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
Generative AI in Fashion: Top 10 Use Cases & Examples
89% of all companies across different sectors are switching to digital technologies, and the generative AI in fashion industry is not an exception. McKinsey reports that in 2021, fashion brands and companies invested approximately 1.7% of their income in emerging technologies. Moreover, they estimate the figure will rise between 3.0% and 3.5% by 2030.
Workload Automation Security: 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.
Top 5 Stonebranch Alternatives
Stonebranch is a SaaS workload automation solution with a wide range of community-driven pre-packaged integrations. However, it has alternatives that may suit specific users. See our rationale for selecting each tool by following the links below: ~30 WLA tools exist in the workload automation software landscape.
Top 5 Alternatives to Tenable Nessus : Features & Comparison
Several notable options are available in the DAST and vulnerability scanning tools market. We selected the top alternatives to Tenable Nessus based on our research and DAST benchmark.
Generative AI in Retail: Use Cases, Examples & Benefits
Retail businesses strive to enhance customer experiences and loyalty. This requires producing attractive content in various formats, effective marketing efforts, and exceptional customer service. With generative AI, retailers can resolve most of these issues through automation, particularly by enhancing their ability to analyze customer data for more personalized customer experiences.
Azure Logic Apps: 7 Use Cases & Real-life Examples
Businesses face integration challenges across legacy and cloud systems. Azure Logic Apps offers workflow automation to reduce manual effort and system silos. As Azure Logic Apps offer various services, users may get confused about which one to use and when.
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 6 Open Source Sensitive Data Discovery Tools
Based on features and user experiences shared in review platforms, here are the top 6 open-source sensitive data discovery tools: Administrative features Feature descriptions: These functionality (especially data lineage and search capabilities) allow businesses to: Data security features Feature descriptions: Categories and GitHub stars Tool selection & sorting: DataHub DataHub is an open-source unified sensitive
Cloud Inference: 3 Powerful Reasons to Use
Deep learning models achieve high accuracy in tasks like speech recognition and image classification, often surpassing human performance. However, they require large training datasets and significant computational power. Cloud inference provides a scalable solution to handle these demands efficiently. Explore cloud inference, compare it to on-device inference, and highlight its benefits and challenges.
Large Action Models: Hype or Real?
Following the launch of Rabbit, an AI device that can use mobile apps, the term large action models (LAMs) is getting popular. These models move beyond conversation by turning LLMs into “agents” that can connect the siloed, app-driven world without requiring a user to click on apps or integrate an API.
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