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
17 Generative AI Healthcare Use Cases
Healthcare systems are facing increased data volumes, staff shortages, and rising expectations for personalized care. Generative AI is emerging as a key solution by synthesizing unstructured medical data, such as clinical notes, imaging reports, and patient histories, into insights for clinicians and administrators. Explore how generative AI is applied across healthcare delivery, administration, and population…
25 Healthcare AI Use Cases with Examples
A recent study shows that hybrid teams of human clinicians and AI systems make more accurate medical diagnoses, largely because they tend to make different and complementary errors that help correct one another. These findings indicate strong potential of AI to enhance patient safety and promote more equitable healthcare.16 How do healthcare AI systems perform?…
800+ Leading AI Benchmarks
We curated a list with over 800 AI benchmarks for LLMs, GPUs, cloud GPUs, AI agents, tabular AI, and cybersecurity that are not yet saturated. Note that most of the May–June peak corresponds to the period during which we carried out our research. Benchmarks that update continuously are dated to the last time we verified…
Top 12 AI Control Plane Tools for Regulated Deployments
An AI control plane provides a shared layer for operating AI agents and agent-based applications. We compared the top 12 AI control plane tools for enterprise architects, security teams, and AI governance owners planning AI adoption at enterprise scale. Read the methodology to see how we scored these products. Vendor selection criteria: We included vendors…
Top 10 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. 10-20% of AI proofs of concept progress to full deployment.59 A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models. As AI becomes more central to business strategy, infrastructure…
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…
AI Fail: 10 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. By identifying and addressing the underlying issues, companies can mitigate the risks associated with AI and ensure that it is used safely…
Compare AI Revenues Across the Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue increased from $47.5B to $115.2B in a single fiscal year (FY2024 to FY2025, ending January 2024 and January 2025). We tracked revenue data from over 80 AI companies. Explore how revenues shifted across compute, data,…
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results. See our benchmark methodology…
AutoSys: Key Features and User Insights
Interest in Broadcom’s AutoSys is declining (Source: Google Trends) and it has a lower average rating on review platforms compared to most other workload automation tools. This may be partially explained by: Dive into AutoSys’s capabilities, user evaluations, and outlook to learn more: AutoSys was originally developed by CA Technologies and acquired by Broadcom in…
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