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 enterprises can leverage new technologies in AI, agentic AI, cybersecurity (including network security, application security) and data including web data.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
- Answers to Korea24's questions on job loss due to AI, 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
Automated Data Collection Tools & Use Cases in 2026
Automated data collection uses systems to gather, process, and analyze information efficiently. Since automated data comes from multiple sources in various formats, understanding the different types and their origins is essential to implementing it effectively.
Workload Automation vs RPA: Differences to Know
Workload automation (WLA) and robotic process automation (RPA) are valuable technologies for businesses’ automation infrastructure. Both technologies reduce the number of manual tasks in an organization by automating repetitive processes. Both have benefits such as decreasing human error and cost, increasing efficiency, and creating transparency.
Azure Logic Apps in 2026: 7 Real-Life Use Cases
Businesses face integration challenges across legacy and cloud systems. Azure Logic Apps offers an integrated platform for AI‑driven automation and intelligent orchestration, including multi‑agent workflows and connection to Azure AI services such as the Foundry Agent Service. As Azure Logic Apps offer various services, users may get confused about which one to use and when.
Informatica Scheduling: Features, Limitations & Use Cases
Informatica Scheduling is a workload automation/job scheduling feature within the data management platform Informatica PowerCenter. To help you leverage its capabilities, we explore Informatica Scheduling in detail below, focusing on its key functionalities, comparative advantages, limitations, and practical applications in workload automation.
Compare Top 7 Meter-to-Cash Solutions
Utilities must accurately track energy consumption, generate bills, and collect payments across millions of customers. However, fragmented systems, legacy infrastructure, and complex billing processes can make the meter-to-cash (M2C) cycle inefficient and error-prone.
Oracle Cloud Infrastructure (OCI) Scheduler Features & Alternatives
Compare the top Oracle Cloud Infrastructure (OCI) schedulers ranked by integration capabilities, features, and pricing.
Workload Automation Security in 2026: Best Practices & Examples
Businesses must secure workload automation at every level. The following sections outline key risks, best practices for securing automation environments, and real-world examples that highlight the importance of robust security.
XR/AR in Manufacturing in 2026: 7 Real-Life Use Cases
Recent industry research shows that XR device shipments (e.g. lightweight AR or smart glasses for industrial use) grew over 40 % year‑over‑year in 2025. With adoption accelerating, XR technologies are increasingly helping manufacturers improve efficiency, safety, and collaboration. Explore the top 7 use cases of XR/AR in manufacturing with real-world examples.
7 Ways SAP Job Scheduling Optimizes IT Operations
SAP environments are increasingly built on SAP S/4HANA and extended via SAP Business Technology Platform (BTP), shifting job scheduling from on-premise background processing to hybrid and cloud-native orchestration. This complexity requires SAP job schedulers to manage SAP workloads, APIs, event streams and external cloud services in real-time.
Compare 7 Python Job Scheduling Methods
Python job scheduling enables you to execute tasks automatically at specific times or intervals, thereby reducing manual effort and enhancing reliability. Here are the various job scheduling methods in Python, ranging from simple to advanced solutions, along with their advantages and disadvantages: Top Python Job Scheduling Methods 1.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.