Sıla Ermut
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
Generative AI in Marketing: Benefits & Use Cases
The artificial intelligence market in marketing was valued at approximately 15.84 billion USD in 2021 and is projected to exceed 107.5 billion USD by 2028.This rapid growth reflects the increasing reliance on AI-driven solutions to address marketing challenges such as content scalability, personalization, and campaign optimization.
Generative AI Ethics: Concerns and How to Manage Them?
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, has accused Perplexity of reusing its content without consent and even attaching its name to inaccurate answers, showing how these technologies can blur the lines between reliable sources and AI-generated text.
Generative AI Healthcare: 15 Use Cases with Examples
As healthcare systems face rising data volumes, workforce shortages, and increasing demands for personalized care, generative AI is emerging as a critical solution. By generating insights from complex medical data, generative AI healthcare applications offer hospital administrators, clinicians, and researchers new ways to improve decision-making and patient outcomes.
Top 20 Blockchain in Supply Chain Case Studies
Blockchain technology is gaining popularity as a solution to long-standing problems in supply chain management. By offering a decentralized and tamper-proof method for recording transactions, blockchain can address issues related to traceability, transparency, and trust among supply chain partners.
Top 10 Strategies for AI Improvement with Real Life Examples
AI systems achieved remarkable milestones (e.g., exceeding human performance in image and speech recognition); however, AI progress is slowing down as scaling yields fewer benefits. Additionally, AI and ML models degrade over time unless they are regularly updated or retrained.This makes it critical to utilize all levers to improve AI models continually.
Top 122 Generative AI Applications & Real-Life Examples
Based on our analysis of 30+ case studies and 10 benchmarks, where we tested and compared over 40 products, we identified 120 generative AI use cases across the following categories: For other applications of AI for requests where there is a single correct answer (e.g. prediction or classification), check out AI applications.
eCommerce Technologies Use Cases & Examples
A few tech and retail giants dominate the eCommerce sector, which is growing at ~10%/year.To compete, smaller businesses should invest in tech to lower costs and increase customer satisfaction, and boost profitability.
Top 10 AI in Fashion Use Cases & Examples
Creative bottlenecks, inefficient supply chains, and rising consumer expectations are pushing fashion brands to seek smarter solutions. According to McKinsey, generative AI can offer a path forward by adding up to $275 billion to operating profits in the fashion, apparel, and luxury sectors until 2028.
30 Datasets for ML & AI Models
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. Explore different types of existing datasets: custom human-generated, custom machine-generated, natural language processing, open, public government, image, audio, and healthcare datasets to train your machine-learning models.
5 AI Training Steps & Best Practices
AI can boost business performance, but 85% of AI projects fail, often due to poor model training. Challenges such as poor data quality, limited scalability, and compliance issues hinder success. Check out the top 5 steps in AI training to help businesses and developers train AI models more effectively.
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