Consulting Kit

• Identification of the commercial and technical objectives of the SME in relation to the adoption of AI.
• An initial diagnosis is made to evaluate the current situation of the company regarding the use and implementation of AI, identifying key areas where it can be applied.

• Analysis of the existing data in the SME to determine their quality, relevance and format in order to implement them in AI algorithms.
• Suggestions from additional data sources are offered if necessary, and advice is provided to guarantee data governance in accordance with regulations (GDPR, UNE).

• An evaluation of the AI tools that best adapt to the needs and investment capacity of the SME is carried out.
• Personalized recommendations of technologies are offered, considering aspects such as ease of use, scalability and cost. Tools such as Microsoft Azure, Google Cloud, AWS, or open source solutions can be suggested.

• Advice is established to create protocols that ensure adequate and safe management of the data.
• These protocols guarantee the quality, integrity and validity of the data used, complying with international regulations such as GDPR and UNE standards.

• It works together with the SME to develop a use case that applies AI techniques in a specific area of the business (for example, demand prediction, customer segmentation, etc.).
• This case of use allows to demonstrate the practical value that AI can contribute to the company in terms of operational efficiency or process improvement.

• Recommendations are elaborated on how to implement AI in the key areas of the company, with suggestions to automate repetitive tasks through RPA (Robotic Process Automation).
• Total implementation and automation will depend on the company's investment capacity, and advice is offered to test and evaluate solutions.

• Personnel training in the strategic use of selected AI tools, with the objective that they can manage and take advantage of these technologies efficiently.
• Practical guides and manuals will be provided to ensure that the equipment can implement the proposed solutions.

• The strategic objectives of SMEs related to data analysis are identified.
• An initial diagnosis is made to evaluate the current situation of the use and exploitation of data in the company, determining the maturity of the current data management system.

• The existing databases are analyzed to evaluate their location, type, volume and relevance.
• The quality of the data and its ability to integrate into advanced analysis processes are evaluated. Additional sources are suggested if necessary.
• Recommendations are offered to improve the structure of the data according to standards such as the entity-relation (MER) model.

• Different data display tools are evaluated, considering the ease of use, integration capacity with current systems and associated costs such as Tableau, Power BI, Google Data Studio, etc.
• Personalized recommendations of tools are provided that best adapt to the needs of the SME.

• It works next to the SME to define the key performance indicators (KPIS) most relevant to the business.
• The current use of kpis in the company is analyzed and opportunities for improvement are identified.
• The creation of new kpis with metric and calculation formulas that better reflect the strategic objectives of the company is proposed.

• SME is advised on creating data quality policies that include format standardization, data cleaning and coherence between systems.
• Procedures are ensured that comply with data privacy regulations.

• The SME is collaborated to develop a use case that applies the results of the data analysis in a specific business situation.
• The case of use adapts to the real needs of the company, allowing to demonstrate the practical value of data analysis in decision making.

• Training is offered to SME personnel on the selected tools for data analysis and visualization.
• Manuals and practical guides are provided to ensure that the internal equipment can handle these tools effectively.

• Review the results of the basic service, evaluating the current SME situation regarding data analysis.
• Identify databases, quality procedures implemented and previous recommendations on tools used.

• Establish the objectives of the advanced data analysis system, aligning the strategy with the commercial needs of the SME.
• Evaluate the internal data of the SME and its potential to integrate with external sources (such as market studies or public databases).

• Design a centralized architecture for data management and storage, structuring tables, relationships between entities, and ensuring the integrity of primary and foreign keys.
• Produce a detailed action plan for data loading and transformation processes.

• Perform an analysis of advanced tools for management and display of data that are suitable for SMEs.
• Consider factors such as ease of use, costs and compatibility with existing systems.
• Provide tools recommendations that best adapt to the needs of the SME.

• Work together with SME to develop and implement a use case that applies advanced AI techniques, such as predictive models or real -time analysis.
• Measure the results obtained, document best practices and make recommendations on the scalability of the case of use in other business areas.

• Explore additional areas within SME where AI can add value through automation or process improvement.
• Propose new AI applications, such as natural language processing (NLP) or Machine Learning, which can align with the strategic objectives of the company.

Aim:

• Evaluate the current status of company digital sales.

Activities:

• Initial meeting with the management team to understand commercial objectives.
• Current digital sales data analysis (conversions, web traffic, customer behavior).
• Identification of strengths and weaknesses in the current sales strategy.

Aim:

• Establish clear and attainable goals for digital sales.

Activities:

• Co-creation workshop with the sales team to define KPIS.
• Establishment of short, medium and long term objectives.
• Alignment of the objectives with the vision and mission of the company.

Aim:

• Design a comprehensive strategy that maximizes digital sales opportunities.

Activities:

• Market research and competition analysis.
• Client segmentation and creation of Buyer Person.
• Development of an optimized sales funnel for the digital environment.

Aim:

• Ensure that the sales team is prepared for the new strategy.

Activities:

• Workshops and formations in digital sales techniques.
• Training in the use of digital tools and data analysis.
• Sales simulations and digital customer service practices.

Aim:

• Evaluate the performance of the strategy and make necessary adjustments.

Activities:

• Periodic analysis of KPIS and performance metrics.
• Monitoring meetings with the sales team to review results.
• Strategic settings based on collected data and equipment feedback.

Aim:

• Provide a full report on the process and results.

Activities:

• Preparation of a report that details the findings, results and recommendations for the future.
• Presentation of results to the address and interested parties.
• Proposal for an action plan for the continuity of improvement in digital sales.

• MAPO OF BUSINESS PROCESSES THROUGH FLOW DIAGRAMS, BPMN MODELS (Business Process Model and Notation) and/or SIPOC diagrams (Supplier, Input, Process, Output, Customer).
• Identification of bottlenecks and specific automation or digitalization needs.
• Definition of technical and functional requirements for the implementation of solutions.
• Value data identification

• Redesign of financial processes and management control.
• Detection of repetitive processes for automation.
• Application of artificial intelligence.

• Analysis of data visualization tools (Power BI, Tableau, etc.) that best adapt to the needs of the SME.
• Recommendation and implementation of the tool.
• KPI definition to monitor processes efficiency.

• Selection of the use case.
• Measurement of results.

• Training in new tools.
• Change management plan.

• Current systems and processes
• Evaluation of tools and technologies
• Identification of gaps and needs

• Evaluate competition strategies
• Identify emerging trends

• Identify the necessary skills in the team to work with AI and BI
• Development of a data culture

• Selection of appropriate tools
• Integration with current systems
• Definition of use cases

• Development of a strategic plan
• Definition of kpis
• Identification of new opportunities

• Identify a process or area where performance improvement has a significant impact
• Use predictive models, data mining and other analysis techniques to extract value insights that improve competitive positioning.
• Presentation of results

• Kpis monitoring
• Settings and optimizations
• Evolution of the strategy

Analyze existing commercial processes to identify those areas of your SME that could benefit from digitalization.

Design and implement awareness programs to educate your employees about the strategic importance of digital transformation.

Evaluate the tools and technologies already in use, offering you an advice to optimize their performance and align them with your digital transformation objectives.

Develop a clear and personalized strategy that articulates the specific objectives, deadlines and steps for digital transformation.

Establish a framework for continuous innovation and adaptability as digital technologies evolve.

Development and execution of a case of use adapted to the business, using appropriate techniques, which allows to know the advantages of using digital tools.

Identification of opportunities or possible uses of AI in the field of digital transformation

• Front-Office (related to customer interaction): sales, marketing, customer service, orders management.
• Back-Office (internal processes): Inventory management, billing, logistics, quality control.

• Analyze and document how the processes are executed by identifying main activities, actors, tools, times, costs, deficiencies, bottlenecks and recurrent errors.

• Artificial intelligence (AI) to predict purchase behaviors or classify customers.
• Machine Learning to segment customers more efficiently.
• Robotic process automation (RPA) for repetitive tasks, such as data entry.
• Cloud platforms to improve collaboration and scalability.
• Big Data analysis to generate customer behavior insights and optimize marketing campaigns.

• Process cycle time (before and after digitalization).
• Costs associated with the operation.
• Customer satisfaction.
• Error rate or incidents in the process.

• Selected technologies display
• Staff training in new systems and processes
• Performing tests and adjustments of the digital process

• Check the kpis periodically.
• Collect user feedback (internal and external customers).