
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
Top 12 Use Cases of RPA in Hotels & Hospitality Industry
Around 86% of hoteliers were expected to increase their hotel tech investment. RPA in hospitality industry can support such digital transformation initiatives. According to industry reports, hotels won’t return to pre-COVID financial levels before 2023, if not later. RPA’s capability to automate front office and back office tasks can help the hotel industry navigate such disruptions.
Top 35+ Generative AI Tools by Popularity & Category
At AIMultiple, we constantly use generative AI technologies: We built dozens of custom GPTs and benchmarked the latest AI tools such as AI agents, cloud GPUs, and e-commerce AI video generators.
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.
20+ Online Survey Statistics from Reputable Sources
Online surveying helps companies understand the trends in the market and customers’ needs. That’s why understanding its scope is crucial in implementing it in your research practices.
Top 5 MTurk Survey Participant Recruitment Alternatives
Recruiting participants for consumer or market research can be challenging. Crowdsourcing platforms like Amazon MTurk make it easier to find survey respondents and get quick results. However, potential drawbacks, such as response reliability and data quality, can impact the results.
GraphQL vs. REST: Top 4 Advantages & Disadvantages
API architecture decisions have a significant impact on application performance, developer experience, and long-term maintainability. Two dominant approaches have emerged: REST (Representational State Transfer) and GraphQL, each offering distinct advantages for different use cases.
Automated Data Collection Tools & Use Cases
Automated data collection involves using automated systems to gather, process, and analyze information efficiently. Since automated data is produced from multiple sources and comes in various formats, understanding the different types of data and their origins is crucial for effectively implementing data automation.
State of RPA vs RDA: 4 Main Differences
People might confuse RPA with RDA – perhaps that’s why “RPA vs RDA” is one of the most searched queries on Google (Figure 1). And while these two automation tools have similar-sounding names, and share certain similar properties, they are not the same thing.
Top 10 Data Center Automation Tools & Case Studies
In this article, we will go over seven innovative data center automation tools.
API Regression Testing: Importance & Challenges
Software testers and developers use regression testing to create efficient software programs. Research on the challenges of regression testing shows that it accounts for 80% of the total testing cost, while software testing constitutes 50–60% of the total cost of a project.
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