The advertising production engine is undergoing significant transformation. Marketers face increasing pressure to generate more content across more formats with static or shrinking resources. This dynamic often leads to inefficient creative processes, budget overruns, and delayed launches. The XR State of Ad Ops 2026 Report, a comprehensive industry-wide study surveying over 400 marketers and creatives, sheds light on these critical trends, challenges, and opportunities shaping the rapidly evolving advertising landscape.
The Pressures on Modern Ad Operations
Today’s ad operations are characterized by a paradox: a relentless demand for increased creative output coupled with constrained resources and operational inefficiencies. This creates a significant challenge for enterprises aiming to maintain brand presence and engagement.
Content Volume and Resource Strain
The report highlights that 81% of marketers have seen their content production increase in the past year, with 70% producing more ad content. Despite this surge, only 30% believe their resources are keeping pace with demand. This imbalance forces teams to stretch existing personnel and budgets, often leading to burnout and compromised quality. For a large telecom provider, this could mean needing to generate hundreds of localized ad variations for multiple service offerings across digital, social, and broadcast channels, straining in-house creative and agency resources alike. Without adequate resource allocation or process optimization, the creative pipeline becomes a bottleneck, impacting time-to-market for new campaigns and product launches.
Budget Overruns and Visibility Gaps
Despite widespread confidence in financial oversight—92% of respondents rated their visibility into production-related costs as “good” or “excellent,” and 84% reported strong alignment between creative and finance teams—the reality is often different. A striking 70% of marketers report going over budget at least some of the time, and 98% experience late campaign launches. For brands, scope creep and unplanned revisions are primary drivers of budget overages. Agencies, conversely, attribute budget issues to shifting platform and channel requirements. This gap between perception and reality indicates a need for more granular tracking and real-time budget transparency. A global B2B SaaS company, for example, might find individual campaign components exceeding their allocated spend by 15-20% due to multiple rounds of stakeholder feedback and legal reviews, impacting the overall marketing budget and subsequent campaign investments.
Creative Bottlenecks and Wasted Assets
Key bottlenecks consistently slow down the creative process. Budget approvals are a consistent workflow bottleneck for 46% of marketers, causing friction between Chief Marketing Officers (CMOs) and CFOs. Creative concepting (38%) and asset versioning (32%, and the number one bottleneck for brand marketers) also contribute significantly to delays. These internal process breakdowns extend beyond just budgets, impacting legal approvals, handoffs, and content localization efforts. Compounding these issues is the alarming rate of wasted creative: nearly half of all marketers admit to using less than 70% of the assets they produce. Campaign direction changes post-production (44%), assets failing to perform in testing (33%), and budget reallocation (29%) are leading causes of unused content. This represents a direct loss of investment and an inefficiency in the creative supply chain.
Summary: The current ad ops environment is characterized by escalating content demands, budget misalignments, and significant workflow friction. Senior leaders must address these fundamental issues to prevent resource drain and maximize campaign effectiveness.
Evolving Operating Models and Investment Priorities
As enterprises grapple with content demands and budget constraints, operating models are shifting, and investment priorities are realigning across media channels.
In-Housing vs. Agency Dynamics
The report indicates a clear pattern in how work is distributed: early-stage creative and social content are increasingly handled in-house, while more complex tasks remain with agencies. Specifically, 58% of ad versioning, resizing, and adaptation are done in-house, alongside 51% of social content and 44% of branded content. Agencies, on the other hand, handle 51% of post-production, VFX, and finishing, 44% of animation and motion graphics, and 40% of full production with cast and crew. This hybrid model allows brands to maintain control over brand voice and agility for high-volume, quick-turn content, while leveraging specialized agency expertise for high-production-value assets. For a large retail enterprise, this could mean an internal team manages daily social media posts and localized promotions, while external agencies are commissioned for major seasonal campaigns requiring extensive CGI or talent.
What this means for leaders: Organizations must clearly define the scope and handoff points between in-house teams and external agencies. Establish clear SLAs (Service Level Agreements) for both internal and external workflows to ensure consistency in delivery times and quality. Implement a robust digital asset management (DAM) system that can be accessed by both internal and external partners, with clear version control and approval workflows.
Strategic Allocation of Creative Resources
While social channels capture the most attention, the majority of the advertising budget is directed towards CTV, linear television, and podcasts. For brands, CTV is the top investment priority, followed by linear and podcasts. Agencies, however, prioritize linear TV first, then podcasts and CTV. This divergence underscores the need for a unified media strategy that aligns creative production efforts with budget allocation across platforms. A financial services firm might allocate significant budget to CTV for brand awareness campaigns targeting high-net-worth individuals, even if their social media team is generating the highest volume of daily content. The key is to ensure that creative assets are purpose-built or efficiently adapted for these high-value channels, avoiding the common issue of assets being built for the wrong specs.
What to do: immediate priorities (first 90 days)
- Audit current resource allocation: Map creative team bandwidth, agency contracts, and media spend against actual content production needs for major campaigns. Identify areas of misalignment.
- Standardize brief templates: Implement a mandatory, comprehensive brief template for all creative requests, detailing objectives, target audience, format requirements, budget, and approval timelines. This reduces scope creep and rework.
- Implement production tracking: Deploy a project management system (e.g., Workfront, Monday.com, Asana) or specialized ad ops platform to track creative assets from concept to launch, including real-time budget consumption and approval status.
- Establish clear approval matrices: Define roles, responsibilities, and thresholds for creative and budget approvals. For example, creative changes over 10% of original scope require C-level approval, while minor text edits can be approved by a brand manager.
AI’s Transformative Role in Ad Production
Artificial intelligence is rapidly moving from an experimental tool to an integral part of ad production workflows, fundamentally reshaping how content is created and optimized.
AI Adoption and Key Use Cases
The report reveals significant AI integration: 88% of major brands and agencies are actively using or piloting AI in their advertising production, with nearly half leveraging it daily. Full-service agencies lead daily use (62%) to scale VFX, ad testing, and storyboarding output. Production companies (79%) actively use AI in production workflows or pilot tools for specific projects. National and global brands (42%) use AI to accelerate campaign brief writing, image creation, scripting, and voice-overs. Media agencies (36%) primarily use AI for experimentation. Top AI applications across all advertising operations include visual effects (VFX) and compositing (45%), video and motion generation (42%), performance analysis and creative testing (44%), and scripting and copywriting (41%). This widespread adoption signals a shift from curiosity to practical application across the creative lifecycle.
AI for Speed, Not Just Savings
Contrary to expectations, AI’s primary business objectives are centered around speed and creative enhancement, not solely cost reduction. Improving creative quality or personalization (41%) and speeding time to market (38%) are the top priorities, with reducing production costs ranking a distant third (23%). This indicates that leaders view AI as an enabler for performance and efficiency rather than a tool to replace human creative talent. For a global e-commerce brand, AI could generate 50-100 unique ad copy variations in minutes, then analyze their projected performance based on historical data, accelerating the launch of highly personalized campaigns without diminishing creative integrity.
Governance for AI-Generated Content and Talent
As AI’s capabilities expand, the use of synthetic talent and digital replicas is becoming more common, particularly within full-service agencies, where 31% already adopt synthetic talent. This introduces a critical need for robust governance frameworks. While AI can recreate a younger version of a celebrity for an ad campaign (e.g., Adidas’s use of a younger David Beckham), this raises complex questions around rights, approvals, consent, and compensation for original performers and their digital likenesses. Legal and ethical considerations are paramount.
Governance and risk controls:
- Develop AI usage policies: Define acceptable uses of AI for content creation, data analysis, and talent generation. Specify guidelines for brand voice, image accuracy, and ethical content representation.
- Establish clear consent protocols: For digital replicas or synthetic talent, secure explicit, legally binding consent from individuals for the creation, use, and distribution of their AI-generated likeness, including compensation models.
- Implement content verification: Establish processes to verify the originality and ethical sourcing of AI-generated content, mitigating risks of plagiarism, copyright infringement, or bias propagation.
- Red-teaming AI outputs: Proactively test AI-generated content for unintended biases, brand safety violations, or misrepresentations before public deployment.
- Define escalation paths: Clearly outline who is responsible for reviewing and approving AI-generated content, especially in sensitive areas like regulatory compliance (e.g., for healthcare or financial advertising).
Conclusion
The XR State of Ad Ops 2026 Report underscores a period of significant pressure and opportunity for marketing and CX leaders. Addressing the persistent challenges of content demand, budget overruns, and workflow bottlenecks requires a strategic approach to operational efficiency and thoughtful AI integration. Leaders who prioritize transparent budget management, optimize their in-house and agency operating models, and implement robust governance for AI-driven creative will be best positioned to drive measurable outcomes and maintain competitive advantage.
What to Do / What to Avoid
What to Do:
- Implement Integrated Production Platforms: Adopt platforms that provide end-to-end visibility of the advertising lifecycle, from brief to payment and delivery. This centralizes cost management and enables real-time tracking of production.
- Formalize Handoffs and Approvals: Define explicit approval gates with clear stakeholders and timelines. Use RAG (Red-Amber-Green) status indicators in project management systems to highlight bottlenecks proactively.
- Optimize Asset Utilization: Track the usage rate of creative assets. Implement post-campaign analysis to understand why assets are unused (e.g., “asset did not perform well in testing” 33%, “technical specs not met” 24%) and refine production processes accordingly.
- Invest in AI for Speed and Personalization: Prioritize AI deployments that accelerate creative iteration, enhance personalization, and improve time-to-market. Target AI for high-volume, repetitive tasks like initial script drafts, basic image variations, and performance analysis.
- Develop Comprehensive AI Governance: Establish policies, consent frameworks, and compensation models for synthetic talent and AI-generated content. Ensure legal and ethical compliance is embedded from the outset.
What to Avoid:
- Optimizing for a Single Metric: Do not focus solely on creative output volume or ad containment without considering asset utilization, budget adherence, or campaign performance (e.g., Conversion Rate, CSAT/NPS).
- Allowing Scope Creep: Without clear initial briefs and change management protocols, scope creep will inevitably lead to budget overruns (70% experience this) and project delays.
- Fragmented Tooling: Relying on disparate systems for project management, budgeting, and asset management creates data silos and hinders holistic visibility into ad operations.
- Treating AI as a Cost-Cutting Tool First: While AI can offer efficiencies, its primary value in ad ops is currently in accelerating creative processes and improving personalization, not replacing high-value human creative talent.
- Neglecting Legal and Ethical AI Implications: Deploying AI-generated content or synthetic talent without explicit consent, clear usage rights, and proper compensation frameworks introduces significant legal and reputational risks.
