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 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.
40+ Self-Driving Cars Stats
The autonomous vehicles industry reached a turning point in the 2020s. What was once experimental is now entering commercial use, with market valuations reaching $68 billion depending on methodology. Discover self-driving cars stats that outline the current state of deployment, safety, adoption, regulation, and economic impact.
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.
Handle AI Ethics Dilemmas with Frameworks & Tools
Warner Bros. is suing Midjourney, alleging that its AI image generator unlawfully reproduces copyrighted characters, including Superman and Batman. The lawsuit highlights a broader issue: AI systems trained on copyrighted works raise significant concerns about ownership, fairness, and accountability.
AI in Government: Examples & Challenges
Governments worldwide are investing in AI to improve efficiency and service delivery. However, scaling AI initiatives presents challenges, from ethical concerns to bureaucratic resistance. Explore AI in government applications, best practices, and real-world examples.
Few-Shot Learning: Methods & Applications
Imagine a healthcare startup building an AI system to detect rare diseases. The challenge? There isn’t enough labeled data to train a traditional machine learning model. That’s where few-shot learning (FSL) comes in. From diagnosing complex medical conditions to enhancing natural language processing, few-shot learning is redefining how AI learns from limited examples.
Top 5 AI Services 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.
Custom AI: When to Build Your Own Solutions
While ready-made AI tools can meet many business needs, they often fall short in areas that require deep data understanding or specialized workflows. Organizations working in complex or niche industries may find that generic systems don’t fully align with their operations or leverage their proprietary data.
Top 15 Logistics AI Use Cases & Examples
Persistent inefficiencies, rising operational costs, and ongoing supply chain disruptions continue to challenge logistics functions globally. In response, organizations are increasingly turning to artificial intelligence to improve resilience, optimize operations, and achieve measurable gains across inventory, transportation, and procurement.
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