
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
TinyML(EdgeAI): Machine Learning at the Edge
Applications of edge analytics transforming industries and the edge computing market is expected to reach ~$350 by 2027. However, the current approach to edge analytics involves machine learning models trained on the cloud. This introduces latency to the system and is prone to privacy issues.
Top 20 Manufacturing Analytics Case Studies
High maintenance costs, unexpected downtimes, and inefficient processes continue to challenge manufacturers. To stay competitive, companies are leveraging manufacturing analytics to optimize operations and enhance asset performance. Explore the top 20 real-world case studies where manufacturers used analytics insights to cut costs, reduce unplanned downtime, and boost productivity.
Compare 45+ MLOps Tools
Machine Learning Operations (MLOps) brings DevOps principles into machine learning to simplify workflows from model development to deployment and maintenance.
Wu Dao 3.0: China's Version of GPT
In July 2023, the Beijing Academy of Artificial Intelligence (BAAI) unveiled Wu Dao 3.0, the successor to their previous AI system. This new iteration takes a different approach, focusing on helping startups and smaller companies build their own AI applications without sacrificing performance.
How to Build a Chatbot: Components & Architecture
Chatbots are communication channels that enable businesses to be available for their customers 24/7. They serve various purposes across different industries, such as answering frequently asked questions, engaging with customers, and providing deeper insights into customer needs. Understanding chatbots’ underlying architecture is essential to reaping the most benefits.
Top 30+ NLP Use Cases with Real-life Examples
Natural Language Processing (NLP) has become a driving force behind business transformation across industries. The NLP market is projected to hit $53.42 billion by the end of 2025, growing at a remarkable annual rate of 24.76% to reach $201.49 billion by 2031, as companies increasingly look for competitive edges through these technologies.
What is Homomorphic Encryption? Benefits & Challenges
The increasing usage of cloud services and collaboration between companies to monetize data raises concerns over data privacy. Regulations such as the General Data Protection Regulations (GDPR) aim to protect consumers’ privacy, and businesses pay serious fines in case of non-compliance. This creates a tradeoff between data privacy and utility for companies.
Scraping Financial Data Without Coding
While official financial data providers do offer APIs, these are often limited in scope, access, or flexibility especially for real-time or niche data needs. As a result, financial data scraping has become a common approach to collecting such information, typically using technologies like web scrapers, headless browsers, and HTML parsers.
Web Scraping for Recruiters: Top Tools & Techniques
Bright Data’s Data collector automatically extracts publicly available data from LinkedIn for recruiters.
6 Web Scraping Challenges & Practical Solutions
Web scraping, the process of extracting required data from web sources, is an essential tool; however, it is a technique fraught with challenges. See below the most common web scraping challenges and practical solutions to address them.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.