Cem Dilmegani
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 enterprise AI and software.Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and the 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, 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.
Media, conference & other event presentations
- Hosted on American Variety Radio, answering Court Lewis' questions on AGI on April 25th, 2026.
- Answered Korea24's questions on job loss due to AI on November 5th, 2025. Recording available on: Korea24
- 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: 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 10 Google Colab Alternatives
Google Colaboratory is a popular platform for data scientists and machine learning scientists, but its limitations and pricing may not meet your needs. Several alternatives offer unique features and capabilities that cater to different data science needs and scenarios.
Top 20 Sustainability AI Applications & Examples
According to PwC, GenAI could improve operational efficiency, which might indirectly reduce carbon footprints in business processes. By applying generative AI to areas such as logistics optimization, demand forecasting, and waste reduction, companies can reduce emissions across their operations beyond the AI systems themselves.
Recommendation Systems: Applications and Examples
Recommendation systems benefit both businesses and customers by using data to personalize experiences. They help boost sales, increase customer loyalty, and reduce churn by simplifying choices and keeping users engaged. We benchmarked three Python recommendation libraries: LightFM, Cornac BPR, and TensorFlow Recommenders, using the same implicit-feedback dataset and identical preprocessing steps.
Mobile AI Agents Tested Across 65 Real-World Tasks
We spent 3 days benchmarking four mobile AI agents (DroidRun, Mobile-Agent, AutoDroid, and AppAgent) across 65 real-world tasks using an Android emulator with applications such as calendar management, contact creation, photo capture, audio recording, and file operations.
Top 9 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
Top LLMOps Tools & Compare them to MLOPs
The rapid adoption of large language models has outpaced the operational frameworks needed to manage them efficiently. Enterprises increasingly struggle with high development costs, complex pipelines, and limited visibility into model performance.
Compare 9 Large Language Models in Healthcare
We benchmarked 9 LLMs using the MedQA dataset, a graduate-level clinical exam benchmark derived from USMLE questions. Each model answered the same multiple-choice clinical scenarios using a standardized prompt, enabling direct comparison of accuracy. We also recorded latency per question by dividing total runtime by the number of MedQA items completed.
10+ Agentic AI Trends and Examples for 2026
We reviewed and compared Agentic AI trends from several major industry reports, benchmarks, and vendor disclosures. The sources point out that the future of agentic AI isn’t just about improving tools or streamlining business workflows. It’s about integrating AI deeply and transforming business approaches by restructuring current frameworks.
17 Generative AI Healthcare Use Cases
Healthcare systems are facing increased data volumes, staff shortages, and rising expectations for personalized care. Generative AI is emerging as a key solution by synthesizing unstructured medical data, such as clinical notes, imaging reports, and patient histories, into insights for clinicians and administrators.
GPU Software for AI: CUDA vs. ROCm in 2026
Raw hardware specifications tell only half the story in GPU computing. To measure real-world AI performance, we ran 52 distinct tests comparing AMD’s MI300X with NVIDIA’s H100, H200, and B200 across multi-GPU and high-concurrency scenarios.
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