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 11 AIaaS to Enhance Business Efficiency
AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.
Compare AI Revenues Across the Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue jumped from $47.5B to $115.2B in a single year; OpenAI reached about $13B in annual revenue; and Anthropic approached $7B in ARR. We tracked revenue data from over 100 AI companies.
Blockchain Case Studies Across Key Industries
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.
10+ Large Language Model Examples
We have gathered open-source benchmarks to compare leading proprietary and open-source large language models. Choose your use case to find the right model.
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.
Top 10 Price Monitoring Tools: Bright Insights & Competera
Price monitoring tools track competitors’ prices and automatically adjust them to stay competitive. Explore the top 10 price monitoring tools, review their key features, pricing models, and see their search market share breakdown below: Price monitoring tools comparison Table notes: * Global coverage options and high-frequency monitoring included in $2,000 monthly commitment.
Compare Email Marketing Pricing: Top 20 Providers
Choosing an email marketing service that fits your budget is key to effective campaigns. Basic plans often include features such as email design tools, contact management, and limited automation, making them ideal for small businesses. Higher-tier plans offer advanced automation, segmentation, analytics, and integrations, making them suitable for larger enterprises.
AI Rollups: Funding, Investors and Industry Trends
We analyzed 30 investments involving over 130 investors from the past 3 years to understand the current trend for AI rollups. Based on our analysis, we identified investor activity and trends, including the number of investors backing AI rollups, the total funding raised for AI rollups, and the leading industries.
LLM Scaling Laws: Analysis from AI Researchers
Large language models predict the next token based on patterns learned from text data. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used during training.
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