Key Takeaways
- AI-powered bidding and campaign automation now play a central role in Google Ads and Microsoft Advertising. However, automated bidding isn’t the right choice for every campaign. Budget, conversion volume, business objectives, and the level of control needed should guide the strategy.
- AI Overviews, conversational search, and changing search behaviors are influencing how people discover and evaluate brands. Advertisers need to understand how these changes affect visibility, traffic quality, and conversion opportunities.
- Success in 2027 will depend on balancing AI-powered optimization with human strategy, including campaign structure, audience targeting, conversion quality, creative development, and measurement.
- As advertising platforms automate more campaign decisions, advertisers need to focus on the areas they can influence, including messaging, landing page experience, budget allocation, and business outcomes.
Paid search continues to evolve as Google and Microsoft introduce more AI-powered capabilities across campaign management, targeting, creative development, and measurement. For advertisers, these advancements create opportunities to improve efficiency and reach new audiences, but they also introduce new considerations around control, transparency, and performance.
As more campaign decisions become automated, advertisers need to be increasingly thoughtful about the information they’re providing platforms and the results they’re measuring. AI can process signals and optimize campaigns at a scale that would be difficult to manage manually, but it doesn’t understand a business’s priorities, customer relationships, or long-term objectives without the right inputs.
Heading into 2027, the opportunity is to use AI where it adds value while maintaining the strategic oversight needed to connect advertising performance with business results.
What’s actually changed
Google and Microsoft have expanded AI-powered capabilities beyond automated bidding. Google’s AI Max for Search introduces additional automation for search term matching, ad creative, and landing page selection. Microsoft has also introduced AI Max for Search, alongside continued enhancements to Performance Max and other automated campaign capabilities.
These developments offer opportunities to reach relevant audiences and improve efficiency, but advertisers still need to evaluate campaign settings, exclusions, budget allocation, and performance reporting. Greater automation doesn’t eliminate the need for campaign oversight.
Search behavior is also evolving as consumers use AI-generated answers, conversational search, and traditional search engines throughout the research and purchasing process. For advertisers, this means understanding how customers search at different stages of their journey and adapting campaigns accordingly. High-intent search remains valuable, but the path from initial research to conversion may involve more platforms and interactions.
What’s actually working
As platforms take on more campaign optimization, the quality of the inputs and the strategic decisions behind them become increasingly important.
Balancing automation with strategic oversight
One of the challenges with AI-powered campaign management is determining where automation can improve performance and where advertiser control remains necessary. Focusing too heavily on individual bidding adjustments can take attention away from campaign structure, conversion quality, audience relevance, and the business objectives guiding the strategy.
Automated bidding can be effective when campaigns have sufficient, reliable conversion data and clearly defined objectives. However, the appropriate bidding strategy depends on the campaign’s goals, budget, and available performance data.
Practically, that means:
- Clean, well-structured conversion data. If you’re feeding the platform noisy conversion signals (form fills that don’t turn into leads, form fills that turn into unqualified leads, all counted equally), the AI will optimize for noise. Enhanced conversions, offline conversion imports, and value-based bidding all rely on giving the platform good data to optimize against.
- Relevant audience data and campaign controls. First-party customer data, audience signals, exclusions, and geographic targeting can help guide campaign delivery. The role of these inputs varies by campaign type, so advertisers need to understand how each platform uses them.
- Meaningful conversion values. Not every conversion contributes equally to business growth. When possible, connecting advertising platforms with qualified lead data, sales outcomes, or appropriate conversion values helps campaigns optimize toward results that matter beyond the initial form submission.
Creative and landing page experience remain essential
There’s a persistent myth that AI-driven bidding makes creative less important, since the platform is doing more of the optimization work. In our experience it’s the exact opposite. When the auction becomes more competitive and everyone is running similar bidding strategies, the differentiator shifts to what’s actually in the ads. Relevant messaging, a compelling offer, and a landing page that supports the user’s intent can significantly influence campaign performance, regardless of how sophisticated the bidding strategy becomes.
This is especially true on Performance Max, where the platform generates variations of your ad assets and serves them dynamically. If you feed the algorithm strong headlines, distinct value propositions, and quality imagery, it has good material to work with. If you feed it generic filler, it produces generic results at scale.
Plan for changing search behavior
Consumers increasingly use a combination of traditional search engines, social platforms, online communities, and AI-powered tools to research products and services. This creates additional opportunities for advertisers, but it also makes understanding audience behavior more important.
Expanding into new channels should be based on audience research, campaign objectives, available budgets, and the ability to measure results. For some businesses, traditional paid search will continue to provide the strongest opportunities for lead generation. Others may benefit from testing additional platforms as part of a broader digital media strategy.
As advertising opportunities within AI-powered search experiences continue to develop, advertisers should evaluate them carefully rather than assume every emerging placement deserves investment.
As AI-powered search continues to evolve, brands also need to consider how they’re represented in AI-generated answers and recommendations. This brings greater attention to SEO, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO), which focus on improving visibility as consumers discover information through traditional search and AI-driven experiences. A potential customer might first encounter a business through an AI-generated recommendation, then turn to Google to research the company or compare options before converting through a paid ad. Understanding how these interactions influence customer decisions can help businesses better align their paid media, organic search, and AI visibility strategies to move prospective customers toward conversion and ultimately drive measurable business results.
Measurement is messier than it used to be
Measuring paid media performance has become more complex as consumer journeys go across multiple platforms and privacy expectations continue to evolve. Attribution models, platform-reported conversions, and analytics tools each provide useful information, but none offers a complete picture of advertising’s contribution to business results.
Effective measurement starts with accurate conversion tracking and clearly defined business objectives. Advertisers should evaluate platform performance alongside website analytics, CRM data, lead quality, and actual business outcomes whenever that information is available. For larger programs, incrementality testing and more advanced measurement approaches can provide additional insight.
The objective isn’t to collect more metrics. It’s to understand which marketing investments are contributing to meaningful results and use that information to make better decisions.
What we’re changing about how we run paid media
As AI takes on a greater role in paid media, we’re evolving how we plan, manage, and evaluate campaigns for our clients. That means focusing more attention on the strategic decisions that influence performance and making sure automation supports each client’s business objectives.
- Prioritizing conversion quality and measurement. We’re placing greater emphasis on tracking meaningful actions, evaluating lead quality, and connecting campaign performance to actual business outcomes rather than relying solely on platform-reported conversions.
- Taking a more strategic approach to automation. We’re evaluating AI-powered features, bidding strategies, and campaign types based on each client’s objectives, budget, and available data. Automation can improve efficiency, but it still requires oversight and ongoing evaluation.
- Strengthening collaboration between media and creative. We’re bringing audience insights and campaign performance data into creative discussions to help develop messaging, offers, and landing page experiences that support campaign goals.
- Evaluating emerging opportunities. We’re monitoring changes in search behavior, AI-powered advertising, and emerging platforms to identify opportunities that make sense for our clients, rather than recommending new channels solely because they’re available.
As AI continues to influence how paid search campaigns are built, optimized, and measured, businesses need a strategy that evolves alongside the technology. At STIR, we combine AI-powered capabilities with audience insights, performance data, and strategic oversight to help clients make informed marketing decisions and drive measurable business results.
About the author
Maureen Breiner is Digital Media Director at STIR, where she leads paid media strategy and execution across paid search, paid social, programmatic, and connected TV. With extensive experience in advertising and digital marketing, she specializes in developing integrated media strategies, optimizing campaign performance, and helping clients navigate an evolving digital advertising landscape.
