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 12 Open Source Job Schedulers & 5 WLA Tools
Businesses leverage open source job schedulers and workload automation tools to automate IT tasks without paying licensing costs or getting locked in to a specific vendor.
10 AI Procurement Use Cases & Case Studies
As the benefits of artificial intelligence (AI) are appreciated by a greater audience, the number of AI use cases in different industries expand daily. AI in the procurement sector is no different.
AI Apps with MCP Memory Benchmark & Tutorial
We tested four Model Context Protocol (MCP) memory servers to measure which ones actually retain and retrieve context across AI agent sessions. Using LangChain’s ReAct Agent, we connected each server, ran standardized multi-session conversations, and scored them on memory operation accuracy.
Synthetic Users Explained: Top 7 AI User Research Tools
Traditional user research takes weeks: recruiting participants, scheduling sessions, and manually coding transcripts. Synthetic user platforms compress that timeline to hours by generating AI-driven personas you can interview, survey, and test against without the logistics.
Top 7 Open-Source DLP Software
While open-source DLP software offers viable solutions for data protection, larger enterprises often turn to closed-source DLP software solutions for enhanced centralized key management and cloud-native deployment options. Below are the top five open-source DLP tools, evaluated for detection accuracy, deployment complexity, and community support.
20 Chatbot Companies To Deploy in 2026
With 200+ chatbot platforms on the market, the choice isn’t obvious. The right vendor depends on three things: how your team wants to build (drag-and-drop vs. code), which systems you need to connect to, and how much conversation volume you’re actually handling. We compared the 20 most widely used chatbot platforms for building production applications.
Demilitarized Zone (DMZ): Examples & Architecture
A Demilitarized Zone (DMZ) network is a subnetwork containing an organization’s publicly accessible services. It serves as an exposed point to an untrusted network, often the Internet. DMZs are used across various environments, from home routers to enterprise networks, to isolate public-facing services and protect internal systems.
Generative AI ERP Systems: 10 Use Cases & Benefits
Enterprise resource planning (ERP) software helps businesses see the process across different departments so they can make smarter decisions faster. Generative AI, alongside technologies like RPA, has the potential to enhance ERP processes.
Top 20 RPA SAP Use Cases & Examples
SAP is one of the oldest and most valuable ERP systems, with ~ €31 billion in revenue. Though an ERP suite offering automation in many areas, most SAP processes are manual and repetitive, such as accounting processes, transaction management, and reporting.
Large Language Model Training
Integrating existing LLMs into enterprise workflows is increasingly common. However, some enterprises develop custom models trained on proprietary data to improve performance for specific tasks. Building and maintaining such models requires significant resources, including specialized AI talent, large training datasets, and computing infrastructure, which can increase costs to millions of dollars.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.