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
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In the race to feed AI’s insatiable appetite for training data, model builders are increasingly butting heads with the platforms that host the content they depend on. The latest flashpoint is Reddit’s lawsuit against Perplexity AI, which accuses the company of “industrial-scale” evasion of anti-scraping protections and the indirect harvesting of Reddit posts through search engine caches. The case raises a knotty question: When is public web content a legitimate training resource, and when is it legally and/or ethically off-limits? Responses are arriving from both the marketplace and governments, with emerging startups helping content creators monetize AI-harvested data and Europe advancing the Artificial Intelligence Act, which would require firms to disclose or summarize copyrighted training data. The members of the Senior Executive AI Think Tank bring a practical and experienced perspective to the discussion of what responsible data acquisition should look like. Here, they break down where ethical and legal lines should be drawn and what responsible access must entail for AI developers, and they share insightful tips to help platforms rethink their data-licensing and access-control strategies.
expert panel
As artificial intelligence advances at breakneck speed, the question of trust has become more urgent than ever. How do senior leaders ensure that innovation doesn’t outpace safety—and that every stakeholder, from customers to regulators and employees, retains confidence in rapidly evolving AI systems? Members of the Senior Executive AI Think Tank—a curated group of seasoned AI leaders and ethics experts—are confronting this challenge head-on. With backgrounds at Microsoft, Salesforce, Morgan Stanley and beyond, these executives are uniquely positioned to share practical, real-world strategies for building trust even in regulatory gray areas. And their insights come at a critical moment: A recent global study by KPMG found that only 46% of people worldwide are willing to trust AI systems, despite widespread adoption and optimism about AI’s benefits. That “trust gap” is more than just a perception issue—it’s a barrier to realizing AI’s full business potential. Against this backdrop, the Think Tank’s lessons are not theoretical, but actionable frameworks for leading organizations in a world where regulation lags, public concern mounts and the stakes for getting trust wrong have never been higher.
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As artificial intelligence continues its rapid advance—from foundational models to enterprise-scale deployments—questions about sustainability are taking on new urgency. While much of the discourse has centered on the carbon footprint of data centers and model training, sustainable AI must also address long-term economic, labor and societal impacts: How will value from AI be shared? Who bears the downstream risks? Well-designed systems matter not only for performance, but also for fairness, trust and longevity. The Senior Executive AI Think Tank brings together seasoned experts in machine learning, generative AI and enterprise AI applications who offer deep insight into these challenges and opportunities. Below, they explore what truly sustainable AI looks like—beyond energy metrics—and who should be accountable.
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As enterprises scale their use of artificial intelligence, a subtle but potent risk is emerging: employees increasingly turning to external AI tools without oversight. According to a 2025 report by 1Password, around one in four employees is using unapproved AI technology at work. This kind of “shadow AI” challenges traditional governance, security and alignment frameworks. But should this kind of AI use be banned outright? Or can its use be harnessed to spur innovation and encourage creativity and experimentation? The Senior Executive AI Think Tank—a curated group of senior leaders specializing in machine learning, generative AI and enterprise AI applications—has pooled its collective wisdom to help organizations transform unmanaged AI usage from a hidden threat into a structured lever of innovation, enhancing speed, agility and enterprise alignment.
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As major players like OpenAI, Google, Amazon and Anthropic continue to dominate AI infrastructure, smaller businesses and startups face a growing concern: how to compete in a landscape shaped by centralized compute, model development and vast resources. Major tech firms have invested billions in foundational models and own substantial portions of the infrastructure underlying generative AI. This can make it challenging for smaller companies to not only get off the ground, but get ahead. The Senior Executive AI Think Tank brings together seasoned experts in machine learning, generative AI and enterprise AI applications who believe that smaller firms can still win—in different ways. This article explores their insights on how startups should pivot from trying to match scale to leveraging agility, domain expertise and smarter infrastructure choices.
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The web is no longer just a destination—it’s becoming an intelligent partner. OpenAI’s introduction of Atlas, an “agentic browser” that can see, reason about and act directly on web pages, represents a paradigm shift in how people and organizations interact with information. Instead of manually searching, clicking and compiling data, users will soon be able to instruct AI to handle these tasks autonomously—transforming the browser from a viewing window into a dynamic workspace. The shift comes amid accelerating enterprise adoption of AI assistants. A 2025 report by Prialto found that 64% of executives believe AI has positively impacted their productivity. However, only 26% fully trust the AI tools they use, indicating a reliance on human oversight. Atlas promises to eliminate that friction by merging reasoning and execution directly within the browser. To understand how this evolution could redefine the digital workplace, we turned to the Senior Executive AI Think Tank—a curated group of leaders shaping machine learning, generative AI and enterprise AI adoption. Their insights reveal not just how Atlas may transform software expectations, but also how organizations can prepare for a world where browsers act as autonomous partners rather than passive tools.
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.