The Future of AI
7 Tech Trends to Watch in 2025
Contents
Contents
Contents
As we look ahead to 2025, the influence of Artificial Intelligence (AI) and its subsets, Machine Learning (ML), Natural Language Processing (NLP) and Generative AI, in marketing is more prominent than ever. At this year’s Piano Academy Live, industry leaders across disciplines shared their perspectives on AI’s transformative potential. One message resonated loud and clear: AI is not just accelerating—it’s reshaping how businesses operate and use AI to impact customer experiences.
The influence of technology like AI now transcends industries, driving innovation in ways we’re only beginning to understand. For marketers, publishers and business leaders, it’s no longer a question of if AI will impact their work, but how. From powering hyper-personalized experiences to streamlining operations and increasing data-driven decision-making, AI and Machine Learning are becoming the backbone of modern marketing strategies. However, rapid advancements bring with it critical challenges—questions about data privacy, ethical use, sustainability and the balance between automation and human creativity.
As we look ahead to 2025, the influence of Artificial Intelligence (AI) and its subsets, Machine Learning (ML), Natural Language Processing (NLP) and Generative AI, in marketing is more prominent than ever. At this year’s Piano Academy Live, industry leaders across disciplines shared their perspectives on AI’s transformative potential. One message resonated loud and clear: AI is not just accelerating—it’s reshaping how businesses operate and use AI to impact customer experiences.
The influence of technology like AI now transcends industries, driving innovation in ways we’re only beginning to understand. For marketers, publishers and business leaders, it’s no longer a question of if AI will impact their work, but how. From powering hyper-personalized experiences to streamlining operations and increasing data-driven decision-making, AI and Machine Learning are becoming the backbone of modern marketing strategies. However, rapid advancements bring with it critical challenges—questions about data privacy, ethical use, sustainability and the balance between automation and human creativity.
As we look ahead to 2025, the influence of Artificial Intelligence (AI) and its subsets, Machine Learning (ML), Natural Language Processing (NLP) and Generative AI, in marketing is more prominent than ever. At this year’s Piano Academy Live, industry leaders across disciplines shared their perspectives on AI’s transformative potential. One message resonated loud and clear: AI is not just accelerating—it’s reshaping how businesses operate and use AI to impact customer experiences.
The influence of technology like AI now transcends industries, driving innovation in ways we’re only beginning to understand. For marketers, publishers and business leaders, it’s no longer a question of if AI will impact their work, but how. From powering hyper-personalized experiences to streamlining operations and increasing data-driven decision-making, AI and Machine Learning are becoming the backbone of modern marketing strategies. However, rapid advancements bring with it critical challenges—questions about data privacy, ethical use, sustainability and the balance between automation and human creativity.
Own Your Audience in a Post-Platform Era
As Heather Dietrick, Chief Media Officer of Outside Network, Inc., noted at Piano Academy Live 2024, owning your audience is essential for long-term stability. Algorithm changes and policy shifts—especially from major players like Facebook and X—can dramatically impact visibility and revenue, leaving businesses vulnerable. Findings from the 2024 Subscription Performance Benchmarks Report reinforce the importance of building strategies that prioritize audience ownership.
Defining a customer journey to move users from their first visit to loyalty and conversion is the first step in owning your audience. AI-driven personalization allows you to target each user with the right messaging at the right time to move them through that journey. That improves results – Piano propensity models show that users in the highest likelihood to convert segment have 100 times higher conversion rates than users in the lowest segment. Smart, AI-driven targeting also prevents misfires that cost revenue – stopping users too early in their journey could mean they never come back. As referrals from platforms decrease, companies need to make the most of every customer who shows up.
Own Your Audience in a Post-Platform Era
As Heather Dietrick, Chief Media Officer of Outside Network, Inc., noted at Piano Academy Live 2024, owning your audience is essential for long-term stability. Algorithm changes and policy shifts—especially from major players like Facebook and X—can dramatically impact visibility and revenue, leaving businesses vulnerable. Findings from the 2024 Subscription Performance Benchmarks Report reinforce the importance of building strategies that prioritize audience ownership.
Defining a customer journey to move users from their first visit to loyalty and conversion is the first step in owning your audience. AI-driven personalization allows you to target each user with the right messaging at the right time to move them through that journey. That improves results – Piano propensity models show that users in the highest likelihood to convert segment have 100 times higher conversion rates than users in the lowest segment. Smart, AI-driven targeting also prevents misfires that cost revenue – stopping users too early in their journey could mean they never come back. As referrals from platforms decrease, companies need to make the most of every customer who shows up.
Own Your Audience in a Post-Platform Era
As Heather Dietrick, Chief Media Officer of Outside Network, Inc., noted at Piano Academy Live 2024, owning your audience is essential for long-term stability. Algorithm changes and policy shifts—especially from major players like Facebook and X—can dramatically impact visibility and revenue, leaving businesses vulnerable. Findings from the 2024 Subscription Performance Benchmarks Report reinforce the importance of building strategies that prioritize audience ownership.
Defining a customer journey to move users from their first visit to loyalty and conversion is the first step in owning your audience. AI-driven personalization allows you to target each user with the right messaging at the right time to move them through that journey. That improves results – Piano propensity models show that users in the highest likelihood to convert segment have 100 times higher conversion rates than users in the lowest segment. Smart, AI-driven targeting also prevents misfires that cost revenue – stopping users too early in their journey could mean they never come back. As referrals from platforms decrease, companies need to make the most of every customer who shows up.
Privacy-First Strategies
Lead with First- and Zero-Party Data
As businesses shift focus from third-party data, businesses are increasingly relying on first-party and zero-party data—information collected directly from or voluntarily shared by users—to support effective advertising strategies. While privacy regulations drive this shift, AI is critical in analyzing and leveraging this data to deliver meaningful results.
Enabling marketers to analyze first-party data at scale, AI can uncover patterns and insights that were previously derived from third-party tracking. ML models and data analysis platforms can identify customer preferences and predict behaviors, allowing businesses to deliver personalized experiences without violating privacy standards.
In addition, AI-powered tools like mixed-media modeling (MMM) help marketers accurately measure campaign performance. These technologies integrate online and offline data sources, creating privacy-compliant attribution models that reveal ROI across channels. This enables a shift from less reliable third-party data to higher-quality first-party data, allowing marketers to maintain precision and personalization in their campaigns—while fostering trust and engagement in an increasingly privacy-conscious world.
Privacy-First Strategies
Lead with First- and Zero-Party Data
As businesses shift focus from third-party data, businesses are increasingly relying on first-party and zero-party data—information collected directly from or voluntarily shared by users—to support effective advertising strategies. While privacy regulations drive this shift, AI is critical in analyzing and leveraging this data to deliver meaningful results.
Enabling marketers to analyze first-party data at scale, AI can uncover patterns and insights that were previously derived from third-party tracking. ML models and data analysis platforms can identify customer preferences and predict behaviors, allowing businesses to deliver personalized experiences without violating privacy standards.
In addition, AI-powered tools like mixed-media modeling (MMM) help marketers accurately measure campaign performance. These technologies integrate online and offline data sources, creating privacy-compliant attribution models that reveal ROI across channels. This enables a shift from less reliable third-party data to higher-quality first-party data, allowing marketers to maintain precision and personalization in their campaigns—while fostering trust and engagement in an increasingly privacy-conscious world.
Privacy-First Strategies
Lead with First- and Zero-Party Data
As businesses shift focus from third-party data, businesses are increasingly relying on first-party and zero-party data—information collected directly from or voluntarily shared by users—to support effective advertising strategies. While privacy regulations drive this shift, AI is critical in analyzing and leveraging this data to deliver meaningful results.
Enabling marketers to analyze first-party data at scale, AI can uncover patterns and insights that were previously derived from third-party tracking. ML models and data analysis platforms can identify customer preferences and predict behaviors, allowing businesses to deliver personalized experiences without violating privacy standards.
In addition, AI-powered tools like mixed-media modeling (MMM) help marketers accurately measure campaign performance. These technologies integrate online and offline data sources, creating privacy-compliant attribution models that reveal ROI across channels. This enables a shift from less reliable third-party data to higher-quality first-party data, allowing marketers to maintain precision and personalization in their campaigns—while fostering trust and engagement in an increasingly privacy-conscious world.
From Segments to “Segments of One”
Historically, marketing relied on broad audience segmentation, targeting large groups with similar characteristics. AI, and more specifically ML and predictive analytics, has transformed this approach, enabling hyper-personalization—what some call "segments of one." By leveraging data on individual behaviors, preferences and past interactions, tools like dynamic paywalls, personalized recommendations and custom offers create unique experiences for every user based on where they are in the customer journey.
For example, ML models can analyze what type of content a user engages with and tailor future interactions accordingly. This level of personalization not only improves conversion rates but also fosters stronger brand loyalty by demonstrating a deeper understanding of the customer. Businesses that invest in tools to achieve this level of precision will lead in customer satisfaction and, ultimately, retention.
From Segments to “Segments of One”
Historically, marketing relied on broad audience segmentation, targeting large groups with similar characteristics. AI, and more specifically ML and predictive analytics, has transformed this approach, enabling hyper-personalization—what some call "segments of one." By leveraging data on individual behaviors, preferences and past interactions, tools like dynamic paywalls, personalized recommendations and custom offers create unique experiences for every user based on where they are in the customer journey.
For example, ML models can analyze what type of content a user engages with and tailor future interactions accordingly. This level of personalization not only improves conversion rates but also fosters stronger brand loyalty by demonstrating a deeper understanding of the customer. Businesses that invest in tools to achieve this level of precision will lead in customer satisfaction and, ultimately, retention.
From Segments to “Segments of One”
Historically, marketing relied on broad audience segmentation, targeting large groups with similar characteristics. AI, and more specifically ML and predictive analytics, has transformed this approach, enabling hyper-personalization—what some call "segments of one." By leveraging data on individual behaviors, preferences and past interactions, tools like dynamic paywalls, personalized recommendations and custom offers create unique experiences for every user based on where they are in the customer journey.
For example, ML models can analyze what type of content a user engages with and tailor future interactions accordingly. This level of personalization not only improves conversion rates but also fosters stronger brand loyalty by demonstrating a deeper understanding of the customer. Businesses that invest in tools to achieve this level of precision will lead in customer satisfaction and, ultimately, retention.
AI’s Role in Workflow Transformation
AI isn’t just enhancing customer experiences; it’s also transforming internal workflows. In media and publishing, for instance, NLP tools automate time-consuming tasks like subtitling videos, transcribing podcasts and drafting articles. Generative AI models also assist with producing content variations. This gives professionals more time to focus on higher-value activities, such as in-depth reporting or crafting strategic campaigns.
Marketers can adopt similar tools to streamline operations, enabling teams to produce more content, test campaigns faster and focus on creative strategies, as well as quality assurance of output. During Piano Academy Live 2024, Nicolas Aucher, Web Analytics & Data Manager at Bouygues Telecom, highlighted how the organization leverages generative AI to summarize call center interactions—improving efficiency in customer call handling and boosting satisfaction. Just one example of how predictive analytics and automated optimization platforms help fine-tune strategies, empowering teams to work more efficiently and allocate more resources towards higher-value activities across industries and verticals.
AI’s Role in Workflow Transformation
AI isn’t just enhancing customer experiences; it’s also transforming internal workflows. In media and publishing, for instance, NLP tools automate time-consuming tasks like subtitling videos, transcribing podcasts and drafting articles. Generative AI models also assist with producing content variations. This gives professionals more time to focus on higher-value activities, such as in-depth reporting or crafting strategic campaigns.
Marketers can adopt similar tools to streamline operations, enabling teams to produce more content, test campaigns faster and focus on creative strategies, as well as quality assurance of output. During Piano Academy Live 2024, Nicolas Aucher, Web Analytics & Data Manager at Bouygues Telecom, highlighted how the organization leverages generative AI to summarize call center interactions—improving efficiency in customer call handling and boosting satisfaction. Just one example of how predictive analytics and automated optimization platforms help fine-tune strategies, empowering teams to work more efficiently and allocate more resources towards higher-value activities across industries and verticals.
AI’s Role in Workflow Transformation
AI isn’t just enhancing customer experiences; it’s also transforming internal workflows. In media and publishing, for instance, NLP tools automate time-consuming tasks like subtitling videos, transcribing podcasts and drafting articles. Generative AI models also assist with producing content variations. This gives professionals more time to focus on higher-value activities, such as in-depth reporting or crafting strategic campaigns.
Marketers can adopt similar tools to streamline operations, enabling teams to produce more content, test campaigns faster and focus on creative strategies, as well as quality assurance of output. During Piano Academy Live 2024, Nicolas Aucher, Web Analytics & Data Manager at Bouygues Telecom, highlighted how the organization leverages generative AI to summarize call center interactions—improving efficiency in customer call handling and boosting satisfaction. Just one example of how predictive analytics and automated optimization platforms help fine-tune strategies, empowering teams to work more efficiently and allocate more resources towards higher-value activities across industries and verticals.
Sustainability and AI
The Growing Imperative
As AI adoption grows, so does its environmental impact. Data centers powering AI applications consume significant amounts of energy and water, highlighting the need for sustainable practices.
Marketers can prioritize eco-conscious AI usage by optimizing workflows and reducing unnecessary data processing through data unification and minimization. Additionally, they can select tools designed with sustainability in mind. Emerging technologies, such as liquid cooling for data centers and AI models designed for lower energy consumption, offer tangible ways to mitigate environmental impact. Taking a proactive approach to AI’s environmental footprint not only reduces costs but also aligns with rising consumer expectations for responsible business practices.
Sustainability and AI
The Growing Imperative
As AI adoption grows, so does its environmental impact. Data centers powering AI applications consume significant amounts of energy and water, highlighting the need for sustainable practices.
Marketers can prioritize eco-conscious AI usage by optimizing workflows and reducing unnecessary data processing through data unification and minimization. Additionally, they can select tools designed with sustainability in mind. Emerging technologies, such as liquid cooling for data centers and AI models designed for lower energy consumption, offer tangible ways to mitigate environmental impact. Taking a proactive approach to AI’s environmental footprint not only reduces costs but also aligns with rising consumer expectations for responsible business practices.
Sustainability and AI
The Growing Imperative
As AI adoption grows, so does its environmental impact. Data centers powering AI applications consume significant amounts of energy and water, highlighting the need for sustainable practices.
Marketers can prioritize eco-conscious AI usage by optimizing workflows and reducing unnecessary data processing through data unification and minimization. Additionally, they can select tools designed with sustainability in mind. Emerging technologies, such as liquid cooling for data centers and AI models designed for lower energy consumption, offer tangible ways to mitigate environmental impact. Taking a proactive approach to AI’s environmental footprint not only reduces costs but also aligns with rising consumer expectations for responsible business practices.
Experimentation and Iteration
The AI Mindset
AI thrives on experimentation, and businesses must adopt a test-and-learn mindset to unlock its full potential. Not every AI-driven strategy will succeed, but iterative testing allows companies to identify what works and adapt quickly.
For example, publishers experimenting with generative AI tools like text-to-speech or bespoke content recommendation models are discovering niche applications that align with their audience's needs. By remaining agile and open to new possibilities, businesses can continuously refine their AI strategies and stay ahead of competitors.
Experimentation and Iteration
The AI Mindset
AI thrives on experimentation, and businesses must adopt a test-and-learn mindset to unlock its full potential. Not every AI-driven strategy will succeed, but iterative testing allows companies to identify what works and adapt quickly.
For example, publishers experimenting with generative AI tools like text-to-speech or bespoke content recommendation models are discovering niche applications that align with their audience's needs. By remaining agile and open to new possibilities, businesses can continuously refine their AI strategies and stay ahead of competitors.
Experimentation and Iteration
The AI Mindset
AI thrives on experimentation, and businesses must adopt a test-and-learn mindset to unlock its full potential. Not every AI-driven strategy will succeed, but iterative testing allows companies to identify what works and adapt quickly.
For example, publishers experimenting with generative AI tools like text-to-speech or bespoke content recommendation models are discovering niche applications that align with their audience's needs. By remaining agile and open to new possibilities, businesses can continuously refine their AI strategies and stay ahead of competitors.
Human + AI Collaboration
The Key to Authentic Experiences
AI excels at efficiency, scalability and data-driven insights, but it cannot replicate the empathy and creativity that come from human input. The most effective marketing strategies will combine AI’s capabilities with the human touch to deliver truly authentic connections.
While AI can automate repetitive tasks, draft personalized emails or generate product recommendations, humans ensure these outputs align with a brand’s voice and resonate emotionally with the audience. Empathy, cultural context and creativity – at least for now –remain distinctly human strengths. This collaboration ensures that automation enhances customer experiences rather than diminishing them, with marketers playing a critical role in shaping messaging and ensuring it connects meaningfully with audiences.
Human + AI Collaboration
The Key to Authentic Experiences
AI excels at efficiency, scalability and data-driven insights, but it cannot replicate the empathy and creativity that come from human input. The most effective marketing strategies will combine AI’s capabilities with the human touch to deliver truly authentic connections.
While AI can automate repetitive tasks, draft personalized emails or generate product recommendations, humans ensure these outputs align with a brand’s voice and resonate emotionally with the audience. Empathy, cultural context and creativity – at least for now –remain distinctly human strengths. This collaboration ensures that automation enhances customer experiences rather than diminishing them, with marketers playing a critical role in shaping messaging and ensuring it connects meaningfully with audiences.
Human + AI Collaboration
The Key to Authentic Experiences
AI excels at efficiency, scalability and data-driven insights, but it cannot replicate the empathy and creativity that come from human input. The most effective marketing strategies will combine AI’s capabilities with the human touch to deliver truly authentic connections.
While AI can automate repetitive tasks, draft personalized emails or generate product recommendations, humans ensure these outputs align with a brand’s voice and resonate emotionally with the audience. Empathy, cultural context and creativity – at least for now –remain distinctly human strengths. This collaboration ensures that automation enhances customer experiences rather than diminishing them, with marketers playing a critical role in shaping messaging and ensuring it connects meaningfully with audiences.
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