Cem Dilmegani
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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.
Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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
- Cem Dilmegani (September 28, 2017). Post-AI Banking: Millions of jobs at risk as banks automate their core functions. International Banker
- Cem Dilmegani, Bengi Korkmaz, and Martin Lundqvist (December 1, 2014).Public-sector digitization: The trillion-dollar challenge.McKinsey & Company
Conference & other event presentations
- Real Estate and Technology, presented by Hofstra University’s Wilbur F. Breslin Center for Real Estate Studies and the Frank G. Zarb School of Business in 2023 and 2024.
- Radar AI session (June 22, 2023): "Increasing Data Science Impact with ChatGPT".
- Generative AI Atlanta meetup (March 10, 2023): Generative AI for Enterprise Technology.
Sources
- Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
- Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
- Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
- Science, Research and Innovation Performance of the EU, European Commission.
- EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
- Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
- We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.
Latest Articles from Cem
Geonode Proxies: Features, Pricing and Reviews
Companies use proxy providers to maintain anonymity and collect web Companies use proxy providers to maintain anonymity and collect web data. Geonode is a proxy provider with an extensive range of residential proxies. However, it does not provide static residential proxies (ISP) and mobile proxies and has proxy performance issues.
Top 5+ Geonode Alternatives: Pricing & Features
Proxy services are essential for companies to use technologies such as web scraping, ad verification, and market research. We reviewed Geonode and its top competitors including Bright Data, Webshare, Oxylabs, Decodo, SOAX, and IPRoyal.
Top 5 RLHF Platforms: Guide & Features Comparison
As AI adoption grows, with 65% of organizations now regularly using generative AI, selecting the right tools for optimizing AI models has become more crucial than ever. Reinforcement learning from human feedback (RLHF) platforms have emerged as key players in this process.
Top 7 Meter-to-Cash Solutions: A Comprehensive Guide
In the utilities sector, the meter-to-cash process is essential for revenue generation and efficient operations. It involves numerous intricate steps and systems. Meter-to-cash solutions have emerged as an indispensable tool for utility providers. Selecting the right vendor for meter-to-cash solutions can be difficult since there is little information about them.
LLM Data Guide & 6 Methods of Collection
In the expanding AI and generative AI market (Figure 1), large language models (LLMs) have emerged as pivotal. These models empower machines to generate human-like content, heavily reliant on quality data. Here, we present a guide for business leaders on accessing and managing LLM data, offering insights into collection methods and data collection services.
Top 10 Data Crowdsourcing Platforms
With the spread of AI tools like generative AI and chatbots, the demand for AI data services has also increased. One such service is data crowdsourcing platforms, which leverage large groups to gather data, enhancing collection efforts with fast, detailed insights.
Instagram Chatbots: Top 5 Vendors & Use Cases
Instagram is the 2nd-most used social media platform for e-commerce purchases. 44% of “active” Instagrammers use it to shop weekly. Companies across different sectors, such as e-commerce, content creation, organization, and customer service, are looking for Instagram chatbots that offer: We listed top chatbot providers with their features and best practices for optimizing Instagram chatbots.
JDE Scheduler: Pros, Cons & Top 5 Alternatives
The scheduling component of JD Edwards EnterpriseOne, often referred to as “JDE Scheduler,” has long been a reliable task manager. However, as modern infrastructure requirements have changed, so too have the options for task scheduling. We selected the top alternatives based on the features, pricing, and market presence metrics of leading solutions.
AI Data Collection: Risks, Challenges & Tools
AI builders need fresh, high quality data: However, data collection comes with its risks. For example, enterprises need to avoid unethical data collection practices and ensure that data is collected ethically to minimize reputational risk.
Best RAG tools: Frameworks and Libraries
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best. Explore the RAG frameworks and tools, what RAG is, how it works, its benefits, and the current situation in the LLM landscape.
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