Guide
The scale imperative.
Maximizing enterprise impact with AI-powered content production.
Unlock enterprise-scale impact from AI-powered content production.
Modern marketing’s challenge isn’t ideas, it’s scale. As customers demand increasingly tailored experiences, the gap between what marketing organizations need and what they can deliver grows wider. Traditional production models can’t keep pace, and while GenAI shows promise, most pilots stall after early wins due to gaps in quality, governance, and integration.
This guide outlines a three-stage framework to overcome the content bottleneck: build a durable foundation, close gaps in unmet demand, and enable real-time personalization at the edge. Together with Accenture, we surveyed marketers and applied expert analysis to quantify the financial upside of enterprise-scale production. Grounded in this research, the report offers practical steps across people, processes, and technology to help CMOs move beyond pilots to enterprise-wide impact—minimizing waste, accelerating value, and creating a sustainable competitive advantage.
The value of scaled asset production.
To drive growth, marketers must now deliver increasingly targeted experiences at greater speed and scale than ever before. The result is an explosion in the depth and breadth of assets needed, turning content supply into a bottleneck for both marketing execution and enterprise expansion.
Leaders first saw GenAI as a silver bullet to solve this bottleneck, yet most initiatives lose momentum after early wins. Pilots succeed in isolation but cannot scale across the enterprise. The result: a stall-out as teams confront quality, governance, brand adherence, and martech integration gaps.
More than
of creatives’ challenges with content creation relate to asset scaling.
Source: Marketing and Creatives Survey
To avoid this, CMOs must think holistically about scaled production, following an approach that lays a durable foundation first, and then rapidly tackles key areas of value. When done right, this approach unlocks not only efficiency gains, but also faster speed to market and outsized revenue growth.
Our financial model estimates that this transformation can yield up to a 8.5X net ROI over three years, or roughly $55M in incremental value per year for a $30B organization.1 Across industries, ~75% of this upside stems from net-new revenue, while the remaining ~25% comes from efficiency and productivity gains.
1. Calculated based on a $30B organization, to represent an industry average for enterprise-sized organizations. See econometric analysis methodology in the Appendix.
To build a solution that stands the test of time, we recommend leaders focus on three progressive stages:
- Stage 1 – Build a durable foundation for scaled production: Deploy a solution rooted in long-term business and technical priorities, demonstrating early wins to build momentum.
- Stage 2 – Close the gap on unmet demand: Move quickly from efficiency gains to revenue-generating use cases, supporting existing campaign and customer experience needs.
- Stage 3 – Deploy real-time content personalization: Connect AI-driven content production to data and journey orchestration capabilities, enabling intelligent creation at the edge.
As efficiency gains fund the journey, each stage layers on incremental revenue, compounding results over time. What begins as reclaimed hours and reduced production costs in Stage 1 becomes fuel for faster test-and-learn cycles in Stage 2 — unlocking deeper engagement, higher-performing creative, and faster market expansion. By Stage 3, those learnings feed real-time personalization at the edge, turning every customer interaction into both a revenue event and the insight that powers the next one.
The sections that follow dissect each of these stages, flagging common pitfalls, spotlighting the most significant opportunities, and prescribing key actions across people, processes, and technology to ensure long-term success.
Stage 1
Build a durable foundation for scaled production.
Most organizations recognize the value of scaling content production with AI, yet often take a tactical approach, focusing only on near-term outcomes rather than on a solution that can support all aspects of the marketing operation. Common pitfalls include:
- Over-indexing on tech without business alignment: Results in limited adoption or tools that fail to meet company goals, ultimately creating more friction than value.
- Overlooking critical standards for enterprise-grade content: Leads to gaps in brand adherence, quality, IP protection, and legal compliance, driving delays and excessive re-work.
- Not engaging key users early in the process: Causes missed opportunities to inform new workflows, leading to low adoption and slower time to value.
- Treating scaled production as a siloed function: Limits impact due to disconnects with upstream and downstream systems and processes in the content supply chain.
While these missteps can limit impact, organizations that establish a foundation for scaled production can realize value while setting up a scalable operation. Early benefits typically center on creative and operations teams streamlining highly repetitive tasks, such as resizing assets across channels, replacing objects and backgrounds, creating local variations of promotional videos or banners.
Repurposing assets consumes
of creative teams’ time, costing enterprises millions of dollars per year in inefficient labor.
Source: Marketing and Creatives Survey
How to unlock value
To avoid common pitfalls, organizations should focus first on defining overarching goals and enterprise principles for content creation. Once this is established, the following needs across people, process, and tech should be addressed:
People
Create early advocates and enablers. Identify key domain experts and “power users” to inform, test, and ultimately evangelize the solution. Creative teams will play a critical role here and see early benefits, both from elevating their craft and from new opportunities to expand their roles. Key groups to focus on include:
- Core creatives: With a scaled production solution, these teams will spend less time on time-consuming tasks and refocus it on higher-value work, such as ideation, testing, and curation of new concepts.
- Creative technologist: A critical emerging role that will collaborate with brand and IT teams to create and deploy design systems for the organization, inclusive of AI models, templates, brand services, and scaled workflows.
Process
Clarify decision-making and priority workflows to tackle. Define how the organization will leverage scaled production, and how decisions will be made:
- Identify decision owners and governance for the new capability and share this information widely across key stakeholder groups.
- Prioritize initial workflows and content types for scaled production, along with an initial view of the next opportunities. Perhaps more importantly, define where not to overindex on AI and automation (e.g., brand campaigns, core photography, etc.).
- Proactively map upstream and downstream workflows that will need to evolve as production scales (e.g., campaign brief creation, metadata tagging, QA and approvals).
Tech
Define technical, AI and brand requirements. Align on core needs within IT and martech teams, as well as critical design standards to:
- Ensure creative, brand, legal and security teams are included, covering elements such as IP protection, commercial usability, brand customization, and support for essential formats (e.g., image, vectors, video, 3D).
- Identify interoperability needs with existing creative and marketing execution tools (e.g., workflow automation and digital asset management systems).
- Confirm that scaled content production outputs align with the organization’s security and AI ethics principles.
Bringing it to life
Estée Lauder scales content production with Adobe.
As the parent company to over 25 iconic brands, Estée Lauder sought to accelerate customer acquisition and maintain mindshare. The company leveraged Adobe Firefly Services to streamline production and meet the staggering demand for hundreds of thousands of assets. Yuri Ezhkov, vice president, Creative Center of Excellence, noted: “We have a trusted partner in Adobe to provide generative AI technologies that are safe for commercial use, with tools that enable our design teams to operate more nimbly and be free to focus on ideating.” Read more here.
Stage 2
Close the gap on unmet demand.
As scaled production matures, solving unmet growth needs across products, markets, segments, and marketing moments can unlock significant revenue gains. The following are key use cases to prioritize:
- Personalizing content: Expanding production to more granular audience segments can lift engagement and conversion while surfacing new insights into the audiences themselves (more granular content = more granular data and vice versa).
- Localizing assets: Scaling content to meet regional needs increases relevance and expands market share. When done right, it can also safeguard the brand from “rogue” creation by under-supported local teams.
- Faster content refreshes: Enabling teams to update experiences more quickly and frequently is especially critical for growth marketing efforts and seasonal campaign strategies.
- Deeper experimentation: Boosting asset permutations enables a new level of A/B testing–experimenting more deeply with attributes, subjects, backgrounds, etc., ultimately surfacing winning combinations for target audiences.
How to unlock value
To realize this opportunity, organizations must enable and evangelize additional functions, define a new set of business processes, and ensure deeper interconnectivity across the marketing tech stack. Consider the following actions across people, process, and tech:
People
As scaled production expands, ways of working will need to change across a wider set of marketing teams. The following functions should be prioritized for readiness:
- Campaign planning and execution: Prepare to run campaigns faster, more frequently, and with higher levels of targeting.
- Marketing operations: Revisit existing processes across the content supply chain to reflect a deeper content pipeline (e.g., asset routing, tagging, review and approvals).
- Testing and optimization: Be ready for faster and deeper levels of testing to optimize content. Evaluate existing audience and journey practices that can be leveraged.
- Data and technology: Ensure that content data, architecture, and activation capabilities are connected and ready to support broader and faster execution.
Process
Revisit select workflows to streamline execution. Marketing teams will need to examine and adjust current processes to support scaled production. Teams should:
- Leverage AI and automation to auto-tag metadata and route asset variants to the right repositories and subfolders in the DAM.
- Define standards for review and approvals. Once hero assets are approved, AI-assisted audits on quality and brand compliance may be sufficient for most derivative assets produced at scale.
- Ensure local nuance is preserved by empowering regional teams to apply last-mile edits after central production, optimizing for tone, cultural sensitivities and local needs.
Tech
Advance integration with the content supply chain. Ensure interoperability and more advanced data practices for content. Below are a few areas of focus:
- Ensure scaled production capabilities are integrated into upstream and downstream systems (e.g., campaign planning tools, DAM, owned, earned and paid activation channels).
- Advance content data strategy and taxonomy to streamline activation, inclusive of defining and enforcing new metadata fields for asset variants.
- Create pathways to share performance insights across teams like design, brand, and creative technologists to fine-tune creative direction and enterprise-wide design systems.
Bringing it to life
Driving immersive ecommerce experiences at Gatorade
In October 2024, Gatorade launched a pioneering digital experience on its ecommerce platform that enables consumers to personalize its iconic squeeze bottles. Using Adobe Firefly Services, the brand generated hundreds of thousands of unique, on-brand designs, empowering creative self-expression for their athletes that boosted both engagement and brand loyalty. Xavi Cortadellas, senior director of marketing at Gatorade, noted, “Now athletes everywhere can engage with our brand in a fun, accessible way... personalization possibilities are virtually endless, deepening our direct connections with consumers and reinforcing brand loyalty.” Read more here.
Stage 3
Deploy real-time content personalization.
Customer journeys now unfold in milliseconds, spanning apps, web, email, marketplaces, and live experiences. Yet most creative workflows follow a waterfall process, missing the moment and limiting marketing agility. This stage marks a shift from treating asset production as a standalone step to embedding it as an activation layer to power personalization at scale.
Forward-thinking marketers are already mapping plans to integrate AI, customer data, and edge decisioning to create and deliver context-aware content in real-time. This shift brings added complexity, requiring tighter alignment across people, tech, and workflows. The question is no longer if content should scale — it’s how it plugs into the existing operation as an intelligent, responsive capability.
The following capabilities will be worth initial focus as you enter this phase:
- Dynamic content creation: Generating assets at the edge based on triggers and live signals from your customer experience orchestration engines.
- Unified content profiles: Applying customer data principles to content; mapping attributes to asset variants such as segments, campaigns, style, tone, color, layouts, and objects used.
- Self-optimizing systems: Enabling AI to continuously test, learn, and iterate, scaling the most effective assets and attributes for each audience to maximize performance gains.
How to unlock value
At this juncture, organizations must shift mindsets as well as operations to think of content as a responsive, intelligent capability integrated into personalization platforms. This will enable nuanced, sequential storytelling, where each message builds on the last to meet customers with relevance and precision at every moment. Think of the actions below as you enter this phase:
People
Advance governance for real-time production. As content creation moves to the edge, a new and expanded set of roles will be needed. We recommend starting with the following:
- Intelligent Content Centers of Excellence (COEs): A cross-functional body charged with advancing the capability and providing overall governance. This team should be connected to — but distinct from — existing data, AI, or personalization COEs to ensure focused execution.
- Content architects: An existing role that will expand from defining and maintaining data frameworks for content (e.g. taxonomy, metadata models), to enabling this data to trigger creation in the moment.
- AI content engineers: These team members will design content workflows triggered by data signals, working closely with content architects, decisioning experts and creative technologists in leveraging generative and agentic AI capabilities.
Process
Rethink business processes to support edge creation. To unlock the full opportunity, organizations must move beyond static or waterfall-type workflows and build flexible, responsive processes for dynamic asset creation. Consider taking the following actions:
- Define clear guidelines for when real-time content creation adds value as not every experience will require 1:1 content personalization in the moment.
- Align real-time content needs to priority journey moments, mapping content by funnel stage, audience segment, and channel fit to trigger dynamic creation and assembly.
- Extend existing data-driven triggers and decision rules that guide journey automation to content, ensuring the right asset is created at the right time, for the right customer.
- Institute AI guardrails that stipulate how far models may alter copy, imagery, logos, or offers — keeping all outputs within brand-approved templates, tone, and pricing parameters.
Tech
Advance the tech stack to power an always-on content engine. To seize this opportunity, organizations must build a fully integrated tech and data ecosystem. Consider the following actions to enable production of content at the edge:
- Define and implement a content data schema and taxonomy to govern the parameters of real-time content creation.
- Select (or upgrade to) content activation platforms that support edge creation and delivery, so assets render within milliseconds and as close to the end user as possible.
- Ensure tech stack integration across customer data, journey orchestration, media platforms, and measurement system — without this, content will remain siloed, limiting personalization and blocking insights from flowing back into production.
- Route engagement, conversion, and other performance metrics back to the decision engine and core creative teams, enabling models and humans to co-optimize experiences without additional manual lift.
Moving into action.
1. Executive accountability, not just sponsorship
Marketing leadership must own the mandate and set the long-term vision, tying AI investments to business growth and driving the shift from siloed pilots to a holistic solution for the enterprise.
2. Flexible, value-driven investment
Shift away from annual budget rigidity. Adopt venture-style funding that doubles down on what works and pulls back from what doesn’t; rapidly scaling content engines, personalization models, and data integrations that drive results.
3. Change as a core competency
Future-ready marketing organizations embed change as core to their day-to-day. That means rethinking roles, retraining teams, and redesigning workflows on an on-going basis to turn AI from a tool into a growth multiplier.
Act decisively on these enablers and the phase-specific guidance in this paper to transform early wins into a sustained competitive advantage.
Methodology
Research approach: Adobe and Accenture partnered to estimate the financial impact of a content creation and production transformation. Insights are rooted in four research components: survey data, expert interviews, econometric analysis and secondary research.
Marketing and Creatives survey: A 30-question online survey gathered insights from 1,047 creatives, marketing and other teams who actively work on content creation during October and November 2024.
- Geography: Respondents were based in the US and Canada.
- Roles: All respondents held full-time positions at the Manager level or above within marketing or advertising function and are extremely involved in content design, content strategy, content production, marketing analytics and performance measurement, or brand management.
- Industries: Retail (n=229), Consumer Packaged Goods (n=200), High Tech (n=150), Financial Services (n=150), Pharmaceuticals (n=150), and Media and Entertainment (n=168).
- Statistical significance: Survey results maintain a margin of error of ±8 percentage points for percentage-based statistics and ±2 percentage points for estimates such as averages, assuming a 95% confidence level for both.
Expert interviews: 38 individual interviews were conducted to gain qualitative insights across industries. The following subject matter experts were interviewed:
- 9 creative and design experts (panel of Accenture Employees)
- 15 Accenture industry experts (3 in Retail, 4 in CPG, 1 in Financial Services, 2 in Pharma, 3 in High Tech, 2 in Media and Entertainment)
- 8 Accenture marketing transformation experts (panel built including former CMOs and advisors)
- 6 Adobe experts (comprised of marketing and industry leaders)
Econometric Analysis: The following outlines the assumptions and sources used to build the value case model to quantify the value of transforming content creation and production workflows:
- Company size: All calculations based on an organization with $30B annual revenue
- Value capture: 30% in Year 1, 50% in Year 2, and 100% in Year 3
- Risk adjustment: 20% to reflect realistic estimates
- Discount rate: 10%
- Costs: Projected ROI calculations based on estimated benefits and estimated costs of implementing AI-tools
- Estimated benefits: Projections are based on survey respondents’ self-reported estimates on the impact of AI-tools in current end-to-end workflows in content creation and production. Findings from the survey pool were then validated by Accenture experts by industry based on observed outcomes from supporting clients in implementing Gen-AI in marketing functions.
- Estimated AI-tool implementation costs: Calculations based on estimated incremental costs for technology licensing and support (provided by Adobe), and change management and training required to realize value opportunities.
- Operational efficiency: Includes time savings from streamlined tasks, increased asset reuse, and simplified review and approvals (based on fully loaded hourly costs), in addition to reduction of hard costs (e.g., physical production costs and agency spend)
- Revenue growth: Projected revenue lifts from enhanced content impact from more personalized and targeted campaigns and higher volume of content, based on industry average CTRs, impressions and conversion rates
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