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
Large Language Models: Complete Guide in 2026
Large language models are now central to artificial intelligence because they can understand natural language and generate text with high accuracy. They use transformer architecture and deep learning to process large amounts of training data and learn patterns in human language. Their usefulness spans many tasks, from answering questions to analysing documents.
AI Presentation Maker: Gamma vs. Canva, vs. SlidesGO
We evaluated the top 5 AI presentation makers by examining their capabilities across 9 dimensions with 4 different prompts to assess how well they handle various scenarios: AI presentation maker benchmark results Review the methodology and evaluation criteria to understand how we determined these results.
Top 123 Generative AI Applications & Real-Life Examples
Based on our analysis of 30+ case studies and 10 benchmarks, where we tested and compared over 40 products, we identified 120 generative AI use cases across the following categories: For other applications of AI for requests where there is a single correct answer (e.g., prediction or classification), check out AI applications.
World Foundation Models: 10 Use Cases & Examples ['26]
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.
AI Image Detector Benchmark: SightEngine & Wasit AI ['26]
As these synthetic visuals grow more realistic and accessible, the ability to detect them has become a critical concern for upholding generative AI ethics, combating misinformation, and ensuring image authenticity. We compared the top 7 AI image detectors across 5 dimensions and found that most perform no better than a coin toss.
15 Blockchain Case Studies Across Key Industries in 2026
A recent forecast projects the blockchain market will reach 943 billion U.S. dollars by 2032, growing at a CAGR of 56%.While the potential is massive, executives face uncertainty due to the varying maturity of blockchain solutions across industries.
AutoSys in 2026: Key Features and User Insights
Interest in Broadcom’s AutoSys is declining (Source: Google Trends) and it has a lower average rating on review platforms compared to most other workload automation tools.
Top 9 AI Infrastructure Companies & Applications in 2026
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
AI Video Pricing: Compare Runway, Synthesia & Invideo AI
AI video pricing can differ significantly across platforms, influenced by factors such as output quality, customization options, and features. As more businesses and creators turn to AI for efficient video production, understanding these pricing models becomes essential.
Federated Learning: 5 Use Cases & Real Life Examples
According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.
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