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
What is Model Drift? Types & 4 Ways to Overcome
Based on my 2 decades of experience helping enterprises adopt advanced analytics solutions, model drift is the largest reason for production model performance declines. Businesses are able to move only a small share of their AI models to production. And then within 1-2 years, performance of most models deteriorate due to model drift.
Sentiment Analysis: Steps & Challenges
Sentiment analysis is growing in popularity as it turns raw, unstructured text data into interpretable insights for business through sentiment analysis. However, tangible use cases for sentiment analysis and the fundamental steps of this method may not be clear.
Web Scraping vs Data Mining: Why the Confusion?
Web scraping and data mining are sometimes confused with each other because they are both linked to extracting value from something that is valuable only when processed. However, the definitions are quite different, and not understanding the difference can cause not realizing how these processes can create value for businesses.
Top 3 Web Scraping Use Cases for Marketing
Web scraping enables many decision-making processes to become more data-driven. Especially in marketing, web scraping has numerous applications, but the variety of use cases can also make it more challenging to gauge which ones apply for your business and would be more beneficial.
Ethical & Compliant Web Data Benchmark
As enterprises scale their web data operations, compliance, data, and risk executives increasingly evaluate the associated ethical, reputational, and legal risks. We benchmarked 5 leading web data collection services across 3 dimensions and tested each service with more than 20 potentially unethical scenarios.
Top 10 Open Source Job Schedulers & WLA Tools
Businesses leverage open-source job schedulers and workload automation tools to automate IT tasks without paying licensing costs or getting locked in to a specific vendor.
Digital Transformation for Telecoms with Case Studies
The telecommunication or telecom sector is a ~$1.5 trillion market that makes communication possible worldwide. As remote work becomes more widespread, consumer needs continuously change in the telecom sector, demanding better services from telecom service providers. Like every other sector, the telecommunications sector can also benefit from digital transformation to improve its services.
Top 5 Technologies Improving Insurance Fraud Detection
According to the FBI, insurance fraud (excluding health insurance) costs more than $40 billion annually in the U.S. alone.(Insurance Fraud. FBI. Accessed: February/11/2025.) Technological tools such as artificial intelligence (AI), the Internet of Things (IoT), machine learning, and blockchain can be used by insurers to more effectively detect and prevent insurance fraud.
Top 20+ AI Chip Makers: NVIDIA & Its Competitors
Based on our experience in assessing GPUs during efficiency, concurrency and multi-GPU benchmarks covering 24 models, we identified the top AI hardware companies for data center workloads.
Audio Annotation
A subset of data annotation, audio annotation, is a critical technique for building well-performing natural language processing (NLP) models. These models offer numerous benefits to organizations, including analyzing text, speeding up customer responses, and recognizing human emotions. In this article, we take a deep dive into audio annotation to understand its importance for businesses.
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