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Ecommerce technology is moving marketing agencies away from routine production and toward integrated commerce work: applying AI under human supervision, turning fragmented data into decisions, improving product content, optimizing retail media, and helping brands get discovered beyond their own websites. The change is reshaping what clients need from agencies; the available evidence does not show that agencies as a whole are being replaced.
What is changing in agency work?
AI tools, retail media, commerce data, and new product-discovery surfaces are changing the tasks agencies perform and the outcomes clients expect. Faster execution is one part of that shift. Clients also need work that is accurate, measurable, relevant to their commerce systems, and distinctive enough to build a brand.
The evidence comes from different surveys of agencies, large-brand commerce leaders, and shoppers. Their percentages describe their own samples and questions, not one combined population or a universal forecast.
How is AI changing marketing agencies?
More work is assisted by AI, but oversight still matters
Forrester’s 2026 research on U.S. marketing agencies reports that nine in 10 use generative AI and half use agentic AI for marketing execution. Within that research, 81% use generative AI and 63% use AI agents primarily to enhance staff productivity and impact. Forrester also reports that agencies use generative AI for tasks including creative work, strategy, media, ideation, content, competitive analysis, and reporting.
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Some reported uses are particularly routine: 74% of agencies in Forrester’s 2026 U.S. research use generative AI to summarize documents and communications, while 70% use it for research and competitive intelligence. These figures show adoption and use, not proof that AI has improved campaign results or replaced a particular job.
Forrester reports that 63% cite accuracy and bias as generative-AI barriers, 62% cite legal concerns, and 55% cite privacy and security. Among barriers to agentic AI, 54% cite a lack of expertise and 51% cite data-infrastructure gaps. Those concerns make workflow design part of the service: agencies need to decide which tasks suit automation, connect tools to authorized client data, check outputs, and define when a person must intervene.
Efficiency is not the same as effectiveness
Forrester reports that 61% of agencies classify AI as a cost of doing business, while 31% plan to monetize agentic AI within 24 months. Those are reported views and intentions, not evidence of a settled pricing model. Forrester analyst Jay Pattisall cautions: “AI has fundamentally transformed marketing agencies, but the industry is at risk of mistaking efficiency for effectiveness.”
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For a brand, the useful question is not simply whether an agency uses AI. Ask what the saved time enables: stronger concepts, faster testing, better customer experiences, or more responsive optimization. If the only demonstrated benefit is cheaper production, efficiency may not translate into marketing value.
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Why are data and agency performance under greater scrutiny?
A CommerceIQ-commissioned Qualtrics study of 240 ecommerce leaders at brands with annual revenue of at least $300 million found that 56% named data trust and quality as their top challenge, while 46% said their data was not actionable. In the same study, 42% said they lacked time to make decisions and 40% said there was too much data to process.
The same respondents described pressure on agency relationships: 76% said their commerce teams rely on agencies, 55% said agency costs were too high relative to results, and 40% reported slow response times. CommerceIQ also reports that 49% allocate 15–30% of budget to agency fees alone. These results describe the surveyed large-brand commerce leaders; they should not be treated as a measure of every ecommerce business or agency relationship.
This combination points to a more demanding brief. An agency may need to work across product, inventory, customer, and campaign context rather than deliver an isolated channel report. KPMG identifies fragmented technology stacks, governance, cybersecurity, and talent as constraints on retail modernization, reinforcing that integration is an operational challenge as well as a marketing one. A dashboard by itself does not resolve poor data quality, unclear permissions, or a lack of decision-making capacity.
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In CommerceIQ’s commissioned 2026 study of 240 ecommerce leaders at brands with at least $300 million in annual revenue, respondents named these areas as leading AI investment priorities:
| Priority | Share of surveyed leaders naming it | What it can mean for agency work |
|---|---|---|
| Retail media optimization | 26% (CommerceIQ-commissioned Qualtrics study, 2026) | Campaign planning, budget allocation, analytics, and optimization across retail media activity. |
| Product detail page and content optimization | 19% (CommerceIQ-commissioned Qualtrics study, 2026) | Maintaining accurate product information and developing useful content variants for commerce surfaces. |
| Predictive demand | 17% (CommerceIQ-commissioned Qualtrics study, 2026) | Connecting demand signals to planning and marketing decisions, where the brand’s data and systems support it. |
These are investment priorities reported by one survey sample, not universal rankings or a forecast of agency revenue. They nonetheless show why commerce-specific operating knowledge can matter alongside traditional creative and media skills.
Retail media needs evidence, not just optimization claims
CommerceIQ respondents named retail media optimization their leading AI investment priority. Separately, Skai describes performance proof and measurement as hurdles to increased retail media investment: brands want evidence before scaling, while limited measurement frameworks can make performance difficult to validate. Skai says retail media leaders plan to shift generative-AI use toward campaign management, optimization, and analytics.
That makes measurement design and cross-platform planning plausible areas of agency value, but it does not justify an unsupported return-on-investment promise. A credible proposal should state the baseline, assumptions, attribution approach, and what evidence will count as improvement.
Product content becomes an operational discipline
Product detail pages, feeds, and content variants need to be accurate and usable wherever a product is sold or discovered. Adobe’s 2026 retail research points to data and measurement readiness, automated content pipelines, and workforce skills as factors that distinguish organizations scaling AI from those remaining in pilots. For agencies, that makes content governance and product-information quality strategic concerns, not merely copy-production tasks.
Where are shoppers discovering products?
Discovery is no longer confined to a brand’s own website or conventional search. Adobe and Oxford Economics’ 2026 research, based on surveys fielded in October and November 2025, reports that one in four shoppers turn to AI-powered platforms ahead of brand websites when making purchase decisions. NRF’s 2026 consumer research reports that 41% use AI assistants to research products, 33% to look for reviews, and 31% to search for deals.
McKinsey describes product research in conversational AI tools alongside conventional search and frames an AI-mediated advertising economy as an emerging shift. Deloitte likewise describes journeys becoming more intent-driven and AI-mediated, with data readiness, trust, and experience design across owned and third-party surfaces as important considerations. Neither establishes one future channel as dominant. For brands, the practical implication is to make product information clear and consistent across the places customers may ask questions, while preserving a useful way to understand how discovery contributes to results.
Why do trust and brand distinction limit automation?
More automation can increase the importance of accuracy, privacy, transparency, and a recognizable brand voice. In NRF’s 2026 consumer research, 52% said they were comfortable sharing data, while 83% reported multiple overlapping privacy concerns. The two findings can coexist: comfort with some data sharing does not mean customers have no reservations about how information is used.
Adobe says consumer readiness for fully agentic interactions is still evolving and recommends transparency and human oversight. Forrester’s findings on accuracy, bias, legal exposure, and privacy point to similar constraints for agency workflows. Human review is therefore not simply a brake on automation; it can be the control that protects product claims, customer data, and creative quality.
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CommerceIQ’s commissioned study also reports that 82% of respondents expect AI investment to increase in the next 12–18 months and 71% are familiar with or actively using AI agents. Among its respondents, 82% say unified business context is critical, 53% require security and compliance, 49% require integration with existing tools, and 43% require human-in-the-loop oversight. These are reported priorities among surveyed commerce leaders, not proof that every organization has the same requirements or maturity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should ecommerce brands evaluate in an agency?
Use the following questions to compare agencies against the work your business actually needs. These are practical evaluation criteria drawn from the reported challenges and priorities, not a validated scoring system.
- Data fit: Can the team work with reliable product, inventory, customer, and campaign context, and explain how it connects to your existing systems?
- Measurement: Will it agree on baselines, attribution assumptions, reporting cadence, and appropriate evidence of impact before claiming performance improvements?
- Speed with control: Can it respond quickly while defining human review, escalation, and auditability for automated work?
- Privacy and security: Can it explain data permissions, handling practices, and its compliance process for the tools and workflows it proposes?
- Creative distinction: How will productivity gains improve ideas and customer experiences rather than only reduce production cost?
- Commerce expertise: Does its practical experience match your needs in retail media, product content, demand planning, or cross-channel discovery?
CommerceIQ’s survey also reports that 55% of its respondents consider agency costs too high relative to results and 40% report slow response times. Rather than treating those as universal benchmarks, use them to make expectations explicit in an agency brief: response standards, decision owners, deliverables, and the evidence used to judge the work.
What the available evidence can—and cannot—show
The figures in this article come from distinct research populations and should not be averaged or compared as if they were one survey. Forrester’s 2026 announcement reports U.S. agency findings but does not provide the full report methodology on the announcement page. CommerceIQ says Qualtrics surveyed 240 ecommerce leaders at brands with revenue of at least $300 million; the accessible topline report does not establish that those results represent smaller brands or the wider agency market.
Adobe and Oxford Economics say they surveyed 3,000 executives and practitioners and 4,000 customers globally, with fieldwork in October–November 2025; the retail and consumer-goods findings refer to a defined subset of respondents. NRF’s consumer figures describe its 2026 consumer research. Their populations and question wording differ, so a percentage from one source should not be read as directly confirming a percentage from another.
The evidence supports a shift in capabilities and client expectations. It does not establish how many agencies will disappear, that AI adoption itself caused better or worse agency performance, or that any single tool or platform resolves data, measurement, or trust problems.
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