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Cem Dilmegani

Cem Dilmegani

Principal Analyst
706 Articles
Stay up-to-date on B2B Tech

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

Conference & other event presentations

Sources

  1. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
  2. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
  3. Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
  4. Science, Research and Innovation Performance of the EU, European Commission.
  5. EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
  6. Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
  7. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

Latest Articles from Cem

AIJul 24

AI Fail: 4 Root Causes & Real-life Examples

Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.

DataApr 4

Web Scraping for Machine Learning: From HTML to ML

~54.7 billion people around the world have been recorded to use the internet, creating 1.7MB of data every second. Crawling this exponentially growing volume of data could provide many opportunities for breakthroughs in data science.

AIJun 18

Synthetic Data for Computer Vision: Benefits & Examples

Advancements in deep learning techniques have paved the way for successful computer vision and image recognition applications in fields such as automotive, healthcare, and security. Computers that can derive meaningful information from visual data enable numerous applications such as self-driving cars and highly accurate detection of diseases.

AIJul 9

Bot As A Service (Baas): Definition & Platforms

Technology as a service delivers tech benefits to businesses without on-premise tools or long-term investments. BaaS providers let businesses use chatbots or RPA bots on a pay-as-you-go basis, avoiding licensing and extensive training. Bot-as-a-Service (BaaS) has been gaining popularity, and businesses that adapt will have increasing advantages over those that don’t.

Enterprise SoftwareJul 9

Top 10+ Workload Automation Use Cases

When asked, “Which automation technologies are the key drivers of enterprise digital transformation?” 47% of business and IT leaders identified workload automation (WLA) as their top choice, with robotic process automation (RPA) following closely behind.

Enterprise SoftwareOct 1

RPA Web Scraping: Tips and Techniques

Web scraping is the act of collecting data from websites to understand what information the web pages contain. The extracted data is used in multiple applications such as competitor research, public relations, trading, etc.

DataSep 26

Top 20+ Synthetic Data Use Cases

Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. It is estimated that synthetic data will be preferred over real data in AI models by 2030.

AIJun 11

Top 5 Travel Chatbots with Use Cases & Examples

A recent survey found that 65% of travel industry leaders see chatbots and virtual assistants as the most significant applications of generative AI. Chatbots act as personal travel assistants, helping customers find flights and hotels, offering budget-friendly options, and recommending tailored packages and promotions.

Enterprise SoftwareJun 16

12 Digital Transformation Trends & Use Cases in Education

The COVID-19 pandemic has accelerated digital transformation in education as nearly 1.5 billion students worldwide became distanced from their classrooms. However, online education is not the only way digital technologies transform the teaching and learning experience. We explore how digital transformation affects education with key technologies and trends.

Enterprise SoftwareApr 3

RPA for Reporting: Guide with 17 Use Cases

Reporting is a vital process in all businesses to gain insights about business finances, growth, employees, and customers. However, reporting is a repetitive and time-consuming task, where 51% of report delivery departments have to deliver the same data several times, and 50% of managers are not satisfied with the speed of delivery.