
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 8 Use Cases of Sentiment Analysis Marketing
“This was great!” “I had a horrible experience.” The sentiments in these sentences can be inferred from certain words, such as “great” and “horrible”. By analyzing the sentiments of reviews, feedback, and other customer interactions, businesses can improve their marketing campaigns.
RPA Pricing Compared Across Leading Vendors
We reviewed leading RPA pricing in detail to identify the lowest RPA vendor license fees: Calculating the total cost of ownership (TCO) for RPA software is complex because there are various factors that affect RPA pricing, such as: See license costs of market-leading solutions for different scenarios with different numbers and types of licenses: RPA pricing
Top 6 Data Collection Methods for AI and Machine Learning
While some companies rely on AI data collection services, others gather their data using scraping tools or other methods. See the top 6 AI data collection methods and techniques to fuel your AI projects with accurate data: Overview of AI data collection methods 1. Crowdsourcing Online talent platforms, such as crowdsourcing platforms, have various benefits.
Data Collection: 10+ Methods Across 6 Key Use Cases
The role of data has become paramount for digitally transforming enterprises. Whether it’s marketing or AI data collection, businesses have become increasingly reliant on accurate data collection to make informed decisions; it’s essential to have a clear strategy in place. This article explores the top techniques for data collection across different sectors and use cases.
Python Yellow Page Scraper: How to scrape Yellow Pages
Yellow pages provide easy access to a variety of services/businesses, which may not all show up in your Google search. Search engines report results based on relevance to the search term, whereas online yellow pages show results based on geographic areas.
Top 10 Sustainability AI Applications & Examples
According to PwC, GenAI could improve operational efficiency, which might indirectly reduce carbon footprints in business processes. Companies can implement strategies to reduce energy consumption during the development, customization, and inferencing stages of AI models. By leveraging GenAI applications, companies can offset emissions in other areas of their operations.
Top 14 Types of NFTs
Collectors and NFT enthusiasts buy and trade different types of non-fungible tokens, but what is the difference between different types of NFTs? We explain different NFT types and give examples for each. NFT Market Place Market Overview & Metrics Types of NFTs and Use Cases Art 1.
Top 8 Computer Vision Construction Use Cases & Examples
Adopting AI technologies, such as computer vision, is accelerating in the construction industry, offering solutions for safety, efficiency, and quality control. However, challenges like an aging workforce and high-risk tasks continue to hinder widespread implementation. Explore the top 8 use cases with real-life examples where computer vision construction technologies already impact construction sites.
7 Ways SAP Job Scheduling Optimizes IT Operations
Enterprise automation plays a key role in business digitalization. Enterprise Resource Planning (ERP) systems, including SAP, the largest ERP by sales, are at the center of most enterprises’ businesses and are involved in numerous processes.
XRay AI: Definition, Use Cases & Examples
The integration of artificial intelligence (AI) to radiology is not a futuristic vision; it’s already redefining clinical workflows, particularly in X-ray imaging. AI tools are now embedded into imaging systems, enabling real-time decision support and improving workflow efficiency, image quality, and clinical accuracy.
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