Definitions, applications and limitations of artificial intelligence at work. Explore guides on marketing, data, automation and business processes.
AI marketing uses artificial intelligence technologies to support marketing activities, from data analysis and content creation to customer segmentation and campaign personalisation.
Businesses can use AI tools to better understand consumer behaviour, automate certain tasks and support decisions.
Applications depend on the technologies used.
Predictive models analyse information to estimate behaviour and outcomes, while generative AI can produce drafts of text, images and other content.
Data analysis to identify trends and support decisions.
Marketing automation
Automating workflows, communications and repetitive tasks.
Content marketing
Supporting the production and review of digital content.
CRM
Analysing customer information and segmentation.
Advertising
Supporting campaign planning, analysis and optimisation.
AI can support productivity and information analysis, but it does not automatically guarantee better results.
Quality depends on the data, skills and how the tools are used.
Business AI School offers courses dedicated to artificial intelligence applications in digital marketing.
AI for e-commerce can support product recommendations, demand forecasting and personalisation. Understanding data, integrations and limitations helps you decide which applications to assess for your online store.
AI for e-commerce can support product recommendations, demand forecasting and personalisation. Understanding data, integrations and limitations helps you decide which applications to assess for your online store.
Artificial intelligence is used in various areas of cybersecurity to support data analysis and the identification of potentially unusual events.
Analytical models can help identify behaviour that differs from expectations.
Machine learning techniques can support the classification of security-related events and information.
AI tools can help organise data, but significant decisions require human oversight and appropriate processes.
Systems can produce false positives, errors and incorrect classifications.
Adoption requires attention to data security, monitoring and the verification of results.
AI can support the analysis of information used in inventory management, demand forecasting and logistics planning.
Historical data analysis can help estimate the quantities needed during particular periods.
Analytical technologies can help identify behaviour and trends relevant to planning.
Digital systems can support the processing of information about goods handling, transport and operations.
Effectiveness depends on reliable data and integration with business systems.
Using artificial intelligence in business requires attention to responsibility, oversight, transparency and regulatory compliance.
Accountability involves assigning responsibilities, documenting decisions and demonstrating how processes and systems are managed.
Responsible management requires clear roles, risk assessment and oversight procedures proportionate to how technologies are used.
AI systems must be used in accordance with applicable rules on protecting information and personal data.
Obligations depend on the type of system, its use and the organisation’s role. Information must be kept current using official regulatory sources.
Artificial intelligence can support data analysis, information processing, content creation and process management.
Start with your organisation’s needs to identify the most useful opportunities.
Marketing: analysis, content and automation.
Sales: data management and opportunity classification.
Organisation: support for information processing.
Analysis: statistical and predictive models.
Support: search and information support systems.
Reducing certain manual tasks, supporting analysis and making information easier to access.
Actual benefits must be assessed against costs and processes.
Define goals, identify activities, assess tools, train people and measure results.
Artificial intelligence changes some professional activities and creates new needs for skills development.
Effects depend on the sector, the tasks and how technologies are adopted.
Some operations can be automated or supported by AI tools, while others require interpretation, responsibility and interpersonal skills.
Understanding AI systems, evaluating outputs and working with data and digital tools are becoming increasingly important.
Updating your skills helps you understand change and use new tools.
The impact involves a range of activities, from marketing and programming to data analysis and process management.
Employment forecasts should not be presented as certain without supporting sources.
Artificial intelligence is used in various advertising platforms to support analysis, automation and ad delivery.
AI technologies can contribute to ad selection, bid management and performance analysis.
Before using automated tools, define campaign goals and select appropriate metrics.
Monitoring, conversion tracking and results analysis remain essential.
Automation does not guarantee better results in every situation and requires appropriate data and oversight.
Predictive analytics uses historical data and mathematical models to estimate future behaviour, values or events.
It is applied in various contexts, from demand forecasting to evaluating customer behaviour.
A predictive process generally involves collecting and preparing data, selecting a model, training, evaluation and using predictions.
Appropriate metrics and test data are needed to understand the reliability of results.
Predictive techniques can support marketing, logistics, sales analysis and planning.
Artificial intelligence can support activities connecting marketing and sales through data analysis and information organisation.
Data can be used to classify potential customers and identify relevant characteristics.
CRM systems organise commercial information that can also be used for analytical activities.
Data analysis can help make communications more relevant to customer segments.
Evaluate the results of activities using metrics aligned with your goals.
Retention strategies require an understanding of customers, their behaviour and their relationship with the business.
AI can support data analysis and the identification of segments relevant to customer retention.
Customer retention is the ability to maintain commercial relationships with customers over time.
Historical data can be used to examine the frequency, value and characteristics of interactions.
Some models can estimate the probability of specific behaviours, such as churn.
Segmentation can support the development of relevant communications, with due regard for privacy.
CRM systems organise information about customers and commercial interactions.
Integrating AI features can support analysis, classification and information management.
Traditional CRM records and organises information. AI features can add predictive analysis, classification and other support tools.
Incomplete or incorrect data can reduce the reliability of analysis.
Personal information must be used in accordance with applicable rules and permissions.
Machine learning in marketing uses models developed from data to classify behaviour, identify groups and estimate outcomes. Understanding how they work helps assess which problems they can address and their limitations.
Chatbots can provide information and support certain customer service activities.
Generative AI solutions can process natural language requests and formulate responses from available information.
Rule-based chatbots follow predefined paths. Generative systems can provide more flexible responses, but require appropriate checks.
The quality of information also depends on the content and sources used by the system.
Provide ways to escalate to human operators when a system cannot answer correctly.
Accuracy, service quality and data management need to be monitored.
Computer vision includes techniques that allow computer systems to analyse images and video.
Applications include classification, feature recognition and identifying visual elements.
Models can be developed to assign images to specific categories.
Some systems can identify objects and estimate their position within images.
Results can be affected by image quality, training data and operating conditions.
AI for e-commerce: recommendations, sales analysis and personalisation.
data storytellingData storytelling and reporting with AI: turn data and charts into clear explanations.
AI in marketingAI in marketing: content, analysis and automation for everyday work.
ChatGPT for marketingChatGPT in marketing: briefs, editorial ideas and content review.
big data marketingBig data and marketing: sources, data quality and customer analysis.
neuromarketingNeuromarketing and AI: attention, communication and behavioural data analysis.
RFMRFM and CLV: recency, frequency and purchase value to understand customers.
Machine learning for marketingMachine learning in marketing: segmentation, classification and predictive models.
Would you like information about AI courses or training opportunities for your business?
Tell us what interests you and what you need.
