Artificial intelligence continues to rapidly transform digital marketing, becoming a strategic factor for companies seeking efficiency, personalization, and scalability in their campaigns. In light of the latest innovations in the field of AI, a more in-depth analysis is warranted regarding the potential of two approaches that have recently gained greater prominence: predictive AI and generative AI.

While predictive AI focuses on pattern analysis to forecast future behaviors and generate insights, generative AI elevates creative automation, producing highly personalized content tailored to the user's context. Today, she is one of the main focuses of attention and investment for marketing teams in companies of various sizes and sectors.

SecondMcKinsey dataGenerative AI has the potential to move between $2.6 trillion and $4.4 trillion in the global economy annually, with 75% of this value generated in four main areas, including marketing and sales. For reference, the value is higher than the GDP of the world's major economies in 2024, except for the United States (US$ 29.27 trillion), China (US$ 18.27 trillion), and Germany (US$ 4.71 trillion).

This in itself helps demonstrate the impact of adopting new technologies based on generative AI and how they will be predominant for advertisers seeking differentiation and ROI maximization. But the question remains: are there other paths that can be explored? And the answer is, without a doubt, yes.

Composite AI: Why combining different AI models can make a difference

Although generative AI is currently in the spotlight, the importance of predictive AI models for digital advertising so far is undeniable. Your role is to transform large volumes of data into actionable insights, enabling precise segmentation, campaign optimization, and predictions about consumer behavior. RTB House data indicates that solutions based on Deep Learning, one of the most advanced fields of predictive AI, are up to 50% more efficient in retargeting campaigns and 41% more effective in product recommendations compared to less advanced technologies.

However, Deep Learning algorithms can be improved when combined with other models. The logic behind this is simple: combining different AI models can help solve various business challenges and contribute to the enhancement of cutting-edge solutions.

At RTB House, for example, we are advancing in combining Deep Learning algorithms (predictive AI) with generative models based on GPT and LLM languages to improve the identification of audiences with high purchase intent. This approach allows algorithms to analyze, in addition to user behavior, the semantic context of the visited pages, refining the targeting and placement of displayed ads. In other words, this adds an extra layer of precision, resulting in improvements in the overall performance of the campaigns.

With the growing concern for privacy and regulations on the use of personal data, solutions based on generative and predictive AI represent a strategic alternative to maintain personalization in environments where direct user information collection becomes more restricted. As these tools evolve, it is expected that the adoption of hybrid models will become a market standard, with applications that contribute to the optimization of campaigns and the results generated for advertisers.

By integrating predictive and generative AI models, companies demonstrate how this approach can transform digital marketing, delivering more precise and efficient campaigns. This is the new frontier of digital advertising – and the brands that embrace this revolution will have a significant competitive advantage in the coming years.

In this context, the question for advertisers is not which AI model to adopt in their marketing strategies, but how they can combine them to achieve even more efficient results and with an approach more aligned with the future of digital advertising.

Video monitoring has evolved significantly with the integration of artificial intelligence, becoming a proactive and predictive tool in the field of security. According to the study "Video Surveillance AI Market Size and Share Analysis: Growth Trends and Forecasts (2025-2030)The size of the AI market for video monitoring is estimated at $5.98 billion in 2025 and is expected to reach $11.76 billion by 2030.

The combination of AI and security devices enables advanced analysis of different scenarios in real time, reducing false alarms and improving the detection of relevant events. However, the implementation of these technologies faces several challenges, from privacy and cybersecurity to scalability and cost.

Reduction of false alarms

One of the main challenges of AI-powered video monitoring is reducing false alarms. Traditional security systems generated many unnecessary alerts due to environmental factors such as plant movement, animals, or adverse weather conditions. AI has improved the accuracy of analysis through the use of advanced algorithms for facial recognition, behavioral analysis, and object classification. Furthermore, the incorporation of thermal cameras and radar sensors optimized detection in environments with low visibility or extreme weather conditions, increasing the effectiveness of security systems.

Regulatory compliance

Another major challenge is privacy and compliance. The ability of AI-powered video surveillance systems to identify and track individuals has raised legal and ethical concerns. Regulations such as the General Data Protection Regulation (GDPR) in Europe and similar laws in other regions require companies to take measures to ensure data privacy. The implementation of techniques such as image anonymization and data encryption is essential to balance security with the protection of individual rights. Furthermore, the development of solutions that allow users to control access to their information is a growing trend in the industry.

Cybersecurity

Cybersecurity is another critical aspect of AI-powered video monitoring. Networked systems can be vulnerable to cyberattacks, risking the integrity of data and the security of facilities. To reduce these risks, it is essential to implement advanced security protocols such as multi-factor authentication, end-to-end encryption, and regular software updates. Furthermore, the adoption of edge systems that process data on the device reduces exposure to external threats and improves response speed.

Implementation and scalability

Finally, the implementation cost and scalability pose barriers for many end users. Although AI video monitoring solutions have proven to be highly effective, adoption remains limited due to a mistaken perception of high acquisition and infrastructure costs. However, advances in hardware and software are enabling the development of more affordable and modular solutions, which can be progressively integrated according to the needs and budget of each organization. Optimizing return on investment (ROI) through systems that not only detect threats but also generate automated responses is essential for investing in AI-powered video surveillance.

Thus, the integration of artificial intelligence into video monitoring has revolutionized the security sector, offering more efficient and accurate solutions. Meanwhile, it is essential to address the challenges related to privacy, cybersecurity, false alarm reduction, and scalability to ensure the responsible and effective use of this technology. As AI continues to evolve, video surveillance systems are expected to become increasingly intelligent, autonomous, and accessible, establishing themselves as an essential tool for security in urban, industrial, and commercial environments.

In recent years, the social media landscape has changed drastically. If before brands and content creators could reach large audiences organically, today that reality seems to be increasingly distant. The algorithms of major platforms—such as Instagram, Facebook, TikTok, and even LinkedIn—have significantly reduced the organic reach of posts, forcing companies and influencers to invest in paid media to ensure visibility. But what is behind this change and what are the alternatives for those who want to continue growing without relying solely on ads?

Organic reach, the number of people who view a post without promotion, has been declining year after year. On Facebook, for example, this number was already above 16% in 2012, but currently it ranges from 2% to 5% for business pages. Instagram is following the same path, increasingly prioritizing paid or viral content. TikTok, which emerged as a more democratic alternative, has also been adjusting its algorithm to favor sponsored content and creators who invest in the platform.

This drop in organic reach is no coincidence. Social networks are companies and, as such, need to generate revenue. The main way these platforms monetize is through ad sales, which means that the less free reach a profile has, the more it will be encouraged to pay to reach its audience.

Therefore, social networks lost their status as "networks" and have become, in fact, "social media," where visibility is increasingly conditioned on financial investment. The original concept of connecting people has been replaced by a business model that prioritizes the display of sponsored content, making paid traffic a necessity for those who want to grow on the platforms.

Large brands, with robust marketing budgets, are able to absorb this impact and invest heavily in paid media. Small businesses and independent creators, on the other hand, face increasing challenges to grow and engage their audience without spending money.

However, it is worth noting that paid traffic on social media is still accessible. Today, with less than R$ 6 a day, any small business can boost content and reach potential customers. This democratized access to digital advertising, allowing more entrepreneurs to gain visibility. However, this dependence on platforms also means that without investment, exposure can be extremely limited.

Another side effect of this change is the homogenization of the content. With networks prioritizing sponsored or highly viral content, feeds are becoming increasingly standardized, making it difficult to diversify voices and niches.

Despite the difficulties, some strategies can still help brands and creators grow without relying solely on paid advertising. In the method I use and teach, called Social Media Metamorphosisaccess here), I argue that in order to achieve greater success on social media, brands need to follow an important sequence to increase their reach:

1 – To beFirst of all, brands need to clearly express their values, behaviors, and mission. The audience connects with authenticity, not just with products or services.The essence of the brand must be demonstrated in practice, not just in words.

2 – Knowledge:Sharing knowledge and expertise by providing content that solves problems and adds value to the audience.

3 – Sell:Only after building authority and relationships does offering products or services become more natural and effective. When the brand has already demonstrated who it is and what it knows, sales become a consequence.

In other words, before talking about what it sells, the brand needs to show what it is and what it knows.This approach creates more connection and engagement, making the digital presence stronger.

Additionally, some strategies can still help expand organic reach without relying solely on paid ads:

Bet on valuable contentPublications that generate genuine interaction, such as polls, questions, and debates, still manage to achieve good reach.

Strategic use of Reels and ShortsShort and dynamic formats, especially those that follow trends, continue to be driven by the platforms.

Community and engagementCreators who strengthen their relationship with their audience—by responding to comments, engaging in Stories, and encouraging participation—tend to maintain a more stable reach.

SMO (Social Media Optimization) for social networksUsing the right keywords in the bio, captions, and hashtags helps improve content discoverability.

Exploration of new platformsAs networks like TikTok and LinkedIn adjust their algorithms, new spaces may emerge with better opportunities for organic reach.

Exploration of new platformsInstead of betting everything on a single platform, such as Instagram, it is essential to diversify your digital presence. Platforms like TikTok, Pinterest, LinkedIn, X, Threads, and YouTube offer new showcases for businesses.

Every new social network that emerges is a new showcase for your business. All of them are indexed by Google, and by distributing content across various platforms, your digital presence becomes more robust. Unfortunately, many still see digital marketing as synonymous with Instagram, which limits growth potential. Betting only on one network can be risky, as any change in the algorithm can directly impact the results.

The current scenario makes it clear that organic reach will not return to what it was before. However, this does not mean that it will disappear completely. The challenge for brands and creators will be balancing investments in paid media with strategies that maintain their relevance and connection with the audience, ensuring that their message continues to reach the right people – with or without advertising investment.

Vinícius Taddone is the marketing director and founder of VTaddone®www.vtaddone.com.br