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
Composite AI: Techniques & Use Cases
Despite significant investments in generative AI, many organizations are still struggling to demonstrate its tangible business impact. In 2024, companies spent an average of $1.9 million on GenAI initiatives, yet fewer than one in three AI leaders say their CEOs are satisfied with the return on those investments.
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
According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.
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.
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 Top 12 AI Ethics Dilemmas with Real Life Examples
Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is not just an academic or a societal concern but a reputational risk for companies, no company wants to be marred with data or AI ethics scandals that impact companies.
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