As we move into 2025, artificial intelligence is poised to make some of its most significant leaps yet. AI will no longer simply analyze data—it will become a true partner in decision-making. According to Mridula Rahmsdorf, Chief Revenue Officer at IKASI, the integration of causal AI with generative AI and large language models (LLMs) is set to redefine how industries operate and decisions are made.
Rahmsdorf recently shared her predictions for 2025 and beyond, outlining five key trends that highlight how causal AI will reshape industries and customer interactions.
Prediction 1: Deeper Integration with Existing AI Takes Decision-Making to a New Level
“AI’s evolution hinges on moving from correlation to causation,” Rahmsdorf explains. For years, correlational models have been excellent at identifying patterns but have fallen short in explaining why those patterns occur. The integration of causal AI with generative AI and LLMs in 2025 will change that, offering decision-making systems capable of presenting not just scenarios but also their underlying causes. 
This integration will enhance decision-making in industries that deal with complex, multi-factorial scenarios. Businesses will be able to navigate conflicting indicators with confidence, relying on causal AI to differentiate between meaningful signals and noise. “It’s not just about accuracy anymore—it’s about insight,” Rahmsdorf notes.
Prediction 2: Greater Confidence Expands Critical Use Cases Across Verticals
As causal AI matures, its applications will expand dramatically across industries, Rahmsdorf predicts. Increased confidence in its ability to deliver accurate, actionable insights will unlock new possibilities in fields like healthcare, finance, and government services.
“Causal AI is transforming how we approach some of the most complex problems,” Rahmsdorf explains. In healthcare, providers will use it to predict disease onset, enabling early intervention and individualized treatment plans. Organizations like Kaiser Permanente are already leading the way, and Rahmsdorf expects to see more providers following suit in 2025.
In finance, causal AI will power sophisticated trading algorithms, optimizing returns while managing risk. Retailers, meanwhile, will refine loyalty programs, promotions, and pricing with unparalleled precision. Even governments will benefit, using causal AI to evaluate the impact of policy decisions and improve public services. “The potential for material impact across verticals is immense,” Rahmsdorf asserts.
Prediction 3: Increased Community and Open Source Development Accelerates Progress
The collaborative nature of open-source projects will play a critical role in advancing causal AI in 2025. Rahmsdorf highlights efforts by tech giants like Google, AWS, and IBM to democratize access to cutting-edge causal AI frameworks.
“Open-source initiatives are leveling the playing field,” Rahmsdorf says. These projects allow startups, researchers, and public entities to leverage advanced tools without incurring prohibitive costs. While challenges like scalability, performance, and quality control remain, Rahmsdorf is optimistic that collaborative efforts will address these hurdles.
She also emphasizes the ethical implications of broader access. “As adoption increases, we need to ensure responsible use of the technology,” Rahmsdorf warns. But she believes that open-source collaboration will ultimately accelerate innovation while making causal AI more accessible.
Prediction 4: Cross-Disciplinary Collaboration Drives Better Models
According to Rahmsdorf, the true power of causal AI lies in its ability to integrate insights from diverse disciplines. In 2025, data scientists will increasingly collaborate with experts from fields like social sciences, economics, and healthcare to develop models that are both technically robust and contextually relevant.
“Causal AI models are only as good as the knowledge they’re built on,” Rahmsdorf explains. By involving domain experts in their development, companies can ensure that these models address real-world challenges effectively. She points to fields like education, supply chain management, and environmental science as areas where cross-disciplinary collaboration will have a profound impact.
Prediction 5: Refined Automation Enables Real-Time Causal Inference
Perhaps the most transformative trend Rahmsdorf foresees is the rise of automated causal discovery methods. In 2025, these tools will allow organizations to identify cause-and-effect relationships in real-time, dramatically improving decision-making speed and accuracy.
“This is where causal AI becomes a game-changer,” Rahmsdorf says. Automated discovery will enable systems to adapt instantly to changing conditions, providing actionable insights on demand. Whether optimizing a supply chain during a crisis or personalizing customer experiences, real-time causal inference will give businesses an unparalleled competitive edge.
Rahmsdorf also notes that advancements in computing power and algorithms will make these capabilities increasingly accessible. “The organizations that embrace this technology will be the ones that thrive in 2025 and beyond,” she predicts.
Looking Ahead
Mridula Rahmsdorf’s predictions paint an exciting picture of AI’s future. By integrating causal AI with existing technologies, expanding its use cases, fostering open-source development, encouraging cross-disciplinary collaboration, and enabling real-time inference, 2025 promises to be a pivotal year for AI innovation.
For businesses, the implications are profound. Causal AI doesn’t just identify patterns—it explains why those patterns exist, allowing organizations to make smarter, more informed decisions. As Rahmsdorf puts it, “2025 will be the year AI moves from analysis to understanding, unlocking new levels of value across industries.”
Photo by Ivan Slade on Unsplash
