Performance marketers are being asked to hand algorithms the keys to campaign execution while simultaneously proving that every dollar delivers measurable business value.

That tension is real. But the solution isn’t to fight automation or surrender full control. The path forward lies in getting crystal clear about the business outcomes you want AI to optimize for — and identifying where human oversight remains non-negotiable.

This balancing act took center stage during the September MarTech Conference panel featuring Maria Corcoran, manager of performance media at Jiffy.com; Anthony Tedesco, global performance media lead at Cisco Systems; and Jiaxi Zhu, head of analytics at Google, moderated by Christina Inge, CEO of Thoughtlight.

AI makes clear objectives more important, not less

Giving up granular control over campaigns feels uncomfortable when you’re accountable for the bottom line.

Corcoran noted that adapting to AI requires looking beyond basic campaign settings. Marketers need to ensure AI truly understands how the target audience, product catalog, website, and surrounding content connect.

Zhu emphasized that clear prioritization is key. Algorithms cannot maximize every metric simultaneously without trade-offs. The critical step is defining the primary objective.

“As long as you’re meeting that goal,” Zhu noted, whether it was achieved “with AI or not with AI” becomes a secondary question.

Tedesco highlighted a common hurdle: the final conversion event isn’t always the best signal for training an algorithm. Enterprise B2B companies care about pipeline and revenue, but those events often occur too infrequently to feed data-hungry AI models. The sweet spot is identifying proxy signals that happen often enough to train the system while keeping campaigns aligned with high-value business outcomes.

Know when to let the algorithm take control

There are areas where delegating to AI makes strategic sense.

For Tedesco, real-time bidding is a prime example. An algorithm evaluates thousands of contextual signals during a search auction far faster than any human operator can. Creative asset assembly is another — models can quickly test and identify which combination of copy and visuals resonates best with a specific user segment.

However, Corcoran’s experience with Google Performance Max shows why automation still requires close monitoring.

Jiffy.com operates four distinct business lines. During a Performance Max campaign test, the tool delivered a strong overall ROI, but allocated budget disproportionately toward a single business line — one that hadn’t funded the initiative.

That test provided a valuable strategic lesson: Jiffy’s site architecture wasn’t clearly differentiating its service lines for AI models. What started as a campaign trial became an opportunity to improve how the brand’s digital infrastructure communicates with automated systems.

Don’t throw out the metrics that still matter

As search evolves, measuring brand presence within AI-generated summaries is becoming essential. Tedesco shared that Cisco monitors AI visibility metrics to track how large language models interpret and cite their content.

At the same time, core performance metrics remain foundational.

“You don’t necessarily need to reinvent the wheel,” Tedesco noted. Traditional funnel metrics continue to serve as reliable anchors even as new signals are introduced.

Corcoran relies on core B2C metrics like LTV:CAC and cost per acquisition, using AI to streamline cross-channel data analysis, uncover attribution discrepancies, and evaluate how influencer or user-generated content impacts performance.

AI doesn’t require abandoning proven metrics—it gives you clearer visibility into what drives them.

AI’s immediate payoff may be operational

Beyond campaign management, AI’s most immediate value lies in eliminating repetitive admin work.

Zhu pointed out that AI significantly reduces friction in foundational analysis, troubleshooting, and campaign setup, freeing teams to focus on strategy and cross-functional leadership.

Tedesco pointed to ad trafficking — a rules-based task that can be transformed into a streamlined, push-button process. He also sees massive potential in self-service analytics.

While custom data joins previously required SQL expertise or weeks of waiting on analytics queues, natural-language AI tools now surface those insights in minutes.

Corcoran uses tools like Claude to unify financial data, ad metrics, site analytics, and sales data into cohesive reports. Her primary goal was practical: eliminating three hours of daily reporting tasks.

By leveraging AI to handle complex tasks like running n-grams or correlation analyses, performance marketers can expand their analytical capabilities without needing a data science degree.

Don’t confuse AI adoption with AI success

Industry adoption is still in its early stages. A conference poll revealed that 58% of attendees are experimenting with AI for performance analysis, 23% are exploring potential use cases, and just 11% have fully implemented the technology into their workflows.

Zhu cautioned against measuring success solely by tool adoption or platform log-ins. The real indicator of success is whether AI applications improve actual business outcomes. Establishing clear benchmarks before testing ensures you scale only what works.

Give AI guardrails, not unlimited control

Managing automation comes down to setting boundaries.

“You have to find that balance of automation and autonomy that makes sense for your business,” Tedesco advised.

Think of campaign architecture as creating guardrails. Establishing a clean data taxonomy gives AI systems the structure they need to generate accurate insights and execute workflows reliably.

Corcoran recommends taking a measured approach to live system integrations, opting to use AI extensively for background analytics before giving automated tools direct access to active, market-facing budgets.

AI isn’t replacing the discipline of performance marketing; it’s raising the bar. The core mission remains unchanged: reaching the right audience, delivering meaningful messaging, and driving business growth. AI processes data faster, but marketers set the direction, validate the data, and define the boundaries.

The most important strategic question isn’t just how AI changes campaign management—it’s how AI changes the way customers interact with your business. That is where sustainable growth begins.

Watch the September 2026 Martech Conference for free, on demand.

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