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
SAP Process Orchestration: Top Solutions, Features, Integrations
We examine the core aspects of SAP PO, understanding its functionalities, benefits, and applications in the modern business landscape.
Top 10 Conversational AI Platforms
Enterprises need scalable conversational customization to affordably delight their customers and manage a high volume of queries. With over 200 conversational AI platforms available, choosing the right one can be challenging.
Top 7 Sentiment Analysis Challenges
Words are the most powerful tools to express our thoughts, opinions, intentions, desires, or preferences. However, the complexity of human languages constitutes a challenge for AI methods that work with natural languages, such as sentiment analysis. Explore sentiment analysis challenges and ways to improve sentiment analysis accuracy: Top 7 challenges in sentiment analysis 1.
Web Scraping vs Data Mining: Why the Confusion?
Web scraping and data mining are sometimes confused with each other because they are both linked to extracting value from something that is valuable only when processed. However, the definitions are quite different, and not understanding the difference can cause not realizing how these processes can create value for businesses.
Top 30 Workload Automation Case Studies
In this article, 30 workload automation case studies are compiled.
50 RPA Statistics from Surveys: Market, Adoption & Future
RPA has gained popularity since the 2000s due to quick implementation and digital transformation.
Alpha Network is a Waste of 15 Seconds per Day
Alpha Network is a game-like application where the user clicks on a button once a day and receives free coins for 24 hours. Much like Eagle Network and Pi Network it has red flags circling its whitepaper and anonymous creators.
UiPath vs IBM RPA: 14 Features Compared
A comparison of IBM RPA and UiPath will be conducted across 14 categories. To minimize biases and ensure a user-driven benchmarking of the features, the winner of each category will be determined by the RPA platform with the highest number of positive user reviews.
Top 9 RPA Use Cases & Examples in Finance
We focus on RPA use cases in finance, such as automated record-keeping and financial control. Some of those use cases are: Why is RPA important in finance? RPA’s usage is growing in the finance department because it is effective in handling repetitive, mundane, back-office tasks.
Python RPA: 8 Benefits for Developers
The intersection of robotic process automation (RPA) and Python can revolutionize the intelligent automation landscape. Even though RPA software bots are useful across a wide range of industries, between 30% and 50% of RPA projects fail.
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