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Cem Dilmegani

Cem Dilmegani

Principal Analyst
360 Articles
Stay up-to-date on B2B Tech
Cem has been the principal analyst at AIMultiple for almost a decade.

Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, Washington Post, global firms like HPE, NGOs like World Economic Forum and supranational organizations like European Commission. [1], [2], [3], [4], [5]

Professional experience & achievements

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. [6], [7]

Research interests

Cem's work focuses on how enterprises can leverage new technologies in AI, agentic AI, cybersecurity (including network security, application security) and data including web data.

Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and the tech team support Cem in designing, running and evaluating benchmarks.

Education

He graduated as a computer engineer from Bogazici University in 2007. During his engineering degree, he studied machine learning at a time when it was commonly called "data mining" and most neural networks had a few hidden layers.

He holds an MBA degree from Columbia Business School in 2012.

Cem is fluent in English and Turkish. He is at an advanced level in German and beginner level in French.

External publications

Media, conference & other event presentations

Sources

  1. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
  2. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
  3. Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
  4. Science, Research and Innovation Performance of the EU, European Commission.
  5. EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
  6. Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
  7. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

Latest Articles from Cem

Enterprise SoftwareFeb 16

eCommerce Technologies Use Cases & Examples

The eCommerce sector continues to expand by ~10% each year as more consumers shift their purchasing habits online and seek faster and more convenient digital experiences.This growth is also accompanied by increasing competition, making it essential for businesses to understand how technology is shaping customer expectations.

DataFeb 16

Top 20+ Synthetic Data Use Cases

Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. It is estimated that synthetic data will be preferred over real data in AI models by 2030.

DataFeb 16

Web Scraping Roadmap in 2026: Insights from 30M Requests

We crawled more than 30 million web pages using more than 50 products from 6 leading web data infrastructure companies. Our goal was to determine which solutions truly handle the complexities of enterprise-level scraping.

Enterprise SoftwareFeb 16

Top RPA Tools / Vendors & Their Features

Based on our experience with RPA software during our RPA benchmark as well as external market presence metrics like number of reviews and employees, we selected the leading and emerging RPA providers.

AIFeb 16

Embedding Models: OpenAI vs Gemini vs Cohere

The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever. We benchmarked 11 leading text embedding models, including those from OpenAI, Gemini, Cohere, Snowflake, AWS, Mistral, and Voyage AI, using ~500,000 Amazon reviews.

Enterprise SoftwareFeb 15

Top 10+ SAP Workload Automation Tools & Use Cases in 2026

According to SAP Corporate Fact Sheet, 99 of the top 100 global organizations use its ERP software, representing 87% of worldwide commerce. Many of these companies also run non‑SAP systems alongside SAP, which can create inefficiencies in workload management.

Enterprise SoftwareFeb 15

Redwood SAP Partnership in '26: Clean core support for SAP

With over 30 years of experience in automation, Redwood has worked alongside SAP to support a wide range of enterprise automation initiatives. Redwood SAP partnership has supported numerous enterprise automation projects. See the services that SAP users can access through Redwood, the history of this partnership, and the dynamics of their collaboration.

Enterprise SoftwareFeb 13

Best 9 Network Monitoring Tools in Windows

We tested network monitoring tools designed for Windows environments to evaluate their performance in real-world deployments. Our goal was to find solutions balancing reliability, usability, and deep integration with Microsoft systems. Protocol Support: Tools needed to handle multiple communication protocols: SNMP, TCP, ICMP, and IPMI.

DataFeb 12

Geonode Proxies 2026: Pricing and Performance

We reviewed Geonode and its top competitors, including Bright Data, Decodo, Webshare, and Oxylabs. Decide whether Geonode is a suitable solution for your specific proxy use cases based on its features and pricing. Geonode alternatives comparison Geonode overview Geonode specializes in residential proxies and datacenter proxies. The proxy service provider offers two residential package types: unlimited and pay-as-you-go.

AIFeb 11

Large Multimodal Models (LMMs) vs LLMs

We evaluated the performance of Large Multimodal Models (LMMs) in financial reasoning tasks using a carefully selected dataset. By analyzing a subset of high-quality financial samples, we assess the models’ capabilities in processing and reasoning with multimodal data in the financial domain. The methodology section provides detailed insights into the dataset and evaluation framework employed.