Sıla Ermut
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 Computer Vision Use Cases with Examples
With the global computer vision market projected to reach US$30 billion in 2025 (see the graph below), business leaders face a critical challenge: identifying where it delivers ROI, from healthcare diagnostics to automated logistics.
10 Computer Vision Agriculture Use Cases & Examples
Labor shortages, resource inefficiencies, and environmental pressures increasingly challenge agriculture. Climate change adds to this burden through extreme weather, water scarcity, and rising pest threats, further straining productivity and sustainability. Computer vision offers targeted solutions by enabling automation and data-driven insights across critical farming operations.
17 Computer Vision in Healthcare Use Cases & Examples
Even though Hinton, a Turin award recipient, claimed that radiology would be automated by 2021, such accelerated automation hasn’t occurred.However, AI-driven computer vision in healthcare is still expected to increase precision in surgery, medical imaging, and real-time patient monitoring, while enabling faster and more reliable decision-making.
Top 20 Manufacturing Analytics Case Studies
High maintenance costs, unexpected downtimes, and inefficient processes continue to challenge manufacturers. To stay competitive, companies are leveraging manufacturing analytics to optimize operations and enhance asset performance. Explore the top 20 real-world case studies where manufacturers used analytics insights to cut costs, reduce unplanned downtime, and boost productivity.
AI Fail: 4 Root Causes & Real-life Examples
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.
Federated Learning: 5 Use Cases & Real Life Examples
McKinsey highlights inaccuracy, cybersecurity threats, and intellectual property infringement as the most significant risks of generative AI adoption.Federated learning addresses these challenges by enhancing accuracy, strengthening security, and protecting IP, all while keeping data private.
Meta Learning: 7 Techniques & Use Cases
Training and fine-tuning a typical machine learning (ML) model can take weeks and cost thousands. Meta learning helps cut this down by leveraging prior learning experiences to accelerate training, reduce costs, and improve generalization. Explore key meta learning techniques and use cases in fields like healthcare and online learning.
Top 10 Applications of Deep Learning in Manufacturing
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 10 Healthcare Analytics Use Cases with Examples
The $28 billion healthcare analytics marketis transforming how providers, payers, and life sciences organizations compete, and companies that move now can seize the advantage. By delivering solutions that drive predictive care, reduce costs, and optimize operations, analytics unlocks new revenue streams and strengthens customer loyalty in a healthcare industry racing toward data-driven performance.
No-Code AI: Benefits, Industries & Key Differences
Many small businesses lack the necessary technical resources to implement AI effectively. No-code AI directly addresses this gap by enabling rapid AI deployment without requiring coding skills. Ideal for small businesses, entrepreneurs, and professionals in fields like marketing, education, or healthcare, no-code AI tools help simplify testing, integration, and iterative improvements.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.