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Sıla Ermut

Sıla Ermut

Industry Analyst
121 Articles
Stay up-to-date on B2B Tech

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

Enterprise SoftwareNov 25

Prices of Top 5 IT Service Management (ITSM) Software

IT Service Management (ITSM) tools that support incident, problem, change, and knowledge base management offer diverse pricing models. See IT service management pricing details of the top 5 providers and the feature guide for small businesses and enterprises. ITSM pricing comparison Note: Pricing information is obtained from vendor websites.

AINov 25

Audience Simulation: Can LLMs Predict Human Behavior?

In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.

AINov 24

Large Language Models: Complete Guide

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.

AINov 24

Compare Multimodal AI Models on Visual Reasoning

We benchmarked 8 leading multimodal AI models on visual reasoning using 98 visual-based questions. The evaluation consisted of two tracks: 70 Chart Understanding questions testing data visualization interpretation, and 28 Visual Logic questions assessing pattern recognition and spatial reasoning. Visual reasoning benchmark See our benchmark methodology to learn our testing procedures.

AINov 21

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.

AINov 21

LLM Parameters: GPT-5 High, Medium, Low and Minimal

New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.

AINov 20

Relational Foundation Models: SAP vs. Gradient Boosting

We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the full semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets.

AINov 20

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.

AINov 18

World Foundation Models: 10 Use Cases & Examples

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.

AINov 18

Top 20 Predictions from Experts on AI Job Loss

AI could eliminate half of entry-level white-collar jobs within the next five years. These job losses could affect the global workforce faster than previous waves of technological change. By 2027, millions of jobs may be displaced or significantly altered. While some roles will evolve, the workforce must prepare for a sharp increase in disrupted employment.

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