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Sıla Ermut

Sıla Ermut

Industry Analyst
73 Articles
Stay up-to-date on B2B Tech

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

Enterprise SoftwareJun 18

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.

AIJun 17

LLM Observability Tools: Weights & Biases, Langsmith

LLM applications have expanded from single turn chat into multi step agents that call tools, query databases, and coordinate with other models, which makes their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected.

AIJun 17

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. 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.

Enterprise SoftwareJun 16

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.

Enterprise SoftwareJun 16

IT Asset Management (ITAM) Pricing Comparison

Finding the right IT Asset Management (ITAM) solution is key to controlling costs, reducing risks, and gaining full visibility into your IT infrastructure. Designed for IT managers, procurement teams, and SMEs, this comparison highlights how different pricing models and feature sets align with varying business needs.

Enterprise SoftwareJun 16

Prices of Top 6 IT Service Management (ITSM) Software

IT Service Management (ITSM) tools that support incident, problem, change, and knowledge base management offer diverse pricing models. See IT service management pricing details of the top 6 providers and the feature guide for small businesses and enterprises. ITSM pricing comparison Note: Pricing information is obtained from vendor websites.

AIJun 16

25 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.

AIJun 16

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.

AIJun 16

Top 11 AI in Fashion Use Cases & Examples

Faced with creative bottlenecks, inefficient supply chains, and rising consumer expectations, fashion brands are seeking smarter solutions. McKinsey estimates that generative AI could boost operating profits in the fashion, apparel, and luxury sectors by up to $275 billion by 2028.

AIJun 15

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 when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.