Tatev Malkhasyan
October 16, 2025
14
minutes read
AI has moved from pilot projects to the center of the marketing stack. It cuts manual work, personalizes messages at scale, and tunes spend as results come in—while people set goals, voice, and guardrails.
Marketers aren’t adopting AI for novelty; they’re using it to remove manual work, tailor experiences, and optimize spend in real time. McKinsey notes that personalisation is now a baseline expectation—71% of consumers expect tailored interactions and most feel frustrated when they don’t get them—so the pressure to operationalise data-driven messaging has intensified.
On the operations side, AI is freeing teams to focus on higher-value work. Salesforce reports that organisations using AI in service functions see widespread time and cost benefits, and a strong majority plan to expand their investment. These gains compound when AI surfaces insights automatically (for example, summarising conversations, prioritising cases, or forecasting outcomes).
Early results suggest the payoff is material. Recent research covered by MediaPost found that eight in ten marketers using generative AI report ROI, with respondents citing improvements in productivity, content quality, and cost control. As adoption broadens and models become more capable, the conversation is shifting from “should we use AI?” to “where does it improve performance first—and how do we govern it?”
Two realities will shape the rest of this article. First, customer expectations for relevance keep climbing, and AI is increasingly the mechanism that delivers it at scale. Second, privacy and compliance requirements are tightening, which means marketers must pair automation with clear guardrails. We’ll map where AI delivers the most value today, highlight practical use cases, call out risks, and lay out what to prepare for in 2026.
💭 AI pays off fastest when goals are clear, data is dependable, and teams agree on what “good” looks like.
AI in digital marketing is a set of techniques that let software learn from data and make or suggest decisions that improve campaign outcomes. It combines three core capabilities:
In practice, AI plugs into the marketing loop—ingest data, score or generate, act, measure—and repeats that cycle continuously. A model might, for example, score audience propensity, select a creative variant, set a real-time bid, and then adjust budgets based on observed performance. Over time, the system learns which combinations of audience, context, and creative deliver the strongest return.
💭 Treat AI as a system—collect signals, decide, act, learn—not as a single tool.
💡 For deeper context on how these capabilities show up in programmatic media and TV, see AI Digital’s perspective on how AI is changing the programmatic game and the rise of AI in TV advertising.
AI matters because it makes marketing faster and more precise. Models automate routine work, personalize messages for each customer, and adjust bids and creative in real time based on performance. The sections below cover the practical gains: lower operating costs, scalable personalization, smarter in-flight optimization, and, ultimately, stronger ROI.
💭 Start where repetitive work slows you down; automation turns reclaimed hours into momentum.
AI takes repetitive work off the table and speeds up routine execution, from summarizing customer conversations to drafting responses and triaging requests.
In Salesforce’s 2024–2025 research, 95% of decision-makers at organizations using AI report time and cost savings, and 92% say generative AI improves service quality—a double effect that lowers operating costs while keeping response times tight.
For media operations, automation extends to bidding, budget reallocation, creative testing, and measurement—workflows that once soaked up analyst hours.
💡 See how this connects to execution in practice via Smart Supply’s integration with AI Digital’s stack.
Personalization isn’t a “nice to have” anymore. As said in the introduction, McKinsey’s 2025 update reiterates a durable finding: around 71% of consumers expect personalized interactions, and 76% are frustrated when they don’t get them. AI makes that level of relevance practical by scoring propensity, selecting content, and sequencing messages per individual—continuously and at volume.
💭 Personalization works when messages change with context, not just with names.
Modern buying platforms use models to evaluate each impression and adjust bids, creatives, and frequency in near real time. On the supply side, AI features such as bid shading and win-rate prediction reduce media waste while preserving reach; production results in RTB systems show lower eCPM/eCPC/eCPA and higher surplus when algorithmic shading is applied.
Industry coverage also shows AI curation and optimization moving deeper into the sell side, tightening feedback loops between buyers and SSPs.
💡 For a practical view of how this plays out in inventory and yield decisions, see AI Digital’s post on supply-path improvements: Supply-side optimisation with AI.
AI’s value shows up in performance metrics: eMarketer reports that a majority of US marketers cite increased performance as a clear benefit of generative AI, linking model-assisted workflows to better campaign results.
On the ground, autonomous optimization can translate into outsized gains. A frequently cited example: Harley-Davidson’s NYC dealership used an AI platform to expand prospecting and lift leads by 2,930% during its trial period—an early but vivid demonstration of data-driven targeting and creative iteration improving outcomes.
AI’s strategic edge comes from how it reshapes day-to-day workflows. It compresses production cycles, surfaces insights without manual digging, and turns customer signals into timely actions across channels. The sections below outline where those advantages show up first—and how to operationalize them.
AI shortens creative timelines and widens the set of ideas you can test.
The Content Marketing Institute reports that 89% of marketers use generative AI tools, most commonly to brainstorm topics (62%), summarize material (53%), and write first drafts (44%)—evidence that AI now underpins everyday copy tasks rather than side experiments.
💡 For a practical framework on when to use models for ideation, variation, and brand-safe guardrails, see AI Digital’s guide to GenAI in creative media strategy.
💭 Use AI for volume; keep humans for voice. That balance preserves brand equity.
AI is raising the bar on speed and quality in service channels—and customers are rewarding brands that get it right.
Zendesk’s 2025 CX Trends reports that “CX Trendsetters” using AI see 33% higher acquisition, 22% higher retention, and 49% higher cross-sell revenue; consumers also expect AI-driven interactions to feel tailored (61%) and 63% say they’ll switch after just one bad experience. A cited case study shows AI resolving 44% of incoming requests and cutting resolution time by 87%.
Adobe’s 2025 Digital Trends adds a forward look: teams already seeing ROI from genAI anticipate better-quality customer interactions (58%) and more consistent communications (50%) over the next 12–24 months.
Leaders are shifting from reactive reporting to prediction. Twilio Segment’s State of Personalization notes 89% of decision-makers say AI-driven personalization will be critical in the next three years, and 86% expect a move from reacting to proactively predicting what customers want—underscoring why propensity models, LTV predictions, and churn risk signals belong in planning cycles, not just post-mortems.
Performance follows: BCG’s Personalization Index finds that personalization leaders grow revenue 10 percentage points faster per year than laggards and report higher customer-satisfaction scores, linking predictive targeting and individualized experiences to commercial outcomes.
💭 Forecasts guide budget; post-mortems refine the model. You need both.
AI discovers segments that manual rules miss by clustering behaviors, content affinities, and context. Gartner notes that marketing teams are using generative and predictive AI to augment and accelerate core marketing operations—content, audience selection, and measurement—shifting effort from manual analysis to governed automation.
Agentic capabilities are beginning to automate in-flight decisions—swapping creatives, tuning bids, and reallocating budget as performance shifts. Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI, enabling more autonomous day-to-day decisions; at the same time, they caution that many early projects will be scrapped before maturity, so governance and value tests matter.
💡 For a deeper dive into how this applies in media buying, see AI in DSPs: How demand-side platforms use artificial intelligence to optimize advertising.
AI shows up in the work you do every day. It drafts first passes, answers routine questions, forecasts outcomes, and tunes bids—then learns from results to improve the next round. The sections below walk through the main jobs where teams are applying it right now.
Generative tools now sit inside the apps marketers use every day, so drafting and iterating copy is faster.
eMarketer reports that 80% of creatives use generative AI somewhere in their process, and 40% use it end-to-end—evidence that first drafts and variant testing have gone mainstream.
Google has rolled generative writing into Docs and Gmail via Gemini for Workspace, while Slides can generate visuals from prompts, tightening the brief-to-asset loop. Adobe’s 2025 Digital Trends likewise describes genAI as a “copilot for ideation,” helping teams produce more options and refine tone before human edits lock brand voice.
AI assistants handle common intents—order status, returns, FAQs—and prep summaries and suggested replies so agents close the rest faster.
McKinsey documents a European bank that replaced a rules-based bot with a gen-AI system that was 20% more effective within seven weeks, and a heavy-equipment maker that cut average resolution time from ~125 minutes to seconds, saving €150k–€300k per day in reduced downtime.
Looking across programs, IBM’s Institute for Business Value reports 65% of customer-service leaders expect gen-AI + conversational AI to increase CSAT, and finds growing operational use of partial automation in feedback handling (49%), retention (48%), and onboarding (47%).
Predictive models estimate conversion likelihood, churn risk, and expected LTV, then push those signals into planning, audience building, and budget decisions. Out of the box, Google Analytics 4 exposes purchase and churn probability plus predictive audiences, which teams can activate in media and lifecycle flows.
Adoption is rising: Twilio Segment’s 2025 CDP report notes a 57% surge in predictive traits as companies operationalize first-party data for next-best action.
Forecasting also helps catch drift early; Google documents how conversion modeling and Consent Mode restore signal for bidding and measurement when data is incomplete.
💡 Pair this with a rigorous take on metrics—see Why your marketing metrics are lying about growth for avoiding proxy traps.
On the buy side, AI scores each impression, adjusts bids, rotates creative, and manages frequency in real time; on the sell side, curated supply and supply-path optimization (SPO) reduce fees and improve quality.
eMarketer reports that ~46% of advertisers plan to use AI for bidding and mid-flight optimization in 2025.
Industry evidence shows tangible savings from smarter supply: an SPO case study reported a 40% CPM reduction while maintaining viewability and video completion rates.
AdExchanger’s explainers and coverage frame SPO as the new normal for programmatic video and CTV.
💭 Curated supply is a strategy, not a filter—choose partners that explain their path to the impression.
💡 For a practical implementation, see Smart Supply, and check out the primer: Programmatic advertising: What it is, how it works, and why it matters in 2026.
AI accelerates keyword research, topic clustering, internal linking, and content scoring so pages better match search intent.
Usage is widespread: Semrush finds 58% of businesses use AI for researching topics, and many apply it to briefs and drafting.
Google’s guidance is clear: AI-generated content is acceptable when it’s helpful and people-first, so quality standards still apply.
Operationally, performance matters as much as copy—faster sites convert better, and multiple studies (e.g., Cloudflare’s explainer) link page speed to conversion lift, making AI-assisted audits and testing valuable for UX and revenue.
AI’s impact shows up most clearly in live programs. The examples below focus on what changed, how it worked, and the measurable outcome—lift in revenue, lower acquisition costs, faster resolution, or stronger engagement. We’ll start with e-commerce personalization, then look at AI-driven ad targeting, email and lead scoring, and generative creative.
Below is a practical, task-based AI for digital marketing toolkit. Pick the pieces that fit your stack and governance model.
💡 If you want an AI marketing intelligence layer for planning, optimization, and transparent insights, see Elevate and the launch overview AI Digital launches Elevate.
Start with a compact writing stack that’s safe for brand and easy to govern. Use one primary assistant for drafting and a few task-specific tools for briefs, variants, and QA.
Pick creation tools that plug into your existing workflows and asset libraries. Prioritize editability, license clarity, and export options your team already uses.
Your intelligence layer should turn first-party data into decisions. Favor platforms that explain recommendations, connect to downstream channels, and report on business KPIs.
Use automation to orchestrate journeys across channels without losing control. Start with proven lifecycle tasks, then layer in predictive triggers as data quality improves.
AI is powerful, but it introduces operational, legal, and creative risks that need clear guardrails.
💡 For a deeper strategic view of blind spots and how to close them, see The biggest AI blind spot in advertising.
AI depends on customer data, which brings scrutiny around consent, purpose limitation, retention, access, and explainability. In walled environments, scale often comes with less transparency into how decisions are made, making accountability harder to prove.
💡 Read more: Walled gardens: the hidden cost for digital advertisers.
Practical guardrails
Weak signals lead to weak decisions. Inconsistent tracking, biased samples, and proxy metrics can push models toward the wrong audiences or claims. As third-party cookies fade, gaps widen unless first-party data and durable identifiers are in place.
💡 For context on the shifting signal mix and durable identifiers, see In a cookie-less world: new challenges and opportunities.
Practical guardrails
Generative tools write and design quickly, but they don’t know your brand’s nuance without help. Left unchecked, outputs can sound generic, misstate claims, or miss the brief.
Practical guardrails
Results hinge on integration and change management. Teams often underestimate the effort to connect data sources, rework workflows, and maintain models. Costs span licenses, infrastructure, and new skills.
Practical guardrails
💭 Speed without governance is just risk arriving sooner.
AI’s next phase is about systems that learn continuously and act across channels with light human oversight. Expect tighter use of first-party data, model-driven orchestration, and multimodal search shaping how people discover products. The sections below outline where this is heading and how to prepare.
Generative models are moving from copy helpers to engines that tailor offers, imagery, and tone for each customer at scale. McKinsey’s 2025 work describes marketers using genAI to produce highly relevant messages—text and visuals—at high volume while staying within brand guardrails, turning personalization into a repeatable system rather than one-off tactics.
Omnichannel programs are becoming “AI-orchestrated”: models reconcile signals across web, apps, ads, email, and retail, then time the next action automatically. McKinsey notes that personalization is shifting from isolated use cases to end-to-end workflows; Gartner, for its part, underscores both the promise and the operational discipline required to deliver relevance across touchpoints.
Marketers should plan for queries that are spoken, snapped, or circled—then make products discoverable in those formats. Google says people now run 12B+ visual searches each month with Lens (and “nearly 20B” monthly as of late 2024), with a meaningful share tied to shopping intent. Voice remains steady usage in many segments, and GWI highlights weekly adoption among key consumer groups. Optimize metadata, imagery, and product feeds accordingly.
Prediction is table stakes; autonomy is next. Gartner projects that by 2028, 15% of day-to-day work decisions could be made autonomously—yet also warns that many early “agent” projects will be scrapped before 2027 without clear value and governance. Expect campaign agents that adjust bids, rotate creative, and re-segment audiences under human oversight.
💭 The edge won’t come from models alone—it will come from how you design the loop between insight and action.
AI is now part of the core toolkit in digital marketing. Used well, it reduces manual workload, unlocks personalization at scale, and tightens the link between spend and outcomes. The constant is the pairing: algorithms do the heavy lifting on analysis and execution; people set goals, shape the story, and apply judgment. That mix—AI for throughput, humans for direction—delivers measurable gains today and sets teams up for what’s next.
Looking ahead, expect faster creative iteration, richer first-party data use, and more autonomous optimization under clear guardrails. Teams that invest in clean data, practical governance, and outcome-based measurement will be in the best position to capture compound returns as models improve.
Want help putting this into practice? AI Digital combines transparent technology with hands-on expertise to deliver performance you can trust. We operate a DSP-agnostic, Open Garden approach for neutral, cross-platform execution and insight, avoiding black-box bias and giving you full control over where budgets go. If you’d like a quick assessment or a pilot plan, reach out to AI Digital. We can review your current stack, map fast wins, and stand up measurable tests that prove value before you scale.
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Inefficiency
Description
Use case
Description of use case
Examples of companies using AI
Ease of implementation
Impact
Audience segmentation and insights
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
No. AI handles high-volume tasks—drafting variants, triaging data, automating bids—so marketers can focus on positioning, creative direction, and strategy. It’s an amplifier, not a substitute.
Plans are increasingly data-first and iterative. Teams set business KPIs, instrument clean data, and let models inform audience selection, creative testing, and pacing—then review results and refine.
It speeds content production, personalizes experiences at scale, forecasts outcomes, and optimizes spend in near real time. The net effect: fewer manual steps, better relevance, and clearer links to revenue.
Use it to accelerate research, briefs, copy and image variants, and message testing—within brand guardrails and human review. Treat genAI as a fast first draft and an experimentation engine, not final authority.
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