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
Agentic AI in ITSM: Use Cases with Examples in 2026
Agentic AI in ITSM marks a practical shift in how organizations manage IT operations and service delivery. Instead of relying on static automation or predefined workflows, agentic AI enables contextual reasoning, allowing AI agents to act autonomously within IT environments.
Generative AI in Marketing: AdsGency AI, Creatify & Jasper
A survey of 5,000 marketers worldwide revealed that their top priority was “implementing or leveraging AI”, showing a growing recognition of its potential in marketing. In particular, generative AI is becoming a critical component of marketing operations, offering capabilities that support more efficient content production, data-driven decision-making, and enhanced content scalability, personalization, and campaign optimization.
Speech-to-Speech Software: Use Cases & Examples [2026]
Language barriers often create friction in conversations, slowing down collaboration, travel, and even critical services like healthcare. Speech-to-speech (S2S) technology addresses this problem by converting spoken input into natural-sounding speech in another language or style.
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.
Custom AI: When to Build Your Own Solutions in 2026
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.
10 Steps to Developing AI Systems in 2026
IBM identifies the top AI adoption challenges as concerns over data bias (45%), lack of proprietary data (42%), insufficient generative AI expertise (42%), unclear business value (42%), and data privacy risks (40%).These obstacles can hinder AI implementation, slow innovation, and reduce the return on investment for organizations adopting AI technologies.
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.
Few-Shot Learning: Methods & Applications in 2026
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.
Control-M for Enterprise Workload Automation in 2026
Control-M by BMC Software helps teams coordinate and automate data and application workflows across environments, including mainframes, the cloud, and hybrid systems. It gives users a single place to schedule jobs, track progress, and handle dependencies.
Top 7 AI Content Assistants: Features & Use Cases in 2026
We compared the top 7 AI content assistants based on their key features, pricing plans, and target audiences, and suggest: Top 7 AI content assistants Note: The table is sorted alphabetically. Feature comparison See AI content assistant features for details on each feature.
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