
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
10 Open Source Data Labeling Platforms
Data labeling, the process of annotating raw data (such as images, text, or audio), is essential for training ML models to perform tasks like classification and recognition. While pre-built solutions exist, they may not always meet specific needs, making open-source platforms a more flexible and customizable alternative. See the top 10 open-source data labeling tools.
5 Reasons for Data Warehouse Automation
The drive for data-driven business decisions, the exponential increase in business data, diversifying data sources and inflexible legacy data warehousing approaches left enterprises relying on a myriad of data marts, enterprise data warehouses and multiple data management tools. This led to complex data warehouse processes.
Data Versioning: Top 3 Benefits & Best Practices
Companies rely on AI/ML models to make business decisions. Effective AI/ML models require high-quality data to make accurate predictions about future conditions. That’s why data is called the new oil for which successful companies need their own refinery.
Top 20 Open Source Chatbot Frameworks
An open-source chatbot framework provides the source code publicly, allowing anyone to use, modify, customize, and distribute it freely. We compiled a list of the top 20 open-source chatbot platforms while highlighting their key and differentiating features.
A General Guide to Internet of Everything (IoE)
The Internet of Everything (IoE) connects things, data, people, and processes using sensors and communication systems, going beyond just device connectivity to a fully integrated ecosystem. IOE solutions create value on healthcare, smart cities, retail, smart homes and industrial processes areas.
Best 6 Use Cases & Benefits of Emotional Chatbots
Emotional chatbots can recognize, interpret, and respond to human emotions during conversations. A use case-focused approach is critical to optimizing the value of emotional AI investments. We identify use cases and real-life examples of emotional chatbots, illustrating their application in healthcare, B2B transactions, governmental entities, banking, retail, and customer support services.
Zero-Knowledge Proofs: How it Works & Use Cases
As businesses collect a vast amount of customer data to gain insights, improve their products and services, and monetize their data assets, they can become vulnerable to cyber threats and data breaches. The cost of breaches is rising every year, reaching ~$4.2M per breach.
15 Best Open Source Web Crawlers: Python, Java, & JavaScript Options
Web crawlers are specialized bots designed to navigate websites and extract data automatically and at scale. Instead of building these complex tools from scratch, developers can leverage open-source crawlers. These freely available and modifiable solutions provide a powerful foundation for creating scalable and highly customized data extraction pipelines.
Types of Virtual Reality & Use Cases
The virtual reality (VR) market has grown significantly, increasing from $6 billion in 2020 to $21 billion in 2025. The COVID-19 pandemic had a notable impact on VR usage, with 71% of users reporting they spent more time using VR during that period. Businesses have also accelerated their adoption of VR technologies.
Top 10+ Emotional AI Examples & Use Cases
The emotion detection and recognition (EDR) market is estimated to reach at ~$50 Bn in 2024, and is expected to reach ~$173 Bn by 2031. Emotion detection and recognition rely on emotion AI to identify, process, and simulate human feelings and emotions.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.