A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.
FreeWheel
Shaping the Future of Advertising with AI
Through this article, Bress emphasizes the importance of cross-functional collaboration and data-driven innovation in driving successful AI initiatives within the advertising industry.
Throughout my career, I’ve worked at the intersection of analytics and technology, focusing on building innovative, data-driven solutions that drive business outcomes. Prior to joining FreeWheel, I held leadership roles where I led analytics teams to design scalable data infrastructures that automated decision-making and enhanced business strategies. At FreeWheel, as the VP of Data Science, I lead a team of data scientists, analysts, and engineers. Together, we leverage data to optimize advertising solutions, improve audience targeting, and develop predictive models that empower buyers and sellers of video advertising to maximize their impact. My role also involves overseeing how we strategically apply data science and AI/ML to enhance our product capabilities, ensuring we stay at the forefront of innovation in the ad tech space.
Fostering Cross-Functional Collaboration in AI Initiatives
Cross-functional collaboration is at the heart of successful AI initiatives. At FreeWheel, we’ve developed a culture of open communication between teams. We hold regular meetings and workshops that bring together data scientists, engineers, product managers, and business stakeholders to ensure we’re all aligned on goals and identify new opportunities for AI development. For new ideas and approaches, we look to move quickly on potential solutions to build a business case for future product development. We iterate quickly and collect feedback from stakeholders regularly to ensure we are pushing forward to a solution our clients will value and adopt. This allows us to create AI-driven solutions that are not only technically robust but also aligned with the strategic goals of the company.
The future of advertising and media will be heavily influenced by AI-driven personalization and automation. In the next five years, I expect to see significant advancements in AI-powered content creation and dynamic ad delivery. Advertisers will increasingly rely on AI to understand audience behavior and preferences at a granular level, enabling real-time adaptation of ad content based on user engagement. AI will also help optimize media buying, reducing waste in ad spending by targeting more relevant audiences. Additionally, we’ll see an increased focus on ethical AI practices as regulatory frameworks evolve to ensure transparency and fairness in advertising.
Preparing for an AI-Powered Future
Preparation for an AI-driven future starts with building a strong foundation of data literacy across the organization. It’s critical that not only data scientists but also business leaders understand the value and limitations of AI. We are investing in training programs that help demystify AI for non-technical teams and encourage interdisciplinary learning. Additionally, we need to ensure our data infrastructure is flexible enough to support the rapid advancements in AI. Staying agile and continuously iterating on models will be key to adapting to the evolving landscape.
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The future of advertising and media will be heavily influenced by AI-driven personalization and automation. In the next five years, I expect to see significant advancements in AI-powered content creation and dynamic ad delivery
Contributions to the Data Science Community
I contribute to the data science community by participating in industry conferences, giving talks on the business applications of AI, and mentoring the next generation of data scientists. I enjoy mentoring others in intellectual property development and hold 15 patents at the intersections of advanced analytics and advertising technology. Engaging with the broader community offers fresh perspectives across industries that I can bring back to my team at FreeWheel. This exchange of ideas across industries helps us stay ahead of the curve, particularly in applying cutting-edge ideas related to AI for real-world business problems.
Emerging Opportunities in Data Science
In the advertising industry, the role of data science will continue to grow as AI advances toward more sophisticated personalization, real-time decision-making, and dynamic ad delivery. One of the most exciting opportunities is the increasing integration of AI-driven personalization at scale, where data science will help advertisers deliver more relevant ads to users while respecting consumer privacy.
Over the next decade, I also see great potential in the rise of connected TV (CTV) and the proliferation of streaming platforms, which will require advanced models to analyze cross-platform viewer behaviors and optimize ad placements across different media channels. AI-based technologies will streamline advertising workflows for agencies, advertisers, and publishers. Advertising will become increasingly frictionless. Additionally, approaches like differential privacy and federated learning will become crucial for effective audience targeting.
At FreeWheel, we are preparing by investing in research and technology that focus on these emerging trends.
Advice for Aspiring Data Scientists
First and foremost, build a strong foundation in mathematics and statistics, as these are the core pillars of data science. Even with the capabilities of AI systems today, a trained data scientist will still be needed to interpret results and validate the output of AI systems. Secondly, aspiring data scientists should establish a habit of continuous learning to keep up with the latest developments related to AI and machine learning. As new technologies and co-pilots make themselves available, they can help a data scientist scale their productivity to new levels. I would suggest focusing on building a portfolio of projects that show not only the application of data science concepts but also some impactful real-world results. The best data scientists use a deep understanding of real-world context to solve problems that provide a meaningful impact. Becoming part of a community and contributing through thought leadership, open-source development, or even mentorship can go a long way to keeping you on a path to long-term success.
