How AI and data increase productivity and operational efficiency in companies.

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The adoption of artificial intelligence has ceased to be just a promise and has become a competitive differentiator for companies seeking to increase productivity and operational efficiency. More than just automating repetitive tasks, AI, machine learning, and GenAI solutions allow for accelerated workflows, optimized strategic decisions, system integration, and scalable operations at lower costs. Case studies from companies like Renault, Mitsui, TVS Supply Chain Solutions, Samsung SDS, and ANZ Bank demonstrate real gains when the technology is applied in a customized way, using proprietary data and models adapted to the business context. In Brazil, BlueMetrics implemented a solution for a large manufacturer of heavy commercial vehicles, reducing production analysis time from 4 hours to just 6 seconds. These examples prove that AI solutions cannot be generic: they need to be custom-developed, with governance and security, to generate concrete and sustainable results.

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Productivity and operational efficiency are central themes for companies seeking growth in highly competitive environments. More than simply cutting costs, it's about finding intelligent ways to extract more value from time, resources, and available human capabilities. In this scenario, artificial intelligence (AI) presents itself as a catalyst for direct impact: applied strategically, it allows for the reduction of manual activities, the optimization of decisions, and the creation of more agile and connected workflows.

The key difference lies in the combination of high-quality data, machine learning models, and the latest generative AI (GenAI) solutions. Together, these technologies not only accelerate existing tasks but also open up opportunities for new ways to organize operations, integrate systems, and support professionals at all levels of the organization.

How AI, data, and machine learning drive productivity and efficiency.

The application of AI to productivity can be observed at different levels of business operations. Below, we explore the most relevant areas for medium and large-sized organizations:

1. Intelligent process automation

While traditional automation has already proven efficient in repetitive and structured workflows, AI expands this potential by handling unstructured data, text, images, and even human interactions. This allows activities that previously required hours of manual work to be transformed into operations executed in minutes, with a smaller margin of error.

Examples include automated email sorting, contract analysis, call center classification, or data extraction from tax documents. These gains are not limited to time saved, but also free up professionals for more strategically valuable roles, such as innovation and customer relationship management.

2. Accelerating workflows with copilots and AI agents.

Copilot tools and AI-based agents act as cognitive assistants that accompany professionals throughout their routines. They suggest answers, anticipate steps, and integrate information from multiple sources, preventing rework and increasing execution speed.

In practice, an analyst can create reports with the support of AI that already provides consolidated data and ready-made visualizations; a manager can plan projects with automatic recommendations on deadlines and priorities; and a sales team can rely on predictive insights into which customers are most likely to buy. This type of acceleration transforms how workflows occur, making work more agile and proactive.

3. Support for decision-making and prioritization

AI is especially valuable when used to analyze large volumes of data and extract patterns that guide strategic decisions. Predictive models can indicate market trends, forecast demand, or identify operational risks, allowing managers to more clearly prioritize where to allocate efforts and investments.

Furthermore, AI assistants can support daily executive tasks by organizing schedules, identifying bottlenecks in timelines, and suggesting resource redistributions. This layer of support helps ensure that time is used more efficiently and focused on the results that truly matter.

4. Systems integration and orchestration

In many companies, technological fragmentation remains an obstacle to productivity. AI can act as an integration layer, connecting systems that historically haven't "talked" to each other. Through natural language processing and machine learning techniques, it's possible to reconcile information from ERPs, CRMs, marketing tools, and BI platforms, creating a single, coherent view of operations.

This intelligent integration reduces redundancies, minimizes manual data entry errors, and allows teams to access information more fluidly and reliably. In practice, the company functions as an interconnected organism, without the friction that usually characterizes complex operations.

5. Continuous innovation and economies of scale.

In addition to immediate gains, the adoption of AI also paves the way for innovation and scalability. Processes that previously depended on a growing number of people can be scaled up through algorithms that learn from data. This means that the company can grow in volume of operations without necessarily growing proportionally in costs or teams.

This gain in scale becomes even more significant when combined with GenAI, which can quickly generate content, simulations, or prototypes, accelerating innovation cycles and reducing the time between idea and execution.

Real-world examples of AI driving productivity and efficiency.

Renault: Digital twins and AI for energy efficiency in manufacturing.

The French automaker Renault has implemented a system of digital twins and AI algorithms at its plant in Palencia, Spain, capable of processing billions of data points per day from cameras, sensors, and 3D scanners. The goal was to optimize quality inspections, reduce waste, and better control energy use.

Results: Since 2021, the project has enabled a 26% reduction in energy consumption per vehicle produced. In addition, it has improved the accuracy of fault detection and increased maintenance and logistics efficiency.

Source: Cadena SER

Mitsui & Co.: Accelerating document review with GenAI

Mitsui, a Japanese conglomerate with a global presence in commerce and projects, faced lengthy document review cycles in international tenders and contracts. To address this challenge, it developed, using the AWS ecosystem, a GenAI-based solution applied to corporate documents, leveraging language models tailored for legal and contractual data.

Results: a 40% to 80% reduction in review time, lower risk of human error, and freeing up specialists for strategic activities such as negotiation and proposal customization.

TVS Supply Chain Solutions: internal assistant with customized LLMs

The logistics company TVS Supply Chain Solutions developed a "Sidekick," an internal AI assistant based on LLMs trained and tuned with the company's own data. The goal was to support employees with internal queries, operational reports, and systems integration.

Highlights: The project not only delivered efficiency gains in day-to-day operations, but also provided important lessons on governance, data security, and organizational acceptance. The experience showed that it is possible to integrate GenAI into mission-critical processes in a controlled manner.

Samsung SDS: Intelligent automation at enterprise scale.

Samsung's technology subsidiary has internally developed the Brity RPA platform, which combines automation bots with AI to interpret logs, recommend processes, and perform administrative tasks in areas such as IT, procurement, and auditing.

Results: In just nine months, the solution was adopted by approximately 15,000 employees, generating an estimated savings of 550,000 work hours. The approach demonstrated that when AI is incorporated into corporate infrastructure, it can free up a massive amount of time and resources.

Source: Wikipedia — Samsung SDS

ANZ Bank: Copilots integrated into software development

The Australian bank ANZ conducted an internal pilot with GitHub Copilot integrated into its software engineering workflows. The project involved approximately 1,000 developers and sought to measure the impact on productivity and code quality.

Results: Teams reported gains in code production speed and higher quality in repetitive programming tasks. The study also revealed governance and standardization challenges, but demonstrated how copilots can generate gains when adapted to the corporate context.

Source: ArXiv

These cases show that adopting AI for productivity goes beyond using generic tools. They involve solutions developed or customized for the context and data of each company, with a real impact on operational efficiency, cost reduction, and economies of scale. Renault, Mitsui, TVS, Samsung SDS, and ANZ Bank are examples of organizations that have successfully transformed critical workflows with AI, demonstrating that the technology, when applied in a targeted way, delivers concrete and sustainable benefits.

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A BlueMetrics case study: linear programming to accelerate analysis and optimize production in industry.

Context

One of the largest truck and bus manufacturers in Latin America, operating throughout the region and with a complete portfolio of heavy vehicles and passenger transport vehicles, was seeking new ways to increase the efficiency of its assembly line. In a sector marked by high operational complexity, deadline pressures, and increasingly tight margins, the company identified that production planning was a critical point for maintaining competitiveness.

Problem

The production feasibility analysis process was done manually, consuming approximately 4 hours per day. This represented about 80 hours of repetitive work per month, subject to human error. In addition to wasted time, planning failures could lead to non-optimized sequencing, line stoppages, and delivery delays, directly impacting productivity and inventory costs.

The challenge was clear: to find a solution that could automate data collection, make the decision-making process more reliable and agile, and at the same time provide accessible information to operations teams.

Solution

BlueMetrics implemented a linear programming-based optimization platform, custom-developed to meet the client's needs. The project involved building a robust data pipeline that automatically extracts information from spreadsheets and internal systems, transforming it into optimized structures for analysis.

From there, the linear programming algorithm calculates the best production sequence in seconds, considering inventory constraints, component availability, and production targets. The solution also includes an intuitive dashboard that presents the results clearly and accessibly, hosted in a cloud environment with scalability and security.

As a result, the analysis went from being manual and slow to being automated, reliable, and virtually instantaneous.

Results

Operational efficiency: reducing analysis time from 4 hours to just 6 seconds, eliminating 99.96% of the effort previously required.

Productivity: Elimination of approximately 80 hours of manual work per month, allowing teams to direct their time and energy towards higher-value activities.

Production optimization: more efficient sequencing, maximizing the number of vehicles produced per period, and better utilization of available resources.

Financial impact: reduced inventory costs and increased revenue potential through optimized production capacity.

Qualitative benefits: increased predictability, greater agility in decision-making, and scalability of the process to other production scenarios.

This case demonstrates how applying optimization algorithms combined with sound data engineering can profoundly transform productivity in industrial environments. By drastically reducing analysis time and making the process more accurate and scalable, BlueMetrics has reinforced its role as a strategic partner in the practical application of AI and process optimization for industry.

Conclusion

The examples presented demonstrate that artificial intelligence can, in fact, generate significant gains in productivity and operational efficiency when applied in a structured way. But they also make one essential point clear: these gains do not happen automatically.

Conversely, when generic solutions are applied without considering the specific reality of each organization, there is a risk of wasted resources, low adoption by teams, and even loss of efficiency.

For the results to be real and sustainable, it is essential that AI solutions are customized to the specific business challenges of each company. This involves three pillars:

• Use of proprietary, well-structured data capable of supporting accurate and contextualized analyses.

• Training and adapting LLMs to the specific domain, ensuring relevant responses and adherence to critical processes.

• Robust layers of security and governance that ensure reliability, information protection, and regulatory compliance.

  
  

In other words, AI cannot be treated as a "one size fits all" technology. Each company has unique characteristics, legacy systems, strategic goals, and constraints that need to be incorporated into the solution design.

This is precisely where BlueMetrics differentiates itself. With over 200 successfully delivered projects for more than 90 clients in the US, Brazil, and Latin America, the company combines expertise in data, machine learning, and GenAI to develop high-impact solutions that are fully aligned with each client's business. This experience allows them not only to implement AI but to make it truly productive, scalable, and strategic.

Therefore, the path to increasing productivity and efficiency with AI lies less in adopting generic tools and more in building tailor-made solutions, supported by solid data, contextualized models, and mature governance. It is this alignment that ensures the technology becomes a competitive advantage, and not just another layer of complexity. Shall we discuss this?

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