In today’s fast-moving media landscape, publishers are grappling with an increasingly fragmented ecosystem. Getting a unified, trusted view of total revenue is more complex than ever, requiring teams to manage dozens of monetization relationships and often resorting to manual spreadsheet work.
To dive deeper into these critical challenges, from navigating data overload to harnessing the power of AI for yield optimization, we sat down with our long-standing partner, Burt Intelligence. This Q&A explores how the Burt and ADvendio integration is actively helping media companies streamline their operations, automate reconciliation, and finally shift their focus from fixing discrepancies to driving real business growth.
The programmatic landscape has become incredibly fragmented over the last few years. From your perspective at Burt, what is the single biggest challenge publishers face today when trying to get a clear picture of their total revenue?
Most publishers today are running dozens of monetization relationships simultaneously – programmatic, direct, curation, audience extension, data licensing, affiliates, social, you name it. The challenge is that every partner reports revenue differently, on different timelines, with different definitions and naming conventions of key metrics like net revenue, gross revenue, billable impressions, etc.
At Burt, we see this data problem as being composed of two sides of the same coin: data complexity and data overload.
By “data complexity,” we mean cardinality, interconnectivity, and dimensionality of the data, as well as the transformation complexity itself. By “data overload,” we mean the volume and velocity of the data, the signal-to-noise ratio, and even just the sheer number of queries you need to run or reports you need to review to successfully operate a digital media business. Many publishers are still trying to stitch this together manually in spreadsheets, which makes it incredibly difficult to get a trusted, unified picture of revenue performance. Burt Intelligence was founded to solve this problem.
Burt and ADvendio have a long-standing integration. How does this partnership specifically help media companies move from just ‘collecting data’ to actually ‘acting on insights’ for their business?
Together, Burt and ADvendio close the gap between raw data and business action.
Burt consolidates and normalizes advertising data from all monetization partners. That data can then be mapped to advertisers, buyers, inventory, deal types, partner accounts, and reporting periods in ADvendio, bringing it directly into commercial and finance workflows. This makes the data useful beyond analytics. It can support reconciliation, revenue attribution, invoicing, campaign records, and client-facing reporting. For publishers, the impact is less manual spreadsheet work, clearer visibility into revenue performance, and faster, more reliable reporting for both internal teams and advertisers.
For a real-world example of this partnership in action, see how Burt and ADvendio helped funda streamline operations and client-facing reporting.
We are seeing AI move from a buzzword to a core operational tool in ad tech. How do you see AI evolving in the next 12–18 months, specifically to help publishers optimize yield and reduce revenue leakage?
Over the next 12 to 18 months, AI in ad tech will move from answering questions to helping teams execute workflows. That shift is already underway.
For publishers, the biggest opportunity is continuous monitoring of revenue and delivery data: identifying anomalies; flagging underdelivery or billing risk; surfacing yield opportunities; and helping teams understand what action to take next. At Burt, we see this as a natural move on our journey from descriptive analytics to prescriptive decision intelligence. The technology has matured to finally allow us to deliver on this promise at scale.
We also expect agentic advertising to develop quickly over the next 12 to 18 months. From our perspective, though, agentic advertising only works if agents have access to trusted, governed data. That is why infrastructure like Burt’s MCP Server matters. However, Sales Agents develop those systems; they will need to understand inventory forecasting, historical performance, and benchmarked pricing if they’re going to respond to Buyer Agent RFPs in commercially sound ways.
The future is not AI replacing revenue, ad ops, or sales teams. It is AI reducing the manual monitoring, reconciliation, and investigation work that slows them down. The publishers who benefit most will be the ones who prepare their data foundation now, so AI can move from “interesting insight” to reliable operational workflow.
Transparency was a major theme in 2025, with buyers and sellers demanding more accountability. How does having an automated, audited data flow, like the one provided by Burt and ADvendio, change the conversation around programmatic integrity?
When data flows are automated and auditable end-to-end, publishers can enter every buyer conversation with confidence. Instead of relying on manually stitched-together reports that may be incomplete or out of date, sellers can present a consistent, verified picture of how a buyer’s campaigns have performed, across channels, deal types, and reporting periods. That kind of transparency builds trust, reduces disputes, and shifts the conversation from reconciliation to strategy. It also creates a clear audit trail that both sides can rely on if questions arise.
Many media teams still spend hours each month manually reconciling spreadsheets. If a publisher could automate that entire workflow today, what does that ‘saved time’ allow their team to focus on instead?
Automating the reporting workflow is ultimately a reinvestment in the parts of the business that drive revenue. Operations teams can shift from being reactive, chasing down data and fixing discrepancies, to being proactive, focusing their time on campaign optimization, yield analysis, and helping sales structure better-performing deals. Account managers get more time with clients, which improves retention. And leadership gets faster access to the insights that actually inform strategy.
It’s a compounding benefit: every hour recovered from manual work is an hour that can go back into growth.
With the rise of new channels like CTV, Retail Media, and DOOH, the ‘data noise’ is only going to get louder. What advice would you give to a media executive who wants to future-proof their tech stack today?
The most important thing media companies can do today is to build for extensibility and data interoperability from the start, not as an afterthought. The biggest mistake publishers can make right now is building their tech stack around individual channels instead of around data interoperability.
New channels will continue to emerge, and every one of them brings different reporting structures, identifiers, metrics, and operational workflows. If each new revenue stream requires another disconnected dashboard or manual reconciliation process, complexity scales faster than the business does.
The publishers that will adapt best are the ones building a flexible data foundation now: centralized, normalized data; clear governance; interoperable systems; and workflows that can evolve as the market changes. That way, when the next channel arrives, and it will, you’re already ready.
