7 April, 2025 POR Luz Castillejo Flor

How to Stay Ahead in the Age of Automation

Automation and Artificial Intelligence (AI) are rapidly transforming the way we work and live. While many people fear that these technologies may replace their jobs, the real threat lies in inaction and failure to adapt. In this article, we will explore how we can adopt a positive perspective on automation and how companies, whether B2B or B2C, can apply these technologies to drive growth and efficiency.

Automation: A Threat or an Opportunity?

Throughout history, technological innovation has always provoked fear of change. From the Industrial Revolution to the current digital age, each advancement has been accompanied by concerns about job security. However, the development of new technologies has also created more jobs, usually in ways we couldn't have predicted.

Concrete Examples of AI Implementation

  1. Enhanced Customer Service: AI can be used to improve customer service through chatbots that respond quickly and intelligently to customer inquiries. In the B2C sector, retail companies can take advantage of chatbots to offer 24/7 assistance, improving customer satisfaction without increasing staffing costs.

  2. Supply Chain Optimization: B2B companies can use automation to improve logistics and inventory management. AI tools can accurately predict demand, allowing companies to adjust inventory levels accordingly, reducing costs related to excess inventory or stockouts.

  3. Personalized Marketing: In marketing, AI can help businesses analyze large volumes of customer data. Both in B2B and B2C, companies can use these insights to create highly personalized marketing campaigns, increasing conversion rates and improving customer experience.

How to Adapt to the Age of Automation

As automation continues to evolve, it is crucial for both individuals and businesses to be proactive in adapting to these new technologies. Here are some key strategies:

Foster a Culture of Continuous Learning

For a company to thrive in a technology-driven environment, it is vital to foster a culture of continuous learning. Employees should be provided with training opportunities to acquire new technical and non-technical skills that are relevant to their field. By investing in the professional development of their employees, businesses can ensure their workforce is equipped to face the challenges of automation.

Integration of Emerging Technologies

Companies must be willing to adopt new technologies and integrate them into their internal processes. This means investing in systems and platforms that can adapt to the changing needs of the market. Cloud-based solutions, for example, can offer businesses the flexibility they need to grow and evolve with technology.

Collaboration Between Humans and Machines

Rather than seeing automation as a threat, companies should consider how humans and machines can collaborate to achieve better results. For example, in manufacturing, machines can handle repetitive and monotonous tasks, allowing employees to focus on more strategic and creative tasks.

Implementation in Large Companies

For large companies, implementing AI at an organizational level may seem like a monumental task. However, the right approach can make the transition easier and ensure that automation is integrated effectively.

Create an Innovation Team

Large companies can benefit from creating a team dedicated to innovation. This team can be composed of technology experts, business leaders, and other key employees working together to identify opportunities for implementing AI across the organization.

Pilots and Testing

Before rolling out new technology on a large scale, it is helpful to conduct pilot projects and tests. This allows businesses to assess the effectiveness of AI in specific areas and make adjustments before a full launch. A phased approach can minimize the risks associated with adopting new technologies.

Measure Results

Finally, it is essential to measure the results of any AI implementation to determine its success. Key performance indicators (KPIs) should be identified and regularly monitored to ensure the technology is meeting business objectives.

Conclusion

Artificial intelligence and automation are not the threat many believe them to be. On the contrary, they offer an unparalleled opportunity for innovation and growth. By adopting these technologies with a positive and proactive mindset, businesses and employees can ensure they are staying ahead in the age of automation. With the right strategies, we can use AI to improve our efficiency, creativity, and, therefore, our professional and personal lives.
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Group your data like a pro: clustering with K-Means and BigQuery ML

Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

  1. You choose how many groups you want (the well-known “K”).

  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

This opens up a wide range of possibilities to enrich your dashboards and marketing analysis:

  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

With K-Means in BigQuery, you can answer questions like:

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

Conclusion

If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 
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