About
I believe technology should empower people rather than complicate their lives. This belief guides my work as I create products that help marketers manage their data effortlessly. At Improvado, I lead innovative projects that centralize marketing data without requiring developers' assistance. It's rewarding to see leading brands like ASUS and General Electric trust our platform—this reinforces my passion for simplifying complex tasks. Throughout my career, I’ve driven transformative initiatives that deliver measurable results. For example, as Product Director at Improvado, I led the development of an AI Revenue Agent that transformed raw data into actionable insights, enhancing customer lifetime value by 35%. This project streamlined decision-making across departments, underscoring my commitment to impactful solutions. In my current role as Vice President of Products, I spearheaded a shift from a traditional reporting platform to a self-serve ETL solution. This change empowered marketers to manage data pipelines independently, reducing time-to-insight by 50% and improving data accessibility for non-technical users. Simplifying complex workflows and enabling teams to focus on strategy continues to drive my work. Beyond my professional life, I mentor startups at Astana Hub and advise innovators at Berkeley SkyDeck. Sharing insights on scaling businesses and leveraging AI fuels my enthusiasm for fostering innovation in the tech industry.
Roman Vinogradov
Published content

expert panel
As enterprise AI adoption accelerates, so too does the complexity of choosing the right foundation. Should companies invest in proprietary platforms like GPT-4 or Claude, or build on open-source models such as Meta’s Llama or Mistral? The answer increasingly lies not in technical specs alone, but in how each option aligns with an organization’s cost structure, data governance needs and long-term innovation strategy. Recent research from McKinsey & Company underscores the growing momentum behind open systems: Over 50% of enterprises already report using open-source AI tools across their technology stack, and 76% expect to increase usage in the coming years. At the same time, proprietary platforms offer speed, reliability and white-glove scalability—often the shortest path to business impact. The trade-offs are real and consequential. To help executive decision-makers navigate these choices, we turned to members of the Senior Executive AI Think Tank—a group of enterprise AI, machine learning and innovation leaders who are shaping the way organizations operationalize artificial intelligence. In the sections below, they break down the pros and cons of each approach and offer actionable guidance on when to build, when to buy and how to orchestrate the right AI model strategy for your organization’s evolving needs.

expert panel
AI‑native startups are scaling faster than ever—some hitting milestones that traditional SaaS firms took years to reach. But things are starting to move even faster. A recent analysis by Stripe suggests AI startups reach $1 million in revenue in about 11.5 months compared with 15 months for the earlier top SaaS models. That velocity comes not just from better algorithms but from a fundamentally different organizational posture. Meanwhile, many legacy firms are still navigating the early stages of adoption—pilots, governance debates, technical debt struggles—and too often fall short of meaningful impact. According to Boston Consulting Group, 74% of companies struggle to derive value from AI, with just 26% achieving scale beyond proof of concept. The Senior Executive AI Think Tank brings together leaders immersed in machine learning, generative AI and enterprise AI applications. Their collective wisdom reveals that competing with AI challengers demands more than tech upgrades—it requires deep structural and cultural shifts. In this article, they explore those shifts and offer actionable strategies for traditional organizations to close the gap.

expert panel
The recent release of TIME’s 2025 TIME100 AI list underscores how much attention is focused on foundation models, generative agents and consumer‑facing AI tools. Yet a closer look suggests that many powerful AI applications are still flying under the radar. That’s where the Senior Executive AI Think Tank comes in—a curated group of experts in machine learning, generative AI and enterprise AI applications who combine technical depth with executive perspective. In this article, they use real-world insight to examine which industries and use cases are underrepresented in lists like TIME’s and explore the biggest AI frontiers that deserve attention now.

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The FDA’s new generative AI tool, Elsa, could signal the start of AI-native government operations—streamlining scientific reviews, increasing public transparency, and reshaping how trust is earned in digital-era governance. But as Elsa ushers in new efficiencies, AI leaders warn: Success depends on human oversight, ethical frameworks, and explainable systems.

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Digg’s relaunch brings generative AI into the heart of content moderation, aiming to scale oversight across online communities. But can AI manage trust, context, and nuance without human judgment? Members of the AI Think Tank weigh in on what this move means for the future of digital community governance.

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AI Think Tank members explore how OpenAI’s upcoming “super-assistants” could impact daily habits, reduce mental load and enhance productivity for consumers everywhere.
Company details
Improvado
Company bio
Improvado's AI Agent is a sophisticated tool designed to enhance marketing analytics through advanced automation and intelligence. It offers features such as Campaign Intelligence, providing deep insights into campaign performance, and Automated Data Analysis, ensuring accurate processing of marketing data. The AI Agent also generates comprehensive metadata for Snowflake data, enhancing usability for analytics and reporting. Additionally, it ensures high data quality and compliance through advanced data profiling, and facilitates data activation by transforming and routing data back into operational tools for actionable insights.













