The future of AI in marketing: How AI is changing digital marketing

AI is now ingrained in the infrastructure marketers use to plan, analyze, create, optimize, and execute digital campaigns. From generative AI that accelerates creative development to predictive tools that help teams understand performance signals, AI is reshaping how marketing work gets done across the full campaign lifecycle.

Recent StackAdapt research found that 91% of surveyed brand marketers and agencies currently use AI tools in marketing or advertising, while 86% use AI regularly or for most tasks. AI is also delivering measurable value, with 88% reporting some form of AI-driven performance improvement.

As it stands, the future of AI in marketing will be defined by how deeply AI becomes part of workflows, roles, and decision-making. 

This article explores how AI is changing digital marketing—and advertising—today, and where that change is headed next.

Generative AI is moving from content creation to creative systems

Generative AI is already changing how marketing teams approach content and creative production. It can help marketers brainstorm campaign ideas, draft ad copy, summarize research, adapt messaging for different audiences, localize assets, generate social media variations, support email marketing, and experiment with video concepts. 

The impact extends beyond “AI writes copy” and into faster creative iteration across channels, formats, and audience contexts.

Speaking with The AI Advertising Podcast, James Targett, Creative Project Manager at StackAdapt’s Creative Studio, describes the scale advantage of AI-assisted creative work. 

“We can produce 50 headlines as quickly as we could historically produce one,” he said, adding  that the team still keeps humans involved to curate, refine, and improve the strongest options.

The current data shows how quickly this behavior is becoming part of advertising workflows:

• StackAdapt’s recent research found that 69% of advertisers use AI for creative development, making it one of the most common areas where AI is already being applied. 
• McKinsey estimates that organizations adopting AI-powered marketing capabilities can achieve two- to threefold productivity improvements while reducing execution-related work by 60% to 70%, underscoring that AI’s value extends far beyond content generation.

The next stage of generative AI is less about producing individual assets and more about supporting connected creative workflows. Instead of generating a single ad or email, AI will help marketers build systems that create, adapt, test, and refine creative across audiences, channels, and campaign stages.

Rather than asking AI to write one headline or produce one image, marketers will increasingly use it to generate multiple creative directions, personalize messaging at scale, recommend new variations based on performance, and continuously optimize assets throughout a campaign.

As those systems become more capable, the marketer’s role becomes even more valuable. Creative teams will spend less time producing every asset manually and more time shaping brand voice, setting creative direction, evaluating ideas, and ensuring that AI-generated work is distinctive, relevant, and aligned with business goals.

Predictive AI will help marketers move from hindsight to foresight

Today, many teams use AI to understand what happened in a campaign—such as which audiences engaged, which creatives performed, where spend was most efficient, and which channels contributed to results—faster. 

That analysis is already becoming a core part of marketing workflows. 

In our recent research, StackAdapt found that:

• 77% of advertisers use AI for reporting and summaries.
• 74% use it for performance analysis and insights. 
• 47% cited better audience targeting as an AI-driven performance improvement.

The next step is using AI to look further ahead. 

Predictive AI can help marketers forecast how audiences, creatives, channels, and budgets are likely to perform before changes are made. Instead of waiting for performance shifts to appear in reporting, marketers can identify opportunities such as high-value audiences, stronger creative combinations, or channels likely to deliver better returns, alongside possible risks earlier in the campaign planning and optimization process.

This has implications across digital marketing:

• In audience discovery, predictive AI can help identify high-value segments that are more likely to engage or convert.
• In campaign planning, it can support forecasts for reach, spend, conversion volume, and efficiency.
• In creative strategy, it can help teams understand which messages or formats are more likely to resonate with different audiences.
• In optimization, it can help marketers decide where to adjust budgets, bids, or targeting before performance issues become harder to correct.

As these capabilities mature, marketers will be able to forecast opportunities, anticipate risks, and understand which audiences, creatives, channels, and budgets are most likely to drive results.

Marketing automation will become more intelligent, contextual, and human-controlled

Marketing automation has traditionally focused on reducing repetitive work: generating reports, adjusting bids, managing budget pacing, segmenting audiences, optimizing campaigns, and speeding up campaign setup.

Those capabilities will still matter, but AI is pushing automation into a more strategic role. As Mary O’Brien, Head of Programmatic at PMG, puts it, “Those more manual, repetitive execution-focused tasks… are the first things that we’re looking for AI to take over from us.”

Instead of simply completing predefined tasks, automation is becoming more contextual, using campaign goals, performance signals, audience behavior, and platform intelligence to support better optimization. That shift gives marketers more space to focus on planning, strategy, and the decisions that require human judgment.

The data shows efficiency gains are already visible. 

In StackAdapt’s research, advertisers pointed to AI-driven improvements across campaign workflows: 

• 62% cited reduced time spent on manual optimizations.
• 56% cited faster campaign setup or launch.
• 53% cited faster optimization cycles.

These findings show why AI-powered automation is gaining momentum: it helps teams move faster through the operational work of managing campaigns.

But the future of marketing automation will depend on more than speed. It’ll depend on whether marketers trust AI to act in ways that reflect their goals, constraints, and business priorities. 

StackAdapt’s recent research also found that advertisers are most comfortable with AI when humans remain involved in the decision process, with stronger support for AI recommending actions, preparing tasks for approval, or acting within human-defined rules than for fully autonomous operation.

That gap points that automation is headed toward controlled delegation: AI that can recommend optimizations, prepare campaign changes, and act within clearly defined boundaries. 

Marketers will still need to set the goals, define the guardrails, approve higher-risk changes, and stay accountable for outcomes. In that model, automation becomes less about replacing manual work and more about helping teams manage complexity with greater confidence.

AI assistants and agents will change how marketers interact with platforms

AI-powered assistants are changing the way marketers work inside digital advertising platforms. 

Instead of relying on menu-based workflows, marketers will increasingly use natural language to ask questions, explore performance, and take action.

This shift is part of a broader move toward agentic AI. 

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously through agentic AI. 

In marketing, that points toward a future where AI assistants do more than answer questions, and will instead help marketers:

• Interpret campaign performance.
• Recommend next steps.
• Prepare campaign changes.
• Support decisions across planning, optimization, and reporting workflows.

For advertising, context will be critical. 

Research on AI agents and online ads found that AI agents interact with ads differently than humans, favoring features like keywords and structured data over purely visual or emotional cues. 

That’s why as AI becomes more involved in digital experiences, advertising systems will need to be structured, explainable, and connected to the signals AI can actually interpret.

The challenge is that recommendations alone aren’t enough. 

In a survey of 687 marketers, StackAdapt found that only 6% of marketers said they act on in-platform AI recommendations almost always. Advertisers are more likely to act when recommendations include a clear explanation or rationale, or when they have a clear tie to a KPI they care about.

That’s where AI assistants and copilots will need to evolve. Generic AI tools may help with isolated tasks, but advertising teams need AI that understands:

• Campaign goals
• Audience strategy
• Creative assets
• Forecasts
• Performance trends
• Business context

The next stage will be more context-aware assistants that help marketers ask better questions, understand what matters, and take action with human judgment and control at the center. 

This points toward the rise of AI-powered workspaces where marketers can ask questions, understand what matters, and act within the same workflow.

Personalization will move from audience segments to adaptive experiences

AI is changing personalization from a channel-by-channel tactic into a more adaptive experience across the customer journey. 

StackAdapt’s state of personalization research found that 76% of brand marketers use email for personalization, followed by web and app experiences at 65%, paid social at 52%, and paid search at 43%. The research also found that brands plan to expand personalization into more channels, like display, native, video, paid social, CTV, retail media, and digital audio, over the next year.

The challenge is that personalization in digital marketing has often been limited by fragmented data and disconnected workflows. And as privacy rules evolve and signal loss continues, marketers can’t rely on the same data flows to power personalized experiences across channels.

IAB found that 95% of data and advertising decision-makers expect continued legislation and signal loss, while two-thirds expect state privacy laws to reduce their ability to personalize messaging.

IAB also notes that budgets are shifting toward channels that use first-party data, but a lack of interoperability across platforms makes it harder to measure and optimize personalized experiences across the full journey.

That makes AI’s role more important, but only when it can work from the right signals. 

StackAdapt’s personalization research shows that marketers see AI having the greatest impact over the next two to three years in areas like targeting and segmentation, creative production and optimization, measurement and attribution, and cross-channel orchestration.

The future of personalization is more adaptive marketing that can connect who the customer is, what they’re doing, where they’re engaging, and how campaigns are performing. 

With AI, marketers will be able to move beyond broad audience segments and deliver experiences that adjust based on context and intent.

Omnichannel marketing will shift from fragmented execution to orchestration

Marketers are often planning campaigns in one place, reviewing performance in another, adjusting budgets somewhere else, and trying to connect paid and owned media through separate reports, dashboards, and tools. 

In traditional demand-side platform workflows, even a single campaign decision can require users to move across multiple views before they can understand what happened and respond accordingly.

This fragmentation is an industry-wide issue. McKinsey research found that 47% of martech decision-makers cite stack complexity, system integration, and data integration challenges as key blockers that prevent them from getting value from their martech tools. 

McKinsey also argues that AI creates an opportunity to move away from disconnected platforms toward an integrated marketing operating model built for end-to-end journey orchestration.

The same challenge shows up in advertising workflows. StackAdapt’s AI delegation research found that: 

• Only 19% of respondents said their AI tools are fully integrated into marketing and advertising workflows. 
• 49% cite fragmented data pipelines as a barrier to AI delegation. 
• 41% cite CRM or first-party data not being integrated with buying platforms.

The future of AI in marketing will depend on reducing the distance between insight and action. AI will be most valuable when it can bring together:

• Planning
• Audience discovery
• Creative context
• Forecasting
• Performance analysis
• Optimization
• Execution

This is where omnichannel marketing shifts from fragmented execution to orchestration: AI can help connect context, intelligence, and action in one workflow, so marketers spend less time moving across tools and more time making confident decisions across paid and owned channels.

AI is also expanding the number of channels available. AI-native environments such as ChatGPT and other large language models (LLMs) are emerging as a new layer of digital discovery, where people research products, compare options, and make purchase decisions through conversations rather than traditional search. 

As LLM advertising evolves, marketers will need to think about how these new AI-powered channels work alongside search, social, display, video, and email as part of a coordinated marketing strategy.

Reporting will become the starting point for action, not the end of the campaign

Reporting has traditionally been treated as a way to look back: what happened, which channels performed, where ad spend went, and whether the campaign met its goals. 

AI is changing that role. 

As reporting becomes faster, more automated, and more interpretive, it can become an active part of campaign decision-making while campaigns are still in flight.

This shift is especially important in digital marketing, where performance can change quickly across audiences, creatives, channels, and budgets. 

AI-powered reporting can help summarize what’s happening, identify unusual performance patterns, surface risks, and recommend where marketers should focus their attention.

StackAdapt’s research shows that advertisers are looking for this kind of proactive intelligence:

• 41% want AI to flag risks before they escalate.
• 41% want AI to summarize insights.
• 39% want recommendations tied to campaign goals.
• 35% want AI to explain why performance changed.

Those findings point to a larger shift in how reporting will function. Marketers will still need dashboards and performance summaries, but the greater value will come from AI that can turn reporting into an always-on feedback loop.

Instead of waiting until the end of a campaign to understand what worked, teams will be able to see what needs attention earlier, understand the likely cause, and decide what action to take next.

In that model, reporting becomes part of a continuous optimization loop. AI helps marketers move from monitoring performance to improving it in real time, while humans provide the judgment needed to evaluate recommendations, align changes with campaign goals, and decide when action is appropriate.

Marketers will move from operating campaigns to guiding AI-assisted decisions

As AI becomes more embedded in marketing workflows, the marketer’s role will shift toward higher-value decision-making. 

Instead of spending as much time manually operating campaigns, navigating dashboards, pulling reports, or making every adjustment by hand, marketers will spend more time setting direction, evaluating opportunities, and deciding how AI should support the work.

That shift will change what campaign management looks like. Marketers will increasingly be responsible for:

• Defining campaign goals and success metrics.
• Setting rules, constraints, and approval paths.
• Interpreting AI-generated insights and recommendations.
• Applying brand, creative, and business judgment.
• Deciding when AI can act, when it should prepare an action, and when a human needs to approve the next step.

StackAdapt’s research shows that advertisers are open to giving AI a larger role, but not without the right conditions. They need recommendations that are specific to the campaign, supported by clear rationale, connected to KPIs, and easy to review or control before AI earns more authority in the workflow.

The future of AI in marketing is still human-led

AI is reshaping how teams create content, gain insights, automate workflows, personalize experiences, analyze performance, and manage omnichannel campaigns. 

But AI’s value will depend on how well marketers direct it. 

Strategy, judgment, brand stewardship, governance, and accountability will become even more important as AI takes on a larger role in marketing decisions.

AI can help surface what matters, recommend next steps, and reduce operational work, but marketers remain central to deciding what actions are right for the brand, the audience, and the business.

AI won’t remove humans from the process, but it will give marketers the context, intelligence, and control to make better decisions faster.

That’s where Ivy Studio^TM fits into the next phase of AI-powered advertising. 

Ivy Studio is an AI-first advertising hub that brings planning, analysis, optimization, and execution into one unified experience. 

Built on StackAdapt platform intelligence and the live context of campaigns, audiences, creatives, forecasts, performance, and business goals, Ivy helps marketers move from intent to action through natural language.

For marketers, that means less time spent on operational work and more time focused on the work where human expertise creates the most value: evaluating opportunities, making confident decisions, guiding strategy, and applying creative and business judgment. 

Request a demo today to find out how to use Ivy Studio to get the most of your marketing efforts.