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
AI in Government: Examples & Challenges
AI in government is no longer a hypothetical or early-stage experiment. Public institutions are moving from isolated pilot projects to large-scale and systemic adoption of AI across core government functions: from social services and healthcare to transportation, public safety, and administrative operations.
eCommerce Technologies Use Cases & Examples
The eCommerce sector continues to expand by ~10% each year as more consumers shift their purchasing habits online and seek faster and more convenient digital experiences.This growth is also accompanied by increasing competition, making it essential for businesses to understand how technology is shaping customer expectations.
LLM Observability Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
Compare Top 4 Self-Checkout Systems
Many retailers continue to face challenges at the checkout line, especially during peak hours when long waits, limited staffed checkout lanes, and rising labor constraints converge. These delays affect the overall shopping experience and place additional pressure on store staff who must balance payment assistance, customer questions, and other in-aisle tasks.
Top 9 AI Providers Compared
The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack.
AI Agents in Marketing: Tools & Examples
Research shows that 50% of organizations using generative AI plan to launch agentic AI pilot programs in 2025.AI agents in marketing represent a significant shift in the industry, introducing systems that can reason, make decisions, and act with minimal human oversight.
Top 5 Price Monitoring Tools
A key challenge for businesses is maintaining competitive pricing while adapting to market fluctuations. Price monitoring tools help solve this challenge by tracking competitors’ prices and providing insights for more dynamic pricing decisions.
Top 20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.
23 Healthcare AI Use Cases with Examples
Healthcare systems are under growing pressure from rising patient data volumes and increasing demand for personalized care. Healthcare AI applications have emerged as a powerful solution to these problems by optimizing processes, enhancing diagnostic accuracy, and improving patient outcomes.
LLM Scaling Laws: Analysis from AI Researchers
Large language models are usually trained as neural language models that predict the next token in natural language. The term LLM scaling laws refers to empirical regularities that link model performance to the amount of compute, training data, and model parameters used when training models.
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