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 5 RLHF Platforms: Guide & Features Comparison ['26]
As AI adoption grows, with 65% of organizations now regularly using generative AI, selecting the right tools for optimizing AI models has become more crucial than ever. Reinforcement learning from human feedback (RLHF) platforms have emerged as key players in this process.
AI for Mental Health: 7 Use Cases with Real-Life Examples
Mental health challenges are a worldwide concern, especially after the COVID-19 pandemic, which saw an estimated 76 million additional cases of anxiety disorders.This heightened stress strained healthcare systems and increased demand for mental health support. Yet, traditional care faces barriers like professional shortages, high costs, and social stigma.
XRay AI: Definition, Use Cases & Examples in 2026
The integration of artificial intelligence (AI) to radiology is not a futuristic vision; it’s already redefining clinical workflows, particularly in X-ray imaging. AI tools are now embedded into imaging systems, enabling real-time decision support and improving workflow efficiency, image quality, and clinical accuracy.
Top 6 Radiology AI Use Cases for Improved Diagnostics
Radiology teams are under pressure from growing scan volumes, staff burnout, and the risk of diagnostic mistakes. These challenges are making it harder to deliver timely and accurate care. AI is helping to ease the burden by accelerating image analysis, minimizing errors, and facilitating more informed decisions.
Top 10 Applications of Deep Learning in Manufacturing ['26]
Deep learning, a subset of artificial intelligence and machine learning, uses predictive analytics to extract insights, improve productivity, reduce defects and maintenance costs, and accounts for approximately 40% of the annual value generated by all analytics approaches.
Top 20 Blockchain in Supply Chain Case Studies in 2026
Blockchain technology is gaining popularity as a solution to long-standing problems in supply chain management. By offering a decentralized and tamper-proof method for recording transactions, blockchain can address issues related to traceability, transparency, and trust among supply chain partners.
Top 5 Computer Vision Security Applications & Examples
As global crime rates rise, the demand for scalable and efficient security solutions is growing. The market is projected to reach $39 billion by 2029, growing at a CAGR of approximately 10% from 2025 to 2029. Conventional manual surveillance is limited by human capacity; personnel can only monitor footage for short periods with inconsistent accuracy.
Google's AI Strategy and 11 Key Developments in 2026
Google’s current artificial intelligence strategy emphasizes advanced integration of AI models into enterprise solutions, with significant investments focused on improving functionality, accuracy, and efficiency. Check out 10 recent developments for businesses considering implementing Google’s AI tools and platforms into their workflows. 1. Gemini 2.5: Google’s advanced multimodal AI Google DeepMind launched Gemini 2.
Top 5 Computer Vision Automotive Use Cases & Examples
Automotive firms face rising safety, cost, and efficiency pressures. Computer vision helps address these challenges by enabling automation, quality control, and accident prevention. Explore the top 5 computer vision automotive use cases that business leaders can leverage to stay competitive.
Inverse Reinforcement Learning: Use Cases & Examples
Inverse reinforcement learning is an approach in machine learning where machines infer the goals or reward structures that guide an expert’s behavior by observing their actions rather than receiving explicit instructions. Discover what inverse reinforcement learning is, how it works, and the top industry use cases with examples.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.