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
AI Scientist: Automating the Future of Scientific Discovery
AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.
Top 10 AI Agents in Marketing
Research shows that 50% of organizations already 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 10 Agentic AI in Supply Chain Tools & Use Cases
Forecasts suggest that by 2030, half of cross-functional supply chain management solutions will integrate agentic AI capabilities. This widespread adoption will enable global enterprises to reduce exposure to supply chain disruptions and achieve more consistent performance.
Context Engineering: Maximize LLM Grounding & Accuracy
LLMs often struggle with raw, unstructured data such as email threads or technical documents, leading to factual errors and weak reasoning. We benchmarked systematic context engineering and achieved up to +13.0% improvement in task accuracy, confirming that structured context is key to enhancing performance in complex tasks.
Frugal AI: Principles, Use Cases & Real-life Examples
From healthcare providers in remote regions to manufacturers optimizing production lines, many industries face limits in budget, infrastructure, and energy use when adopting artificial intelligence. Frugal AI addresses these constraints by prioritizing efficiency, sustainability, and inclusivity, enabling AI systems to deliver measurable value with minimal resources.
Top 20 Supply Chain AI Companies with Examples
From demand forecasting and inventory optimization to last-mile delivery and supplier negotiations, AI enables supply chain companies to process complex data, respond to disruptions more quickly, and make more informed decisions across global networks.
AI Agent Deployment: Steps and Challenges
Organizations are increasingly relying on AI agents to manage tasks that once required constant human effort, such as responding to customer queries, automating workflows, or coordinating data across different systems. While these agents can extend productivity and reduce operational load, their value is realized only when they are deployed correctly in production.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come with different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and various parameters, including high, medium, low, and minimal. Below, we explore the differences between these versions of the models by gathering their benchmark performances and the costs to run these benchmarks. Price vs.
Audience Simulation: Can LLMs Predict Human Behavior?
In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.
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