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
Top 5 AI Services to Enhance Business Efficiency
AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.
Top 6 Social Media Post Generator Benchmark
Generative AI is playing a significant role in the creation and management of social media content. As more tools offer features like caption writing, image selection, and post scheduling, it’s helpful to understand how they compare.
Top 10 Marketplace Optimization Tools with Examples
Brands selling on eCommerce marketplaces face challenges such as high competition, unpredictable demand, and limited product visibility. These issues often lead to reduced profitability and inefficient resource use. Marketplace optimization uses data, automation, and analytics to improve pricing, advertising, and content performance.
Composite AI: Techniques & Use Cases
Despite significant investments in generative AI, many organizations are still struggling to demonstrate its tangible business impact. In 2024, companies spent an average of $1.9 million on GenAI initiatives, yet fewer than one in three AI leaders say their CEOs are satisfied with the return on those investments.
Autonomous Things: Use Cases with Examples
Autonomous things (often shortened to AuT) are physical devices, such as vehicles, robots, and drones, that use onboard sensors, connectivity, and AI to perceive the physical world and autonomously complete tasks with little or no human direction Explore what autonomous things are and how they operate, their most common use cases with real-life examples, and
17 Computer Vision in Healthcare Use Cases & Examples
Even though Hinton, a Turing award recipient, claimed that radiology would be automated by 2021, such accelerated automation hasn’t occurred.However, AI-driven computer vision in healthcare is still expected to increase precision in surgery, medical imaging, and real-time patient monitoring, while enabling faster and more reliable decision-making.
Content Authenticity: Tools & Use Cases
The increasing prevalence of misinformation, deepfakes, and unauthorized modifications has made content verification important. In the United Kingdom, 75% of adults believe that digitally altered content contributes to the spread of misinformation, underscoring the need for reliable verification methods.
50+ 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. We identified 50+ datasets to train and evaluate machine learning and AI models.
Conversational AI for Sales: Applications & Real-Life Examples
By combining natural language processing, machine learning, and integration with customer data systems, conversational AI tools enable sales teams to handle routine tasks, qualify leads, and engage in personalized conversations with prospects and customers.
Top 10 Agentic AI in Supply Chain Tools & Use Cases
Forecasts suggest that by 2030, half of cross-functional supply chain management solutions will integrate agentic AI capabilities. This widespread adoption will enable global enterprises to reduce exposure to supply chain disruptions and achieve more consistent performance.
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