Marketing is at the forefront of a transformation being driven by artificial intelligence (AI). AI implementation is transformational at scale, but it is not uniform.
To gain an understanding of the integration of AI in marketing processes, Outcomes Rocket surveyed more than 1,000 marketers across various functions and company sizes. The results provided a clear image of a rapidly becoming inseparable from the functioning of marketers, generation, and planning.
Saul Marquez, the founder and CEO of Outcomes Rocket, recently answered our questions on why AI is becoming a central aspect in marketing, the role of agentic AI in the workplace as compared to generative AI and the necessity to implement integrated and upskilling strategies.
Demand Gen Report (DGR): Saul, thanks for taking time to talk with us today. What percentage of marketers have adopted AI in their processes, and how does this vary across organization sizes?
Saul Marquez: Thanks for having me. Our report reveals that nearly 9 out of 10 marketers (89.5%) have integrated AI into their processes. While adoption is high across organizations of every size, small businesses are turning to AI most aggressively as a way to sharpen their competitive edge. Smaller teams often have fewer layers of approval and can move faster when adopting new tools. Larger organizations, on the other hand, may have the resources to invest more deeply in AI, but their adoption curve is sometimes slowed by bureaucracy, integration challenges, and legacy systems.
DGR: What are the top three AI tools used for data analytics in marketing, and how do they support decision-making?
Marquez: The three used for data analytics in marketing are Google Analytics, Tableau, and Looker.
Google Analytics gives real-time visibility into customer behavior and how people engage with campaigns, which helps teams adjust on the fly. Tableau takes it further by turning complex data into visuals that make patterns easy to spot, so leaders can see where to double down or pull back. Looker connects multiple data sources to provide a fuller picture of the customer journey and campaign impact.
In practice, marketers are using these tools to refine strategies, allocate budgets more intelligently, and anticipate what their customers need next. It’s a shift from hindsight to foresight, and that’s where AI-driven analytics is proving its real value.
DGR: Why is Gen AI the most widely adopted AI technology in marketing, and what are its primary use cases? How does ChatGPT dominate the generative AI space, and what advantages does it offer over competitors?
Marquez: It tackles one of the industry’s biggest demands: content creation. Marketers are under constant pressure to produce more, faster, and with greater personalization and Gen AI makes that possible. From ad copy to blog posts to email campaigns, it’s now central to how teams create at scale. Within this space, ChatGPT has clearly emerged as the leader. Its versatility and natural language capabilities set it apart. Compared to competitors, it offers smoother user experience and broader adaptability, which means it integrates seamlessly into daily workflows.
DGR: How is agentic AI being adopted, and what potential does it hold for the future of marketing?
Marquez: While overall AI adoption in marketing is remarkably high, only about a quarter (24.3%) have actually experimented with agentic AI. Most teams are still relying on assistive tools, like generative AI for content, rather than systems that can act more autonomously. The potential here is enormous. Agentic AI could eventually manage campaigns end-to-end, from testing creative to reallocating budgets in real time, without needing constant human input. We’re still early in that journey, but as confidence grows, agentic AI has the power to transform marketing from being reactive to truly self-optimizing.
Right now, adoption is low because agentic AI requires trust, new workflows, and often integration with existing systems. But as success stories emerge and technology becomes more accessible, it’s likely we’ll see agentic AI follow the same curve generative AI did. The companies that start exploring it early may gain a lasting competitive advantage, because agentic AI has the potential to fundamentally reshape how marketing is run.
DGR: What challenges do marketers face with AI-generated content, and how do they address these issues?
Marquez: One of the clearest findings from our research is that AI still comes with challenges. In fact, 93.4% of marketers say they regularly encounter issues such as factual errors, bias, or results that simply miss the mark. Because of this, 71.1% report they would never publish AI outputs without reviewing or editing them first. That kind of human oversight has become non-negotiable: AI can accelerate production, but it still needs a human eye to ensure accuracy, relevance, and brand alignment.
To address these challenges, many marketers build an editing layer into their workflows. They treat AI drafts as starting points that are polished by humans before publication. Others are training AI models with brand-specific data or refining prompts to reduce off-brand or irrelevant results. On top of this, marketers are increasingly relying on plagiarism checks, fact-checking tools, and bias detection systems to catch errors before content goes live.
For AI to be a fully trusted partner, it must become more transparent in how it generates outputs and more reliability in reducing errors.
DGR: What percentage of marketers have received formal training on AI tools, and how does this impact their confidence in using AI?
Marquez: It’s notable that most companies still aren’t providing formal training on AI— 80.8% of marketers say they haven’t received any structured guidance from their employers on how to use these tools. That lack of preparation shows up in confidence levels. Without training, many marketers feel uncertain about how to get the best out of these tools or even how to spot when the outputs are flawed. Instead of using AI strategically, they often stick to surface-level tasks or rely heavily on trial and error. This can create hesitation, second-guessing, and an overall sense that they’re not in full control.
DGR: How has AI improved productivity for marketers, and what is the average time saved per week?
Marquez: Our research shows that 86% are saving time, with the average gain being 4.74 hours per week. That’s nearly half a workday back, which can make a big difference in how teams prioritize their efforts. Instead of being tied up in repetitive production tasks, marketers can shift more attention to strategy, creativity, and testing new ideas. Over time, this kind of time recovery reshapes entire team dynamics by allowing more space for innovation and long-term planning.
DGR: How do marketers plan to increase AI investment, and what areas are they focusing on?
Marquez: Marketers are making it clear that AI is becoming a long-term investment priority. The plans for increased spending are about deepening integration into core marketing functions. The biggest areas of focus are content creation, customer insights, and personalization. Content has been the first obvious win, but the next wave of investment is leaning toward analytics and decision support. Personalization is also rising on the agenda, with teams looking to use AI to deliver more tailored customer journeys on scale.
DGR: What strategies are recommended for organizations to integrate AI effectively while addressing workforce preparedness and ethical considerations?
Marquez: To integrate AI effectively, organizations need a strategy that combines workforce readiness with clear ethical guardrails. On the workforce side, this starts with training and upskilling. Too many marketers are left to figure out AI on their own, which limits confidence and slows adoption. Companies that invest in structured learning programs, prompt engineering skills, and cross-team experimentation build a culture where AI feels like an enabler instead of a risk.
At the same time, ethical considerations can’t be an afterthought. Organizations should establish guidelines for transparency, bias detection, and responsible use.
A few strategies that work in practice:
- Build internal programs to teach employees how to use AI effectively, from prompt-writing to evaluating outputs. This helps replace trial-and-error with confidence and consistency.
- Give teams safe spaces to test AI on real workflows, share learnings, and build trust in the tools. Small pilots often lead to broader adoption.
- Set clear rules on where and how AI can be used. Transparency, accountability, and fairness should be embedded into every use case.
- Be clear with both employees and customers about how AI is being used.
DGR: What are the expected advancements in AI technologies that will impact marketing in the next few years?
Marquez: The next few years are set to bring a major shift in how marketers use AI, with a few key advancements standing out. Generative AI (79%) is expected to lead, as most marketers see improved content creation tools as the biggest game-changer. The demand for faster, more personalized, and scalable content will keep this technology at the center of marketing innovation.
Other areas to watch are predictive analytics (55%) and hyper-personalization (54%). As customers expect experiences tailored to their individual needs, AI will give marketers the ability to deliver relevance at scale in ways that were not possible before.