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 15 Accounting AI Agents
Tools like Dext, AutoEntry, and Hubdoc have automated data extraction and transaction posting. But these systems are fundamentally still rule-based, often requiring accountants to jump between spreadsheets. Thus, after reviewing the documentation and watching demos, we picked the top 15 AI-based accounting agents: These web-based ERP frameworks can be used for bookkeeping and integrated with…
Pros & Cons of Top 7 RPA Alternatives to Consider
Robotic Process Automation (RPA) is a beneficial technology that can automate up to 70-80% of rules-based processes.5 However, ~40% of companies fail to reach their expectations of cost reduction after RPA implementation.6 This is because RPA is not the right fit for every process, and there are potential pitfalls of RPA implementation, such as costly…
DGX Spark vs Mac Studio & Halo: Benchmarks & Alternatives
NVIDIA’s DGX Spark entered the desktop AI market in 2025 at $4,699, positioning itself as a “desktop AI supercomputer”. It packs 128GB of unified memory and promises one petaflop of FP4 AI performance in a Mac Mini-sized chassis. See the benchmark results on value and performance compared to alternatives: When comparing systems on the demanding…
GPU Software for AI: CUDA vs. ROCm in 2026
Raw hardware specifications tell 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. While AMD’s MI300X boasts 1,307 TFLOPS compared to NVIDIA’s H100/H200 at 990 TFLOPS, a 32% theoretical advantage, real-world performance is a…
Top 8 Open Source AI Coding Agents
In prior evaluations, we benchmarked both open-source and proprietary Agentic CLIs, focusing on their performance in web development tasks, and some open-source agents performed as successfully as the paid options. Therefore, we also listed the top open-source coding agents for users with privacy concerns. For methodology, see the AI coding benchmark. For more details about…
Compare 10 Python Job Scheduling Methods
Python job scheduling automates tasks at specific intervals to enforce strict execution timelines, catch runtime exceptions, and ensure system state consistency without manual intervention. Here are the various job scheduling methods in Python along with their advantages and disadvantages: Cron is a built-in Unix/Linux scheduler that executes scripts at specific times. It is useful for…
Top 25+ Synthetic Data Use Cases
Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. It is estimated that synthetic data will be preferred over real data in AI models by 2030.40 We listed the capabilities…
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex
We benchmarked 5 RAG frameworks: LangChain, LangGraph, LlamaIndex, Haystack, and DSPy, by building the same agentic RAG workflow with standardized components: identical models (GPT-4.1-mini), embeddings (BGE-small), retriever (Qdrant), and tools (Tavily web search). This isolates each framework’s true overhead and token efficiency. The benchmark consisted of 100 queries, with each framework running the full set…
Python RPA: 8 Use-Cases for Developers
Robotic process automation (RPA) lets software robots run repetitive computer tasks. Python gives developers a flexible way to build those robots in code. The global RPA market sits at about USD 28 billion in 2025 and is forecast to reach roughly USD 247 billion by 2035.45 Yet between 30% and 50% of RPA projects fail,…
Data Loss Prevention (DLP): Types & 6 Challenges
The increased mobility introduces risks of data loss or theft, which can lead to severe financial losses and reputational damage for companies. Effective Data loss prevention (DLP) software needs to prevent the unauthorized movement of private data and personally identifiable information (PII) to limit reputational and financial risk. Explore DLP fundamentals, challenges organizations face when…
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