Looking toward the year ahead, AI experimentation is quickly giving way to widespread AI use. This will be a critical year for advertisers to evolve further by embracing sustainable and scalable AI strategies across their organizations.
This new AI era will benefit advertisers who have integrated comprehensive systems so AI can turn channel, campaign, and real-time performance data into real business impact.
If your company’s questions are changing from "What can AI do?" to "How do we scale AI to its maximum usage?", you’re in the right place. Here are four things you must do to properly scale AI within your advertising operations in 2026 and beyond: make your data AI-ready, break down tool silos, unify your system of record, and pair AI with automation for reliable, autonomous workflows.
1. Make your data AI-ready: clean, structured, and contextualized
2026 will be the year the majority of advertisers take a good, hard look at their data health.
Conceptually, this isn’t a new idea. Digital advertising runs on data. However, in the rush to adopt AI, advertisers risk prioritizing AI adoption over proper data health.
Big mistake.
As AI becomes more integrated into daily AdOps practices, properly prepared data will be the single biggest competitive advantage in advertising. Clean, structured, and well-governed data allows AI to do what it does best: predict and optimize. Setting enterprise-wide standards means data is prepared and ready to use, not just accessible.
Advertisers should also build infrastructures that maintain accuracy and reinforce rigorous data governance, including automated health checks on data syncs.
Beyond data accuracy, though, more advertisers will structure data collection around specific AdOps actions teams take every day. This is what it can look like in practice:
- Improved onboarding: Restructuring client onboarding processes to feed AI models or automated workflows from day one.
- Hands-off reporting: Moving from manual spreadsheet exports to automated, live data streams from multiple channels feeding directly into one place for AI to access.
- Intelligent, on-brand content creation: Standardizing data inputs so AI can instantly generate brand-aligned ad copy, keyword sets, and creative suggestions.
- Live portfolio analyses: Centralizing data into one place so teams can use a singular AI chat interface to analyze performance across accounts and channels, asking questions like "Why did CTR drop?" instead of digging through dashboards.
All of these practices design data for action, ensuring that every AI-generated insight you use to inform your advertising strategies is built on absolute truth.
2. Replace “siloed” AI tools with integrated AI across systems and channels
Advertisers must continue to break down the walls between channels (or workflows) in favor of using AI that supports the entire campaign lifecycle. This means moving away from AI tools that support a specific work stream or channel but don’t share the same data or speak the same language as your other tools.
Why? Because siloed workflows are risky. It’s like hiring a new workforce without training and with no ability to work cross-functionally.
Imagine if AI could see the full picture. AI could analyze performance in one channel, like Google Ads, and immediately use those insights to inform actions or modify copy in another, such as Microsoft Ads or Meta Ads—all without a human manually making the change.
Bringing multichannel data into one place makes this AI dream a reality. Integrated AI solutions, like Muse, have always-on access to real-time data from major ad publishers. Because Muse is embedded directly into the advertising operating system (rather than sitting as a disconnected AI chatbot), it acts as the connective tissue between disparate channels and data.
Integrated AI eliminates the need for manual "copy-paste" workflows by providing a unified layer of intelligence. Instead of your team managing separate, disconnected tools, they can run strategies that take into account the context of your entire portfolio at once.
AI's ability to generate insights and execute strategies across multiple channels simultaneously is finally pushing our industry toward true interoperability. With AI and automation now capable of delivering real cross-channel intelligence, data integration has become a competitive necessity.
3. Unite your systems so AI can be predictable, safe, and valuable
As AI becomes more powerful, the potential for error increases if it isn't managed correctly. The more tools, logins, and systems you use, the more security vulnerabilities you create. Every one-off AI tool is essentially another window left open for a data leak.
This leads to my third recommendation for better AI usage: consolidate your AdOps into a single system of record.
In 2026, more advertisers will centralize their data into a "single source of truth." This means consolidating multichannel performance metrics, creative assets, and strategic guidelines into one unified system instead of scattering them across multiple tools and spreadsheets.
Working out of one system of record for all your advertising has multiple benefits for AI:
- Enhanced security and governance: Centralizing data allows you to control data exchange across platforms within a closed system, minimizing exposure points and security vulnerabilities.
- Improved transparency and collaboration: A unified system creates a bridge between agencies and advertisers where conversations are transparent, organized, and secure.
- Reduced AI errors and strategic drift: Grounding AI in centralized brand rules and historical performance data minimizes hallucinations and keeps strategies aligned with your business objectives (automation can help reinforce these rules, too!).
- Safe AI deployment at scale: Teams can leverage advanced AI capabilities without concerns about data egress or privacy violations, since all intelligence operates within your secure ecosystem.
Scaling AI safely will require building a secure space in which it can operate. Once you have everything in one place, with the right data governance practices in place, AI can do its best work without you worrying about security issues.
4. Use AI and automation together for autonomous task execution
This is perhaps the most exciting shift. We are moving from AI that talks to us through conversational responses to AI that acts for us. This is the era of agentic AI.
Currently, most AI provides passive assistance: it summarizes, it suggests, it writes. It’s cool, but it doesn’t really give advertising teams meaningful time back for the majority of their workload.
Our latest data shows that core AdOps tasks, like launching accounts and making campaign changes, take up 23% of an analyst's monthly working hours. That’s nearly a quarter of their time spent doing the same routine tasks.
In 2026, specialized advertising AI will autonomously execute core, meaningful AdOps tasks like budget reallocation and optimizations. These advertising AI agents will be capable of managing end-to-end workflows and making real-time decisions that typically require constant human oversight.
In this sense, AI agents are less tools and more like digital employees you can “hire” to complete specific AdOps tasks. Each AI agent would have tasks, goals, and the ability to execute across multifaceted workflows by taking findings from your system of record and making changes across multiple channels instantly.
For example, if an agent detects a budget pacing error on a Saturday night, it won't just alert the team via email (an email that the team won’t even read until Monday morning). The AI agent will fix the error, log the change, and notify the team when they log in on Monday.
Agentic AI will also free human teams to focus on high-impact strategy. Instead of double-checking pacing numbers every morning to make sure you don’t overspend, your team will be orchestrating a fleet of agents that handle execution.
The key? Use automation to keep AI-powered workflows in check. Automation can run quality checks on every AI output, making it uniquely suited to ensure that everything AI does is both infinitely repeatable and error-free.
The era of AI-powered AdOps is here
2026 will be the year AI moves from being a supporting player to a core part of how advertisers operate. From here on out, advertising operations will require strong collaboration between human creativity and AI’s unparalleled ability to optimize, execute, and scale.
The most successful advertisers will be those who embrace AI as a strategic partner, leveraging its ability to unify, optimize, and execute at scale. With AI seamlessly integrated into every aspect of AdOps, the opportunities ahead are as exciting as they are transformative. Our team would love to talk with you about making AI-powered automation a reality for your company.
