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
OCR Benchmark: Text Extraction / Capture Accuracy
OCR accuracy is critical for many document processing tasks and SOTA multi-modal LLMs are now offering an alternative to OCR. We tested leading OCR services to identify their accuracy levels in different document types: 2025 OCR benchmark results Product names were shortened above, their full names are listed below.
Alpha Network is a Waste of 15 Seconds per Day
Alpha Network is a game-like application where the user clicks on a button once a day and receives free coins for 24 hours. Much like Eagle Network and Pi Network it has red flags circling its whitepaper and anonymous creators.
Differential Privacy: How It Works, Benefits & Use Cases
Violating data privacy is costly for organizations due to factors such as diminished reputation or regulatory fines. IBM’s 2022 Cost of a Data Breach report states that the average total cost of a data breach is nearly $4.5 million. However, access to private information is required in building solutions to many important business problems.
Federated Learning: 5 Use Cases & Real Life Examples
McKinsey highlights inaccuracy, cybersecurity threats, and intellectual property infringement as the most significant risks of generative AI adoption.Federated learning addresses these challenges by enhancing accuracy, strengthening security, and protecting IP, all while keeping data private.
43 Back Office Automation Examples: RPA, WLA, AI/ML
Many tasks in IT, HR, and finance are repetitive, data-based, and labor intensive, which makes the back-office a great candidate for automation using AI and RPA, and workload automation. According to research, RPA in the back office can reduce 40% of the cost of employees, providing a quick and tangible ROI to organizations.
Tether USDT is possibly a scam but it can remain valuable
Please read our disclaimer on investment related articles. Tether (USDT) is a stablecoin with a claimed value where 1 USDT equals 1 US dollar. Tether Limited, the centralized authority of USDT, has the ability to print tether and therefore is claiming to print something equivalent to US dollars.
Top 12 Use Cases & Examples of Retail Chatbots
Retail chatbots serve as advanced AI-powered assistants that integrate online and in-store interactions. Modern chatbots utilize multimodal inputs, real-time data, and large language models to deliver personalized shopping experiences, streamline workflows, and enhance consumer satisfaction.
Meta Learning: 7 Techniques & Use Cases
Training and fine-tuning a typical machine learning (ML) model can take weeks and cost thousands. Meta learning helps cut this down by leveraging prior learning experiences to accelerate training, reduce costs, and improve generalization. Explore key meta learning techniques and use cases in fields like healthcare and online learning.
Top 5 Insurance Chatbots with Real-life Use Cases
In 2024, the global insurance industry’s premium income increased by approximately 9%, totaling ~$8 trillion, highlighting strong demand and escalating digital investments. At the same time, the global insurance chatbot market is projected to reach a value of $5238 million by 2033, indicating the rapid growth of chatbots in the insurance industry industry.
12+ Data Augmentation Techniques for Data-Efficient ML
Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity. Data-centric AI/ML development practices such as data augmentation can increase accuracy of machine learning models.
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