
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
12 Blockchain Case Studies Across Key Industries
A recent forecast projects the blockchain market will reach 943 billion U.S. dollars by 2032, growing at a CAGR of 56%.While the potential is massive, executives face uncertainty due to the varying maturity of blockchain solutions across industries.
Top 13 Supply Chain AI Use Cases with Examples
The global supply chain management market is projected to reach nearly $31 billion by 2026, reflecting its growing importance across various industries.Yet despite this rise, recent disruptions, such as the COVID-19 pandemic and ongoing geopolitical tensions, have exposed deep vulnerabilities in supply chains, resulting in costly delays and operational inefficiencies.
Cloud Workload Automation: Top Software & Use Cases
Businesses are increasing their flexibility while managing costs by adopting a hybrid cloud strategy. According to Statista, industries have increased their cloud workloads and had an uptick as a response to the COVID-19 pandemic.
Responsible AI: 4 Principles & Best Practices
AI and machine learning are revolutionizing industries, with 90% of commercial apps expected to use AI by 2025 as AI statistics shows. Despite this, 65% of risk leaders feel unprepared to manage AI-related risks effectively.
Top 6 Radiology AI Use Cases for Improved Diagnostics
Radiology teams are under pressure from growing scan volumes, staff burnout, and the risk of diagnostic mistakes. These challenges are making it harder to deliver timely and accurate care. AI is helping to ease the burden by accelerating image analysis, minimizing errors, and facilitating more informed decisions.
Top 5 Computer Vision Automotive Use Cases & Examples
Automotive firms face rising safety, cost, and efficiency pressures. Computer vision helps address these challenges by enabling automation, quality control, and accident prevention. Explore the top 5 computer vision automotive use cases that business leaders can leverage to stay competitive.
23 Healthcare AI Use Cases with Examples
Healthcare systems are under growing pressure from rising patient data and demand for personalized care. A recent study found that 154 of 290 hospital referral regions (53%) experienced workload imbalances, highlighting the strain on resources and the need for more efficient solutions.
Top 15 Use Cases of RPA in the Automotive Sector
Robotic Process Automation (RPA) streamlines repetitive tasks, freeing employees for strategic work. Widely adopted across industries like education, retail, and manufacturing, RPA is now making significant impacts in automotive. It enhances order management, logistics, production, and telematics, boosting efficiency and precision. See how RPA reshapes automotive workflows: Order processing 1.
Top 15 Computer Vision Use Cases with Examples
With the global computer vision market projected to reach US$30 billion in 2025 (see the graph below), business leaders face a critical challenge: identifying where it delivers ROI, from healthcare diagnostics to automated logistics.
10 Computer Vision Agriculture Use Cases & Examples
Labor shortages, resource inefficiencies, and environmental pressures increasingly challenge agriculture. Climate change adds to this burden through extreme weather, water scarcity, and rising pest threats, further straining productivity and sustainability. Computer vision offers targeted solutions by enabling automation and data-driven insights across critical farming operations.
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