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Cem Dilmegani

Cem Dilmegani

Principal Analyst
724 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, 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

Media, 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

Agentic AISep 12

12 Reasons AI Agents Still Aren't Ready

For all the bold promises from tech CEOs about AI agents “joining the workforce” and driving “multi-trillion-dollar opportunities,” the reality is far less inspiring. What we currently have are not autonomous agents, but glorified chatbots dressed in fancy packaging; mostly mimicking scripts. Give them the same task twice, and you’ll often get wildly different results.

Enterprise SoftwareSep 12

Low/No Code RPA Software: Benefits & Best Practices

We analyzed ~80 RPA tools in the market and prioritized those with a low code approach. The below are either market leaders due to metrics like number of reviews and features or provide exceptional pricing.

CybersecuritySep 12

How to Protect Your Business from Website Cloning / Mirroring

Your business may be a mundane B2B business like ours, and you may think that you do not have to protect your website from attacks like cloning. You would be wrong. Eventually, your site may get cloned; it happened to us.

DataSep 12

BI Governance: 6 Implementation Best Practices

The global business intelligence market is projected to be $33.3B by 2025, with more business units adopting BI tools. The importance of business intelligence is increasing. Data-driven decision making, for instance, is five times faster via data access and data analytics.

AISep 12

Top 10 Chatbots in Healthcare: Insights & Use Cases

Healthcare chatbots are AI-powered tools that interact with patients through text or voice conversations. The healthcare chatbot market is projected to reach $1.49 billion in 2025, soaring to an estimated $10.26 billion by 2034. AI-driven chatbots are expected to save the healthcare industry $3.

Enterprise SoftwareSep 11

WLA Migration: Best Practices & Vendor Approaches

Organizations are increasingly reconsidering their existing workload automation tools, with many actively exploring or transitioning to new solutions. Migrating to a newer workload automation system can provide immediate gains in scalability, reliability, and integration capabilities. We define workload automation (WLA) migration, best practices, what to pay attention to, and the differing approaches from different vendors.

DataSep 10

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.

AISep 10

10 Generative AI Supply Chain Use Cases

Artificial intelligence, particularly generative AI, presents new opportunities to address longstanding supply chain challenges. By analyzing large volumes of historical and real-time data, generative AI can produce actionable insights that improve decision-making, efficiency, and resilience. One notable example is Microsoft Dynamics 365 Copilot, an AI-driven assistant integrated into CRM and ERP systems.

Enterprise SoftwareSep 10

30 Intelligent Automation Case Studies / Success Stories

One of the most effective ways to understand how new technology can benefit your organization is through reading case studies of successful implementations.  For this purpose, we have aggregated case studies about intelligent automation from numerous sources. You can filter or sort them by industry (e.g.

Enterprise SoftwareSep 10

Top 25 Use Cases / Examples of Intelligent Automation

Traditional automation approaches can be effective in simple tasks but they often rely on rigid, pre-defined rules and are limited in their ability to adapt to changing circumstances. This can lead to inefficiencies and errors, especially when dealing with complex tasks or data.


Cem Dilmegani | AIMultiple: High Tech Use Cases & Tools to Grow Your Business