GenAI Capabilities Marketers Should Demand From Their Tech Partners

As generative AI becomes a staple in marketing technology, the conversation is shifting. Marketers are no longer asking whether a platform uses AI—they expect that it will. What they’re asking is what that AI actually does. The hype cycle is full of vague promises and clever interfaces, but what matters is whether the intelligence driving the tech is built to solve real marketing problems. It’s time to get more specific—not just about what GenAI is, but about what it should deliver.

The tricky part? It’s all still so new. Most marketers haven’t had the chance to fully define what to expect from GenAI capabilities—or even what to ask their vendors. And because so many tools are racing to check the AI box, it’s hard to separate real capability from surface-level flash.

But there are a few foundational things your tech partners must be thinking about—regardless of whether you’re focused on retail media, paid search, social, or broader campaign strategy. These aren’t wishlist features. They’re the minimum requirements for AI that’s actually built for marketers.

What you need is a clear understanding of the three critical capabilities GenAI should deliver:

• Speed to insight for faster decision-making
• Leverage of your data for positive outcomes
• Productivity to focus on high-impact work

What are the right questions to ask your vendor to figure out whether their AI is truly ready to support your team?

How can GenAI make you faster when the window to act is short?

• In fast-moving commerce media, GenAI has to shrink the gap between signal and action so teams can respond to changing demand, competition, and inventory before performance suffers.
• GenAI that makes you faster quickly surfaces emerging trends, explains what’s happening, and proposes channel-aware fixes you can apply while campaigns are still in flight.
• For time-pressed marketing teams, the most valuable GenAI capabilities continuously monitor performance, prioritize issues, and turn complex data streams into clear, ready-to-use recommendations instead of after-the-fact reports.

Speed isn’t just nice to have—it’s the difference between leading the category and reacting too late. Especially in performance channels where trends shift midweek, competition moves fast, and campaign windows are short.

Most marketing teams don’t have the time (or capacity) to monitor every signal. So when ad spend drops or product demand spikes, the delay in response costs you. GenAI capabilities should close that gap. It should catch the change, understand why it’s happening, and suggest a fix before the results show up in your weekly recap.

According to a 2024 McKinsey study, companies that move quickly to operationalize GenAI are already seeing measurable gains in speed and decision quality, with a small group of high performers pulling ahead on value realization.

And it has to be grounded in your real-world complexity. Retailers behave differently. Social trends change quickly. Paid search is its own animal. GenAI that only reports on “what’s happening” isn’t helping—it needs to help you act while it still matters. That’s why many brands unify channel signals through an integrated retail media solutions hub and paid search platform so GenAI can see performance holistically instead of in silos.

Questions to ask vendors:

• How fast does your AI deliver insights I can act on—minutes, hours, or days?
• Can it surface emerging trends before performance dips?
• Does it help me adjust campaigns in flight, across channels?

Red flags – If the vendor’s AI only delivers insights after the performance has already dropped, it’s not fast enough to support real-time decision-making. If it treats all platforms the same and can’t account for channel-specific behavior, its recommendations will miss the mark. And if it requires your team to interpret vague outputs just to know what to do next, it’s adding friction, not reducing it.

How should GenAI connect the dots between insights and real business outcomes?

• Marketers don’t just need more AI-generated charts; they need GenAI that understands revenue, margin, and lifetime value so every recommendation ladders up to real business goals.
• The strongest GenAI capabilities translate fragmented performance signals into outcome-focused guidance, like where to shift budget, which SKUs to prioritize, or how to protect profitability across channels.
  When GenAI is wired to outcomes, it stops at “insights” only as a midpoint and pushes all the way through to suggested actions, expected impact, and clear explanations stakeholders can trust.
• It’s easy to build GenAI that summarizes data. It’s a lot harder to build GenAI that helps you make better decisions.

Too many tools stop at “insights.” They tell you what happened, but not what to do about it. And they rarely understand the full context of your goals: channel mix, category norms, seasonality, or what performance actually needs to look like in order to move the business.

If the tool can’t help you shift budget intelligently, prioritize what matters, or spot soft areas before they hurt your goals, it’s not helping you be strategic – it’s just another report.

Effective GenAI should be able to map data to outcomes: not just tell you that ROAS is dropping, but also determine why, suggest the adjustment, and project the impact. For example, recent research from Forrester in 2024 found that nearly two-thirds of AI decision-makers are increasing GenAI investment specifically to drive measurable business outcomes, not just experimentation.

Questions to ask vendors:

• How does your AI map insights to business outcomes?
• Can it tell me where to shift spend to maximize ROI?
• Does it surface specific actions I can take to improve results?

Red flags – If the vendor’s AI gives you generic insights that could apply to any brand, it’s not grounded in your goals or data. If it can’t prioritize or explain how a recommendation ties back to revenue, it’s not built for business outcomes—it’s built for optics. And if you find yourself doing the strategy work manually after getting “insightful” summaries, their AI isn’t solving the right problem.

How should GenAI take work off your plate instead of giving you more to manage?

• The real test of GenAI in marketing is whether your team gets meaningful time back to focus on strategy, planning, and stakeholder alignment.
• GenAI that reduces workload doesn’t just chat about performance; it automates the tedious, repeatable tasks that currently burn hours—reporting, issue detection, recaps, and SKU-level triage.
• When GenAI is embedded in your workflows, it acts like an expert teammate: spotting problems, drafting updates, and proposing changes so you can spend less time pulling data and more time making decisions.

Marketing teams don’t need another dashboard. They need time back. The kind that comes from replacing tedious manual tasks with clear, usable, and reliable output.

GenAI should help you do the work, not just talk about it. That means identifying issues, generating recaps, writing campaign updates, summarizing performance shifts, and surfacing problem SKUs before you spend the weekend troubleshooting.

And it needs to meet marketers where they are. That means natural language prompts, intuitive UI/UX, and insights that make sense without a technical background. Whether you’re running a QBR or adjusting bids across thousands of SKUs, the AI should be an extra set of expert hands—not another tool to babysit. Nielsen’s 2025 global marketing survey notes that marketers increasingly see AI as a way to streamline daily workflows and make their efforts “far more impactful” by automating operational tasks

Questions to ask vendors:

• What manual work does your GenAI capabilities meaningfully eliminate?
• Can it generate summaries, recommendations, or outlines without heavy lifting from my team?
• What parts of the workflow does it actually streamline?

Red flags – If the vendor’s AI still requires you to export data and clean it manually, it’s not automation—it’s a disguised to-do list. If it takes multiple prompts or workarounds just to get usable insights, it’s not intuitive—it’s inefficient. And if adoption across your team is slow or inconsistent, that’s usually a signal the AI isn’t built to work the way marketers actually operate.

How is Celeste AI delivering on these three pillars?

• For brands and agencies managing complex commerce media programs, Celeste AI shows what it looks like when GenAI is purpose-built for marketing rather than bolted on as a feature.
• Celeste AI combines GenAI reasoning with channel-specific intelligence so it can explain performance, recommend actions, and help execute changes across retail media, search, and social.
• By living inside the same platform that runs your campaigns, Celeste AI keeps recommendations grounded in real data, real constraints, and the real levers your team actually controls.

Everything above? It’s already live.

Celeste AI, Skai’s first-of-its-kind generative AI marketing agent for commerce media, was built specifically for brands and agencies navigating the complexity of retail media, paid search, and social. It’s not a bolt-on chatbot or a generic prompt layer—it’s trained on commerce-specific data and designed to integrate directly into marketing workflows. .

Celeste doesn’t just generate summaries. It delivers real, actionable guidance: budget reallocations, keyword recommendations, SKU-level insights, anomaly detection, and channel-specific strategy prompts. It benchmarks across brands and retailers, detects issues before they spiral, and recommends what to do next—clearly and in plain language.

What makes it useful isn’t just the intelligence. It’s the integration. Celeste is there when you need it, helping you make smarter decisions, faster. Whether you’re optimizing today or planning next quarter, it’s built to reduce the noise and increase the impact. This aligns with recent Gartner research showing that CMOs expect advances in AI to dramatically reshape their role and outcomes over the next two years, but only when AI is embedded deeply enough to influence everyday decisions.

Because GenAI capabilities shouldn’t just describe the work—it should help you win it.