AI in DSPs: How demand-side platforms use artificial intelligence to optimize advertising

Programmatic buying is now the default. In the U.S., it accounts for 91.3% of digital display ad spend (2024), which means the systems that decide what to bid—and where—shape almost every campaign outcome.

That scale forces automation. A single platform like The Trade Desk can evaluate around 15 million ad queries per second, far beyond what human traders or static rules could handle. Modern DSPs lean on machine learning to score each impression, predict the likelihood of your goal, and set a price in milliseconds. This is the practical answer to how DSPs use AI: impression-level forecasting, dynamic bidding, audience discovery, and supply selection that update as results come in.

The operating model has shifted from hand-tuned line items to AI DSP systems that learn continuously. Instead of fixed parameters, an AI based programmatic DSP platform adapts with every auction and creative exposure, optimizing toward business goals rather than proxy metrics. It’s not just hype either: a late-2023 survey of U.S. programmatic leaders found 52% consider AI essential for DSPs and SSPs, reflecting how deeply these models are now embedded in day-to-day buying.

The sections that follow break down these capabilities—and their limits—so you can evaluate which tools and approaches will actually improve your campaigns.

What is a DSP in advertising?

A demand-side platform (DSP) is a software system that allows advertisers, agencies, and brands to buy digital ad inventory in an automated way. Instead of negotiating directly with publishers, advertisers use a DSP to access multiple ad exchanges and supply-side platforms (SSPs) at once, bidding on impressions in real time.

Through a DSP, a marketer can:

• Define budgets, audiences, and campaign goals.
• Tap into vast pools of ad inventory across websites, mobile apps, connected TV, and other channels.
• Leverage automation to serve ads at the best available price and placement.

What makes a DSP powerful is its ability to evaluate millions of impressions in milliseconds and decide whether or not to bid based on campaign criteria. This allows advertisers to scale campaigns efficiently while retaining control over targeting, spend, and measurement.

The role of AI in modern DSPs

Artificial intelligence sits at the center of how DSPs evaluate impressions, predict outcomes, and decide what to bid in milliseconds. Put simply, how DSPs use AI determines how well they can turn data into performance at scale across display, mobile, CTV, audio, and emerging formats. The biggest buying platforms now frame AI as a built-in “co-pilot” that ingests first-party, third-party, and contextual signals to improve decision quality for every auction.

Why AI matters for programmatic advertising

Programmatic is already the default way digital media is bought in the U.S., which raises the stakes for smarter automation. Insider Intelligence estimates 91.3% of U.S. digital display ad spend was programmatic in 2024, and spending continues to expand into channels like CTV—further multiplying the number of auctions a DSP must judge every second.

At this scale, manual rules can’t keep up. AI models forecast the probability of a desired action (a view, a click, a conversion), weigh price and quality signals, and set bids accordingly. The shift to first-price auctions also makes algorithmic pricing essential, since buyers win and pay what they bid—AI-driven bid strategies (e.g., shading and dynamic floors) help avoid overpaying while maintaining win rates.

Signal loss and privacy changes are another reason AI matters. As identifiers become scarce, DSPs lean on contextual and modeled signals to sustain performance. Leading platforms describe AI systems that combine first-party data with contextual features and identity frameworks to keep targeting, frequency, and measurement effective as cookies and mobile IDs recede.

Finally, marketers need speed with accountability. Trade press and brand buyers have praised AI gains while flagging transparency gaps in all-in-one, black-box optimizers. The takeaway for practitioners: embrace AI for scale and efficiency, but keep governance, measurement guardrails, and human review in place. 

AI buying agents are going to be directing upwards of 80% of digital media buys by 2030 — Ben Hovaness, Chief Media Officer at OMD (part of ad giant Omnicom).

‍💡 For background on buying mechanics, see Programmatic advertising: What it is, how it works, and why it matters in 2026.

AI vs traditional DSP algorithms

Traditional DSP approaches relied on static, hand-tuned rules: fixed audience segments, line-item targeting, and scheduled bid adjustments. They worked when inventory, formats, and IDs were simpler—but they struggle with today’s volume, channel diversity, and auction dynamics.

AI-driven DSPs replace static rules with learning systems that:

1. predict outcomes at the impression level,
2. adjust bids to the likely clearing price in first-price auctions, and
3. continuously re-allocate spend across audiences, creatives, and supply paths based on live feedback. That’s why major buying platforms position AI not as an add-on but as the core of pacing, forecasting, creative selection, and identity-aware frequency.

There’s a trade-off. While AI improves efficiency and results, buyers have pushed for explainability after experiences with opaque “black-box” systems that restrict control and insight into targeting and placement. The industry consensus is moving toward human-in-the-loop models: let AI handle per-impression math and cross-channel rebalancing, while teams set strategy, constraints, and measurement.

⚡ Use AI to win the right auctions; use humans to define what ‘right’ means.

How DSPs use AI to optimize campaigns

AI sits in the engine room of every modern AI DSP, scoring each impression, estimating its value, and deciding what to bid in a few milliseconds. Below are the core ways an AI-based programmatic DSP platform turns data into performance.

Predictive modeling for bid optimization

DSPs train models to predict the probability of an outcome (view, click, conversion) for every auctioned impression, then translate that probability into a bid. You’ll see this described as auction-time or impression-level bidding. Google documents this approach in Smart Bidding, which uses Google AI to optimize for conversions or conversion value in each auction.

On the open web, The Trade Desk’s Koa advertises similar capabilities—scoring impressions, removing under-performing audience segments, and automatically adding promising ones as the model learns. Their “predictive clearing” feature illustrates how the system adjusts bids to an optimal level before submitting them.

Amazon DSP has also detailed machine-learning upgrades that improve bidding and pacing decisions as third-party identifiers recede, underscoring how predictive models now compensate for signal loss.

⚡ Let the model price the impression; let the marketer decide the objective.

Real-time decision-making and dynamic pricing

Because most exchanges now run first-price auctions, buyers pay what they bid. That change pushed DSPs to adopt bid shading and other pricing intelligence to avoid overpaying while maintaining win rates. Trade press has covered how first-price dynamics reshaped DSP behavior, and why bid-shading logic became a staple.

Platforms pair this with continuous feedback loops. For example, The Trade Desk’s Kokai surfaces AI recommendations in-workflow so traders can act quickly, while the underlying models update bids, pacing, and supply selection in real time. Amazon and Google expose controls that let advertisers bring goals, constraints, or custom scoring into the automated system.

Audience segmentation and targeting with AI

Audience strategy has shifted from static segments to learning systems that cluster and expand audiences as performance signals emerge.

The Trade Desk describes Koa Audiences removing weak performers and adding high-potential cohorts in real time.

Lookalike modeling and probabilistic techniques extend reach to users who behave like converters, while contextual AI classifies pages at scale to maintain relevance without user identifiers.

Analyst research also ties smarter segmentation to measurable gains: McKinsey reports that effective personalization can lift revenue by 5–15% and improve marketing ROI by 10–30%, which is why advertisers increasingly pair first-party data with algorithmic audience building inside DSPs.

Fraud detection and brand safety

Invalid traffic and unsuitable content erode outcomes and waste budget, so DSPs integrate AI-driven verification and pre-bid filters. 

Industry standards from the Media Rating Council (MRC) spell out requirements for detecting general and sophisticated invalid traffic; verification providers apply machine learning to spot patterns like botnets, headless browsers, or AI-generated “slop sites.”

Recent lab updates note surges in GIVT and new streaming fraud variants, highlighting why always-on models are needed. 

⚡Treat fraud and suitability as optimization problems—models should block bad supply and re-route budget to quality paths.

💡 For broader guidance on risk controls in buying, see AI Digital’s overview of Safety in programmatic advertising.

AI for cross-channel and cross-device attribution

Attribution has become a hybrid of model types. Data-driven attribution uses machine learning to assign credit across touchpoints and channels, moving beyond last-click and informing smarter budget shifts. Google’s documentation explains how DDA evaluates Search, YouTube, Display, and more to determine each channel’s incremental contribution, while identity graphs and cross-device matching help connect exposures to outcomes. 

Practically, this means your DSP can: 

1. deduplicate reach and manage frequency across devices, 
2. weight touchpoints by their true impact, and 
3. feed those learnings back into bidding and audience expansion so the next dollar is spent in the highest-return channel.

💡 For a supply-side view of how cleaner paths and identity improve measurement, explore AI Digital’s Smart Supply & Smart Supply integrates with AI Digital, delivering a holistic programmatic powerhouse solution.

Generative AI and innovation in DSPs

Generative models now sit alongside bidding and forecasting in an AI DSP, helping teams create, test, and scale more relevant ads at lower marginal cost. The goal isn’t just faster production; it’s a tighter learning loop where creative, targeting, and measurement reinforce one another across an AI-based programmatic DSP platform.

Creative optimization with generative AI

Generative tools accelerate copy, image, and video development, so marketers can ship far more variants and let the DSP’s optimization pick winners quickly. Analysts and trade press report rapid adoption. 

According to Gartner research, adoption is moving fast: 29% of marketing leaders are implementing and 7% are piloting generative AI (early 2024), and by 2025 84% of high-performing organizations use genAI for creative development.

DCO (dynamic creative optimization) has evolved to incorporate genAI, enabling on-the-fly asset assembly and variant generation that responds to context, audience, and price signals.

⚡ Speed only matters if it improves learning per dollar.

IAB’s 2025 video study also points to mainstream use in video: 86% of advertisers already use or plan to use genAI for video ads, with a growing share of creative tailored to audience and context.

💡 For a strategy view of where genAI fits in the creative workflow, see Generative AI in creative media strategy.

AI-driven personalization in ad creatives

Personalization is where how DSPs use AI meets creative at scale. Models can generate headlines, visuals, and product combinations for micro-segments, while the DSP learns which combinations lift outcomes. As mentioned earlier, McKinsey’s research links effective personalization to 5–15% revenue lift and 10–30% marketing ROI gains, which explains why teams pair first-party data with algorithmic creative systems.

Consumer behavior is moving the same way: Adobe reports 39% of U.S. consumers have used genAI for shopping tasks like research and recommendations, a signal that tailored content and offers are becoming the norm.

⚡ Treat genAI as a creative multiplier, not a creative replacement.

Balancing innovation with user experience and privacy

Aggressive personalization must respect consent, data minimization, and explainability. The IAB’s State of Data 2024 found 95% of U.S. advertising decision-makers expect continued legislation and signal loss, pushing teams toward contextual and modeled approaches that don’t rely on third-party IDs. Adobe’s Digital Trends 2024 adds a practical check: 57% of practitioners say ensuring quality and customer trust in AI-generated content is a top challenge.

On platform policy, the FTC has intensified enforcement. In 2024 it issued a final rule banning fake and AI-generated reviews and launched “Operation AI Comply” to pursue deceptive AI claims—guardrails that affect endorsement, influencer, and creative practices.

Limitations and ethical concerns of generative AI

GenAI introduces new risks that a DSP alone can’t solve.

• The IAB Tech Lab’s AI in Advertising Primer and the IAB’s legal whitepaper outline issues spanning IP ownership, privacy, dataset provenance, and disclosure. 
• Deloitte flags operational risks like hallucinations and misinformation that can damage brand trust if outputs are not reviewed. 
• And the IAB notes a real-world learning curve: over 70% of marketers have already experienced an AI-related incident (hallucination, bias, off-brand content), yet many under-invest in governance.

Pragmatically, the fix is process, not just tooling: human review for high-impact assets, strong data governance, clear disclosure in endorsements, and bias testing for creative and targeting models. When those pieces are in place, genAI can safely feed the DSP with diverse, brand-safe variants—and the DSP can do its job of rapid testing and optimization.

How AI enhances programmatic advertising strategies

AI shifts programmatic from manual line-item tinkering to system-level strategy. Instead of weekly tweaks, models learn continuously, reallocating budget, refining audiences, and shaping creative and supply choices as results come in. Below are the strategic upgrades you can expect when AI sits at the center of your buying stack.

Faster planning and smarter budget allocation

Modern buying tools use AI to turn a brief (goal, budget, dates, assets) into a draft media plan, then keep optimizing it as performance data accrues. Platforms report material adoption of these assistants; for example, The Trade Desk says a majority of spend now runs through its Kokai experience, which surfaces AI recommendations alongside execution.

Across the walled gardens, automated suites like Performance Max and Advantage+ have shown they can drive outcomes, even as marketers push for more levers and clarity. The Wall Street Journal sums up the trade-off: automation boosts sales and efficiency, but control and transparency remain sticking points that buyers must manage.

First-party data activation in a low-signal world

As third-party identifiers fade, strategy hinges on collecting and modeling your own data and combining it with contextual signals. IAB’s State of Data 2024 found 71% of brands, agencies, and publishers planned to increase first-party datasets in the next year (with an average 35% expansion among those increasing), explicitly to support media effectiveness under signal loss. MediaPost’s summary of the same trend: less addressability is pushing up costs and forcing smarter data use.

True cross-channel orchestration (including CTV)

AI helps unify planning and frequency across web, mobile, social, audio, and connected TV. That matters because CTV is no longer a side bet: multiple Insider Intelligence reads peg U.S. CTV ad spend around $33.35B in 2025, with double-digit growth and a rising share of total digital.

Strategy wise, that means your DSP needs AI to normalize signals across screens, cap frequency, and move budget to the video environments that are actually driving incremental lift.

Supply path optimization you can measure

AI doesn’t just pick audiences; it also picks paths. Expect curation and SPO to play a larger role in 2025-2026 strategy as buyers seek quality inventory with fewer hops and clearer economics. 

Insider Intelligence highlights curation’s rising importance, while industry audits show progress: ANA’s multi-year transparency work and ISBA/PwC’s supply-chain studies document reduced “unknown delta” when buyers steer spend into auditable private marketplaces. Build your plan around those evidence-based pipes and let models optimize within them.

Measurement that feeds strategy, not just reporting

AI-assisted measurement connects exposure patterns to outcomes and then closes the loop by informing bidding, creative rotation, and channel mix. In practice, teams blend data-driven attribution with incrementality tests and MMM to avoid “last-click myopia,” then let the DSP’s models reweight spend toward proven combinations. The operational goal: fewer static reports, more live reallocation guided by model-detected lift.

Marketers at Digiday’s forums have stressed this point—automation is useful, but only when you can see why the machine is moving dollars.

Human-in-the-loop governance

AI can handle impression-level math; humans still define what “good” means. Buyers are asking for explainability, controls, and safeguards so automation aligns with brand goals and compliance. 

Keep humans focused on guardrails, objectives, and creative direction, while delegating the millisecond-level execution to the DSP. It’s a pragmatic split that acknowledges both the speed of the tools and the need for accountability.

Personalization that scales beyond channels

Personalization isn’t just a creative tactic; it’s a strategic lever when models can predict which audiences and contexts merit richer investment. Brands that use first-party data for key marketing functions have achieved up to 2.9× revenue uplift and a 1.5× increase in cost savings, which is why leading plans pair owned data with contextual AI and dynamic creative to fund the highest-yield audience–message combinations.

📌 Bottom line: strategy becomes continuous. Use AI to speed the cycle from insight → allocation → learning, while reinforcing it with clean first-party data, curated supply, and transparent measurement. The result is a programmatic plan that adapts daily instead of quarterly—and one that’s ready for the next wave of CTV growth and ongoing signal change.

Benefits of AI-powered DSPs for advertisers

AI gives programmatic buyers practical gains they can measure: sharper targeting, lower operating costs, better outcomes per dollar, and clearer visibility into where money goes.

Better targeting and personalization

An AI DSP evaluates context, intent, and first-party signals to decide who should see what, then matches each audience with the most relevant creative. Targeting adapts over the course of the flight rather than staying static.

• Higher relevance at the impression level. Machine learning scores each auction for the likelihood of meeting your goal (view, click, conversion) and prioritizes the users and contexts most likely to respond.
• Personalization that moves the needle. Boston Consulting Group found that brands delivering personalized experiences see 6–10% revenue increases and grow two to three times faster than those that don’t—clear justification for pairing first-party data with DSP modeling to back the audience–message matches that convert.
• Modeled audiences when IDs are scarce. Lookalike expansion and contextual models help sustain relevance as third-party identifiers fade, keeping prospecting efficient while respecting privacy constraints.

⚡ Personalization is most valuable when it informs both the audience and the creative the audience sees.

Increased efficiency and reduced costs

Automation trims the busywork so your team can focus on strategy. With models handling pacing, bidding, supply, and creative rotation, the same budget reaches more of the right impressions.

• Automation replaces hours of manual tuning. AI handles pacing, bid adjustments, creative rotation, and supply selection continuously, freeing teams for strategy and testing.
• Lower acquisition costs from smarter bidding. Google documents cases where auction-time bidding cut cost per acquisition by 40% while increasing conversion rate 36%, illustrating how learning systems can squeeze more from the same budget.
• Personalization lowers waste. McKinsey reports personalization can reduce customer acquisition costs by up to 50%, by aiming messages where they’re most likely to work.

Improved ROI through smarter optimization

Tie optimization to real outcomes, not proxy metrics. As the system learns what actually drives conversions or revenue, it reallocates spend to the combinations that produce incremental lift.

• Budget flows to what works—fast. As models observe outcomes, they reweight spend toward high-return combinations of audience, context, and creative without waiting for a weekly review cycle.
• Auction intelligence prevents overpaying. First-price auctions reward precise pricing; DSP bidding models estimate clearing prices and win where it matters, supporting better return on ad spend over time.
• Evidence from both platform cases and independent research. As mentioned, BCG reports that brands delivering personalized experiences see 6–10% revenue gains and grow 2–3× faster than peers, while Google’s auction-time automation shows impact at the case level—Toyota cut CPA by 33% after shifting to Smart Display with Smart Bidding.

Transparency and actionable insights

Visibility turns data into decisions. With clearer logs and explainable models, you can see which audiences, creatives, and supply paths create value and adjust in near real time.

• Cleaner supply paths, clearer economics. The ISBA/PwC follow-up study found the “unknown delta” in open-web programmatic reduced from 15% to 3%, showing that better log-level access and standardized data can meaningfully improve transparency.
• Independent benchmarks reinforce progress. The ANA’s 2024 Programmatic Benchmark points to improvements in efficiency and transparency, while noting more work is needed to ensure a higher share of spend reaches working media—useful context when you choose partners and set KPIs.
• Insights you can act on. With impression-level data stitched across channels, AI surfaces drivers of performance (creative, audience, placement, time) so traders can set guardrails and run targeted experiments rather than broad guesswork.

⚡ Transparency isn’t just a compliance box—it's how you find the levers that actually move ROI.

Challenges of AI in DSPs

AI gives programmatic buying real advantages, but it also introduces hard problems that teams must manage—legal risk, fairness, control, and implementation complexity. Here’s what to watch and how to respond.

Data privacy and compliance (GDPR, CCPA)

AI-powered targeting depends on data, which brings obligations under privacy laws in the U.S. and abroad.

• Know the rules you’re playing by. In the EU, the GDPR requires a lawful basis (often explicit consent) for personalized advertising. IAB Europe’s Transparency & Consent Framework (TCF) codifies how vendors capture and pass consent signals across the ad chain.
• California has raised the bar. Under the CCPA as amended by the CPRA, consumers must be able to opt out of the sale or sharing of their personal information for cross-context behavioral advertising. The California Privacy Protection Agency has proposed additional updates, including rulemaking on automated decision-making technology (ADMT), which would affect AI-driven profiling and measurement.
• Plan for ongoing signal loss. IAB’s State of Data 2024 found 95% of U.S. brands, agencies, and publishers expect continuing legislation and signal loss, with many expanding first-party data and moving to privacy-safe modeling.

⚡ Privacy compliance isn’t a one-time project—it’s a product discipline you apply to every audience, model, and measurement feed.

💡 For practical steps specific to cookieless buying and modeled audiences, see ‍.

The risk of bias in AI models

Even when targeting parameters are neutral, optimization can skew ad delivery in ways that disadvantage certain groups.

• Peer-reviewed evidence. A widely cited study showed Facebook’s delivery system produced significant skew by gender and race for job and housing ads, despite broad, neutral targeting. The researchers traced the skew to the platform’s optimization choices and market dynamics, not just advertiser settings.
• What to do about it. Use a risk framework (e.g., NIST AI RMF) to assess data, objectives, and outputs; add fairness tests to your experimentation plan; and document overrides when model performance conflicts with equity goals.

💡  A deeper marketing take on blind spots and mitigation is in The biggest AI blind spot in advertising.

Balancing automation with human control

Marketers value AI for scale, but they also need explainability and levers.

• Opaque optimization is a real concern. Buyers continue to press platforms for clarity on what AI is optimizing and why—especially around brand safety, targeting expansions, and creative rotation. Trade coverage has documented these demands and the push for stronger disclosures.
• Keep people in the loop. Human-set guardrails (business objectives, incrementality goals, suitability standards, geo and audience constraints) paired with machine execution is emerging as the workable middle ground. Industry groups like the WFA also emphasize elevating the insight function so teams guide, not just observe, automation.‍
• Don’t forget compliance on creative claims. The FTC’s recent final rule on fake and AI-generated reviews underscores the regulator’s focus on AI misuse in marketing; align influencer, UGC, and testimonial workflows accordingly.

Technical complexity and adoption barriers

Deploying AI in a DSP stack isn’t just a model choice—it’s an operating model change.

• People and process are the bottleneck. Research finds most AI roadblocks are organizational (skills, workflow, governance), not algorithmic. BCG reports ~70% of challenges stem from people and process, with only ~10% tied to the algorithms themselves.
• Data quality and integration matter. Clean, well-governed data is the prerequisite for useful AI. Analysts warn that poor pipelines and fragmented tools derail projects long before model tuning begins.
• Supply-chain complexity persists. Even with improvements, the ANA’s Programmatic Transparency Benchmark shows less than half of spend reaches consumers, highlighting the need for curated supply paths and log-level accountability when you automate buying.

📌 How to de-risk implementation: start with a clear measurement plan (incrementality + MMM alongside data-driven attribution), run fairness and privacy reviews during experimentation, centralize identity and data contracts, and train teams on when—and how—to override the machine.

The future of AI in DSPs: what advertisers should expect in the next 5 years

AI will keep shifting DSPs from rule-based buying to systems that predict, generate, and orchestrate across every screen. Here’s what that means for planning, creative, supply, and measurement.

Advancements in predictive and generative AI

Expect sharper prediction at auction time and richer generation of assets inside the DSP workflow. On the prediction side, large platforms are pushing beyond CPA bidding to lifetime-value and “new customer” goals. 

Google’s 2025 update added AI that predicts which new users are likely to maximize LTV and bids more for them, with reporting that separates high-value new customers from the rest. 

The Trade Desk’s Kokai continues to evolve with features aimed at predicting clearing prices and ranking impression relevance, reflecting a broader push toward impression-level value estimates that adapt to first-price auctions.

On the generative side, DSPs and creative partners are integrating tools that spin up video and image variants for testing at scale. Recent examples include The Trade Desk expanding its generative AI creative marketplace through a Rembrand integration for virtual product ads.

Analysts also expect synthetically generated marketing output to become commonplace; Gartner projected that 30% of outbound marketing messages from large enterprises would be synthetically generated by 2025. 

⚡ The next leap is not just cheaper creative—it’s prediction, generation, and measurement living in the same loop.

💡 For how this evolution plugs into programmatic planning, see The future is now: how AI Digital embraces AI technologies to change the programmatic game.

Integration with CTV, OTT, and new ad formats

CTV is becoming the essential testing ground, with growth projected to outpace traditional TV by 2028. This massive scale is pushing buyers to rely on AI for managing identity, frequency, and supply selection across streaming platforms. 

Expect more shoppable and embedded formats (e.g., virtual product insertion) and tools that normalize signals between web, mobile, and TV so budgets move to the combinations that drive incremental lift.

AI as a service in advertising ecosystems

AI is moving from a single-platform feature to a service layer that brands and agencies can plug into their stack. 

AdMonsters describes the shift as AI as a service—autonomous planning and optimization capabilities delivered by DSPs and partners, with buyers choosing modules for forecasting, pacing, creative, or identity rather than a closed bundle.

Vendors are already positioning around this model: Viant, for example, has promoted ViantAI as a step toward more autonomous buying on CTV-led omnichannel plans.

Expect more API-first offerings, where external AI agents analyze performance, suggest reallocations, or even execute limited tasks across multiple buying endpoints.

⚡ Think of AI less as a button inside a DSP and more as an agent you can call from anywhere in your stack.

Sustainability and efficiency: green programmatic powered by AI

Sustainability will move from side project to buying constraint. 

Scope3’s work has quantified advertising’s emissions and shown that sustainable media can perform while lowering carbon; its 2025 meta-analysis synthesizes 2024 campaign results across verticals to that effect.

Standards are catching up, too. IAB Tech Lab’s Sustainability Playbook and Ad Net Zero’s Global Media Sustainability Framework offer guidance on measuring and reducing emissions in media planning and buying, which is where DSP automation can help by trimming waste, shortening supply paths, and prioritizing cleaner inventory.

Practically, buyers should expect DSPs to add carbon-aware optimization knobs (e.g., preferring direct supply routes when performance is equal), log-level reporting that exposes the emissions impact of different paths, and curation marketplaces that balance ROI and CO₂. 

💡 For a tactical view of how supply controls intersect with performance, see Supply-side optimization with AI. 

📌 What this means for your roadmap: plan for AI that can (1) predict value at the user and impression level, (2) generate and rotate creative variants in minutes, (3) orchestrate across CTV and the open web with shared identity and measurement, (4) plug in as an independent service layer, and (5) optimize for business KPIs and environmental impact.

Conclusion

AI now sits at the core of how a DSP plans, prices, and learns. 

Modern DSPs leverage machine learning for everything from bid calculation and audience discovery to fraud detection and creative generation. These capabilities deliver measurable benefits: better targeting, reduced costs, improved ROI, and actionable insights that inform strategic decisions.

Yet significant challenges remain. Privacy regulations constrain data usage, algorithmic bias threatens fair advertising distribution, and the complexity of AI systems creates adoption barriers for many advertisers. Balancing automation with human control proves particularly crucial, as over-reliance on AI can lead to strategic stagnation.

Looking forward, AI will continue advancing DSP capabilities through improved prediction, generative content creation, and cross-channel orchestration. The next five years will bring AI-as-a-Service models that democratize access to sophisticated algorithms, while sustainability concerns will drive efficiency improvements that benefit both advertisers and the environment.

For advertisers using AI-powered DSPs, five actions will maximize success:

1. Anchor automation to business outcomes. Define your primary KPI (e.g., new-customer ROAS, incremental revenue, qualified leads) and set the measurement plan up front—combine data-driven attribution with incrementality tests and, where useful, MMM. Feed those learnings back into bid strategies and budgets.
2. Invest in durable data and consent. Build first-party datasets with clear permissions. Pair them with contextual and modeled signals so targeting and frequency still perform as identifiers fade.
3. Keep humans in the loop. Use governance guardrails: brand-suitability policies, fairness checks, and override rules for when model behavior conflicts with strategy. Review high-impact creative and targeting changes before scale-up.
4. Curate supply on purpose. Prefer auditable paths (PMPs, curated marketplaces) and require log-level transparency. Treat supply-path optimization as a performance lever, not a procurement exercise.
5. Test creatively, then scale what wins. Use genAI to produce safe, on-brand variants. Combine with DCO and holdouts so you scale only the combinations that show lift, not just clicks.

If you’d like support executing this playbook, reach out to us at AI Digital. We operate with a DSP-agnostic, client-first model and can plug in wherever you need help: strategy and planning, managed execution across channels, supply curation, and AI-enhanced optimisation. Reach out today, and we’d be glad to map these ideas to your goals and current stack.