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Sıla Ermut

Sıla Ermut

Industry Analyst
74 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

AIMay 7

Compare Large Vision Models: GPT-4o vs YOLOv8n

Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count.

Enterprise SoftwareMay 6

Agentic AI in ITSM: 10 Use Cases & Examples

Agentic AI in ITSM marks a practical shift in how organizations manage IT operations and service delivery. Instead of relying on static automation or predefined workflows, agentic AI enables contextual reasoning, allowing AI agents to act autonomously within IT environments.

AIApr 29

Generative AI Ethics: How to Manage Them

Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. 1.

AIApr 28

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.

AIApr 22

Top 125 Generative AI Applications

Based on our analysis of 30+ case studies and 10 benchmarks, where we tested and compared over 40 products, we identified 125 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.

AIApr 21

LLM Market Share: Compare Usage & Adoption

We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: LLM market share comparison by country Read the methodology to see how we measured and calculated these results.

Agentic AIApr 21

15 AI Agents in Marketing Tools & Examples

Research shows that 50% of organizations using generative AI plan to launch agentic AI pilot programs in 2025.AI agents in marketing represent a significant shift in the industry, introducing systems that can reason, make decisions, and act with minimal human oversight.

AIApr 15

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.

AIApr 15

LLM Quantization: BF16 vs FP8 vs INT4

We benchmarked Qwen3-32B at 4 precision levels (BF16, FP8, GPTQ-Int8, GPTQ-Int4) on a single NVIDIA H100 80GB GPU. Each configuration was evaluated on 2 benchmarks (~12.2K questions) covering knowledge and code generation, plus 2,000+ inference runs to measure throughput. Int4 is 2.

Enterprise SoftwareApr 14

Top 10 Price Monitoring Tools

Price monitoring tools track competitors’ prices to enable pricing strategies that respond to market changes and stay competitive as conditions shift. Explore the top 10 price monitoring tools, review their key features and pricing models, and learn how they can enable pricing strategies that respond to market changes.

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