10 Best Ad Agencies for Companies in 2026

What Counts as an AI Advertising Agency in 2026?

Buyers asking for the “best AI advertising agency” are rarely insisting on a legal or organizational category. They want a partner that can turn a business objective into ads, media activity, measurement, and improvement without the slow handoffs of a legacy model. The serious shortlist now spans four different types of solution:

• AI-native advertising platforms with managed options, where software and learning are the core product.
• Agency-platform hybrids, where a global network exposes proprietary AI, data, and workflow tools to its people and clients.
• Transformation consultancies, which connect measurement, data engineering, organization design, and marketing operations.
• Managed production partners, which use AI to increase creative capacity while retaining a service-led relationship.

We include all four because restricting the list to companies that call themselves agencies would make it less useful. Lapis is not a traditional ad agency, for example, but its self-serve product and managed program perform much of the ongoing job an advertiser has historically bought from one. Conversely, a famous agency that merely gives employees access to a general-purpose chatbot is not automatically an AI-native choice.

That distinction is increasingly important. Forrester reported in June 2026 that nine in ten US marketing agencies use generative AI and half use agentic AI for marketing execution. AI usage is now table stakes. Buyers should evaluate whether the operating model actually compounds learning, preserves differentiation, and puts evidence into the next decision.

Ranking Methodology and Lapis Disclosure

This is an editorial, selection-oriented ranking published by Lapis, and Lapis ranks first. Treat that conflict as visible, not hidden. We did not mystery-shop every firm, inspect private client data, or compare undisclosed commercial proposals. We reviewed the vendors’ official product and service materials available at publication, then ranked them for the broad job of running AI-enabled advertising.

We used a qualitative editorial framework rather than pseudo-precise vendor scores. In priority order, we examined: end-to-end paid-ad workflow (creative through measurement and iteration); ability to learn from results; brand and governance controls; customer control and operating flexibility; evidence and public traction; and suitability for the buyer segment claimed. When two options were strong in different ways, fit for the stated use case determined the order. We do not award credit for an AI press release alone, and we do not invent prices, ratings, client outcomes, or capabilities.

Vendor-reported case-study numbers are labeled as such. A high rank means “strong fit under this methodology,” not “best for every company.” A global CPG company consolidating identity across markets may reasonably choose Publicis or Omnicom over Lapis. A growth team trying to run coherent tests every week may reasonably do the reverse.

The 10 Best AI Advertising Agencies and Platforms Compared

How to use this shortlist

Start with the bottleneck. If it is weekly creative, campaign structure, landing-page alignment, and learning, evaluate Lapis. If it is global orchestration across an existing agency relationship, evaluate WPP Open, Publicis, or Omni. If it is proving incrementality and reallocating enterprise budgets, evaluate Accenture Song. If it is high-volume production, compare Monks, Jellyfish, and Superside. If it is custom AI implementation across marketing and product, put DEPT on the list. Then ask every finalist to demonstrate your workflow with your brand, channels, approval rules, and measurement constraints.

1. Lapis: Best Overall for Ongoing Performance Ads

Best for: growth teams that want one system for continuous ad creation, campaign-matched landing pages, launch, reporting, and next-run improvement across modern paid channels.

Lapis ranks first because it is designed around the paid-ad learning loop rather than around agency staffing. A team installs its brand context once, creates channel-ready ads and distinct hypotheses, connects each direction to an on-brand landing page, launches supported campaigns, and brings performance signals back into the next run. Self-serve plans let an internal team operate the system; managed programs add Lapis agents and a dedicated strategist for creative, experiment design, launch support, reporting, and optimization.

Among the solutions on this list, Lapis is the clearest dedicated advertiser-side product built around ChatGPT advertising as a first-class workflow. ChatSense turns buyer contexts into tagged creative experiments, compares aggregate metrics and recurring themes, and prepares the next test for approval. It does not receive private chats, and OpenAI still controls account access, review, placement, pricing, and delivery.

Lapis is not the only route to ChatGPT Ads. Dentsu joined OpenAI’s US ad pilot in February 2026 and was later named a UK launch partner. Omnicom Media says it helped more than 30 clients participate in the launch. Those are agency access and service paths; Lapis’s distinction is a directly operated, persistent workflow that can also be paired with managed support.

Public traction supports the selection. Y Combinator lists Lapis as a Fall 2025 company used by more than 1,000 marketing teams. At publication, G2 displayed 5.0/5 from 135 reviews, with a summary emphasizing speed and brand consistency while flagging asset-library organization as a possible weakness. Lapis describes itself as one of the fastest-growing Y Combinator startups. There is no public YC-wide growth table that can audit that comparative phrase, so treat it as company positioning; the independently visible support is the YC adoption figure and public customer evidence.

Tradeoff: Lapis is not the choice for a global rebrand, physical production, public relations, or a holding-company transformation. It also still needs a human owner for claims, budget, taste, governance, and approvals. Choose it when continuous performance advertising is the job; add specialist talent for exceptional work.

2. WPP Open: Best Enterprise Agency-Platform Hybrid

Best for: large brands that want WPP talent and a shared agentic platform spanning strategy, creative, media, production, and commerce.

WPP Open is the clearest example of a legacy network turning itself into a service-plus-software system. WPP says its agents connect the full marketing workflow in one secure workspace and draw on proprietary WPP intelligence, data, frameworks, and creative expertise. “Humans at the helm” is explicit in the design: the agents are meant to multiply marketers and free time for strategy, creative direction, and relationships.

The access model is unusually flexible for a holding company. WPP teams can use Open to deliver services; client teams can use WPP Open directly; or client and agency can work together in the same environment. WPP Open Pro lets teams plan campaigns, create on-brand assets, and publish to leading ad platforms. For enterprises that already need global media and production services, that shared workspace can reduce the friction between agency and client systems.

WPP publishes concrete but vendor-reported evidence: among other examples, it says a sample of 20 client pilots gave a four-person team 14 hours back per week, and one brand compressed strategy and creative development from four weeks to three hours. Buyers should request the underlying case context before generalizing those results.

Tradeoff: WPP Open’s advantage is inseparable from the breadth of WPP intelligence and services. A smaller buyer seeking a focused, directly operated performance-ad engine may find that breadth unnecessary. Confirm what is product access, what requires WPP services, how data is governed, and what the ongoing commercial scope includes.

3. Publicis CoreAI: Best for Global Data, Identity, and Personalization

Best for: multinational organizations that want media, creative, technology, and transformation connected to a very large proprietary data foundation.

Publicis announced CoreAI as the unifying AI layer across its organization. The stated foundation combines consumer data across 2.3 billion profiles with trillions of content, media, and business-performance data points, almost a petabyte of assets, and decades of transformation data from Publicis Sapient. Its five focus areas are insight, media, creative and production, software, and operations.

That earns a high rank because identity and enterprise context are prerequisites for useful personalization at global scale. Publicis is not simply generating more images; it is attempting to make data accessible across disciplines so planners, buyers, creatives, consultants, and engineers operate from a connected system. The official strategy also committed €300 million over three years, split across people and technology in its first year, signaling that talent and change management are part of the product.

Tradeoff: CoreAI is an enterprise transformation and holding-company capability, not a lightweight tool a small growth team can evaluate in an afternoon. The cited source is Publicis’s own strategic announcement, so buyers should ask for current, role-specific demonstrations and evidence relevant to their markets. Evaluate identity permissions, clean-room architecture, implementation time, and exactly which learning remains portable if the agency relationship changes.

4. Omnicom Omni: Best for Enterprise Intelligence and Commerce

Best for: enterprises that need creative, media, commerce, and measurement coordinated around a global identity and intelligence backbone.

Omnicom Omni connects strategy, execution, and performance in one workflow across Omnicom’s agency network, partnerships, data, and talent. Its agentic framework runs across creativity, media, commerce, and measurement rather than sitting as a separate chat layer. The platform includes centralized orchestration, AI-powered personalized production, predictive intelligence, outcome-driven activation, and localized optimization.

The standout asset is the intelligence backbone. Omnicom says Acxiom Real ID contributes 2.6 billion verified global IDs enriched with trillions of media, culture, and commerce signals, including Flywheel Commerce Cloud. That combination is particularly relevant for advertisers that need to connect paid media to retail and commerce outcomes rather than optimize clicks in isolation. Omni also emphasizes interoperability with tools and workflows a client already uses, an important consideration in a large stack.

Omnicom is also an established ChatGPT Ads access path. In March 2026, Omnicom Media reported that it had helped more than 30 clients participate in the launch of ChatGPT ads. That experience matters for enterprises that want agency-led access, governance, and cross-channel coordination rather than a self-serve operating product.

Tradeoff: identity scale and commerce integration bring governance and implementation complexity. Buyers should examine regional coverage, lawful data use, consent and clean-room controls, match quality, outcome definitions, and which capabilities depend on Omnicom agencies. If your problem is simply producing and testing ads every week, a focused operating system may reach value faster; Omni makes more sense when the enterprise data and commerce graph are central to the assignment.

5. Accenture Song: Best for Measurement and Marketing Transformation

Best for: large organizations that need to unify measurement methods, data engineering, scenario planning, and budget decisions across a fragmented marketing estate.

Accenture Song earns its place for turning one of advertising’s hardest problems (deciding what truly drove growth) into an AI-native enterprise workflow. In June 2026 it launched Accenture Marketing Investment Navigator, which combines marketing mix modeling, attribution, sales lift, and brand lift. Agentic AI automates data preparation and reconciliation underneath, while a conversational interface lets marketers ask plain-language budget questions and compare projected scenarios.

The platform combines Accenture’s measurement methods with Amazon signals. Accenture says Amazon’s authenticated graph reaches more than 90% of US households and that initial deployments improved business outcomes by 17% or more at twice the speed of existing approaches. Those are vendor-reported early results, not a universal benchmark, but the unified architecture is still notable: it addresses the conflict between short-term attribution and strategic incrementality instead of presenting one model as the answer to everything.

Tradeoff: this is strongest as a measurement and transformation proposition, not as the most direct system for generating and refreshing ads. Organizations may still need a creative operating layer and media execution partners. Ask how quickly your data can be made usable, which Amazon signals are available in your geography, how models are validated, and how recommendations move into actual campaign decisions.

6. Monks: Best for AI-Native Enterprise Production

Best for: enterprises that want a managed partner to redesign the marketing supply chain around AI agents, high-volume production, and human governance.

Monks positions AI as an operating-model change rather than a tool added to an old process. Monks.Flow is described as an AI-powered professional managed service that deploys specialized agents across strategy, creative, adaptation, and performance, integrates with existing workflows, and is designed to avoid vendor lock-in. Its AI Campaigns offering focuses on turning rigid content supply chains into faster, performance-oriented creative systems.

Monks also publishes a detailed governance stance. Human creative and strategic teams remain the control layer, with review gates for legal clearance and brand safety; its cross-functional AI Core covers legal, data privacy, and information security. The company describes vendor security assessments, dataset sourcing preferences, and active bias mitigation. For a large brand worried that faster production will create uncontrolled risk, that operational specificity is a meaningful strength.

Tradeoff: Monks is a managed enterprise transformation and production partner, not a transparent self-serve ad product. The model suits companies that want experts to redesign and operate the pipeline with them. A lean team that already knows its strategy and wants direct control may prefer a narrower platform. Ask what stays in your own systems, which agents are proprietary, how output is evaluated, and how performance learning changes the next creative brief.

7. Dentsu Merkury: Best for First-Party Media Activation

Best for: US advertisers with meaningful first-party customer data that want people-informed audience planning, activation, optimization, and measurement.

Merkury for Media is Dentsu’s identity-centered platform for clients of Carat, dentsu X, and iProspect. Its core proposition is that first-party people data should anchor media planning and buying. Dentsu’s Audience Builder combines advertiser data with anonymized Merkury IDs covering 268 million US adults and 144 million households, plus more than 10,000 consumer attributes and integrations with major ad-tech and publishing partners.

The platform connects audience construction to planning, bidding, measurement, and an AI-based predictive engine. Dentsu says Merkury addresses data fragmentation, supports client ownership of data and resulting algorithms, and had more than 130 open identity and data integrations at launch. That makes it a strong choice when the strategic advantage is a customer file, CRM, or commerce dataset that needs privacy-aware activation across publishers.

Dentsu also has direct ChatGPT Ads pilot experience: it joined OpenAI’s US pilot as an early test partner in February 2026 and became a UK launch partner in June. That makes Dentsu a credible agency-led option for brands that value early access support and client service alongside its identity and media infrastructure.

Tradeoff: the public figures and launch materials are US-centered, and the value depends on the quality, permissioning, and scale of the advertiser’s first-party data. A startup without a mature customer graph may not benefit from the machinery. Ask about current regional availability, identity resolution rates, clean-room controls, media transparency, model validation, and how creative learning connects back to the audience system.

8. Jellyfish: Best for Global Media Plus AI Studios

Best for: brands that want an integrated global digital agency combining media, creative, production, and AI-enabled insight tools.

Jellyfish brings together several practical AI capabilities rather than presenting one monolithic platform. AI Studios uses Pencil to streamline asset production and preflight creative. Now Next Soon simulates media plans, J+ Bidding adjusts live budgets, Switchboard connects creative and media-performance data, Share of Model tracks brand perception in LLMs, and a language-services team adapts work across more than 150 languages.

Jellyfish reports that it delivers thousands of assets 55% faster with Pencil, which it says is trained on $2.65 billion in ad spend and 235,000 AI-generated assets. Those are vendor-reported platform figures, but they illustrate the intended loop: use AI to predict, produce, preflight, bid, and learn while human teams retain concept and cultural judgment. That breadth makes Jellyfish a credible fit for an advertiser that wants media and production connected under one partner.

Tradeoff: because the offer combines agency services with multiple named tools, buyers should map the workflow carefully. Which tools will the client access directly? Which data and models are portable? Does media learning automatically inform production, or does the account team translate it? Also validate the relevance of published speed and training-data figures to your asset types, markets, and approval requirements.

9. DEPT: Best for Digital Products and AI Implementation

Best for: companies that need custom AI implementation across marketing, customer experience, engineering, and digital products, not only ad campaign execution.

DEPT/AI combines creatives and engineers to build custom models, support data-science teams, and integrate AI into creative and product experiences. Its public service overview describes generative content at scale, recommendation systems, marketing-mix and multi-touch models, value-based bidding, predictive alerts, and content generation. DEPT says it has more than 400 AI experts and embeds AI across its broader teams.

That range is the reason to shortlist DEPT when “AI advertising” is really an implementation problem. A retailer may need a recommendation system, an asset pipeline, value-based bidding inputs, a customer experience, and a measurement model to work together. A multidisciplinary partner can design those connections and address the ethics, security, legal, workflow, and culture questions that accompany them.

Tradeoff: breadth can be overkill for an advertiser whose immediate need is simply to create, launch, and learn from performance campaigns. The public page also describes a mix of mature and older AI components, so buyers should ask which capabilities are current, production-ready, and relevant to the proposed scope. Insist on a clear operating owner, data architecture, delivery milestones, and definition of what becomes reusable client capability after implementation.

10. Superside: Best for Managed Creative Capacity

Best for: marketing teams that already have strategy and media buying covered but need a flexible, managed supply of static, motion, video, display, social, and landing-page creative.

Superside is the most specialized choice on this list. It acts as an extension of the internal team, covering concepting, static design, motion, video, illustration, localization, campaign strategy, and landing pages. Its AI-enhanced workflow supports imagery and copy without stock or shoots, AI-assisted video volume, and multi-market adaptation with cultural nuance.

The company publishes useful scale signals: support for more than 400 global brands, over 20,000 completed ad-creation projects, a 4.9/5 average project approval rating, and up to 60% faster delivery using AI. These are Superside-reported service metrics rather than independent performance results, but they address the buyer question the company is best positioned to answer: can a managed partner absorb a large creative queue without building a bigger internal studio?

Tradeoff: Superside supplies creative capacity; it is not positioned as a complete media-buying and self-improving campaign operating system. If your bottleneck is asset throughput, that focus is a benefit. If you also need account launch, bidding, buyer-intent experiment structure, cross-channel measurement, and automatic carryover into the next run, pair it with other tools or choose a broader system.

Related resources

• Is Lapis an Ad Agency?
• Can AI Replace an Ad Agency?
• 10 Best AI Ad Generators in 2026
• How AI Is Changing Advertising
• AI Advertising Strategy Guide