
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
Compare Google Dialogflow and Its Competitors
Tech giants such as Google, IBM, Microsoft, Amazon, and Facebook are investing in conversational AI to enable developers to build chatbots easily. These AI-powered chatbots can automate various routine tasks such as sending emails, searching for information on search engines, etc.
State of OCR: Is it dead or a solved problem?
Optical Character Recognition (OCR) is one of the earliest areas of artificial intelligence research. Today OCR is a relatively mature technology and it is not even called AI anymore which is a good example of Pulitzer Prize winner Douglas Hofstadter’s quote: AI is whatever hasn’t been done yet.
Top 7 AI Avatar Generation Tools
When choosing the right AI avatar generation tool, businesses can take into account the following components: Top 7 AI avatar generation tools The table above is sorted based on the number of reviews. Sources: For more on prices, check the pricing comparison.
Handwriting Recognition Benchmark: LLMs vs OCRs
Today, OCR technology provides higher than 99% accuracy with typed characters in high-quality images. However, the diversity in human writing types, spacing differences, and handwriting irregularities causes less accurate character recognition, as shown in the featured image. Thus, tools that read handwriting cannot provide the same accuracy that OCR systems offer on typed characters.
Top 20 Analytics Case Studies
Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making, and enables the launching of more personalized products.
Insight Engines: Top 3 Real-Life Use Cases & 19 Tools
Enterprise search has been a game-changing technology in terms of increasing the productivity of organizations. Enterprise search engines combined with the power of a modern search engine with the company’s internal data.
40 IoT Applications & Use Cases with Real-Life Examples
Worldwide annual revenue on IoT in 2033 is expected to be $934B. IoT enables a myriad of different business applications. Knowing those IoT examples and use cases can help businesses integrate IoT technologies into their future investment decisions.
Explainable AI (XAI): Guide to enterprise-ready AI
As AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This approach is problematic since it prevents transparency, trust and model understanding. After all, people don’t easily trust a machine’s recommendations that they don’t thoroughly understand.
AI Chips: A Guide to Cost-efficient AI Training & Inference
In the past decade, machine learning, particularly deep neural networks, has been pivotal in the rise of commercial AI applications. Significant advancements in the computational power of modern hardware enabled the successful implementation of deep neural networks in the early 2010s.
Autonomous Things: Use Cases with Examples
Autonomous things (often shortened to AuT) are physical devices, such as vehicles, robots, and drones, that use onboard sensors, connectivity, and AI to perceive the physical world and autonomously complete tasks with little or no human direction Explore what autonomous things are and how they operate, their most common use cases with real-life examples, and
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