Sıla Ermut
Sıla is an industry analyst at AIMultiple focused on email marketing and sales videos.
Research interests
Sıla's research areas include email marketing, eCommerce marketing campaigns and marketing automation.She is also part of AIMultiple's email deliverability benchmark. She is designing and running email deliverability benchmarks while collaborating with the AIMultiple technology team.
Professional experience
Sıla previously worked as a recruiter and worked in project management and consulting firms.Education
She holds:- Bachelor of Arts degree in International Relations from Bilkent University.
- Master of Science degree in Social Psychology from Başkent University.
Her Master's thesis was focused on ethical and psychological concerns about AI. Her thesis examined the relationship between AI exposure, attitudes towards AI, and existential anxieties across different levels of AI usage.
Latest Articles from Sıla
Top 15 Logistics AI Use Cases & Examples
Persistent inefficiencies, rising operational costs, and ongoing supply chain disruptions continue to challenge logistics functions globally. These pressures are straining traditional systems, reducing service reliability, and limiting organizations’ ability to scale. In response, companies are increasingly turning to artificial intelligence to enhance end-to-end visibility, strengthen resilience, and optimize core functions.
Top 12 IT Service Management Tools
We evaluated the top 10 IT service management tools based on user experience, performance, and feature set. Explore our findings to see how these leading solutions differ in areas including AI features, communication integrations, DevOps, monitoring & security connections, and deployment options.
No-Code AI: Benefits, Industries & Key Differences
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background.
eCommerce Technologies Use Cases & Examples
The eCommerce sector continues to expand by ~10% each year as more consumers shift their purchasing habits online and seek faster and more convenient digital experiences.This growth is also accompanied by increasing competition, making it essential for businesses to understand how technology is shaping customer expectations.
World Foundation Models: 10 Use Cases
Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing.
Time Series Foundation Models: Use Cases & Benefits
Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.
E-Commerce AI Video Maker Benchmark: Veo 3 vs Sora 2
Product visualization plays a crucial role in e-commerce success, yet creating high-quality product videos remains a significant challenge. Recent advancements in AI video generation technology offer promising solutions.
25 Healthcare AI Use Cases with Examples
Healthcare systems are under growing pressure from rising patient data volumes and increasing demand for personalized care. Healthcare AI applications have emerged as a powerful solution to these problems by optimizing processes, enhancing diagnostic accuracy, and improving patient outcomes.
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.
Large World Models: Use Cases & Examples
Despite advances in large language models, artificial intelligence remains limited in its ability to understand and interact with the physical world due to the constraints of text-based representations. Large world models address this gap by integrating multimodal data to reason about actions, model real-world dynamics, and predict environmental changes.
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