Agencies have never had more ways to innovate, or more pressure to prove that innovation pays off. A McKinsey study released in June 2026 found that 86% of marketers are excited about what AI makes possible, yet fewer than 10% of organizations have captured real business value from their AI initiatives.
At the Agency Growth Exchange, we sat down with Patrizia Vassallo, Director of Analytics at JUST Global, to talk through the questions agencies are asking right now:
We’ll walk through what we shared on the panel, including a practical framework for agency readiness, a clearer way to think about build-versus-partner decisions, and what we believe will be table stakes within the next 12 months.
When we work with agencies at Adswerve, three signals tell us a lot about how ready a team is to move from experimenting to scaling when it comes to AI solutions and automation.
The first is data maturity. Does the agency have a solid data foundation, and does the team have access to the data points it needs to build solutions that address actual business challenges? Without that foundation, even the most promising AI initiative tends to stall before it delivers.
The second is data fluency across the organization. The technical team doesn't need to be the only group that understands the data. Media planners and account leads need to be able to articulate what they're trying to solve, speak the data well enough to ask the right questions, and spot whether the results make sense against reality.
The third is organizational alignment. Can the agency bring the right stakeholders into the process, build agreement around a solution, and make sure what gets developed can actually be adopted and deliver value across teams? Even a well-built solution only goes so far if it can't gain traction beyond the group that created it.
As Patrizia put it during the panel, JUST Global needed "approved tools, data we trusted, and documented SOPs [when scaling AI capabilities]. If you can't describe how the work gets done today, an agent won't fix it." That squares with what we see at Adswerve every day.
None of these signals are pass-or-fail criteria. An agency that's still building its data foundation isn't disqualified from innovation. But that's where a media and technology partner can step in to bridge the gap and help build the runway.
Once an agency has a clearer picture of its readiness, the next question is where to invest. The temptation is to build everything in-house, but that approach rarely scales, and it often comes with overhead that's hard to justify against the return.
A smarter starting point is an ecosystem audit, which evaluates your team's core strengths, your data assets, the partners already in your orbit, and where the gaps are. When you do look outside, are you choosing collaborators who help you build durable capabilities your team can retain over time, or are you renting another black-box tool?
Marketing mix modeling is a clear example of this dynamic. We regularly hear from agencies that spent a year paying a hefty vendor subscription for a single-client model. By the end of that year, the math didn't add up. For the same investment, they could have partnered with us to own three custom Google Meridian models, with ongoing maintenance covered. The subscription made the entry easy, but it didn't build anything the agency could keep.
Patrizia framed the decision well at the panel, advising, "[We] build when it captures our expertise and we can maintain it. Buy when a product already does it well and the total cost makes sense. [We] partner when the gap is specialist capability or capacity." The question most agencies skip, she added, is who maintains it. "Building is easy. Keeping it accurate six months later is the hard part."
The goal isn't to own every capability in-house. It's knowing what makes your agency distinct, doubling down there, and using partners to extend your reach where the technical depth would be too expensive to build and maintain alone.
Agencies that understand the full economics of an AI investment before they commit tend to make better build-versus-partner decisions and end up with solutions that actually scale. The key is planning beyond day one.
AI shifts where costs sit rather than eliminating them entirely. Manual effort gives way to infrastructure, API usage, token costs, and ongoing maintenance. Knowing that upfront is an advantage, because it means agencies can budget for the full picture and choose architectures that scale cost effectively from the start.
Token costs, for example, add up quickly depending on the type of model an agency uses. Even decisions about how data flows through different systems (real-time versus near-real-time processing, for example) can affect the overall cost dramatically.
These decisions might seem purely technical, but they shape the business case. When an agency plans for total cost of ownership from the beginning, it can build a solution that grows with the client base rather than one that requires renegotiation every time it scales.
A partner who understands those economics upfront helps agencies make better decisions about what to build, how to build it, and whether the investment can scale without requiring proportional increases in headcount or infrastructure. The goal is to invest in AI where the return is clear and where the solution creates room for the agency to grow.
If there's one thing agencies should stop waiting for permission to try, it's using technology to take the friction out of the operating layer from campaign trafficking and tagging to data normalization and reporting. There's an enormous amount of margin and capacity to recover just by making the work happening behind the scenes more efficient.
Looking 12 months out, an AI-enabled execution layer will be table stakes. Whether it's built into the platforms themselves or agencies are developing their own capabilities, more of the execution is going to be automated. The agencies that recognize this early have a chance to redefine where they add the most value.
When execution speed is no longer a differentiator, the value moves to what's harder to automate: human judgment.
At Adswerve, we talk constantly about keeping humans in the loop. As the execution layer gets automated, an agency's people become the true differentiator. The value shifts to human judgment: the strategy, the measurement, and the commercial instinct to know what to activate and why. AI handles the plumbing, while people handle the decisions that move the business.
This shift also opens the door for agencies to rethink how they price and package their work. Not everyone needs to overhaul their commercial model overnight, but agencies that start pricing around business outcomes, rather than time and materials, will be better positioned to capture the value their strategy and judgment actually deliver.
Agency innovation doesn't require a bigger team or a larger technology budget. It requires clarity about what you're good at, honesty about where the gaps are, and the right partners to fill them. At Adswerve, we work with agencies to run the ecosystem audits that surface those gaps, build custom measurement solutions like Meridian marketing mix models, and deliver the engineering depth that lets agencies scale innovation without scaling overhead. If your team is thinking through where to build, where to partner, and how to make AI investment pay off, we'd love to talk.