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
RPA in Food Industry: Top 15 Use Cases
When we think of restaurants or the food industry in general, this is what normally comes into mind: The food and beverage industry relies on efficient operations both in the kitchen and behind the scenes.
Top 3 Amazon Mechanical Turk Alternatives
This analysis explores some downsides to using Amazon Mechanical Turk, or MTurk, a popular AI data collection and market survey platform. It also compares the top Amazon Mechanical Turk alternatives on the market. Readers interested in MTurk alternatives usually fall under 3 categories; select yours to see relevant alternatives for your business.
Top 3 Appen Alternatives for Workers & Customers
Appen, an AI data service provider, faces challenges that may explain its declining popularity. We compared the top alternatives to Appen in the AI training data space. The alternatives to Appen depend on your goals. Explore alternatives for Appen’s: Appen alternatives for workers * Data is from Trustpilot, as it primarily consists of worker reviews.
Traditional vs. Online Survey Research
Conducting survey research helps businesses collect data from customers, employees, or the public. Collecting data with traditional methods, such as paper-pencil or telephone, is costly, time-consuming, and cannot keep up with the digitally transforming world. Thanks to online survey research tools, businesses can quickly reach a broad audience’s opinion and make necessary adjustments.
Audio Data Collection for AI: Challenges & Best Practices
As the demand for voice recognition and virtual assistants grows , so does the need for audio data collection services. You can also work with an audio or speech data collection service to acquire relevant training data for your speech processing projects.
Sentiment Analysis Machine Learning: Approaches & 5 Examples
It is not surprising that the use of AI in the workplace has increased by 270% from 2015 to 2019, considering the data available and its exponential growth.
Video Data Collection: Challenges & Best Practices
Video data is crucial for training computer vision (CV) systems, particularly with the increasing demand for autonomous vehicles and CV-enabled technologies. Here, we explore what video data collection entails, the challenges involved, and best practices to consider.
Image Data Collection with Best Practices
Computer vision (CV) is revolutionizing industries, from autonomous vehicles to healthcare, but success depends critically on the collection of high-quality image data. Organizations that implement strategic data collection services can achieve higher accuracy in specialized applications, while poor data strategies lead to biased models and compliance violations.
Top +15 API Statistics for Understanding API Landscape
APIs can integrate with new technologies such as IoT and chatbots. We cover 17 API-related statistics to provide a comprehensive picture of the trends and landscape of APIs. General API statistics Figure 1. Growth of different data types Source: Cloudflare as the main factors behind adopting APIs in their organization( see Figure 2).
Ethical & Legal AI Data Collection
Disruptive technologies, such as AI, ML, the Internet of Things (IoT), and computer vision, require various types of data to operate. This data often includes biometric data, such as facial images and voice recordings. Collecting and managing such data requires multiple ethical and legal considerations, which, if disregarded, can lead to expensive lawsuits and significant reputational damage.
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