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Sıla Ermut

Sıla Ermut

Industry Analyst
115 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

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 13

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. Models tested include GPT-5, Gemini 2.5 Pro/Flash, Claude 4.5 Sonnet/Haiku 4.

Enterprise SoftwareNov 7

Agentic AI in ITSM: Use Cases with 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.

AIOct 22

Top 7 AI Content Assistants: Features & Use Cases

We compared the top 7 AI content assistants based on their key features, pricing plans, and target audiences, and suggest: Top 7 AI content assistants Note: The table is sorted alphabetically. Feature comparison See AI content assistant features for details on each feature.

AINov 11

Top 9 AI Providers Compared

The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack.

AIOct 16

Top 10 Marketplace Optimization Tools with Examples

Brands selling on eCommerce marketplaces face challenges such as high competition, unpredictable demand, and limited product visibility. These issues often lead to reduced profitability and inefficient resource use. Marketplace optimization uses data, automation, and analytics to improve pricing, advertising, and content performance.

AIOct 10

Content Authenticity: Tools & Use Cases

The increasing prevalence of misinformation, deepfakes, and unauthorized modifications has made content verification important. In the United Kingdom, 75% of adults believe that digitally altered content contributes to the spread of misinformation, underscoring the need for reliable verification methods.

AIOct 30

AI Scientist: Automating the Future of Scientific Discovery

AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.

Agentic AINov 14

AI Agents in Marketing: Tools & Examples

Research shows that 50% of organizations already 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.

Agentic AIOct 2

Top 10 Agentic AI in Supply Chain Tools & Use Cases

Forecasts suggest that by 2030, half of cross-functional supply chain management solutions will integrate agentic AI capabilities. This widespread adoption will enable global enterprises to reduce exposure to supply chain disruptions and achieve more consistent performance.

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