEN framework (Role – Instructions – Steps – End goal/Expectation – Narrowing/Novelty) provide practical guidance for crafting effective prompts. For more on prompt engineering, consult OpenAI’s comprehensive documentation

Foundational Use Cases of GenAI in Roadmap Optimization

• Predictive Analysis: Anticipate the impact of future features using algorithms based on historical data. Ask GenAI tools to draw insights from specialized sources, reports, and studies or to analyze user surveys and detect patterns.
• Backlog Automation: Use tools like ChatGPT to efficiently draft epics and user stories.
• Story Mapping: Organize user stories visually to streamline sprint planning.

Advanced Use Case: Building a Comprehensive Roadmap with AI

For a deeper level of application, consider using a GenAI tool, like the widely adopted ChatGPT, as a genuine copilot by feeding it all relevant context and knowledge about your current role. Two potential scenarios could guide this approach:

1. Starting a new business model: You’re a PO entrepreneur creating an MVP.
2. Evolving an existing product: You’re enhancing and implementing new functionalities or processes.

In both cases, the approach involves setting up a custom ChatGPT or maintaining a document that consolidates all the relevant information. Continuously attach and reference this document in your prompts to ensure it serves as a reliable source.

Step 1: Define the Product Vision

Ask the AI to generate a product vision by providing context and objectives. Refine the results until you achieve a solid vision statement, core functionalities, and unique value propositions.

Step 2: Identify Target Personas

The AI can create detailed profiles of potential users. Provide the AI with background information, and within seconds, it can deliver 4–5 personas, complete with needs, interests, and preferences.

Step 3: Generate Jobs to Be Done (JTBD)

Using the defined personas, ask the AI to identify JTBD aligned with your product’s functionalities.

Step 4: Create Epics and User Stories

From the JTBD, prompt the AI to generate epics with acceptance criteria and break them into detailed user stories. Keep saving this information to the reference document for consistency in subsequent prompts.

Step 5: Story Mapping and a Complete Roadmap

With all the user stories, instruct GenAI to create a partial delivery map. In minutes, you’ll have a structured roadmap ready to tailor to your product’s specific needs.

Incorporating this technique into your routine boosts productivity and hones your skills as a meticulous product owner. However, it’s crucial to remain aware of the rapid pace of technological advancements and continuously update your knowledge.

Maximizing GenAI’s Value in Product Management

1. Ongoing Training: Stay updated on the latest features and best practices.
2. Regular Assessment: Periodically evaluate GenAI’s impact to uncover areas for improvement.
3. Balanced Approach: Use GenAI to complement, not replace, human judgment.

Capitole prioritizes continuous learning, enabling each team member to remain at the cutting edge of technology. Leveraging such opportunities is essential for enhancing productivity and advancing toward truly strategic product management. Capitole can also help you maximize your roadmap definition, with or without GenAI, as experts in this area.

We’re witnessing a quiet revolution that’s reshaping the product owner’s role. Integrating GenAI isn’t optional—it’s imperative for those aiming to lead innovation. The future of product development is being written today, and GenAI is the pencil sketching the brightest lines.

The post Optimizing the Product Roadmap with Generative AI Tools appeared first on Capitole.

But what exactly is a Large Language Model? How does this technology work and what are its limitations? What are the uses of this technology in the business world? In the following article we will provide answers to these questions:

What exactly is a Large Language Model ?

An LLM is a natural language model formed by deep neural networks. Its neural networks have been trained on large amounts of data.

The application of statistical and prediction models to natural language is not new.

In the 1980s and 1990s with n-grams and hidden Markov models, the application of probabilistic mathematics to language was developed, giving rise to a variety of tools and methods for creating more flexible data-driven mathematical models.

But it was not until recently that this technology was truly consolidated with the discovery of the Transformer by Google experts, presented in the famous paper “Attention is all you need”. The Transformer is a neural network that attempts to mimic the attention we humans pay to the context of a word or set of words in a body of text. Let’s see it with an example:

When we read the previous paragraph we establish a relationship between the words coco – perro – patas – jugar. If we only read the last sentence (Coco likes to play tag), we do not know if Coco is a dog or a person. However, thanks to our inherited human attention we take into account the context of the whole paragraph. This is how the Transformer created by goodle calculates the relevance between different words in a text corpus.

This discovery led to ChatGPT3, a chatbot based on the foundational Generation Pretrained Model 3 (GPT-3) that revolutionized the world, becoming the chatbot with the highest active user growth in history. Composed of a neural network with 175 billion parameters, it is capable of generating text, understanding language and answering questions in a surprising way.

These capabilities such as reading comprehension, logical inference or even more advanced tasks for a machine, for example explaining why a joke is funny, would be within the reach of the densest models.

Does this mean the end for humans, and will AI take away our jobs as everything can be automated by these models? Not yet, says Meta’s Chief AI Scientist, Yann Lecun in this interview; LLMs have several limitations that make them unreliable if they are not accompanied by the necessary software architectures.

What are their limitations?

One of the major limitations LLMs have is that they are not able to generate data that is outside the training set. For example, if you ask ChatGPT who Steve Jobs is, it will provide an answer about the famous tech entrepreneur. However, if you ask it about the latest sales made in your company’s sales department, it will not be able to give you an accurate answer. This happens because LLMs do not have direct access to the most up-to-date information happening in the world.

But if we give these Chatbots, connected to LLMs, access to the right context, they would be able to answer any kind of question accurately thanks to their writing power and linguistic understanding.

This is why a new software architecture has recently emerged that manages to solve the aforementioned problem. It is called Retrieval Augmented Generation (RAG) and connects a database with a search engine that contains everything relevant to the user. In this way the LLM will be able to access information that he/she was not trained on.

This turns the problem of the lack of context of LLMs into a problem of information management and search, whose solutions have long been studied and developed in the information sector.

The infrastructure describing a RAG architecture is typically composed of:

• An Ingestion Pipeline that injects and fragments the documents into different parts, commonly called chunks. This pipeline will help us to implement different document fragmentation strategies depending on the data they contain.
• The pipeline will connect with an embedding model to vectorize back and forth the input and output data from the database. These models convert document fragments into sophisticated numerical representations.
• Finally, a vector database, which stores and indexes the information for later retrieval. The most common metric for searching and successfully answering user queries is cosine similarity.

Therefore, by basing answers on up-to-date data, RAG reduces the chances of generating incorrect information in the form of hallucinations, because of the tendency to always answer queries. In addition, fine-tuning or re-training of the model for specific knowledge areas (such as apps with knowledge of mining practices or logistics of fashion products) could be investigated. Updating the database may be sufficient in general use cases but there is scientific literature indicating that LLM fine-tuning can increase the accuracy of the RAG-enhanced application.

However, it is also important to identify some disadvantages:

• The effectiveness of the RAG architecture depends heavily on the quality of the search engine configuration, as well as on a good document preprocessing strategy: choosing the right embedding model.
• The contextual message of LLMs is limited: the amount of text with instructions and practical examples for the AI to perform its function. According to the scientific literature when the size of the context increases, the attention span of the actions performed by the models decreases. Therefore, we will have to write the messages following prompt engineering’s expert recommendations to make sure that everything is interpreted and nothing escapes the LLM’s attention.
• There is a notable evaluation difficulty: evaluating a RAG application is difficult due to the non-deterministic or random nature of LLMs which makes the quality of the information generated variable if the application is not properly tuned. Given the difficulty in applying traditional metrics, continuous evaluation and monitoring of these applications is required.

In conclusion, the combination of Large Language Models (LLMs) with the Retrieval-Augmented Generation (RAG) architecture has marked a breakthrough in the area of Natural Language Processing by mitigating some of the key limitations of LLMs, such as hallucinations and access to updated information. RAG improves the accuracy of LLMs by integrating a search engine, without incurring LLM retraining costs. However, the success of this solution depends on the robustness of the vector database search engine and the availability of relevant information.

LLMs can automate repetitive tasks, improve customer service and facilitate content creation, allowing your team to focus on strategic decisions. However, not all tasks benefit from LLMs. For deep analytics or very specific data-driven decisions, RAG can complement the model by providing up-to-date context.

If you want to learn more about how these technologies can transform your business, contact us at Capitole. Our team will help you identify the most effective applications to optimize your daily operations and make the most of artificial intelligence, as well as develop predictive models.

The post What are LLMs and what are their limitations? appeared first on Capitole.