
Cem Dilmegani
Cem has been the principal analyst at AIMultiple for almost a decade.
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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.
Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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 (September 28, 2017). 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
Conference & other event presentations
- 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 (March 10, 2023): 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 18 Web Scraping Applications & Use Cases
We have explained what a web crawler is and why web scraping is crucial for companies that rely on data-driven decision-making. Web scraping is important because regardless of industry, the web contains information that can provide actionable insights for businesses to gain an advantage over competitors.
Roadmap to Web Scraping: Methods & Tools
Data is critical for business and internet is a large data source including insights about vendors, products, services, or customers. Businesses still have difficulty automatically collecting data from numerous sources, especially the internet. Web scraping enables businesses to automatically extract public data from websites using web scraping tools.
Web Crawler: What It Is, How It Works & Applications
Have you ever wondered how search engines such as Google and Bing collect all the data they present in their search results? It is because search engines index all the pages in their archives so that they can return the most relevant results based on queries. Web crawlers enable search engines to handle this process.
AI in Government: Examples & Challenges
Governments worldwide are investing in AI to improve efficiency and service delivery. However, scaling AI initiatives presents challenges, from ethical concerns to bureaucratic resistance. Explore AI in government applications, best practices, and real-world examples.
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.
IoT Implementation Steps & Best Practices
We analyzed insights from industry leaders, IoT experts, and real-world case studies to provide a structured approach to IoT implementation and best practices. Successful IoT deployment requires a clear strategy, starting with defining business objectives, selecting the right technology stack, ensuring security, and integrating IoT with enterprise systems.
10 AP AI Applications in Accounts Payable Processes
We’ve written about accounts payable automation and invoice automation before, where we highlighted that AP processes can be mostly automated and shared criteria to select the right vendor.
Quantum Computing Stats: Forecasts & Facts & Beyond
Quantum computing is an emerging field that has the potential to fundamentally change computing and build smarter machines. To understand the potential of quantum computing (QC), feel free to read our research on QC and its applications.
Few-Shot Learning: Methods & Applications
Imagine a healthcare startup developing an AI system to detect rare diseases, but there’s a problem: there’s not enough labeled data to train a traditional machine learning model. This is where few-shot learning (FSL) comes in.
Top 7 RPA Linux Software to Discover
Most robotic process automation (RPA) bot development is going on in Windows machines since most corporate end users rely on Windows. However, the Linux operating system has particular advantages over MacOS and Windows, and RPA tools can add value to Linux systems as well.
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