How sellers are looking to AI to drive smarter, faster decisions
AI is quickly becoming part of the engine behind modern media, but for sell-side teams, the real story is just taking shape. From forecasting demand to optimizing yield, innovation is accelerating quickly, but speed doesn’t always equal alignment. As adoption grows, a more important question emerges: What tools do media sellers actually need, and how should AI fit into the way their businesses operate?
While innovation is happening quickly, implementation is more complex. The real opportunity needs to go beyond what AI can do to how it connects into existing workflows, supports better decisions, and scales across an increasingly fragmented ecosystem.
Smarter, faster decisions, with human oversight
AI’s potential on the sell-side is real, but expectations remain grounded. According to a recent study, more than half of media sellers (53%) believe AI will make TV advertising better, yet fewer see a fully autonomous future as a given, with only 36% saying autonomous media buying is inevitable. Instead, sellers are focused on something more immediate and practical: using AI to make better decisions, faster.
Across responses, a clear pattern emerges. Media sellers overwhelmingly see AI as a tool for commercial optimization, with nearly half (46%) focused on improving pricing, performance, and overall revenue outcomes. Media sellers also see AI playing a key role in market intelligence (24%) and campaign audience intelligence (19%), signaling growing demand for improved prospecting and more precise targeting capabilities.
Ultimately, AI is seen less as a replacement and more as augmentation, a decision-support layer that strengthens human strategy rather than replaces it.
If you could ask AI one question to instantly improve your selling process, what would that be?1
Themes grouped from open ended responses
Tools to better monetize inventory
Where AI becomes especially powerful is with tools that help bring automated intelligence to inventory management. Sellers are managing many different variables at a time and navigating constant trade-offs that can impact revenue and yield. They are looking for solutions that can help augment inventory signals across all demand channels to improve decision making in a holistic way that maximizes value.
That’s where agentic AI stands out. Rather than optimizing a single outcome, agents can continuously evaluate demand, weigh competing priorities, and dynamically allocate inventory. It’s about efficiency and better orchestration of complex systems.
That aligns directly with what sellers say they need most. When asked which AI capabilities would help increase yield and maximize revenue, the top responses were:1
- Inventory allocation and prioritization (60%)
- Automated classification and tagging (55%)
- AI-assisted pricing strategies (53%)
- Audience value scoring (48%)
- Packaging and bundling recommendations (48%)
Vendors are ready and eager to build solutions for seller agents, but these solutions must be compatible to integrate within their current systems.
Solutions that can work easily with existing workflows
Sellers know that AI can help deliver value, but the real challenge is whether it can fit into the way organizations already operate. According to Deloitte, who surveyed AI leaders, the number one challenge organizations face with adopting agentic AI is in legacy system integration.2 Similarly, a HubSpot report found that 1 in 3 marketers cite integration challenges with existing or legacy systems to be a barrier to AI tool adoption.3
Integration continues to be one of the biggest obstacles to scaling AI. At the same time, it’s also a requirement. On the buy side, 66% of advertisers already use AI planning tools within their existing platforms, not as standalone solutions. AI only works if it works with everything else.4
AI will need industry collaboration to succeed
As AI becomes more embedded in media workflows, two concepts move from technical considerations to business imperatives:
- Interoperability: Can systems work together seamlessly?
- Extensibility: Can they evolve as needs change?
These two pieces directly influence how quickly teams can experiment, adopt new capabilities, and scale what works. Without them, even the most advanced AI solutions risk becoming siloed, adding complexity instead of reducing it. With them, AI becomes a connected layer that enhances the entire ecosystem.
If the future of sell-side AI is agentic decisioning, interconnected workflows, and continuous optimization, then the infrastructure behind it needs to support that reality. For AI to truly succeed, the industry needs to work together to make it easier to integrate new capabilities without heavy lift, connect partners into existing workflows, and to test, iterate, and scale innovation quickly.
This is where the industry is moving – from standalone tools to ecosystems that enable innovation at the edges while maintaining control at the core. Those partners placing a growing emphasis on open, interoperable platforms where innovation is shared will be key in driving this next phase of AI. Because success won’t come from having the most tools – it will come from having the most connected ones.
To see how FreeWheel connects partners and AI tools to drive smarter, more unified outcomes, visit the FreeWheel Partner site.
Sources:
- FreeWheel survey of marketers, agencies, and media sellers conducted by AdExchanger, April 2026, media sellers n=50.
- Deloitte article, AI trends 2025: Adoption barriers and updated predictions, September 15, 2025.
- HubSpot, “AI Trends for Marketers, 2025.” September 16, 2025. Retrieved from eMarketer.
- IAB, State of Data 2026. Base: Planning roles (n=225), Analytics roles (n=205).
