The Tool Desk
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What is changing in agency work
The shift is not simply from people to software. It is from isolated marketing tasks toward connected commerce operations: using reliable product and campaign information, getting content onto the right surfaces, optimizing paid placements, and explaining what the work achieved. AI can speed parts of that process, but strategy, judgment, governance and proof still matter.
AI speeds tasks, while adding review and governance work
Forrester’s 2026 research on US marketing agencies reports that nine in 10 use generative AI and half use agentic AI for marketing execution. Agencies report using generative AI for creative, strategy, media, ideation, content, competitive analysis and reporting. Forrester also says 74% use it to summarize documents and communications, while 70% use it for research and competitive intelligence.
Those figures describe adoption and use, not a measured improvement in campaign performance. Forrester reports that 81% use generative AI and 63% use AI agents primarily to enhance staff productivity and impact; it also warns that an efficiency-only focus can undermine creativity and differentiation. The operational implication is that a capable agency needs to decide which tasks suit automation, connect tools to relevant client information, review outputs, and show how time saved benefits the work—not merely how it reduces production effort.
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Data interpretation becomes part of the service
Commerce teams can have plenty of data without being able to act on it. In a 2026 Qualtrics study commissioned by CommerceIQ, 240 ecommerce leaders at brands with at least $300 million in revenue identified data trust and quality, actionability and decision-making speed as prominent challenges. The study also found that many respondents expect integrated business context and connections to existing tools when adopting AI. For agencies, this raises the value of understanding how product, inventory, customer and campaign information fit together, as well as working within clients’ permissions and governance requirements.
A dashboard or AI tool cannot be assumed to resolve fragmented systems or unreliable inputs. KPMG’s discussion of retail modernization also identifies fragmented technology stacks, governance, cybersecurity and talent as constraints. An agency’s data role should therefore be judged by the quality of its integration and decisions, not by the number of platforms it can display.
Product pages and digital shelf operations move closer to the center
In the CommerceIQ-commissioned study, retail media optimization was the leading stated AI investment priority for 2026, followed by product detail page and content optimization, then predictive demand. These are intentions among that study’s respondents, not a universal ranking of agency services. Still, they point to practical work brands may ask agencies to support: accurate product information, feed quality, useful content variants and updates that reflect product availability and customer needs.
Rank #2
Adobe’s 2026 retail research recommends data and measurement readiness, automated content pipelines and workforce skills as foundations for scaling AI. That makes content operations more than copy production: the agency may need to help maintain consistent, current product information across channels while keeping brand and accuracy checks in place.
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Retail media optimization is also where brands may expect agencies to connect planning with evidence. Skai describes performance proof and measurement as hurdles to increasing retail media investment, and says retail media leaders plan to shift generative AI use toward campaign management, optimization and analytics. That makes cross-platform planning, measurement design and repeatable optimization valuable capabilities—but it does not justify an unsupported promise of return on ad spend. Brands should ask how the agency sets baselines, handles attribution assumptions and distinguishes observed results from incremental impact.
Discovery is no longer confined to a brand’s own website
Product research is spreading across conventional search, retail platforms and conversational AI services. 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. In separate 2026 consumer research, the National Retail Federation reports that 41% use AI assistants to research products, 33% to look for reviews and 31% to search for deals.
McKinsey describes AI-mediated discovery and commerce as a developing shift, while Deloitte emphasizes intent-driven journeys across owned and third-party surfaces. For agencies, a reasonable implication is more attention to making product information understandable and useful wherever shoppers encounter it, while preserving measurement across those surfaces. The evidence does not establish that one new channel will dominate or that brands can reliably attribute every AI-assisted purchase.
Trust and distinctiveness limit what should be automated
Forrester reports accuracy and bias, legal concerns, and privacy and security among agencies’ leading generative AI barriers. Among agentic AI barriers, it identifies lack of expertise and gaps in data infrastructure. Adobe calls for transparency and human oversight as consumer readiness for fully agentic interactions evolves. The NRF’s 2026 research also finds overlapping privacy concerns among consumers even as some use AI assistants for shopping.
These concerns make review rules, data-use boundaries, escalation paths and brand voice practical agency responsibilities. Human review is especially important where a mistake could misstate a product, expose sensitive information or damage trust. Automation can increase the volume or speed of output; it cannot by itself ensure that output is distinctive, lawful or right for a customer.
What the survey figures do—and do not—say
The figures below come from different studies, populations and questions. They should be read as separate signals, not averaged or treated as directly comparable measures.
| Study and population | Reported finding | What it helps a brand assess |
|---|---|---|
| Forrester, 2026 research on US marketing agencies | Nine in 10 agencies use generative AI; half use agentic AI for marketing execution. Forrester also reports that 61% classify AI as a cost of business, while 31% plan to monetize agentic AI within 24 months. | AI adoption is widespread in the surveyed US agency context, but adoption and monetization plans are not evidence of improved client outcomes. |
| Forrester, 2026 research on US marketing agencies | For generative AI, 63% cite accuracy and bias, 62% legal concerns, and 55% privacy and security as barriers. For agentic AI, 54% cite lack of expertise and 51% data infrastructure gaps. | Ask what controls, expertise and client-data safeguards underpin the agency’s AI-enabled work. |
| CommerceIQ-commissioned Qualtrics study, 2026; 240 ecommerce leaders at brands with revenue of at least $300 million | 56% name data trust and quality as their top challenge; 46% say data is not actionable; 42% lack time to make decisions; 40% say there is too much data to process. | Data quality and decision usefulness are live operating concerns for the surveyed larger brands. |
| CommerceIQ-commissioned Qualtrics study, 2026; same described sample | 76% rely on agencies; 49% allocate 15–30% of budget to agency fees alone; 55% say costs are too high relative to results; 40% report slow response times. | Brands can connect agency review to fees, responsiveness and agreed outcomes rather than activity volume alone. |
| CommerceIQ-commissioned Qualtrics study, 2026; same described sample | 82% expect AI investment to increase in the next 12–18 months; 71% are familiar with or actively using AI agents. The leading stated AI investment priorities are retail media optimization (26%), product detail page and content optimization (19%), and predictive demand (17%). | These are respondent expectations and priorities, not universal forecasts or proof of realized returns. |
| CommerceIQ-commissioned Qualtrics study, 2026; same described sample | 82% say unified business context is critical; 53% require security and compliance; 49% require integration with existing tools; 43% require human-in-the-loop oversight. | AI and agency proposals need to fit the brand’s systems and control requirements. |
| Adobe and Oxford Economics, 2026 retail research; surveys fielded October–November 2025 | One in four shoppers turn to AI-powered platforms ahead of brand websites when making purchase decisions. | Brand websites are not the only relevant discovery surface; the figure does not establish which surface caused a purchase. |
| National Retail Federation, 2026 consumer research | 41% use AI assistants to research products, 33% to look for reviews, and 31% to search for deals; 52% are comfortable sharing data, while 83% report multiple overlapping privacy concerns. | Shopping use and privacy concern can coexist; do not treat adoption as blanket consent to data use. |
Forrester’s public announcement gives detailed findings but not the full report methodology. CommerceIQ describes its study as conducted by Qualtrics among the 240 leaders above; the full report is offered separately. Adobe and Oxford Economics surveyed 3,000 executives and practitioners and 4,000 customers globally, with retail and consumer goods as a defined subset. The studies differ in population, geography, methods and questions, so their percentages do not form one common benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an agency or AI-enabled service
Use these questions to compare proposals against your own commerce priorities. They are a practical decision framework, not a validated scoring system.
Best Value
Check whether the agency can use the data that matters
- Which product, customer, inventory and campaign data does the work require?
- How will it connect to existing systems, and what data access or permissions are needed?
- How does the agency handle missing, stale or conflicting information before it reaches a customer or campaign?
Ask how results will be measured
- What baseline and reporting cadence will the agency use?
- Which attribution assumptions are built into the proposed measures?
- Where suitable, how will it distinguish incremental impact from activity that would have happened anyway?
- For retail media, what evidence would justify changing or scaling spend?
Understand speed, review and accountability
- Which tasks are automated, and which require human approval?
- Who owns corrections, escalations and audit records when a system produces an inaccurate or unsuitable result?
- What turnaround time is promised, and how is urgent work prioritized?
Review privacy, security and brand fit
- What information is sent to AI tools, under what permissions and retention rules?
- How does the agency address security, legal review, disclosure and privacy requirements?
- How will human reviewers preserve accuracy, brand voice and creative distinction?
Match commerce capability to the business need
Do not pay for a fashionable capability that is disconnected from the brand’s actual bottleneck. Compare the agency’s hands-on experience in retail media, product content, predictive demand and cross-channel discovery with the work you need done, and agree on what success would look like before expanding scope.
Will AI replace marketing agencies?
The available evidence does not establish that agencies as a whole are being replaced, nor does it show that AI adoption itself caused performance gains or losses. It does show widespread generative AI use in Forrester’s surveyed US agencies, alongside agency-client concerns about data usefulness, responsiveness and the relationship between fees and results in CommerceIQ’s commissioned study. That combination points to changing expectations: clients may increasingly value agencies for integration, commerce judgment, measurement, governance and differentiated creative work rather than routine execution alone.
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