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
Tabular Models Benchmark: Performance Across 19 Datasets 2026
We benchmarked 7 widely used tabular learning models to identify top-performing model families across 19 real-world datasets of varying sizes and structures, covering ~260,000 samples and over 250 total features, with dataset sizes ranging from 435 to nearly 49,000 rows. Tabular learning models benchmark results In the chart, the winning model receives 1 point.
Large Language Model Evaluation: 10+ Metrics & Methods
Large Language Model evaluation (i.e. LLM eval) is the multidimensional assessment of large language models (LLMs). Effective evaluation is crucial for selecting and optimizing LLMs. Enterprises have a range of base models and their variations to choose from, but achieving success is uncertain without precise performance measurement.
Agentic LLM Benchmark: Leading Models Compared
We benchmarked the top Large Language Models (LLMs) across 10 software development tasks by using an agentic CLI tool. We executed ~3,500 automated validation steps per model across both API and UI layers. Agentic LLM benchmark results Each alias ran 3 times across 10 tasks (30 samples per alias, 230 cells per iteration).
AI Agent Performance: Success Rates & ROI
Recent research reveals that AI performance follows predictable exponential decay patterns, enabling businesses to forecast capabilities and differentiate between costly failures and successful ROI-generating implementations. I oversaw 12 AIMultiple benchmarks, including nearly 70 AI agents across more than 1,000 tasks.
AI Agent Traps: 20 Real-Life Incidents
AI agent adoption has outpaced AI agent security: 82% of enterprises now deploy agents, but only 44% have policies to secure them, and one in five organizations has already experienced an agent-related breach.
The LLM Evaluation Landscape with Frameworks
Evaluating LLMs requires tools that assess multi-turn reasoning, production performance, and tool usage. We spent 2 days reviewing popular LLM evaluation frameworks that provide structured metrics, logs, and traces to identify how and when a model deviates from expected behavior.
Backup software benchmark: Acronis vs NinjaOne vs Comet vs MSP360
We benchmarked Acronis Cyber Protect Cloud Backup, Comet Backup, MSP360 Managed Backup, and NinjaOne Backup on identical AWS infrastructure. Each vendor ran a file-mode backup of the same 625,946-file / 50 GB workload and a full image backup of the system disk, then restored the 15 GB medium subdirectory.
40 IoT Applications & Use Cases
Worldwide annual revenue on IoT in 2033 is expected to be $934B. IoT enables a myriad of different business applications. Knowing those IoT examples and use cases can help businesses integrate IoT technologies into their future investment decisions.
Agentic AI for Cybersecurity: 10 Use Cases & Examples
Agentic AI refers to AI systems that combine models like large language models (LLMs) with automated workflows, tool integration, and decision support. These systems assist security teams in SecOps and AppSec by analyzing alerts, automating routine tasks, and supporting investigative work. Agentic AI tools generally operate under human oversight.
Top 14 Intrusion Detection and Prevention Tools in 2026
At its core, intrusion detection and prevention systems (IDPS) monitor networks for threats, alert administrators, and prevent potential attacks. We previously explained real-life use cases of AI IPS solutions.
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