How artificial intelligence is changing the retail sector 

Artificial intelligence is transforming the retail sector because it makes it possible to analyse large volumes of data on sales, inventory, prices, customers and the supply chain and turn them into faster decisions. This allows retailers to anticipate demand, adjust stock, reduce stockouts and excess inventory, optimise promotions and adapt the assortment to each store or channel. 

In digital channels, AI improves recommendations, product search and customer service through virtual assistants. In stores, it facilitates replenishment, analysis of the customer journey, loss detection and frictionless shopping experiences. In addition, generative artificial intelligence helps prepare descriptions, quotes, orders and customer responses more quickly. 

AI to manage inventory and reduce stockouts 

It helps retailers better control inventory by analysing sales, stock movements, seasonality and purchasing behaviour. 

Automatic replenishment and control of perishable products 

AI can identify when a product needs to be replenished and automatically suggest orders or stock movements. In the case of perishable products, it also makes it possible to adjust replenishment according to shelf life and sales speed. 

Assortment optimisation by store and channel 

AI makes it possible to adapt the assortment according to location, customer profile, sales history and trends in each channel. This allows retailers to decide which products to offer, in what quantities and where to place them in order to improve availability, reduce excess inventory and make better use of stock. 

AI to forecast demand and plan supply 

It makes it possible to anticipate demand more accurately through the combined analysis of internal and external information. 

Data that helps anticipate sales 

Forecasts can take into account sales history, seasonality, promotions, customer behaviour, the location of each store or the performance of similar products. They can also incorporate external factors such as weather, trends or market changes. 

Adjustment of purchases, stock and supply chain 

The company can adjust the quantities it purchases, distribute inventory more effectively across stores and channels and anticipate replenishment needs. AI also helps evaluate suppliers, routes and alternative sourcing options when delays or disruptions occur. 

AI to improve customer experience 

It makes it possible to analyse purchase history, searches, preferences and each user’s behaviour to offer a more useful and personalised experience. 

Personalised recommendations and offers 

AI systems can recommend products based on previous purchases, items viewed and the behaviour of customers with similar profiles. They also make it possible to personalise promotions, messages and cross-selling opportunities. 

Chatbots and shopping assistants 

Chatbots and virtual assistants can answer questions, locate products, check availability and guide customers during the purchasing process. They also make it possible to offer immediate support across different channels and resolve frequently asked questions. 

Smart stores and frictionless shopping 

The combination of AI, cameras and sensors makes it possible to create stores where it is easier to locate products, check stock or complete payment. It also helps analyse how customers move through the store, detect areas with low activity and reorganise the space to improve the experience and commercial performance. 

AI in sales and customer service 

It helps retailers transform large volumes of commercial and customer data into useful recommendations, responses and content. 

Cross-selling and next best action 

AI can analyse each customer’s purchase history, preferences and interactions to suggest complementary products or indicate the most appropriate next commercial action. This helps personalise communication and detect sales opportunities. 

Automation of quotes, orders and queries 

It can also extract information from emails, documents or forms to prepare quotes, register orders and draft responses to frequently asked questions. In addition, it facilitates product search, comparison of alternatives and identification of substitutes according to customer needs. 

After-sales service and incident resolution 

In after-sales service, generative AI can summarise the customer history, consult internal policies and suggest responses to resolve incidents more quickly. It also helps classify cases, detect recurring problems and decide when a query should be transferred to a specialist. 

Other artificial intelligence solutions for retailers 

In addition to improving inventory, demand and customer experience, artificial intelligence can be applied to other areas of the retail business to increase profitability, reduce losses and make better commercial decisions. 

• Price and promotion optimisation: Using variables such as demand, available stock, competitor prices, margin and purchasing behaviour, AI helps adjust prices, decide which products to promote, when to do it and through which channel. 

• Loss, fraud and anomaly detection: By analysing sales, returns, checkout operations and inventory movements, AI identifies unusual behaviours and helps detect possible theft, incorrect charges, internal fraud or stock discrepancies. 

• Location selection and commercial space analysis: AI compares demographic data, costs, footfall, competition and consumption habits. It also analyses how customers move inside the store and, based on this, reorganises products, aisles or promotions. 

How to implement AI in a retail company 

Implementing AI requires defining specific objectives, having reliable data and checking that the solution integrates correctly with the company’s systems. 

La entrada AI in retail: how to improve inventory, demand and customer experience  se publicó primero en Acevedo.

How artificial intelligence is transforming logistics management 

Artificial intelligence is changing the way companies plan, monitor and optimise their logistics operations. Its ability to analyse large volumes of information makes it possible to connect data from ERP systems, warehouse management software, transport platforms and IoT devices to obtain a more complete view of the supply chain. 

Based on this data, AI can anticipate changes in demand, adjust inventory levels, identify possible delays and recommend routes or alternative sourcing options. It also facilitates the automation of warehouse tasks, shipment monitoring and the detection of incidents before they affect deliveries. 

AI to anticipate demand and adjust inventory 

AI makes it possible to analyse demand trends and relate them to stock levels, pending orders and logistics capacity. 

Forecasts based on internal and external data 

AI models can combine previous sales, seasonality, orders, promotions and customer behaviour with external information such as weather, regional events or market changes. 

Reduction of stockouts and excess inventory 

More accurate forecasting makes it possible to keep quantities aligned with real needs. The company can anticipate which products are at risk of running out and which ones are accumulating more units than necessary. 

Replenishment and inventory distribution 

AI also helps decide when to replenish, what quantities to move and in which warehouse or distribution centre each product should be located. This facilitates a more balanced distribution of inventory and improves order preparation. 

Transport and route optimisation with AI 

AI makes it possible to analyse variables such as traffic, weather, delivery points, carrier availability and vehicle capacity to organise journeys with greater precision. 

Route planning and delay prediction 

AI models can compare different routes and recommend the most suitable one according to current conditions. They also estimate arrival times, identify shipments at risk of delay and suggest alternatives when incidents occur. 

Fleet management and last mile 

In fleet management, AI helps assign loads, drivers and vehicles according to their availability, capacity and location. In the last mile, it makes it possible to organise stops more effectively, adapt routes when unexpected issues arise and prioritise specific orders. 

Artificial intelligence in warehouse operations 

AI helps organise warehouse tasks by analysing orders, stock levels, routes and resource availability. 

Product location and order picking 

AI analyses purchase frequency, turnover and products that are usually ordered together to recommend their location within the warehouse. It also helps define more efficient picking routes and prioritise urgent orders or perishable goods. 

Robotics, sorting and error control 

Robots and AI-based systems can support storage, picking, internal transport and product sorting. Cameras and sensors make it possible to check that the correct item has been picked, detect packaging errors or identify shipments sent to the wrong location. 

Document and query automation 

AI can extract information from invoices, delivery notes and bills of lading, create shipping labels and summarise the history of an order. It also makes it possible to answer queries about availability, transport conditions or shipment status. 

Visibility and resilience in the supply chain 

Artificial intelligence makes it possible to monitor the movement of products, vehicles and materials throughout the supply chain. 

Shipment tracking and cargo conditions 

AI can use data from sensors and IoT devices to know the location of shipments and monitor variables such as temperature, humidity or the condition of the goods, generating alerts so the team can intervene. 

Risk detection and alternative sourcing 

Predictive models analyse information about suppliers, routes, weather, ports or possible disruptions to identify risks before they affect supply. They can also recommend alternative suppliers, carriers or routes. 

Returns management and reverse logistics 

AI helps identify products with a high return rate and detect possible defects or design errors. It also makes it possible to classify returns and direct each item to the appropriate process. 

How to implement AI in logistics and supply chain 

Implementation should begin by identifying the logistics processes that generate the most inefficiencies, errors, delays or costs. Then, it is necessary to prepare reliable data and connect the ERP, the warehouse management system, transport platforms and IoT devices to obtain an integrated view of operations. The best approach is to start with a pilot project in a specific area, such as demand forecasting, measure its impact using defined indicators and, once the results have been validated, scale it progressively. 

Benefits of applying AI in logistics 

Artificial intelligence makes it possible to make better use of data from inventory, warehouses, transport and demand. 

• Reduction of costs and delivery times: Route, load, inventory and warehouse process optimisation helps reduce unnecessary journeys, fuel consumption, storage costs and delays. 

• Greater efficiency and service quality: AI speeds up repetitive tasks, improves order accuracy and makes it possible to anticipate incidents in order to offer more reliable deliveries. 

• More sustainable and adaptable operations: More precise planning makes it possible to make better use of vehicles, reduce emissions and avoid excess inventory. 

Challenges of incorporating artificial intelligence into logistics 

Although AI can improve the efficiency of logistics and the supply chain, its implementation requires: 

La entrada AI in logistics and supply chain: how to improve routes, inventory and demand forecasting  se publicó primero en Acevedo.

Why artificial intelligence has become strategic for banks 

Artificial intelligence has become a strategic technology for banking because it makes it possible to analyse large volumes of data, anticipate risks and improve processes that directly affect efficiency and security. Banks can use it to detect suspicious transactions, assess solvency, automate internal tasks and obtain useful information to make decisions faster. 

It also responds to the new expectations of customers, who are looking for more agile, personalised digital services that are available at any time. Virtual assistants, financial recommendations and self-service tools help improve customer service without increasing employees’ workload. 

Financial control and risk management with AI 

Artificial intelligence helps banks analyse large volumes of data on transactions, customers and operations to identify risks more quickly. 

Transaction analysis and anomaly detection 

AI models compare each transaction with historical patterns and usual behaviours to identify unusual movements. They can detect sudden changes in amounts, frequency, location or recipients and generate alerts for transactions with a higher risk level. 

Solvency assessment and credit decisions 

AI makes it possible to process financial and credit information to estimate each applicant’s payment capacity and default risk. This speeds up the assessment of loans, cards or credit extensions and helps maintain more consistent criteria. 

Fraud and anti-money laundering prevention 

AI systems can connect transactions, accounts, identities and behaviours to detect signs associated with fraud or money laundering. They also help prioritise alerts according to their relevance, speed up investigations and improve the monitoring of suspicious activity. 

AI in digital banking security 

AI helps strengthen the security of banking applications, platforms and operations through the continuous analysis of data to detect threats quickly, verify user identity and reduce the risk of fraud across digital channels. 

Real-time cyber threat monitoring 

AI systems can monitor network traffic, access and platform activity to identify patterns that may indicate a possible cyberattack. 

Identity verification and prevention of impersonation 

AI can analyse identification data during customer onboarding or login and detect false identities, manipulated documents or impersonation attempts. 

Protection of data, operations and digital channels 

AI helps control access, detect unauthorised use and continuously review the activity of applications, websites and APIs. 

Banking process automation 

Artificial intelligence makes it possible to streamline tasks that require reviewing information, validating data and coordinating several stages of a process, helping to reduce response times in internal operations and customer-facing services. 

Document processing and verification 

AI can extract, classify and check information from documents, helping to onboard customers, reduce transcription errors and detect incomplete data, inconsistencies or possible manipulation. 

Loan processing and compliance control 

In loan management, AI can organise documentation, consult internal and external information, evaluate financial data, flag possible risks, review compliance requirements and detect suspicious transactions. 

Automation of internal tasks 

Banks can also use AI to summarise reports, classify requests, transcribe calls, prepare communications or update records. 

How AI improves relationships with customers and employees 

Artificial intelligence makes it possible to offer more agile service through virtual assistants capable of answering queries, guiding users through operations and facilitating self-service. It also helps personalise products, recommendations and communications according to each customer’s needs and behaviour. Within the bank, copilots support analysts, managers and customer service teams by searching for information, summarising documents and preparing responses. 

Generative AI in banking: opportunities and limits 

Generative artificial intelligence can help banks work with large volumes of unstructured information, speed up internal tasks and make knowledge easier to access. However, its use in a regulated sector requires strict controls: 

Benefits of AI in the banking sector 

AI helps improve both internal operations and customer-facing services. 

• Greater accuracy and operational efficiency: AI reduces manual tasks in processes such as document review or request classification, making workflows more agile. 

• Reduction of fraud, errors and costs: It identifies unusual transactions, detects possible threats and flags inconsistencies before they have a greater impact, reducing errors. 

• More agile and personalised services: Analysis of behaviour and needs makes it possible to offer more relevant products and recommendations. Virtual assistants facilitate continuous support and query resolution. 

The future of artificial intelligence in banking 

The future of banking will be shaped by broader process automation, more accurate risk detection and digital services capable of anticipating each customer’s needs. AI will make it possible to optimise workflows, personalise recommendations and offer more complete self-service tools, combining speed and availability with human support when the case requires it. 

The use of AI agents will also increase to coordinate processes such as request handling, document verification or compliance control. However, its progress will depend on banks properly integrating their data and systems, maintaining effective human supervision and strengthening the security, explainability and governance of models. 

La entrada AI in banking: how to improve financial control, risk management and process automation  se publicó primero en Acevedo.

What is artificial intelligence in finance?

Artificial intelligence in finance refers to the use of technologies capable of analysing data, identifying patterns, automating processes and generating forecasts that support better financial decisions. It is used both by financial institutions and by finance departments in companies across all industries.

Unlike traditional tools, AI solutions can process large volumes of information, connect data from different sources and improve their results as they work with new information. This helps reduce manual tasks, produce faster and more accurate analyses and support business financial planning, as companies can compare scenarios and assess how specific decisions could affect the business.

Applications of AI in corporate financial management

Artificial intelligence can be applied to different tasks within the finance department.

AI agents in finance: what they are and what they are used for

AI agents in finance are systems capable of analysing data, reasoning, planning and executing actions to achieve a specific objective. They do not simply answer questions or follow a fixed sequence. They can retrieve information, decide which steps to take and interact with tools such as ERP systems, databases and financial applications.

Within the finance department, they coordinate processes involving several tasks, such as collecting data, preparing reports, updating forecasts, reviewing budgets and detecting unusual transactions. Their purpose is to streamline work and decision-making while operating under defined permissions, limits and human supervision.

How to implement AI solutions for finance

Implementing artificial intelligence in the finance department requires more than simply adding a new tool. The company must define which processes it wants to improve, assess the quality of its data and establish secure integration with the systems it already uses.

1. Identify the processes that can be automated

Start by identifying manual, repetitive or time-consuming tasks, such as preparing reports or updating forecasts. It is advisable to begin with a specific process, define the expected outcome and confirm that automation delivers a measurable improvement.

2. Centralise and prepare financial data

AI solutions need complete, up-to-date and well-structured information to provide reliable results. The company should therefore review where its data comes from, remove duplicates, establish a common source of information and define who can access each data set and how sensitive financial information will be protected.

3. Choose the right AI solutions for finance

The tool should be selected according to the process to be improved, the volume of information and the systems used by the company. Some solutions focus on reporting, while others specialise in forecasting, treasury management, anomaly detection or accounting automation. Ease of use, scalability, security and integration capabilities should also be considered.

4. Integrate AI with the ERP and financial tools

To avoid creating new information silos, AI should be connected to the ERP, accounting applications, banking platforms and analytical tools used by the company. This integration provides access to up-to-date data and allows tasks to be carried out within existing workflows.

5. Measure results and optimise processes

Once the solution has been implemented, the company should assess whether it reduces processing times, lowers the number of errors and improves the quality of financial information. Indicators may include the number of hours spent preparing reports or the accuracy of forecasts. Based on these results, the company can refine the models, expand the data sources and apply AI to other processes.

How to apply AI in finance with Oracle Cloud ERP

It centralises financial and operational information while incorporating artificial intelligence capabilities into business processes. For companies still working with on-premises systems or disconnected tools, ERP Cloud provides access to up-to-date information, improves integration and creates a stronger foundation for implementing AI solutions.

Benefits of applying artificial intelligence to finance

Some of its main benefits include:

• Reduction of manual tasks and errors: AI automates repetitive processes, reduces workloads and lowers the risk of errors when handling information.
• Faster and more accurate financial analysis: By reviewing large volumes of data in a short time, identifying patterns and highlighting relevant changes, AI enables more comprehensive analysis.
• Improved planning and decision-making: It facilitates the creation of forecasts and the comparison of different scenarios, providing more information for assessing important decisions.
• Access to up-to-date financial information: When integrated with the ERP and other tools, AI works with recent data and provides a clearer overview. This makes it possible to detect changes quickly and act before they have a greater impact.

Risks and challenges of AI in finance

Although artificial intelligence can improve financial management, its implementation also presents risks that must be controlled.

• Data quality, privacy and security: Incorrect or incomplete data can produce unreliable analysis, while insufficient protection may expose sensitive information.
• Transparency and explainability of results: AI systems may generate forecasts or recommendations without clearly showing how they reached their conclusions. The finance team must be able to understand, justify and review the results before using them.
• Technological dependence and human oversight: Automating too many processes without control mechanisms can increase dependence on technology. AI should support the finance department rather than replace professional judgement.
• Regulatory compliance and AI governance: Companies must ensure that their use of AI complies with regulations on data protection, security and information management. They must also define clear limits on the actions these systems can perform.

The future of finance with AI

The future of finance will be shaped by greater automation and the use of predictive analytics to anticipate changes in revenue, expenses, budgets and treasury. Integrating AI with ERP systems will allow companies to work with connected and up-to-date financial data. Even so, important decisions will continue to require human oversight and knowledge of the business.

La entrada AI for finance: how to automate reporting, forecasting and financial control se publicó primero en Acevedo.

The role of Artificial Intelligence in industrial transformation

Artificial intelligence is changing how industrial companies manage production, maintenance and quality. Its ability to process large volumes of information makes it possible to identify patterns, anticipate problems and recommend adjustments that would be difficult to detect through manual reviews.

Machines, sensors, production lines and management systems continuously generate data. AI can connect this information to provide a clearer understanding of equipment performance, adjust parameters in real time, reduce waste and adapt production to changes in demand. It helps create more flexible, efficient and sustainable processes, improves decision-making and enables companies to respond more quickly to changing situations.

AI for optimising industrial production

AI can analyse data from machinery, sensors and production lines to identify inefficiencies and improve process performance.

• Real-time process and parameter adjustment: AI models can monitor variables such as temperature, pressure and material usage. When they detect a deviation, they help adjust the relevant parameters to maintain performance.
• Demand-based production planning: By analysing sales history, seasonality, orders and market trends, AI can anticipate changes in demand and organise manufacturing, shifts, available capacity and materials, helping to avoid both overproduction and shortages.
• Waste reduction and resource optimisation: AI helps identify the stages where more raw materials, energy or time are being used than necessary. It can then suggest improvements to reduce waste, avoid rework and align resource use with actual production volumes.

Predictive maintenance with artificial intelligence

AI provides a clearer view of the actual condition of machinery, anticipates potential breakdowns before they cause downtime and supports intervention when data reveals signs of wear or abnormal behaviour.

• Sensor data to anticipate failures: Industrial sensors collect information on variables such as energy consumption and operating speed. AI analyses this data to identify patterns and detect changes that may indicate an upcoming failure.
• Less downtime and better maintenance planning: AI helps anticipate breakdowns, extend equipment lifespan, schedule interventions during periods of lower activity, prepare the necessary spare parts in advance and coordinate maintenance teams more effectively.

AI for improving quality control

AI enables continuous inspection of products and processes to detect errors before they reach the customer.

• Computer vision for detecting defects: These systems use cameras and AI models to inspect parts, components or products during manufacturing. They can quickly identify cracks, deformations, assembly errors, colour differences or measurements outside the permitted tolerances.
• Traceability and root cause analysis: AI can also link each defect to data from the machine, production batch, materials used, shift or process conditions. This makes it easier to identify the source of the problem, determine whether it affects other units and apply corrective measures.

Smart manufacturing and connected factories

Smart manufacturing connects equipment, systems and data to provide a more complete view of plant activity. Artificial intelligence analyses this information in real time, detects inefficiencies and helps adjust production.

• Integration of machinery, MES, SCADA and ERP: This connection brings operational and management data together in a single environment. As a result, the company can link plant performance with production orders, inventory, costs and delivery times.
• Digital twins, cobots and advanced automation: Digital twins create virtual representations of machines, production lines or plants to simulate changes and anticipate their impact before applying them in the real environment. Cobots work alongside people on repetitive or physically demanding tasks, while advanced automation enables processes to be adapted more quickly and accurately.

Other applications of industrial Artificial Intelligence

In addition to improving production, maintenance and quality, artificial intelligence can also be applied to other areas:

How to implement AI in industrial processes

Implementation should begin by identifying the processes with the greatest potential for improvement, such as those that cause downtime, errors, waste or high operating costs. The next step is to prepare the data and connect machinery, sensors and management systems so that the company can work with reliable and up-to-date information.

It is advisable to start with a pilot in a specific process and measure results such as time savings, fewer failures or increased productivity. Once the solution has proved effective, it can be extended gradually to other production lines, equipment or plants.

Challenges of applying Artificial Intelligence in industry

Although artificial intelligence offers significant opportunities, its implementation requires companies to overcome several obstacles.

• Legacy systems and lack of integration: Many industrial companies use old machinery and applications that are not designed to share data.
• Data quality and availability: Complete, reliable and up-to-date information is required to produce accurate results. Incomplete, duplicated or siloed data can limit the effectiveness of AI.
• Employee training and resistance to change: It is important to provide training, explain the benefits of the technology and involve employees so that they understand how to use the new tools.

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There is no single AI-powered ERP that is suitable for every organisation. Some platforms stand out in large companies with complex processes, while others are better suited to mid-sized businesses, growing companies or industries with highly specific requirements.

Top 5 ERP Systems with Artificial Intelligence in 2026

Before comparing the solutions, it is worth reviewing several factors:

• Company size and complexity.
• Processes to be automated.
• Included predictive and generative capabilities.
• Availability of AI agents.
• Integration with current tools.
• Data security, permissions and governance.

1. Oracle ERP Cloud

It is one of the most comprehensive alternatives for companies looking to incorporate artificial intelligence directly into their financial and operational processes. The solution combines predictive, generative and agentic AI to automate transactions, improve operational visibility and coordinate end-to-end processes. Oracle is developing applications powered by teams of specialised agents capable of understanding objectives, making decisions and executing actions within business workflows.

2. SAP Cloud ERP

It is designed for organisations that need to manage complex processes across finance, supply chain, manufacturing and other business areas. It embeds artificial intelligence into core processes to automate tasks, provide contextual insights and make it easier to interact with the system through conversational language. One of its most notable capabilities is Joule, which interprets user intent and coordinates specialised agents to execute workflows, analyse data and support decision-making across different areas.

3. Microsoft Dynamics 365 Business Central

It is aimed primarily at small and mid-sized businesses that want to incorporate artificial intelligence without adopting an overly complex platform. Its Copilot capabilities help users query data, summarise information, complete records and work with sales documents, product information and other areas of the system.

• Sales Order Agent: Interprets requests received by email, checks products and prepares quotes or orders for review.
• Payables Agent: Monitors supplier invoices, extracts the relevant information and creates drafts that are subsequently validated by the person responsible.

4. Oracle NetSuite ERP

Oracle NetSuite ERP is a cloud-based solution designed for growing companies that need to centralise finance, inventory, purchasing, orders and operations. Its artificial intelligence capabilities are applied to task automation, financial analysis and the interpretation of business data. Its main agents include Sales Order Agent, which interprets requests received by email and prepares quotes or orders, and Payables Agent, which extracts data from supplier invoices and creates drafts for validation. The platform also introduces improvements for reconciliations, planning and the analysis of payment-related risks. In inventory and operations, NetSuite can generate summaries that highlight trends, causes and relevant changes.

5. Odoo

Odoo is a modular ERP platform that brings together applications for accounting, sales, CRM, inventory, manufacturing, e-commerce and other business areas. It is generally well suited to small and mid-sized businesses that want to start with a specific scope and add new modules as the company evolves. Odoo 19 includes artificial intelligence capabilities that allow users to interact with agents, query database records, use information from documents and execute specific actions. Its main advantage lies in its flexibility and the integration of multiple functions within a unified experience. However, the final scope of its AI capabilities will depend on the configuration, the installed modules and the customisations implemented.

Business Areas Where an AI-Powered ERP Delivers the Most Value

An ERP system with artificial intelligence can improve different areas of a company because it works from a shared base of financial, commercial and operational data.

Finance, Accounting and Treasury

AI can classify transactions, automate reconciliations, detect potential errors, generate forecasts for collections, payments and cash flow, and identify deviations or unusual transactions.

Inventory, Production and Procurement

The ERP can analyse demand, stock levels, orders and supplier lead times to anticipate purchasing or manufacturing requirements.

Sales, CRM and Customer Service

In the commercial area, AI can identify opportunities, recommend actions and analyse each customer’s behaviour. It also facilitates automated responses, case creation and access to interaction histories.

Reporting and Decision-Making

It can transform ERP data into understandable reports, forecasts and alerts. Managers can query information using natural language, detect relevant changes and compare different scenarios.

How to Choose the Best ERP with Artificial Intelligence

The decision should be based on the processes the company needs to improve, rather than solely on the number of AI features advertised by each provider. It is important to confirm which capabilities are actually available, whether they are included in the chosen licence and how much configuration they require.

Before making a decision, it is worth reviewing:

• Which tasks and processes the system can automate.
• Whether the AI is built in or depends on additional tools.
• Which agents are available and what actions they can perform.
• How the ERP will connect with existing applications.
• Which permissions, controls and activity logs it provides.
• The total cost of licences, usage and implementation.
• Which partner will support the configuration and ongoing development of the ERP.

The best ERP with artificial intelligence will be the one that applies the technology to specific business problems, works with reliable data and maintains human control over sensitive decisions. AI should improve day-to-day management and decision-making, rather than simply becoming an additional feature.

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Insolvency proceedings are a situation that concerns many companies, especially when they start accumulating debt, payment delays or treasury pressure. However, entering insolvency proceedings does not always mean the immediate closure of the business.

In many cases, this procedure can help organise debts, protect business activity and seek a viable solution with creditors. The key is to act in time, analyse the financial situation properly and rely on specialised advice.

What are insolvency proceedings?

To understand what insolvency proceedings are, it is useful to start with a simple idea: they are a legal procedure that is activated when a company, self-employed professional or individual cannot regularly meet their payment obligations.

Their purpose is to organise the insolvency situation, protect creditors’ rights and assess whether there is a real possibility of continuity. If the company is viable, an agreement may be sought. If it is not, the procedure may end in liquidation.

For this reason, insolvency proceedings should not be seen only as the end of a company, but as a legal route to organise a complex economic situation.

When a company enters insolvency proceedings, the process becomes subject to court supervision. From that moment on, debts, assets, creditors, business viability and possible solutions are analysed.

This may involve changes in company management, limitations on certain decisions and greater oversight of financial operations. Even so, the company can continue operating if there is activity and a real possibility of continuity.

Types of insolvency proceedings

There are different types of insolvency proceedings, depending on who requests them and the situation the company is in.

Voluntary insolvency proceedings are usually more advisable when the company detects the problem in time, as they allow it to anticipate the situation and show a more organised attitude towards insolvency.

What happens if my company enters insolvency proceedings?

One of the most common questions is: what happens if my company enters insolvency proceedings? The answer depends on the financial situation, the level of debt and whether the business remains viable.

In general terms, several things can happen:

• Certain debt claims are suspended or reorganised.
• All creditors are identified.
• The company’s assets are analysed.
• Whether activity can continue is reviewed.
• Possible payment agreements are studied.
• An insolvency administrator may be appointed.
• It is assessed whether an agreement or liquidation is appropriate.

Insolvency proceedings should not be understood only as a failure, but as a mechanism to organise a critical situation. In some cases, they allow the company to gain time, negotiate with creditors and avoid rushed decisions.

How insolvency proceedings work

Insolvency proceedings usually follow several stages, although they may vary depending on the specific case and the type of debtor.

1. Situation analysis

Before filing for insolvency proceedings, it is important to review the debt, treasury position, contracts, assets, pending payments and business viability. At this stage, good business financial planning can help anticipate scenarios, organise priorities and make decisions with greater control.

2. Filing for insolvency proceedings

The request can be filed by the company itself or by a creditor. When insolvency is already real, it is important to act quickly and not wait for the situation to worsen.

Filing for insolvency proceedings on time can facilitate a more organised management of the process and reduce the risk of making rushed decisions.

3. Court declaration

The court analyses the request and, if the requirements are met, declares the insolvency proceedings. From that point, the procedure formally begins and the situation with creditors is organised.

At this stage, the financial documentation, asset situation and the company’s ability to continue operating are reviewed.

4. Insolvency administrator

In certain cases, an insolvency administrator is appointed to supervise the financial situation, review documentation and participate in the management of the procedure.

Their role is to help organise the information, assess the company’s real situation and protect the interests of the parties involved.

5. Report, inventory and list of creditors

During the procedure, the company’s assets, rights, debts and creditors are identified. This part is essential to understand what is owed, who it is owed to and what resources are available.

It is also important to have clear and updated financial information. Financial consolidation solutions make it possible to obtain a more accurate view of the financial situation, especially in companies with several entities, subsidiaries or complex structures.

6. Agreement or liquidation

If the company is viable, it may try to reach an agreement with creditors. This agreement may include payment extensions, debt reductions or new payment conditions.

If there is no real possibility of continuity, the procedure may end in liquidation.

Liquidation in insolvency proceedings

Liquidation in insolvency proceedings occurs when it is not possible to maintain activity or when no viable solution is approved to pay the debts. In that case, the company’s assets are sold to pay creditors according to the established order.

Liquidation may mean the closure of the company, but it can also help organise the end of activity and prevent the situation from deteriorating further.

This stage is not always reached. If the company acts in time and has a viable model, alternatives such as agreements, debt restructuring, sale of productive units or continuity with adjustments may be considered.

In some cases, when the company retains assets, customers or a viable productive unit, corporate transactions such as a business merger may also be assessed to preserve part of the activity or integrate it into a stronger structure.

Can a company exit insolvency proceedings?

Yes, it is possible to exit insolvency proceedings, but it will depend on the company’s real situation. A company with temporary liquidity problems is not the same as a company with no activity, no revenue and no ability to generate cash.

Some ways to overcome insolvency proceedings include:

• Reaching an agreement with creditors.
• Restructuring debt.
• Reducing costs and reorganising activity.
• Selling non-strategic assets.
• Maintaining the productive unit if the business is viable.
• Improving financial management and payment planning.

Exiting insolvency proceedings requires a realistic plan. It is not enough to postpone payments; the company must show that it can generate enough income to meet its future commitments.

How to overcome insolvency proceedings

To overcome insolvency proceedings, the most important thing is to act quickly and not wait until the situation becomes irreversible.

Some key recommendations are:

• Review the financial situation as soon as possible.
• Have a clear treasury forecast.
• Identify which debts are most urgent.
• Analyse whether the business remains viable.
• Negotiate with creditors before the conflict increases.
• Prepare accounting and financial documentation.
• Avoid new debts that worsen the situation.
• Rely on specialised legal and financial advice.

The sooner the problem is detected, the more options there are to preserve activity, negotiate payments and avoid liquidation.

Common mistakes when entering insolvency proceedings

Some mistakes can worsen the company’s situation and reduce its chances of recovery:

• Waiting too long to act.
• Hiding financial information.
• Continuing to accumulate debt without a clear plan.
• Not speaking with creditors.
• Confusing a temporary lack of liquidity with structural insolvency.
• Not preparing the documentation properly.
• Making decisions without advice.
• Thinking that insolvency proceedings always mean closure.

Entering insolvency proceedings does not have to mean the end of a company. It can be a tool to organise debt, protect activity and seek a viable way forward. The difference usually lies in acting in time, analysing the numbers realistically and making decisions before the situation becomes impossible to redirect.

La entrada Insolvency proceedings: what they are and how to overcome them se publicó primero en Acevedo.

What does it mean to modernise an on-premise infrastructure?

Modernising an on-premise infrastructure means updating the systems, servers, applications and technology processes that still depend on physical or local resources, so they can respond better to current business needs. It does not always mean completely abandoning the company’s own environment or moving everything to the cloud, but rather reviewing which part of the infrastructure should be maintained, which part can be optimised and which workloads should move to a cloud or hybrid model.

In many companies, on-premise solutions remain critical because they support key operations, sensitive data or applications that cannot be changed overnight. The problem appears when that infrastructure starts generating high costs, rigidity, integration difficulties, performance issues or continuity risks. At that point, modernisation stops being a technical improvement and becomes a strategic decision.

A well-planned modernisation helps reduce dependence on obsolete hardware, improve security, make scaling easier, connect systems more effectively and prepare the organisation for new digital tools. The goal is not to move everything to the cloud just because it is fashionable, but to build a more flexible, secure and sustainable infrastructure aligned with the company’s operational reality.

Signs that your on-premise infrastructure has become obsolete

An on-premise infrastructure does not become obsolete simply because the cloud exists, but when it starts to limit operations, increase maintenance costs or prevent the company from evolving with agility. Many organisations live for years with servers, applications and on-premise solutions that still work, but require more and more effort to maintain, integrate or scale.

Increasingly high maintenance costs

One of the clearest signs is that maintaining the infrastructure is becoming increasingly expensive. This can include server renewal, storage expansion, licences, specialised support, energy consumption, physical space and technical team hours. When a large part of the budget is allocated to keeping old systems running instead of improving capabilities, the infrastructure starts to become a brake on the business.

Scalability and performance problems

On-premise infrastructures depend on installed capacity. If the company needs more resources, it usually has to buy, configure and deploy new hardware, which slows down the response to changes in demand. This can be seen in slow systems, occasional outages, saturation at critical moments or difficulty growing without new investments.

Difficulty integrating new solutions

Another common sign is that connecting the current infrastructure with new tools becomes complex. Integrating cloud solutions, SaaS applications, ERPs, CRMs, analytics or automation platforms may require custom developments, fragile connections or manual processes. The result is often scattered information, duplicated data and little ability to work with a unified view of the business.

Security, support and continuity risks

When systems become outdated, unsupported by the manufacturer or difficult to patch, the risk of vulnerabilities and failures increases. In addition, if incident recovery depends on manual processes or old infrastructures, any outage can directly affect business continuity. Modernisation makes it possible to reduce exposure, improve data protection and strengthen recovery capacity.

On-premise, cloud or hybrid model: key differences

Choosing between on-premise, cloud or hybrid infrastructure does not depend only on technology, but on what the company needs to protect, scale and control. Each model offers a different balance between investment, flexibility, security, maintenance and growth capacity. That is why, before modernising, it is important to understand what each option provides and in which scenarios it fits best.

On-premise infrastructure

On-premise infrastructure is based on servers, storage, networks and applications hosted on the company’s premises or in a directly controlled environment. Its main advantage is the level of control over data, configuration and security, which is important in regulated sectors or with very strict internal requirements. In return, it requires more investment in hardware, maintenance, updates, technical staff and failure recovery capacity.

Cloud infrastructure

Cloud infrastructure allows companies to consume technology resources from an external provider, under a flexible and scalable model. Instead of buying and maintaining their own servers, companies can increase or reduce capacity according to their needs, access advanced services and delegate part of the maintenance, security and updates. It is especially useful when the objective is agility, availability and less dependence on physical infrastructure.

Hybrid infrastructure

Hybrid infrastructure combines on-premise and cloud environments within the same technology strategy. It allows companies to keep the most critical, sensitive or difficult-to-migrate systems locally, while moving other workloads, applications or services that need more flexibility to the cloud. This model is often the most realistic option for companies with legacy systems, because it enables progressive modernisation without putting operational continuity at risk.

When it makes sense to migrate to the cloud

It makes sense to migrate to the cloud when the current infrastructure needs more flexibility, scalability and adaptability than an on-premise environment can provide. It is especially recommended if the company has demand peaks, needs to deploy new services quickly, wants to reduce internal maintenance workload or seeks access to more advanced technologies without constantly investing in its own hardware.

It also makes sense when systems must be available from different locations, when the business is growing quickly or when local infrastructure has become a fixed cost that is difficult to justify. In these cases, the cloud allows the company to move to a more flexible model, where resources are better adjusted to the real needs of each moment.

When it makes sense to keep on-premise solutions

Keeping on-premise solutions may still be appropriate when there are very strict requirements for control, security, latency or regulatory compliance. Some companies need to keep certain data, applications or critical workloads in their own environments because their sector, internal policy or the nature of their operations requires it.

It may also make sense when an application works correctly, is deeply integrated with internal systems or migrating it would involve more risk than benefit. In these cases, modernisation does not necessarily mean moving everything to the cloud, but optimising what already exists, strengthening security, improving performance and ensuring continuity.

When to choose a hybrid infrastructure

Hybrid infrastructure is often the most balanced option when the company needs to modernise without disrupting operations. It allows critical or more sensitive systems to remain local, while moving to the cloud those workloads that require more scalability, availability or ease of integration.

This approach fits especially well in organisations with legacy systems, complex dependencies or processes that cannot be migrated all at once. With a hybrid model, the company can move forward in phases, reduce risks and take advantage of the benefits of the cloud without giving up control over certain technology assets.

How to approach infrastructure modernisation without putting operations at risk

Modernising an infrastructure should not be approached as a sudden change, but as a controlled process. Before moving workloads, replacing systems or incorporating cloud services, it is necessary to understand what exists, what depends on what and what impact any change would have on the company’s daily activity. The goal is to move towards a more flexible and secure infrastructure without causing unnecessary interruptions.

Evaluate systems, workloads and dependencies

The first step is to analyse the current infrastructure: servers, applications, databases, networks, integrations, users and associated processes. This assessment makes it possible to identify which systems are critical, which workloads consume the most resources, which technologies are obsolete and which dependencies could create problems during a migration or update.

Without this diagnosis, any decision is made blindly. An application may seem simple to move, but it may depend on an old database, a specific integration or an internal process that affects the entire project.

Prioritise critical applications and processes

Not everything should be modernised at the same time. It is advisable to classify applications according to their importance for the business, their technical complexity and the risk involved in changing them. The most critical systems require more careful planning, prior testing and clear contingency plans.

In many cases, the best approach is to start with less sensitive workloads or with processes where the benefit is high and the risk is acceptable. This allows visible improvements to be achieved without compromising operational continuity.

Define a phased roadmap

Modernisation should move forward in stages: assessment, design, testing, partial migration, validation and scaling. This roadmap makes it possible to control the impact of each change, correct deviations and avoid making the project depend on one large migration.

Working in phases also makes it easier to combine models. A company can keep certain on-premise solutions, move others to the cloud and progressively build a hybrid architecture according to its priorities and restrictions.

Monitor performance, costs and security

Once modernisation has started, it is essential to continuously measure the behaviour of the new infrastructure. Performance, availability, resource consumption, cloud costs, security alerts and user experience must all be reviewed.

This monitoring makes it possible to verify whether modernisation is meeting its objectives and detect problems before they affect the business. Modernisation does not end with migration or an architecture change: it requires follow-up, adjustment and continuous improvement to ensure the infrastructure remains efficient, secure and sustainable.

La entrada Modernising on-premise infrastructure: when to migrate to the cloud or choose a hybrid model se publicó primero en Acevedo.

Subsidiary companies are a common option for businesses that want to grow, enter new markets or separate different lines of business. This model allows a parent company to control another company while maintaining a separate legal and operational structure. It is important when a company wants to expand, reorganise its activity or create an independent entity to operate in another country or sector.

What is a subsidiary company?

A subsidiary company is a company that is wholly or partially owned by another company, known as the parent company. The parent company has a controlling stake in the subsidiary and can influence its strategic, financial and operational decisions.

The subsidiary has its own legal personality. This means it can have its own name, management, tax obligations, contracts, accounting and legal responsibilities.

For example, an international company may create a subsidiary in Spain to sell its products, hire local staff and operate under Spanish regulations, while the parent company maintains overall control of the business.

Difference between subsidiary company, affiliate and branch

The terms subsidiary and affiliate are often used in a similar way, as both refer to a company controlled by a parent company. The most important difference is usually between a subsidiary and a branch.

The difference matters because it affects legal liability, taxation, accounting and day-to-day management.

What is a subsidiary company used for?

Creating a subsidiary company can make sense when a company wants to grow with a more organised structure.

Some common uses include:

• Entering a new country or market.
• Separating a line of business.
• Protecting the parent company from certain risks.
• Managing independent brands or projects.
• Facilitating investment or acquisition operations.
• Adapting better to local regulations.
• Organising the group’s financial information more effectively.

For example, a company that wants to expand internationally may create a subsidiary to operate in the target country through its own legal entity.

Advantages of creating a subsidiary company

One of the main advantages of a subsidiary is that it allows different activities to be legally separated. This can help limit risks, organise management more effectively and make it easier to control each business unit.

It can also be useful for international expansion, as the company can operate as a local entity and adapt better to the market, taxation and regulations of the country.

Main advantages include:

• Greater legal separation between activities.
• Better risk control.
• Possibility of operating in new markets.
• Differentiated management by country or business line.
• Better tax and operational adaptation.
• Greater financial control within the group.

When a company manages several entities, it is important to have financial consolidation tools that allow data to be unified and the group’s overall performance to be analysed.

Challenges of a subsidiary company

Although a subsidiary can bring many advantages, it also involves more management. Creating a new company means assuming tax, accounting, legal and administrative obligations.

Some common challenges include:

• Greater accounting complexity.
• Incorporation and maintenance costs.
• Need to comply with local regulations.
• More financial reporting.
• Coordination between parent company and subsidiary.
• Management of taxes, contracts and documentation.

That is why, before opening a subsidiary, it is advisable to analyse whether it is really the best option or whether a branch, a local partnership or another simpler formula may be sufficient.

How to open a subsidiary company

Knowing how to open a subsidiary company depends on the country where it will operate, although the process usually follows a series of common steps.

1. Define the purpose of the subsidiary

Before starting the formal procedures, the parent company must be clear about why it wants to create the subsidiary. Opening a company to sell in another country is not the same as doing so to separate a business line or launch a new brand.

At this stage, it is advisable to define the market, the required investment, the team, the risks and the financial objectives. Good business financial planning helps assess whether the new company will be viable.

2. Choose the legal form

The next step is to choose the most appropriate legal form. In Spain, many subsidiaries are incorporated as a Limited Liability Company or a Public Limited Company, depending on the size of the project, the capital and the shareholder structure.

3. Prepare the documentation

After that, the necessary documentation must be prepared: company name, articles of association, shareholder details, share capital, registered address and corporate purpose.

The articles of association must define how the company will operate, what activity it will carry out and how its management will be organised.

4. Incorporate the company

The incorporation is usually formalised before a notary through a public deed. After that, the company must be registered with the Commercial Registry and obtain its tax ID number in order to operate.

It will also be necessary to complete the corresponding tax, employment and administrative procedures before starting activity.

Examples of subsidiary companies

A simple example would be a foreign technology company that creates a subsidiary in Spain to sell its solutions, hire a local team and invoice Spanish customers.

Another case would be a business group that decides to separate its logistics division into its own company. This allows it to better control costs, staff, assets and results for that specific line.

It can also happen when a company acquires another company and keeps it as a subsidiary within the group. In these cases, the parent company controls the acquired company, but may keep its brand, team and operations.

In growth or integration operations, a business merger can also be analysed when the objective is not to maintain separate companies, but to combine structures.

Financial management of a subsidiary company

Once the subsidiary has been created, the parent company needs to control the financial information, costs, results and evolution of each company within the group.

This is especially important when there are several subsidiaries in different countries, currencies or tax frameworks. In these cases, having integrated systems helps reduce errors, improve visibility and make decisions with updated data.

An ERP or business management solution can facilitate the connection between the parent company and its subsidiaries, centralising finance, purchasing, sales, inventory, reporting and operational processes.

What to consider before opening a subsidiary

Before opening a subsidiary company, it is advisable to review some key points:

The key is not to create a subsidiary only for structural reasons, but because it brings a real advantage to the business.

Common mistakes when creating a subsidiary company

Some mistakes can complicate the creation and management of a subsidiary:

• Opening the company without a clear strategy.
• Not properly analysing tax and legal obligations.
• Choosing an unsuitable legal form.
• Not defining the relationship between parent company and subsidiary.
• Duplicating unnecessary processes.
• Not having integrated financial reporting.
• Not assessing maintenance costs.
• Treating the subsidiary as a simple delegation.

A subsidiary company can be a very useful tool to grow, enter new markets or separate activities. The key is to define its purpose properly, choose the right structure and have systems that allow financial and operational information to be controlled from the start.

La entrada Subsidiary companies: what they are, how to open one and examples se publicó primero en Acevedo.

Scaling a startup is not only about selling more, hiring more people or entering new markets. A company can grow in revenue and still not be truly scalable if every new customer means more costs, more operational workload and more internal complexity.

The key is to ensure that the business can increase revenue, customers and market presence without the required resources growing at the same pace. That is why, before asking how to scale my startup, it is important to analyse whether the business model, processes, technology and team are prepared to grow sustainably.

Although there are many ways to scale a business, in the case of a startup the challenge is doing so without losing agility, financial control or adaptability.

What does it mean to scale a startup?

Scaling a startup means increasing business volume while maintaining efficiency. In other words, growing without always depending on more working hours, more people or proportional investment.

A scalable startup is one that can repeat its model across different customers, markets or segments without having to rebuild its processes from scratch. To achieve this, it needs a clear value proposition, measurable acquisition channels, defined internal processes and a technology structure capable of supporting growth.

The difference between growing and scaling lies in profitability. Growing can mean selling more. Scaling means selling more while keeping costs and operations under control.

What makes a startup scalable?

To understand what makes a startup scalable, several elements of the business must be reviewed.

First, there must be a repeatable model. If every customer needs a fully customised solution, it will be difficult to increase volume without increasing costs. On the other hand, if the product or service can be delivered in a similar way in many cases, the startup will have greater growth capacity.

It is also important that costs do not increase at the same pace as revenue. Automation, technology and process improvement allow the team to manage more customers without multiplying the internal structure.

In addition, a scalable startup needs predictable acquisition channels. It is not enough to win customers occasionally. It is necessary to know how much it costs to acquire a customer, what return that customer generates and which channels have the greatest growth potential.

How to scale a startup step by step

1. Validate the market before growing

Before scaling, the startup must confirm that there is a real need in the market. This means validating that customers understand the value proposition, are willing to pay and continue using the product or service after the first purchase.

Positive signs include repeat purchases, recommendations, good customer retention and clear feedback on the value provided by the solution.

Scaling without validating the market can lead to cash flow problems, dissatisfied customers and an operation that is difficult to sustain.

2. Analyse your key metrics

A startup should not scale without knowing its numbers. Revenue is important, but it is not enough to know whether the business is sustainable. Some key metrics are:

If revenue increases but costs grow at the same pace or faster, the startup is not scaling correctly. That is why good business financial planning helps control investment, anticipate needs and make better decisions before accelerating growth.

3. Automate repetitive processes

Automation is one of the keys to scaling. A startup that depends too much on manual tasks will have more difficulty growing without increasing costs.

Some processes that can be automated are:

• Welcome emails.
• Sales follow-up.
• Lead management.
• Invoicing.
• Reports.
• Internal reminders.
• Initial customer support.
• Contact segmentation.

Automating does not mean losing closeness with the customer. It means freeing up the team’s time so they can focus on higher-value tasks.

At this point, it may be useful to review how artificial intelligence helps scale a business with AI, especially when there are repetitive processes, manual tasks or large volumes of data.

More advanced solutions can also come into play, such as AI agents, which make it possible to execute tasks, analyse information and support decision-making across different business processes.

4. Standardise the way of working

In the early stages, many startups work with informal processes because the team is small. However, when customers, sales and tasks increase, that improvisation can become a problem.

To scale, it is advisable to document processes such as:

• Customer acquisition.
• Opportunity qualification.
• Onboarding.
• Product or service delivery.
• Support.
• Incident management.
• Metric tracking.

Having clear processes allows growth not to depend only on a few people and makes it easier for the team to work in a more organised way.

5. Build profitable acquisition channels

To scale a startup, acquisition must be measurable and sustainable. It is not just about attracting more leads, but about acquiring profitable customers.

Channels can include SEO, Ads, content, email marketing, partners, referrals or outbound sales. The important thing is to identify which ones have the best conversion, the lowest acquisition cost and the greatest growth potential.

Depending on a single channel can be risky. That is why a scalable strategy usually combines several channels and measures the performance of each one.

6. Prepare the team to grow

Scaling also means organising the internal structure. It is not always about hiring more people, but about defining responsibilities more clearly.

Each person must know what decisions they can make, which processes they lead and how their work is measured. This avoids bottlenecks and allows the startup to grow without losing agility.

At this stage, it may also be useful to outsource tasks that are not part of the core business, especially if this helps maintain a more flexible structure.

How to measure startup scalability

To know how to measure the scalability of a startup, it is advisable to analyse different areas of the business:

If every new customer generates too much internal workload, too many costs or too many incidents, the startup still needs to improve its foundation before scaling.

When to consider expansion into new markets

Once the model works, the startup can consider growing into new segments, regions or countries. However, expansion must be based on prior analysis.

Before taking the step, it is advisable to study the market size, competition, legal requirements, entry costs and internal capacity to serve new demand.

If the startup already has solid processes and a validated model, it can assess strategies to expand a business internationally without losing operational control.

Common mistakes when scaling a startup

Some common mistakes are:

• Scaling before validating the product.
• Hiring without having clear processes.
• Not measuring customer acquisition cost.
• Depending on a single acquisition channel.
• Automating without taking care of the user experience.
• Growing revenue without reviewing profitability.
• Expanding into new markets too early.

Knowing how to scale a startup involves much more than selling more. A scalable startup needs a repeatable model, clear processes, technology, profitable acquisition channels and metrics that allow decisions to be made with data.

Before accelerating growth, it is important to review whether the product is validated, whether the numbers are sustainable and whether the team can take on more volume without losing quality.

Scaling well is not about growing fast at any cost, but about building a solid foundation to increase revenue, customers and market reach without losing control.

La entrada How to scale a startup se publicó primero en Acevedo.