Sıla Ermut
Sıla is an industry analyst at AIMultiple covering AI models, AI infrastructure, AI governance, and enterprise applications of AI.
Research interests
Sıla’s research focuses on the use of AI in marketing, healthcare, supply chains, and sustainability. She also covers AI evaluation, governance, ethics, and adoption.Her work includes researching and comparing AI technologies, analyzing their business applications, and contributing to AIMultiple’s technology benchmarks.
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 12 SEO AI Use Cases with Case Studies
As algorithms change and consumer expectations rise, it has become more challenging to compete for accessibility in search results. Conventional SEO techniques, which depend on manual research and minor updates, frequently fall behind these developments. AI-powered SEO tools address this challenge by automating complex tasks and aligning content more precisely with user intent. Explore the…
Top 12 AI Avatar Generation Tools
When choosing the right AI avatar generation tool, businesses can take into account the following components: We tested 7 AI avatar generation tools and compared their visual (resolution and export capabilities) and voice (number of languages supported and voice cloning availability) features, as well as their pricing plans. We signed up for the free trial…
Top 11 AI in ITSM Use Cases & Examples
Leveraging AI for IT service management (ITSM) tools supports organizations in terms of: See the top 11 use cases of AI in ITSM, examples, and benefits of leveraging AI in ITSM. AI-native ITSM refers to a new way of managing internal support and IT operations where artificial intelligence is not an added feature but part…
LLM Market Share: Compare Usage & Adoption
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: Read the methodology to see how we measured and calculated these results. The United States dominated web visits across all four months, consistently accounting for 85.5–90.5%.…
No-Code AI: Benefits, Industries & Key Differences
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background. Recently, no-code AI has expanded…
Compare Large Vision Models: GPT-4o vs YOLOv8n
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count. To ensure a fair comparison, all images were resized to…
Top 20 ITSM Case Studies
Leveraging IT Service Management (ITSM) tools is essential for businesses aiming to increase the efficiency of their IT operations and enhance service delivery. Explore ITSM case studies across various industries such as education, manufacturing, transportation, and telecommunications, and see how companies have leveraged ITSM solutions to overcome service management challenges, improve efficiency, and boost customer…
LLM Parameters: GPT-5 High, Medium, Low and Minimal
Some LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. We used the GPT-5 family in our…
Federated Learning: 7 Use Cases & Examples
Federated learning (FL) enables models to learn from decentralized data while keeping sensitive information private and ensuring compliance with data localization and privacy laws. Explore what federated learning is, how it works, common use cases with real-life examples, potential challenges, and its alternatives. Federated learning supports a wide range of AI systems where data sensitivity,…
Control-M for Enterprise Workload Automation
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. The platform also connects with popular cloud services, data tools, and DevOps systems, making it easier to manage production…
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