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 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 13 GAN Use Cases
While GANs pioneered many early generative AI applications, particularly in image synthesis and style transfer, most consumer-facing generative AI tools today rely on diffusion-based architectures or related approaches such as flow matching and diffusion transformers (DiT). However, GANs remain important in specific domains, such as super-resolution, face restoration, the generation of synthetic tabular or healthcare…
Top 20 Email Server Software: Features & Pricing
There are two main use cases for email servers. If you are looking for: Sorting: The list ranks providers, with sponsored entries shown first along with their respective links. All non-sponsored providers are listed in order of the total number of B2B user reviews collected from G2 and Capterra. Pricing details and feature information were…
AI Video Pricing: Compare Synthesia & Invideo AI
AI video pricing can differ significantly across platforms, influenced by factors such as output quality, customization options, and features. As more businesses and creators turn to AI for efficient video production, understanding these pricing models becomes essential. Dive in for a detailed comparison of the top AI video tools, highlighting what each service provides at…
+100 Datasets for ML & AI Models
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified over 100 datasets to train and evaluate machine learning and AI models. This category includes datasets and benchmarks designed for training and evaluating advanced language…
Top 20+ Predictions from Experts on AI Job Loss
As a McKinsey consultant, I helped enterprises adopt new technologies for a decade. My quick answers: Note: The size of the plots is correlated with the size of the job loss prediction. The percentages referenced in our analysis are derived from assumptions about overall job displacement. In specific scenarios, these assumptions included potential job gains…
Top 10 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. 10-20% of AI proofs of concept progress to full deployment.61 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. As AI becomes more central to business strategy, infrastructure…
LLM Latency Benchmark by Use Cases in 2026
We benchmarked 11 top large language models with a total of 1,320 requests, splitting reasoning and non-reasoning models, and measured first-token latency, per-token latency, and overall response time. You can find details on how we measured latency here. We report reasoning and non-reasoning models separately. Reasoning models spend several seconds thinking before the first visible…
Top LLMOps Tools & Compare them to MLOPs
LLMOps platforms handle the operational side of running large language models: deployment, monitoring, evaluation, and cost management. We examined top LLMOps tools, their core features, pricing models, and how they differ from each other to help identify the best fit for various use cases. A breakdown of each metric is provided below: LLMOps platforms support…
Top 5 Open-Source Agentic AI Frameworks in 2026
We benchmarked 4 popular open-source agentic frameworks across 2,000 runs (5 tasks, 100 runs each per framework), measuring end-to-end latency, token consumption, and architectural differences. We examined how the frameworks themselves influence agent behavior and the resulting impact on latency and token consumption. LangGraph is the fastest framework with the lowest latency values across all…
Agentic Mesh: The Future of Scalable AI Collaboration
Theoretical discussions around AI agents dominate the market, but production implementations lag behind. McKinsey introduced the agentic mesh to solve this gap, yet real-world deployments still hit severe bottlenecks in integration, agent isolation, and operational reliability.89 Below, we analyze these live failure points and demonstrate how an event-driven mesh architecture enables controlled scaling at enterprise…
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