4 September, 2025 BY Luz Castillejo Flor

INBOUND HubSpot 2025 updates: how AI transforms your CRM

The big shift: from isolated data to complete decisions

Let’s be honest: most companies don’t make decisions with complete data. They use about 20 percent and leave the rest buried in emails, calls, or tickets. This means that when leadership reviews the pipeline, marketing analyzes conversions, or service helps a customer, they’re working with an incomplete picture.

What HubSpot is rolling out in this Fall 2025 release isn’t just “nice features.” It’s a real shift: your CRM stops being a simple database and becomes the hub where all information connects, makes sense, and is activated with AI.

The promise is clear: fewer spreadsheets, less copy-paste, faster and smarter decisions.

Smart CRM: when your data finally makes sense

One of the biggest pains is how we view information. Sales often has to draw insights from endless spreadsheets, marketing gets lost in scattered click rates, and service lacks a clear panel to track trends.

 

3-1

With Smart CRM you can now:

  • Set up flexible views that show the same data as a table, chart, or dashboard.

  • Adapt information to each team’s language:
    • Sales sees the pipeline clearly, what’s moving forward and what’s stuck.
    • Marketing tracks conversion funnels and drop-off points.
    • Service spots spikes in issues before they escalate.
    • Leadership reviews dashboards with the metrics that really matter.

The result: decisions that used to take weeks now happen in days, sometimes in hours.

Self-Generating CRM: goodbye manual data entry

If you ask a sales team how much time they spend entering data into the CRM, the answer is usually uncomfortable. It’s not uncommon for them to spend over 20 percent of their day on admin tasks.

With the Self-Generating CRM, this problem shrinks:

  • Conversational Enrichment: the system extracts relevant info from emails or calls and updates customer records automatically.

  • Smart Properties: a “data agent” answers specific questions about accounts or prospects and fills that information directly into the CRM.

In client tests, this has meant 2.4 extra hours per week spent selling, not typing. The key isn’t just turning it on, but defining the right questions that help you close more opportunities.

4-2

Breeze Customer Agent: 24/7 Support and Sales Without Increasing Your Team

Many businesses face a dilemma: invest in a large support team or compromise the customer experience.

With Breeze Customer Agent, that dilemma disappears. This AI-powered agent handles chat, email, WhatsApp, Facebook, and even voice. It qualifies leads, answers frequently asked questions, and knows when to hand off an issue to a human.

Properly configured, it can:

  • Reduce over 50% of repetitive tickets.

  • Increase visitor-to-opportunity conversion.

The key is in the setup: if the agent works with a generic knowledge base, it will give generic answers. Train it with your CRM and industry data, and the customer experience improves significantly.

 

1-3

Data Studio: Clean Datasets Without Manual Work

Marketing teams typically lose an average of six hours per week exporting data, cleaning Excel sheets, and uploading it back to the CRM.

Data Studio eliminates this cycle:

  • Connect Google Sheets or Excel to the CRM and have them update automatically.

  • Integrate data warehouses like Snowflake and combine them with HubSpot records to segment and launch more precise campaigns.

The difference: you’re no longer feeding AI incomplete pieces of data, but providing real, unified intelligence.

6-2

AI-Powered email: less time, more relevance

Most campaigns fail because they are generic. HubSpot’s new tools turn simple prompts into complete campaigns with design, copy, and real personalization.

What you get:

  • Emails created in minutes, not days.

  • Unique content for each contact, no coding needed.

  • Better engagement because AI predicts the best send time.

Teams using it report 25% less time spent on creation and higher open and click rates.

Breeze Studio: Agents That Understand Your Business

Until now, AI acted as an assistant giving generic drafts. With Breeze Studio, it becomes virtual teams working as if they were part of your organization.

Three types:

  • Marketing: generate content aligned with your brand voice.

  • Sales: qualify and route leads based on your real criteria.

  • Service: resolve cases using your knowledge base and internal processes.

The difference comes from the context you provide: your buyer personas, business rules, and priorities. The more specific, the more useful.

2-1

The Piece That Holds Everything Together: Your Data Strategy

Here’s the uncomfortable truth. None of these updates will work if your CRM is full of duplicates, poorly created properties, and scattered data. AI needs a clean, consistent foundation.

At Hike & Foxter, we’ve seen this repeatedly:

  • We unify profiles so the same customer doesn’t appear three times.

  • We connect relationships across interactions—emails, calls, tickets.

  • We map business logic within the CRM so AI understands real stages and criteria.

The result: your CRM stops being a partial snapshot and becomes a complete model to guide decisions.

What You Should Do Right Now

  • Review your properties and clean up duplicates.

  • Connect the basic channels: emails, calls, and tickets.

  • Set up flexible views so each team sees what they need.

  • Activate automatic enrichment and define five key questions for Smart Properties.

  • Launch your first dataset in Data Studio.

  • Test an agent in Breeze Studio with a single clear objective (e.g., qualifying leads in chat).

What’s at Stake

HubSpot’s updates are not “just another step.” They’re an opportunity to stop making blind decisions and start managing a CRM with complete, actionable data.

If you want to see the impact these updates could have on your business, on September 10 we’ll host a webinar on INBOUND HubSpot 2025, where we’ll explore in depth how to get the most out of all these new updates.

PLANTILLAS WEBINAR (3)

PREVIOUS

TIPS DE EXPERTOS

Suscríbete para impulsar tu negocio.

LATESTS ARTICLES

4 September, 2025 POR Luz Castillejo Flor
crm automatización Hubspot Automation

INBOUND HubSpot 2025 updates: how AI transforms your CRM

The big shift: from isolated data to complete decisions

Let’s be honest: most companies don’t make decisions with complete data. They use about 20 percent and leave the rest buried in emails, calls, or tickets. This means that when leadership reviews the pipeline, marketing analyzes conversions, or service helps a customer, they’re working with an incomplete picture.
SEGUIR LEYENDO
2 September, 2025 POR Alba Fiorella Aviles
ia Personalization omnichannel

Personalized marketing at scale: what AI on your data really means

Personalization is no longer optional.
Brands that understand this are already integrating AI into their marketing systems. Not to personalize a first name in an email, but to build unique, real-time experiences across channels, based on actual data.

This doesn’t depend on a single tool. It requires a clear strategy. It involves your data, your technical architecture, and your teams. Here’s what you need to make it work at scale.
SEGUIR LEYENDO
29 August, 2025 POR Luz Castillejo Flor
advanced analytics data architecture machine learning

Synthetic Data: How to Turn Information into Business Value

Artificial intelligence (AI) and machine learning increasingly rely on high-quality data. However, obtaining large volumes of real-world information can be expensive, time-consuming, and, in many sectors, limited by privacy regulations. This is where synthetic data comes into play—a technology that allows the creation of artificial information with properties similar to real data, offering versatile solutions for training models, automating processes, and protecting privacy.

Although the concept may seem futuristic, synthetic data is already transforming sectors such as finance, healthcare, manufacturing, and automotive, enabling small and medium-sized enterprises (SMEs) to compete with resources comparable to those of large corporations.

What is synthetic data?

Synthetic data refers to artificially generated datasets designed to mimic the statistical characteristics and patterns of real data. It is created using statistical techniques, generative neural networks, or advanced AI models such as transformers and variational autoencoders.

Unlike real data, synthetic data can provide a balance between utility and privacy, as it does not contain identifiable information about real individuals. This makes it an ideal tool for training AI algorithms, testing systems, and generating insights without compromising security or individual rights.

Types of synthetic data

Synthetic data can be classified by format and level of synthesis:

Main formats

  • Tabular: Useful for relational databases and statistical analysis.

  • Text: Used in natural language processing (NLP) and automated content generation.

  • Multimedia: Includes images, videos, and unstructured data, essential for computer vision, object recognition, and image classification.

Level of synthesis

  • Fully synthetic: Generates completely new data without using identifiable real-world information. Ideal for training models in scenarios where original data is scarce, such as financial fraud detection.

  • Partially synthetic: Replaces only sensitive information from real data while preserving structure and patterns. Highly useful in clinical and medical research.

  • Hybrid: Combines real and artificial data, offering a balance between realism and anonymization, suitable for customer analysis or system testing.

Techniques for generating synthetic data

Various methodologies exist to create synthetic data, from traditional approaches to advanced AI-based techniques:

  • Statistical methods: Rely on the distribution and correlation of data, generating new samples through random sampling or interpolation/extrapolation, especially for time series or tabular data.

  • Generative Adversarial Networks (GANs): Consist of a generator producing data and a discriminator distinguishing real from artificial data. Iterative training allows the creation of images and datasets almost indistinguishable from real ones.

  • Transformer models: Process sequences of data through encoders and decoders, capturing complex patterns and relationships in text or tabular data, forming the basis of models like GPT.

  • Variational autoencoders (VAE): Compress input data into lower-dimensional representations and then reconstruct artificial variations, useful for images and time series.

  • Agent-based modeling: Simulates complex environments with autonomous entities interacting under defined rules, generating behavioral data applicable to transportation, epidemiology, or financial markets.

Key benefits of synthetic data

  • Customization and control: Enables the creation of datasets tailored to specific needs, improving analysis and data management.

  • Efficiency: Eliminates slow, costly collection of real data, and being pre-labeled, accelerates AI model training and process automation.

  • Data protection: Without containing identifiable information, it helps comply with privacy regulations and avoids intellectual property issues.

  • Richness and diversity: Allows inclusion of extreme cases, outliers, or underrepresented groups, enhancing coverage and model robustness.

Challenges and considerations

While synthetic data offers many advantages, careful implementation is necessary:

  • Bias: It can inherit biases from the original data. Integrating multiple sources and diversifying training sets can mitigate this.

  • Model collapse: Training a model repeatedly on only artificial data can degrade performance. Combining real and synthetic data prevents this issue.

  • Balance between accuracy and privacy: Adjusting the amount of personal data retained versus statistical fidelity is critical depending on the use case.

  • Verification: Testing and validation are required to ensure the quality and consistency of generated data.

Use cases by sector

  • Automotive: Enables training of autonomous driving systems, improved traffic simulations, and transportation optimization without relying on real incidents.

  • Finance: Used for fraud detection, risk assessment, and simulation of complex financial scenarios while protecting sensitive customer information.

  • Healthcare: In clinical trials and pharmaceutical development, synthetic data enables creation of artificial medical records, anonymized clinical datasets, or medical images for research without compromising privacy.

  • Manufacturing: Supports training of computer vision models for quality inspection and predictive maintenance using synthetic sensor data, anticipating failures and optimizing industrial processes.

How to start with synthetic data in your company

  1. Identify data needs: Determine what information is missing or hard to obtain due to legal or logistical constraints.

  2. Choose appropriate tools: Libraries and solutions such as Synthetic Data Vault or predefined IBM datasets simplify data generation.

  3. Pilot small projects: Start with a limited dataset to validate quality, usefulness, and reliability.

  4. Integrate and train models: Use synthetic data alongside real data to train AI systems and improve predictions or automations.

  5. Monitor results: Evaluate effectiveness, adjusting parameters and techniques as needed.

Conclusion

Synthetic data is a strategic tool that combines security, efficiency, and scalability. It allows companies to train AI models faster and more accurately, explore new scenarios without risk, and protect sensitive information.

For small and medium-sized enterprises, it represents a unique opportunity to compete in innovation and automation without relying solely on costly or limited real data. Adopting this technology can enhance efficiency, data protection, and AI quality, making it a fundamental ally in digital transformation.

 
 
SEGUIR LEYENDO
27 August, 2025 POR Luz Castillejo Flor
chatgpt Automation AI Agents

Agentic AI: How Intelligent Automation Empowers SMEs

Artificial intelligence is no longer an exclusive resource for large corporations—it has become a key enabler of growth at any business scale. What once sounded futuristic is now within reach for startups and SMEs seeking to compete in increasingly dynamic markets. Within this landscape, one of the most promising innovations is agentic AI, a technology that doesn’t just execute commands but reasons, decides, and acts autonomously.

The term might sound technical, but the idea is simple: while traditional AI solutions work like assistants that wait for instructions, agentic AI becomes an independent collaborator that understands context, learns from experience, and makes decisions aligned with your business objectives. For a small company, this means saving time on repetitive tasks, responding better to customers, and making faster, data-driven decisions.

What agentic AI means for an SME

Automation has always been seen as a path to boosting workplace efficiency. However, it has often been limited to rigid workflow programming, chatbots with predetermined responses, or management systems that only work within fixed parameters. Agentic AI breaks that barrier: it doesn’t just obey—it interprets and adapts.

Take a simple example. A traditional chatbot answers FAQs but fails when faced with off-script requests. An AI agent, on the other hand, remembers past interactions, understands the customer’s history, and can even anticipate what that person might need. If someone repeatedly checks the price of a service, the AI doesn’t just reply—it can suggest scheduling a sales call or automatically send a personalized proposal.

The impact goes far beyond sales. Agentic AI can organize invoices, filter emails, identify inventory trends, or prioritize business opportunities. For small businesses, often operating with limited resources and lean teams, having this type of support can mean the difference between stagnation and growth.

How intelligent automation transforms operations

One of the most appealing aspects of agentic AI is its ability to operate independently, without constant human supervision. This frees teams from routine tasks and allows them to focus on higher-value, strategic activities.

Some of the most relevant benefits for SMEs include:

  • Always-on customer service: digital agents available 24/7 that not only respond but learn from customer tone and questions to deliver personalized solutions.

  • Faster commercial processes: identification of potential customers based on behavior, lead prioritization, and automated follow-ups.

  • Real-time data analysis: instead of waiting for weekly or monthly reports, AI agents can instantly detect consumption patterns or stock issues.

  • Frictionless scalability: the technology grows with the business, adapting to demand peaks or an expanding client base without immediately hiring extra staff.

In other words, agentic AI doesn’t replace teams—it becomes a strategic partner that multiplies their capabilities.

La Inteligencia Artificial en atención al cliente: 10 maneras de usarla - Cepymenews

Real applications in small businesses

To better understand the value of agentic AI, let’s ground it in concrete scenarios:

  • Automated marketing: AI agents monitor campaigns, adjust budgets, and predict the best times to publish on social media—making every marketing dollar work harder.

  • Smart sales: if a client visits the pricing page multiple times, AI flags them as a hot lead and automatically books a meeting with a sales rep.

  • Customer support: a digital agent can handle basic questions about hours, policies, or products and escalate sensitive cases to a human team.

  • E-commerce shopping assistants: they remember customer preferences and suggest relevant products, improving the shopping experience and boosting average order value.

These applications aren’t futuristic—they already exist in accessible tools that let SMEs start small and scale gradually.

How to get started

The process of adopting agentic AI can be broken into simple phases:

  1. Identify pain points: start with time-consuming, low-value tasks like repetitive inquiries or database updates.

  2. Choose the right tools: you don’t need a massive investment. There are AI solutions designed for SMEs that integrate with existing CRMs or POS systems.

  3. Run a pilot project: begin with something manageable, such as a chatbot for FAQs. This validates results without major risks.

  4. Train the agent: outcomes depend on the quality of input data. Feed the system customer info, product details, and past interactions.

  5. Measure and improve continuously: track performance and adjust responses or workflows as the AI learns about your business.

The key is to start small, validate results, and expand functions strategically.

Common challenges and how to overcome them

Of course, every innovation comes with obstacles. The most common when implementing agentic AI in small businesses are:

  • Learning curve: it may feel complex at first. Rely on tutorials, vendor support, and gradual training.

  • Data security: a valid concern. The solution is choosing platforms that guarantee encryption, transparency, and regulatory compliance.

  • Fear of losing the human touch: automation doesn’t mean dehumanization. Use AI for repetitive tasks, reserving humans for emotional and strategic interactions.

  • Tight budgets: many SMEs worry about costs, but today there are affordable options built into CRM systems with no hidden fees or heavy upfront investments.

Each challenge has a solution, and the balance of effort versus benefit clearly favors adoption.

Chatbots vs Conversational AI: Is There Any Difference?

A long-term growth tool

More than a trend, agentic AI represents a new paradigm in business management. For small companies, it can become a true growth driver, freeing time, reducing costs, and improving customer experience.

Its most compelling aspect is continuous learning: the more it interacts, the more precise it becomes. This means your automation investment doesn’t stagnate—it improves over time, something traditional processes rarely achieve.

The real question is no longer whether small businesses can afford to implement agentic AI, but whether they can afford not to—when automation and AI are setting the pace of the market.

Conclusion

Agentic AI is here to stay. Its ability to act independently, learn from every interaction, and adapt to market changes makes it one of the most powerful tools SMEs can adopt today.

Implementing it doesn’t require tech expertise or big budgets. All it takes is identifying repetitive tasks, launching a pilot, and letting automation do the rest.

If your goal is to enhance workplace efficiency, increase sales, and secure long-term business growth, agentic AI is the partner you’ve been waiting for. The next strategic decision for your company won’t be taken alone—you’ll take it with the help of artificial intelligence.

 

Schedule a meeting

SEGUIR LEYENDO
VIEW MORE
data
Mallorca 184, 08036
Barcelona, Spain