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
Compare Large Vision Models: GPT-4o vs YOLOv8n
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count. To ensure a fair comparison, all images were resized to…
Top 20 Sustainability AI Applications & Examples
By applying generative AI to logistics optimization, demand forecasting, and waste reduction, companies can reduce emissions across their operations beyond the AI systems themselves. Discover sustainability AI applications with real-world examples that leverage AI to build a smarter, more efficient, and more sustainable future. What do researchers say about sustainability and AI? SourceConceptsDetails Artificial Intelligence…
+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. Large Language Models (LLMs) and Agentic AI datasets Dataset / BenchmarkDescriptionFree / PaidLast…
DAST Software Pricing Comparison: Burp Suite, Nessus & More
With over 20 DAST tools on the market, selecting the most suitable one can be challenging given their varying features and pricing options. We’ve compiled publicly available information on vendors’ pricing strategies, making it easy to get an overview and estimate the likely costs you may face. Top DAST software prices VendorsFree TrialPrice InsightVM Rapid7…
Top 15 Data as a Service Companies
Data fuels generative AI and enterprise innovation. Data as a Service (DaaS) is a cloud computing model that provides data on demand to users, usually on a subscription basis. This streamlines data collection and management. See the top data as a service companies and data types they provide, the key features such as data analytics,…
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. Agentic AI frameworks benchmark 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…
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. See benchmark results including real-world performance comparison, costs and execution times: Mobile AI agents performance comparison DroidRun Highest…
Top 5 Vulnerability Scanning Tools
Vulnerability scanning tools identify security weaknesses in networks, applications, and systems. Organizations evaluate these tools based on scanning methods (DAST/IAST/SCA), SIEM integration, deployment options, and pricing. Our analysis covers five vulnerability scanning solutions based on technical capabilities and market presence. Follow the links to see our rationale and a detailed explanation of the tools below:…
CRM AI Systems: Top 5 Vendors and Key Features
We tracked 50+ product announcements from major CRM vendors, analyzed integration launches from Salesforce, HubSpot, and Microsoft, and cross-referenced adoption data from 15+ industry research reports to understand which AI CRM features actually deliver value versus marketing hype. ClickUp dominates search interest with a 60-70% share, well ahead of the field. Pipedrive holds a steady…
Agentic Mesh: The Future of Scalable AI Collaboration
While much has been written about agent architectures, real-world production-grade implementations remain limited. This piece highlights the agentic AI mesh, a concept introduced in a McKinsey report. 58 We will examine the challenges that emerge in production environments and demonstrate how our proposed architecture enables controlled scaling of AI capabilities. Challenges in agentic systems As…
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