11 February, 2025 BY Sara Fernández

Creativity and AI: Strategic Allies

Creativity has been for centuries one of the most unique and valuable characteristics of human beings. It is the result of imagination, emotion and a personal touch that allows us to develop innovative and artistic ideas. However, with the rise of technology and artificial intelligence (AI), we are faced with a question that has generated debate: is AI a friend or a competitor in the creative industries? In this article, we will focus on how AI can function as a strategic ally, enhancing creativity in both B2B and B2C environments.

Artificial intelligence as a collaborative tool

Transforming traditional creative processes

In recent decades, AI has emerged as a technology capable of transforming traditional processes, including those in the creative industries. Rather than being a replacement, AI acts as a collaborative tool that assists artists, designers and creatives in the development of their projects. Instead of seeing AI as a threat, we must harness its potential to enhance human creativity.

For example, in graphic design, tools that integrate AI can analyze complex datasets to generate patterns or design suggestions that may not have been considered by humans. This allows designers to explore new possibilities and enrich their creative process.

Applications in marketing and advertising

Another prominent application of AI in the creative industries is in marketing and advertising. In B2C environments, companies can use AI algorithms to create more personalized and effective campaigns. By analyzing consumer data, AI can help identify trends and preferences, allowing for the development of advertising messages that resonate better with the target audience.

For example, large companies can implement automated marketing platforms that optimize advertising campaigns in real time, adapting to consumer behavior and thus increasing the effectiveness of their marketing efforts.

Use cases in B2B and B2C

Innovation in content production

Content production is an area where AI can have a significant impact. In a B2B environment, companies, especially marketing agencies, can use AI tools to generate informative or creative content on a large scale. This not only reduces costs but also allows for the creation of content pieces in different languages and formats, adapting to the specific needs of clients.

A practical case would be the use of AI for the creation of blogs or technical articles that meet SEO parameters, optimizing visibility in search engines and increasing traffic to the client's website.

Personalization of experiences in the retail sector

In the B2C sector, AI can be used to personalize the customer experience in online stores. Large companies in the retail sector already use algorithms that study the purchasing behavior of users to offer personalized product recommendations. This not only improves the user experience but also increases conversion rates and customer loyalty.

For example, e-commerce platforms can integrate AI to analyze the purchasing and interaction habits of their customers, allowing for better product recommendations and personalized offers that increase sales and customer satisfaction.

Implementation in large companies

Data analysis for decision making

Large companies have a large amount of data that, if analyzed properly, can provide valuable insights. AI can process this data efficiently, helping companies to better understand their market and make more informed decisions. This applies to both B2B and B2C, where understanding the customer and the market is crucial for success.

For example, sentiment analysis using AI can help companies to better understand customer opinions on social media, allowing for product or communication strategies to be adjusted quickly and effectively.

Optimization of internal processes

In addition to improving the customer experience, AI can also be used to optimize internal processes within companies. In a B2B context, companies can use machine learning to improve operational efficiency, from supply chain management to automated customer service.

For example, AI-powered chatbots can handle basic customer inquiries effectively, allowing human employees to focus on more complex cases.

Conclusion

Artificial intelligence offers endless possibilities to improve and enhance creativity in the creative industries. Far from being a competition, it acts as a powerful ally that complements and amplifies human capabilities. In both B2B and B2C environments, the implementation of AI can generate efficiencies, personalization and new business opportunities.

The important thing is to adopt an open and collaborative mindset, allowing AI to be a strategic complement on the path to innovation and business success. At the end of the day, it is we, humans, who control the narrative of how technology can be used to improve our lives and creative environments.
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16 July, 2025 POR Alba Fiorella Aviles
crm Data management AI Automation

The seventh wave of AI is redefining CRM and data strategy

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14 July, 2025 POR Luz Castillejo Flor
Marketing strategy Tracking data stratergy third-party cookies

How the end of third-party cookies impacts your marketing strategy

The announcement of the definitive elimination of third-party cookies marks a turning point in the digital ecosystem. This is not just a technical adjustment in browsers: we are talking about a structural change in the way companies collect data, activate advertising campaigns, and manage customer relationships.

And although it may seem like a distant issue or one exclusive to large corporations, the reality is that it affects any business that uses digital advertising, email marketing, retargeting strategies, or affiliate programs.
That’s why understanding its impact and knowing how to prepare is key to staying competitive.

What are third-party cookies and why are they disappearing?

Third-party cookies: the foundation of digital marketing until now

A third-party cookie is a file placed on your browser by a provider other than the website you are visiting.
For example, if you visit a blog that uses Google or Facebook ads, those systems install cookies that track your behavior—even when you browse other sites.

Thanks to those cookies, advertisers could:

  • Follow you throughout your browsing.

  • Show you ads based on your interests and behavior.

  • Measure the impact of their campaigns.

  • Build detailed profiles without requiring you to register or provide data.

In short: third-party cookies were the backbone of programmatic advertising and retargeting.

Why are they being eliminated?

The official reason is user privacy protection.
More and more users demand control over their personal data and how it’s used. Regulations like GDPR in Europe and CCPA in California have forced major players (Google, Apple, Mozilla) to move toward a more privacy-friendly model.

But there is another angle:
Google, owner of Chrome and a leader in digital advertising, is redefining the game to maintain market control and limit competition. By eliminating third-party cookies, Google ensures that only those who manage first-party data or operate within its platforms can effectively reach users.

The three major pillars changing after the elimination of cookies

1. Campaign measurement and attribution

Until now, measuring the impact of a multichannel campaign (ads, email, web visits) relied on attribution models based on cookies.
For example:

If a user saw an ad on Instagram, clicked on a Google ad, and then made a purchase on the website, cookies helped trace that path.

What happens without third-party cookies?

  • Conversions attributed to third parties will decrease.

  • The user journey will be harder to track.

  • “Last-click” or “multi-touch” measurement becomes less reliable.

How to adapt?

  • Prioritize first-party data measurement by connecting your CRM with analytics platforms.

  • Implement solutions like Google Enhanced Conversions or server-side tagging, which allow more accurate measurement without relying on cookies.

  • Explore proprietary attribution models, such as integrating sales or CRM systems with analytics tools.

2. Audience segmentation and activation

The end of retargeting as we knew it.
Without third-party cookies, platforms can no longer create audiences based on behavior across different websites. This directly affects:

  • Programmatic advertising.

  • Dynamic retargeting campaigns.

  • Affiliate campaigns based on cross-site tracking.

How to adapt?

  • Enhance your first-party data: encourage registration, subscriptions, and account creation.

  • Use activation tools like Customer Match (Google Ads) or Audiences (Meta), which let you upload your own data to reach those users on their platforms.

  • Work on lookalike strategies based on your own customer data, not third-party data.

  • Leverage contextual advertising by showing ads related to the content being consumed—without needing to know the user’s identity.

3. First-party data management and value

The direct consequence of this change is that first-party data becomes the most valuable asset of a digital company.
Without the ability to buy audiences based on cookies, you need to build your own database with real, interested users with whom you can maintain a direct relationship.

This means:

  • Developing acquisition strategies based on value: lead magnets, quality content, incentives for registration.

  • Creating automated, personalized communication flows from your CRM.

  • Focusing on the quality of the relationship, not just the quantity of impacts.

How to adapt?

  • Strengthen your lead generation strategies and improve your registration forms.

  • Implement a CDP (Customer Data Platform) if you handle large volumes, or ensure your CRM is well integrated with your marketing platforms.

  • Take care of the user experience to avoid intrusive practices like aggressive pop-ups or forced capture.

What alternatives does the market propose after the elimination of cookies?

  • FLoC and Privacy Sandbox (Google): Google proposes alternative systems based on cohorts, where users are grouped by interests without being individually identified. These proposals still generate debate over their effectiveness and privacy.

  • Data Clean Rooms: Secure environments where data from different parties (advertisers, platforms) can be matched without revealing user identities. Costly but necessary for major advertisers.

  • Contextual advertising: Making a comeback. Showing ads related to the content being visited, with no need to know who the user is.

  • Server-side models: Collecting and activating data from the server side is a technical alternative for measuring and segmenting without relying on traditional cookies.

What should companies do to adapt (and not just survive)?

  • Invest in a data strategy:
    Organize, structure, and connect your databases with your marketing tools.
    First-party data is a strategic asset—not just a list of emails.

  • Train your teams:
    Not just the marketing department. Sales, customer service, IT… everyone needs to understand the value of data and how it’s managed.

  • Strengthen customer trust:
    Transparency and good privacy management will be differentiators. Clearly explaining how you use data builds trust and, in the long term, conversion.

  • Commit to personalized omnichannel experiences:
    The CRM should be the center of a strategy where the user receives coherent impacts across all channels (web, email, app, social).

  • Prepare for new measurement methods:
    Invest in server-side solutions, predictive models, and tools that allow you to measure impact beyond cookies.

Conclusion: Threat or opportunity?

The end of third-party cookies is not the end of advertising or digital marketing.
It is the beginning of a new paradigm where companies that invest in:

  • Building their first-party data.

  • Truly integrating their systems.

  • Personalizing based on a deep understanding of the customer.

… will be the ones to take the biggest slice of the pie.

Because if one thing is clear, it’s that data remains important…
You just have to earn it now.

Schedule a meeting

SEGUIR LEYENDO
9 July, 2025 POR Luz Castillejo Flor
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No solid base, no AI performance: the challenge of the Data Foundation

In a business context where AI has become the new standard for efficiency and scalability, many organizations face a paradox: they have advanced technology, but they fail to achieve consistent results. The issue usually isn’t the algorithm—it’s the foundation. The Data Foundation is the true determinant of success or failure for any AI, automation, or CRM strategy.

This is confirmed by the latest TDWI (Transforming Data With Intelligence) study, published in June 2025, which warns that more than 49% of companies still lack a database ready to scale artificial intelligence projects.

The Data Foundation: more than just infrastructure

Having a modern data platform doesn’t mean having a solid foundation. The TDWI study emphasizes that an effective Data Foundation must meet three conditions:

  • Data quality and governance from the source
  • Scalable and connected architecture
  • Real-time activation capability

When a company fails in any of these three areas, AI becomes more of a promise than a real business lever.

Key findings from the study

Here are some of the main conclusions of the report:

Only 10% of companies claim to have a fully operational Data Foundation.
40% report severe limitations due to poor data quality, silos, or outdated processes.
Most organizations suffer from fragmentation across data sources, preventing a 360-degree view of the customer.
55% of companies already using AI operationally do so despite their technical limitations, not because of their strengths.

In other words, many companies are running with a backpack full of ballast. And that limits the performance of their AI, automation, or CRM tools.

Why does this matter for your CRM or marketing?

At Hike&Foxter, we see it frequently: companies investing in advanced CRMs, analytics platforms, or generative AI engines… without first securing the technical and structural foundation of their data.

The result:

  • AI models that fail in production.
  • Automations triggered incorrectly.
  • Unreliable analytics reports.
  • Inconsistent customer segmentations.

All of this can be avoided with a well-designed Data Foundation, connected to key processes and with controlled data flows.

How to build a real Data Foundation

These are the phases we recommend implementing if you want to turn your data architecture into a competitive advantage:

1. Technical and functional audit

Before incorporating AI, it's important to review:

What data sources exist and how they are integrated
The degree of duplication, obsolescence, or noise they contain
Where the main bottlenecks are (latency, format, access)

2. Standardization and governance

Without a common taxonomy and control rules, any automation attempt will be fragile. This involves:

Defining unified structures (customers, products, interactions…)
Establishing automatic validation rules
Creating clear roles: who creates, modifies, or validates data?

3. Connected and flexible architecture

A data warehouse alone is no longer enough. You need to:

Connect CRM with analytics, automation, and digital channels
Use scalable environments (Snowflake, BigQuery, Azure Fabric)
Consider data mesh or federated architecture if there are multiple business units

4. Real-time activation

The value of AI lies not just in predictive analysis but in its ability to act.

Therefore:

Connect your Data Foundation with activation tools (such as Customer Data Platforms, personalization engines, RPA)
Ensure data flows in real time
Prioritize use cases with direct business impact (retention, up-selling, lead scoring…)

Conclusion

Investing in AI, automation, or CRM platforms without a solid Data Foundation is like building a house on sand.
Before thinking about “which model to use,” you should ask yourself “what data feeds it and how is it governed?”

A robust and well-connected infrastructure not only improves your current projects but also prepares you for what’s next: autonomous agents, contextual decisions, predictive personalization, and end-to-end automation.

Want to strengthen your Data & Tech Foundation?

At Hike&Foxter, we help you build the digital foundations your business needs to grow with confidence.

Schedule a meeting

SEGUIR LEYENDO
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Artificial Intelligence digital transformation Google I/O search engine

Google transforms its search engine with Artificial Intelligence.

In May 2025, Google took a decisive step toward transforming the world’s leading search engine.
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