Forrester predicts that rising artificial intelligence costs will force business-to-consumer (B2C) marketing leaders to scale back one-third of their AI initiatives in 2027, as usage-based pricing makes automated workflows, generated content and agent interactions increasingly expensive. The research firm's 2027 predictions also point to renewed investment in customer experience talent, a stronger emphasis on human-generated content and the emergence of conversational shopping. Together, the forecasts suggest that B2C leaders will face growing pressure to balance AI adoption with measurable returns, consumer trust and human expertise.

AI Adoption Faces a Cost-Control Test

Forrester’s forecast signals a shift in how B2C organizations may evaluate AI investments. As marketing technology vendors increasingly adopt usage-based pricing, the cost of running AI systems can rise with every interaction, generated asset and automated workflow.

This pricing model creates a challenge for marketing leaders scaling agentic AI and generative content across campaigns. A workflow that appears efficient at pilot stage may become expensive when deployed across large customer bases, multiple channels and high volumes of interactions.

Rather than treating AI adoption as an objective in itself, organizations will need to evaluate initiatives against measurable business outcomes, including conversion rates, customer retention, campaign efficiency and service costs. Some projects may be reduced or discontinued if their incremental value does not justify their operating expense.

Forrester's prediction that one-third of B2C AI initiatives will be scaled back is a forecast, not a reported outcome. Its significance lies in the possibility that cost management will become a more prominent part of marketing technology governance in 2027.

CX Teams Could Rebuild AI Expertise

Forrester also expects customer experience staffing to rise by 20% following two years of widespread cuts. The anticipated hiring would focus on roles such as natural language processing engineers, data scientists and AI experience designers.

The forecast challenges the assumption that AI adoption necessarily means continuously reducing headcount. Organizations need specialists who can identify appropriate use cases, prepare and interpret data, evaluate system performance and design experiences that work for customers.

For B2C brands, this could mean shifting some investment from general operational staffing toward technical and experience-design capabilities. The objective would be to identify where automation improves customer outcomes and where human intervention remains necessary.

The forecast does not specify whether the projected 20% increase applies across all CX employers or a particular segment of the market. Companies should therefore treat it as a directional prediction rather than a universal staffing target.

Human-Generated Content Returns to the Foreground

As AI-generated material becomes more prevalent across digital channels, Forrester predicts that 50% of brands will make human-generated content a top priority again.

The shift reflects a growing strategic challenge for marketers: increasing content production does not automatically increase audience trust or differentiation. When similar tools can produce large volumes of comparable material, original expertise, customer experiences and documented evidence can help brands establish credibility.

Human-generated content need not mean abandoning AI. Marketing teams can use AI to support research, analysis and production while retaining human responsibility for factual accuracy, editorial judgment and brand voice.

For brands, the important distinction is between using AI to improve content workflows and relying on automation to replace the experience or evidence that makes content valuable.

Agentic Commerce Could Change Shopping Behavior

Forrester predicts that one in 20 consumers in the US and UK will abandon traditional shopping for conversational interactions. This points toward a potential expansion of agentic commerce, in which AI assistants help consumers research products, compare options and complete routine purchases.

The forecast presents an opportunity for brands to reconsider how product information, pricing, availability and purchasing processes are made accessible to AI-driven systems. However, consumer adoption will depend on more than convenience. Trust, transparency, control and confidence in purchase decisions will influence whether shoppers delegate transactions to AI agents.

For marketers and digital commerce teams, preparing for this shift means evaluating both the technical requirements of agent-enabled transactions and the customer protections needed to support them.

Market Landscape

Forrester’s predictions highlight three pressures converging on B2C technology strategy: the operating costs of AI, demand for credible brand communication and changing customer expectations around digital commerce.

Marketing platforms that make AI costs transparent and demonstrate measurable outcomes could become more attractive as buyers scrutinize return on investment. At the same time, brands will need stronger data, technical and experience-design capabilities to manage automation responsibly.

The competitive question is no longer simply how many AI tools an organization can deploy. It is how effectively those tools support profitable growth, useful customer experiences and credible brand relationships.

Strategic Outlook

B2C leaders should prepare for 2027 by reviewing AI initiatives against clear financial and customer-outcome measures. Usage-based costs need to be monitored alongside the value generated, while CX workforce planning should account for the skills required to govern and improve AI systems.

Brands should also protect investment in distinctive human expertise and assess where conversational commerce could change product discovery or purchasing behavior. The broader lesson from Forrester’s forecasts is that sustainable AI adoption will depend on disciplined investment, not deployment volume alone.

Top Insights

• AI costs could trigger retrenchment: Forrester predicts rising usage-based expenses will force B2C marketing leaders to scale back one-third of their AI initiatives in 2027.

• CX hiring may rebound: The firm forecasts a 20% rise in CX staffing, emphasizing technical and experience-design roles needed to support valuable AI use cases.

• Human content regains importance: Forrester expects 50% of brands to prioritize human-generated content as synthetic material becomes more widespread.

• Conversational shopping may expand: One in 20 US and UK consumers could abandon traditional shopping for conversational experiences, according to the forecast.

• Investment discipline becomes central: AI spending, customer trust and human expertise will need to be evaluated together rather than as separate strategic concerns.

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Results Driven Marketing® is highlighting AI search visibility as an emerging consideration for businesses in Mount Pleasant, South Carolina, and the wider Charleston area. The agency says its services help local companies strengthen their online presence through relevant website content, accurate business information and clearer demonstrations of expertise. The announcement reflects growing interest in how AI-powered search tools influence local discovery, although it provides no performance data showing that the agency’s services have increased client visibility in AI-generated answers.

AI Search Adds Another Route to Local Discovery

Local businesses have traditionally focused on search engine rankings, business listings and websites to reach customers researching nearby services. AI-powered search introduces another discovery pathway: instead of presenting only a list of links, some tools generate conversational responses that summarize information and identify businesses relevant to a user's question.

For a Mount Pleasant homeowner searching for a plumbing company or a Charleston business owner comparing marketing agencies, those responses can influence which providers receive initial consideration.

However, visibility in AI-generated answers is not guaranteed. Platforms differ in how they retrieve, evaluate and present information, and inclusion can vary according to the query and available sources. Businesses therefore need to approach AI search as an additional discovery channel rather than a replacement for established SEO practices.

Results Driven Marketing Focuses on Local Business Information

Results Driven Marketing serves Mount Pleasant, Charleston and surrounding Lowcountry communities. Its service offering includes AI visibility, search engine optimization, website development, pay-per-click advertising and content marketing.

The agency says its AI visibility services focus on strengthening digital presence through relevant website content, consistent descriptions of services and locations, and information that communicates a business's expertise.

These activities align with broader search optimization practices: helping people and search systems understand what a company offers, where it operates and which customer needs it addresses. For local service providers, clear service pages and consistent business details can provide a useful foundation for discovery across traditional and AI-powered search environments.

The announcement does not specify which AI platforms the agency targets, how it measures AI visibility, or whether its services have produced verified improvements in AI-generated recommendations. Businesses evaluating providers should therefore ask how visibility is tracked and how reported results connect to qualified leads or revenue.

Why Local Service Providers Should Pay Attention

Contractors, law firms and other service-based businesses often compete for customers who are actively comparing providers in a specific geographic area. Being clearly represented online can help potential customers understand service availability, experience and areas served before making contact.

AI visibility adds another consideration to that process. Businesses may need to assess whether their websites answer common customer questions directly, whether location and service information is accurate, and whether their expertise is supported by useful, specific content.

These measures do not guarantee that an AI assistant will recommend a particular business. They can, however, improve the clarity and usefulness of the information available to prospective customers and discovery systems.

AI Visibility as Part of a Broader Marketing Strategy

Results Driven Marketing positions AI visibility alongside its existing digital marketing services rather than presenting it as a standalone guarantee of customer acquisition.

That distinction matters as businesses encounter new services marketed around generative engine optimization (GEO), the practice of improving the likelihood that content is understood and referenced by AI-powered search and answer engines. Because platforms and measurement methods continue to evolve, businesses should assess these services using transparent reporting and practical outcomes.

Useful indicators may include referral traffic from identifiable AI platforms, qualified inquiries, local search performance and the accuracy of information presented about the business. No single metric captures every AI-driven interaction, particularly when a user receives an answer without clicking through to a website.

The agency also cites industry recognition, including being named among the Philadelphia Business Journal's Top 50 Women-Run Businesses. The announcement does not provide a year for that recognition.

Market Landscape

AI-powered search is creating additional pressure on local businesses to maintain accurate, useful and well-structured online information. Local SEO remains relevant, while GEO adds a focus on whether content can be surfaced and referenced in generated answers.

For agencies, the challenge is to translate this developing discipline into measurable business value without promising placement that no provider can control. Clear methodology, realistic expectations and reporting tied to customer acquisition can help distinguish substantive services from unsupported visibility claims.

Strategic Outlook

Mount Pleasant businesses can treat AI search visibility as an extension of their existing digital strategy. Priorities include maintaining accurate business details, building useful service and location pages, answering customer questions clearly, and evaluating performance across multiple discovery channels.

For Results Driven Marketing, the opportunity is to show how its AI visibility services complement established SEO and advertising work. Demonstrable results, transparent measurement and clear distinctions between optimization efforts and guaranteed outcomes will be important to building credibility in this emerging market.

Top Insights

• AI search adds a discovery channel: Conversational AI tools can summarize information and identify local providers, influencing how potential customers research services.

• Accurate information remains foundational: Clear service descriptions, locations and evidence of expertise can help customers and discovery systems understand a business.

• Visibility is not guaranteed: AI platforms differ in their retrieval and answer-generation methods, so agencies cannot promise inclusion in generated recommendations.

• Measurement needs business context: AI referrals, qualified inquiries and local search performance can help businesses assess whether optimization efforts support customer acquisition.

• Results Driven Marketing expands its positioning: The agency promotes AI visibility alongside SEO, website development, PPC and content marketing, but the announcement includes no quantified client results.

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Impact Factory has released Version 1.9 of its AI-powered marketing platform, introducing an integration that lets businesses access its marketing capabilities through compatible AI assistants, including ChatGPT and Claude. The update also adds four APIs for approved developers, extending the company's automated content-production workflows into third-party applications. The move reflects a broader shift toward conversational access to business software, where users can request marketing work through existing AI interfaces instead of learning another platform.

MCP Connects AI Assistants to Marketing Workflows

The new integration uses the Model Context Protocol (MCP), a standard that enables compatible AI assistants to connect with external tools and services. Impact Factory says subscribers can connect the platform through their assistant’s settings and request marketing work conversationally without leaving their preferred AI environment.

The company lists ChatGPT, Claude, Grok, Cursor, VS Code, Claude Code, Codex, Gemini CLI and Windsurf among the environments intended for use, subject to each platform’s MCP support and configuration requirements.

Available capabilities span advertising scripts and video or audio assets, website creation and evaluation, social media content, campaign strategy, competitor research and brand management. The integration can also draw on existing brand profiles, marketing materials and previous projects, allowing users to build on prior work rather than repeatedly supplying background information.

A key limitation remains: the integration does not independently place ads, publish content to external platforms or spend media budgets. Those actions remain within Impact Factory’s existing publishing and media-placement workflows. The distinction separates marketing creation and analysis from the operational steps required to launch campaigns.

MIA Offers an Alternative to External Assistants

Version 1.9 expands access to capabilities already available through Impact Factory’s own conversational interface, which is led by MIA, its Marketing Intelligence Advisor and virtual chief marketing officer.

MIA helps users develop strategies, coordinate specialized AI agents, create campaigns, analyze performance and make marketing decisions through natural-language interactions. Customers can continue working directly with MIA or use a compatible external assistant to access the platform.

The model addresses a usability challenge for smaller businesses: sophisticated marketing tools can require time and expertise to learn. By making conversational requests the primary interaction method, Impact Factory aims to reduce the friction between identifying a marketing need and starting the work.

However, ease of access does not eliminate the need for review. Businesses still need to assess the accuracy, brand suitability and quality of AI-generated material before approving it for use.

Four APIs Extend Agentic Content Production

The release also introduces four APIs that approved developers can use to incorporate Impact Factory’s marketing capabilities into third-party products, applications and automated workflows. Developers can apply through the company’s website.

Impact Factory describes these APIs as agentic production pipelines rather than simple endpoints that generate an individual image, video clip or audio segment. According to the company, each pipeline coordinates multiple AI systems and production processes to deliver a finished marketing asset from a natural-language instruction.

If the approach works reliably at scale, it could allow software providers and businesses to embed more complete marketing-production workflows into their existing tools. The announcement, however, does not provide API pricing, performance benchmarks, throughput limits or comparative production-quality data.

Market Landscape

The launch sits at the intersection of conversational AI, MCP-based integrations and agentic automation. Rather than requiring customers to work exclusively inside a vendor’s interface, platforms can expose capabilities through AI assistants and connect specialized services to broader workflows.

For marketing technology providers, this creates a new distribution opportunity. It also raises questions around permissions, data access, quality assurance and the consistency of results across different AI environments. The practical value will depend on how securely integrations handle customer assets and how reliably they complete complex tasks.

Strategic Outlook

Impact Factory’s Version 1.9 positions the platform as both a direct marketing workspace and a service that can be accessed through other AI tools. Its developer APIs extend that strategy beyond individual users toward embedded marketing automation.

The key test will be whether conversational access and coordinated production pipelines deliver measurable gains in time, quality and cost without compromising brand control. For buyers, compatibility alone is not enough: permissions, human approval, publishing controls and transparent pricing will remain important evaluation criteria.

Top Insights

• Conversational access expands: MCP integration lets subscribers request marketing work through compatible AI assistants instead of relying solely on Impact Factory’s interface.

• Marketing capabilities cover multiple workflows: Users can access campaign strategy, advertising assets, websites, social content, competitor research and brand materials.

• Creation remains separate from execution: The integration does not independently publish content, place ads or spend media budgets.

• APIs target embedded automation: Four APIs let approved developers incorporate the company’s agentic marketing-production pipelines into third-party applications and workflows.

• Performance remains unproven in the announcement: Pricing, production benchmarks and measured time or cost savings were not disclosed.

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RAD Intel is using its October 2026 edition of RADAR, titled Think Again, to examine how marketers apply human judgment when AI-powered research presents multiple possible campaign directions. The editorial publication combines an exercise in audience intelligence with analysis of creative effectiveness and generative engine optimization (GEO), positioning research as an input to marketing decisions rather than a substitute for strategic thinking.

Same Research, Different Marketing Decisions

The latest RADAR edition centers on What Does It Take to Change a Marketer’s Mind?, an editorial exercise built around a LEGO campaign brief. Three marketers from RAD Intel, AV Communications and RAD Amplify first develop their recommendations, then reconsider them after reviewing the same audience research produced by RAD Intel’s decision intelligence platform.

Their responses illustrate three distinct ways research can influence strategy. One marketer narrows her target audience, another maintains her original recommendation because important cultural questions remain unanswered, and a third retains the audience but changes the creative approach.

A finding that one audience was 184 times more likely than a random comparison group to build and share custom LEGO Technic mechanisms also failed to dictate a single recommendation. Although the data indicated strong existing enthusiasm, it did not independently resolve how the campaign could attract buyers.

The exercise offers a view into the reasoning behind marketing recommendations, including where evidence strengthens a strategy and where further investigation is needed. RAD Intel does not present it as a campaign-performance study, an important distinction when interpreting the example.

Maria Brown, chief portfolio marketing officer at RAD Intel, said the company’s marketing team regularly makes audience, creative and investment decisions across as many as five brands. The publication aims to make the reasoning behind those decisions more visible, including what marketers still need to test.

Creative Effectiveness Requires More Than Prediction

The edition also examines the challenge of evaluating creative work when professional judgment, consumer testing and predictive analytics point in different directions.

The feature Creative Effectiveness and the Problem With Good Taste uses examples including Coca-Cola’s AI-generated holiday advertising to explore how marketers assess creative quality. Its central question is relevant to teams introducing generative AI into campaign development: does a prediction about likely audience response provide enough evidence to choose one creative direction over another?

AI can expand the number of concepts teams generate and help them analyze audience signals. But more options do not automatically produce better decisions. Marketers still need to establish what a campaign is trying to achieve, determine whether the evidence addresses that objective and decide which assumptions require testing.

That distinction is particularly important when predictive analysis is treated as a proxy for actual campaign outcomes. Research can inform a creative decision, but performance still needs to be evaluated against defined business and audience measures.

RADAR Connects Editorial Content With GEO

RAD Intel is also using RADAR as a generative engine optimization content engine. GEO focuses on improving the likelihood that content is discovered, understood and referenced by AI-powered search and answer systems.

The Think Again edition supports this strategy through original reporting, named contributors, a concrete marketing exercise and analysis of specific decision-making challenges. These elements give the publication a defined subject and practical examples rather than relying solely on broad claims about AI’s role in marketing.

For RAD Intel, the stated objective is to make relevant expertise easier to discover when prospective customers research marketing challenges or assess potential partners. The content also supports visibility for Lickly, the company’s self-service AI platform for marketing decision intelligence, alongside the agency expertise represented by RADAR’s contributors.

However, the announcement does not provide traffic, citation or referral data demonstrating that RADAR’s approach has already improved visibility in AI-generated answers. Its GEO strategy is therefore best understood as a stated publishing objective, not a verified performance result.

Market Landscape

Marketing technology vendors are increasingly combining audience research, predictive analytics and generative AI to help teams develop and evaluate campaigns. This creates an opportunity to make research more accessible across marketing workflows, but also increases the need to distinguish useful evidence from findings that merely appear compelling.

RAD Intel’s editorial approach focuses on the human decision-making layer: how marketers interpret research, identify unanswered questions and translate findings into creative or targeting choices. Its GEO strategy adds a distribution dimension, emphasizing how content is structured and presented for discovery by AI-powered search tools.

Strategic Outlook

For marketing leaders, the practical takeaway is to make the reasoning behind AI-assisted decisions explicit. Teams should document which audience insights support a recommendation, which assumptions remain untested and what results would justify changing course.

For publishers and MarTech providers, RADAR illustrates one approach to GEO: create distinctive, evidence-led content around real marketing problems, explain the reasoning clearly and avoid treating AI visibility as a guaranteed outcome. The next measure of success will be whether this content attracts qualified audiences and earns meaningful references across AI-powered discovery channels.

Top Insights

• AI does not replace marketing judgment: RADAR’s LEGO campaign exercise shows how three marketers can interpret identical audience research and reach different strategic recommendations.

• Audience signals need context: A finding showing 184-times-higher likelihood of LEGO Technic engagement did not independently determine the campaign strategy.

• Creative effectiveness remains contested: Marketers must weigh professional opinion, consumer testing and predictive analysis rather than relying on a single measure of creative quality.

• GEO is a publishing strategy, not a guaranteed outcome: RAD Intel is using original reporting and structured editorial content to pursue visibility in AI-powered search.

• Performance evidence remains essential: The announcement outlines RADAR’s approach but does not disclose measured improvements in AI citations, referral traffic or campaign outcomes.

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Car dealerships are facing a search environment in which visibility increasingly depends on more than traditional Google rankings. Alex Melen, co-founder of digital marketing agency SmartSites, has released the second edition of Automotive Search Marketing: A Car Dealer's Guide to Google & AI Search, adding guidance on AI-generated search answers, dealership visibility and measurement. The book targets dealership principals, general managers and marketing directors navigating the shift from conventional search engine optimization (SEO) toward AI search visibility and generative engine optimization (GEO).

Automotive SEO Expands Into AI Search

The second edition retains the book's existing guidance on organic search and paid advertising while adding five chapters focused on AI-powered discovery. The new material examines Google AI Overviews, Google's AI Mode and ChatGPT, including how dealerships can improve their chances of appearing in AI-generated answers, advertise in emerging AI search environments and measure visibility when a search does not produce a website click.

The release reflects a changing challenge for automotive marketing teams. Traditional dealership SEO focuses on rankings, local search visibility, website traffic and conversions. AI-powered search introduces another layer: systems that synthesize information from multiple sources and recommend businesses or products directly within generated answers.

For dealerships, that shift raises questions about how vehicle inventory, model comparisons, pricing, service information, customer reviews and business profiles influence discovery. Melen argues that useful, specific content is central to visibility, particularly pages that answer customer questions directly and provide concrete information rather than generic promotional copy.

The book also includes a 12-point AI readiness checklist designed for dealers to use when evaluating their website providers and search marketing strategies.

First-Party Data Highlights New Visibility Patterns

SmartSites says the new edition draws on first-party data from dealerships managed by the agency rather than relying exclusively on industry averages.

Among its reported findings, ranked and numbered buying guides have become the most frequently cited content format in AI answers. A publishing initiative that produced 831 pages across 31 dealerships in summer 2026 reportedly saw 87% of those pages indexed and generating impressions within five weeks.

The company also reports that model-research pages roughly quadrupled AI-feature impressions for one Honda dealership over eight weeks. These figures suggest that detailed, model-specific content may help dealerships become more visible as buyers research vehicles through conventional search and AI-generated responses.

However, the results are based on SmartSites' own client data. The announcement does not provide the full methodology, comparison groups or independent validation needed to determine whether the results generalize across dealerships, manufacturers or markets.

Melen also claims that AI assistants recommend approximately 1% of business locations, compared with more than one-third appearing in Google's local map pack. The comparison highlights a potential measurement gap, but the announcement does not define the datasets or methodology behind those figures.

Dealers Need Better Control of Marketing Data

Beyond content strategy, the book challenges dealerships to examine how their digital marketing relationships are structured. Melen criticizes original equipment manufacturer (OEM)-certified vendor programs, arguing that intermediary arrangements can add fees between dealerships and the work being performed.

He also emphasizes dealer ownership and access to Google Ads, Google Analytics and Google Search Console accounts. These platforms provide data about advertising expenditure, website behavior and organic search performance, helping dealerships evaluate vendors against measurable outcomes.

That advice becomes more important as AI search adds new visibility signals that may not translate directly into clicks. Marketing teams will need to distinguish between appearing in an AI-generated answer, attracting qualified website traffic and ultimately generating leads or vehicle sales.

The competitive landscape includes automotive-focused marketing agencies, website providers and broader digital marketing platforms offering SEO, paid search, local listing management and analytics. AI visibility is becoming another service differentiator, although dealerships should look for transparent reporting rather than promises of guaranteed inclusion in AI answers.

For dealer principals and marketing directors, the book's central message is that search strategy is expanding, not disappearing. Traditional SEO and paid search remain important, while AI-generated answers create additional discovery opportunities that require new measurement approaches.

The second edition of Automotive Search Marketing: A Car Dealer's Guide to Google & AI Search is available on Amazon.

Market Landscape

Automotive search marketing is evolving as Google integrates AI-generated answers into search and consumers increasingly use conversational AI tools to research products and services.

• Traditional SEO: Helps dealership websites rank for vehicle, service and location-specific searches.

• Paid search: Enables dealers to compete for high-intent queries and track advertising costs and conversions.

• Local search optimization: Supports visibility through Google Business Profiles, reviews and local search results.

• AI search optimization: Focuses on making dealership information useful, accessible and understandable to AI-powered answer systems.

• Marketing analytics: Helps dealers connect visibility and engagement with leads, appointments and sales.

The opportunity is to coordinate these channels rather than treat AI search as a replacement for established marketing methods.

Strategic Outlook

Dealerships should assess whether their websites answer buyers' questions clearly, publish accurate vehicle and pricing information, maintain detailed local business profiles and provide useful model-specific resources.

They should also retain access to their marketing accounts and establish reporting that distinguishes AI visibility from traffic, leads and revenue. As AI search measurement develops, transparent methodology and independently verifiable outcomes will be important when evaluating agency claims.

Top Insights

• Automotive SEO is expanding into AI search: The updated guide covers Google AI Overviews, AI Mode and ChatGPT, helping dealership marketers adapt to changing vehicle discovery patterns.

• Detailed buying content may improve visibility: SmartSites reports that ranked buying guides are frequently cited in AI answers, highlighting the potential value of structured, useful content.

• First-party data informs the new edition: Reported dealership examples include faster page indexing and increased AI-feature impressions, although independent validation is not provided.

• Measurement is becoming more complex: AI-generated answers may influence buyers without generating clicks, requiring dealers to distinguish visibility from leads and vehicle sales.

• Marketing data ownership remains essential: Direct access to advertising, analytics and search accounts helps dealerships assess vendor performance and retain control over their marketing investments.

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UK marketing leaders are reporting better results from artificial intelligence even as fewer say they use it, according to Censuswide’s 2026 Voice of the UK CMO report. Generative AI adoption among chief marketing officers fell from 89% in 2025 to 62% in 2026, while 64% of AI adopters said the technology had exceeded expectations. The findings point to a more selective approach to marketing AI, alongside a growing concern that executives may be overestimating consumers’ comfort with AI-generated advertising and brand communications.

AI Adoption Is Giving Way to Selective Deployment

The decline in reported usage does not necessarily mean marketing leaders are abandoning AI. Instead, the findings suggest that some CMOs are becoming more selective about where the technology earns a place in their operations.

Among adopters, 99% reported at least one benefit. Better campaign targeting was the most frequently cited advantage, at 43%, followed by improved analysis at 41%. Higher-quality content and greater personalization each ranked at 40%. The share saying AI had exceeded expectations rose from 58% in 2025 to 64% in 2026.

That combination—lower reported usage but greater satisfaction among adopters—could indicate a shift from broad experimentation toward more targeted applications. However, the survey does not establish why usage declined, so it would be premature to attribute the change solely to better tool selection or improved return on investment.

The report also exposes a continuing debate over creativity. While 40% of CMOs said AI fully or mostly enables creativity, 44% said it fully or mostly hinders it. The split suggests that AI's impact depends on how teams deploy it: automating repetitive production may free time for creative work, while excessive reliance on generated material could make campaigns less distinctive.

Consumer Comfort Remains a Blind Spot

The gap between executive perceptions and consumer attitudes is one of the report's clearest warnings. When asked how consumers mainly feel about AI in marketing and advertising, 44% of CMOs believed consumers were comfortable with it. Consumers themselves were most likely to describe their feelings as uncomfortable, at 29%.

That 29% represents the most common response, not a majority of consumers. Even so, the difference highlights the risk of treating AI adoption inside marketing departments as evidence of customer acceptance.

For brands, transparency and context matter. AI-assisted audience analysis or campaign optimization may be less visible to consumers than synthetic imagery, generated endorsements or automated interactions that appear human. Marketers should therefore assess customer expectations by use case instead of applying one blanket policy to every AI application.

Brand Risk and Budget Pressure Complicate the Picture

AI is only one part of the challenge facing UK marketing leaders. Concern about reputational backlash has nearly doubled: 47% said it is always on their minds when planning marketing activity, compared with 24% in 2025. Consumers are also divided over brand activism, with 51% wanting or expecting brands to take public positions on social or political issues and 49% disagreeing.

Budget constraints add another layer. Although 66% of CMOs expect marketing budgets to increase, 99% reported at least one budget challenge, and 47% said funds were already committed to planned activity.

The report also links organizational integration with stronger internal support for marketing. Among CMOs whose marketing and PR teams were fully integrated, 65% said stakeholders completely understood marketing's importance, compared with 32% where collaboration was campaign-based. Complete leadership buy-in stood at 83% and 51%, respectively.

For enterprise marketing teams, the broader lesson is that AI effectiveness depends on more than access to tools. Clear governance, customer research, cross-functional alignment and credible measurement are essential to turning AI capabilities into durable business value.

Market Landscape

The Censuswide findings reflect a maturing AI marketing market in which adoption rates alone are becoming a weaker indicator of success. Enterprise platforms from Salesforce, Adobe and Microsoft continue to integrate AI into customer data, content production, analytics and campaign workflows.

For marketing leaders, the competitive question is increasingly how to balance automation with customer trust and creative differentiation. Censuswide's results suggest that more selective deployment may be worthwhile, but the survey does not prove that reducing AI usage directly improves outcomes.

The consumer perception gap is particularly important for brands using AI in customer-facing communications. Marketers need to distinguish between behind-the-scenes optimization and visible AI-generated experiences, measuring acceptance and performance for each use case.

Strategic Outlook

The next phase of AI marketing will require CMOs to evaluate technology based on demonstrated business value rather than adoption targets alone. Campaign targeting, analytics and personalization are promising applications, but brands must also assess creative quality, customer sentiment and reputational risk.

Integrating marketing and public relations teams, investing in market research and establishing transparent AI policies could help organizations make better decisions. The objective is not to use less AI for its own sake, but to deploy it where it improves performance without weakening customer trust.

Top Insights

• AI adoption is becoming more selective: Generative AI usage among UK CMOs fell to 62%, while 64% of adopters said AI exceeded expectations, suggesting a shift toward evaluating practical value.

• Consumers may be less comfortable than executives assume: Forty-four percent of CMOs believe consumers are comfortable with AI marketing, while discomfort is the most common consumer response at 29%.

• Marketing benefits remain measurable: Better campaign targeting, improved analysis and higher-quality content rank among reported AI benefits, giving CMOs specific areas for evaluating performance.

• Creativity remains contested: With 44% saying AI hinders creativity and 40% saying it enables it, implementation choices appear central to marketing's creative future.

• Integrated teams report stronger leadership support: Fully integrated marketing and PR teams report greater stakeholder understanding and leadership buy-in, highlighting the importance of organizational alignment.

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Tourism and attractions businesses are getting another route to AI-powered marketing as GlobalTix partners with Mavic to help operators promote experiences and reach travellers. The partnership combines GlobalTix’s attractions booking and distribution platform with Mavic’s AI-driven marketing capabilities, which cover content planning, creation, social publishing and performance optimization. The move targets a practical challenge for tourism operators: generating demand before a traveller makes a booking, often without the budget or staff needed to run a full-scale digital marketing operation.

GlobalTix Adds AI Marketing Capabilities to Its Attractions Platform

GlobalTix has partnered with Mavic, an AI brand marketing platform, to give its network of attractions, tour operators and activity providers access to tools designed to manage more of their marketing workflows. The partnership is available to GlobalTix partners, who can contact their account managers to explore Mavic’s services.

The collaboration addresses a gap in the travel technology stack. Booking and distribution platforms help operators sell tickets and manage reservations, but they do not necessarily solve the challenge of attracting customers in the first place. Operators must still maintain social media accounts, produce engaging content, respond to changing traveller interests and measure which campaigns generate demand.

Mavic aims to bring those tasks into a more connected workflow. Its platform supports marketing planning, the creation of on-brand images, videos and copy, publishing across social channels, performance tracking and optimization. The companies also offer access to managed marketing services for businesses that need additional support.

For smaller attractions, the appeal is reducing the amount of time and specialist expertise required to maintain a consistent online presence. Larger operators with established marketing teams could use automation to handle routine production and publishing tasks, allowing employees to concentrate on campaign strategy, brand positioning and audience development.

Moving Beyond AI Content Generation

The partnership reflects a broader shift in marketing technology from standalone generative AI tools toward platforms that coordinate multiple stages of execution. Generating a caption or promotional image is relatively straightforward; maintaining a publishing schedule, preserving brand consistency and evaluating performance across channels requires a more sustained process.

Mavic is positioning its platform around that end-to-end workflow rather than content generation alone. For an attraction, for example, a marketing team could use AI-assisted content to promote a seasonal event, publish material across social channels and review engagement to inform subsequent campaigns. Actual results would depend on audience relevance, creative quality, distribution and the operator’s ability to convert interest into bookings.

The distinction matters in tourism, where demand can fluctuate with seasonality, weather, school holidays, destination trends and local events. Smaller businesses may lack dedicated marketing staff to respond quickly to these changes. Automation could help them maintain visibility, although it cannot replace accurate local information, authentic storytelling or a compelling visitor experience.

Connecting Marketing With Travel Commerce

GlobalTix operates in the attractions, tours and activities market, connecting experience providers with booking and distribution channels. Adding marketing capabilities through Mavic could help extend its value proposition from transaction infrastructure toward demand generation.

That does not mean the partnership has demonstrated an increase in bookings or revenue. The announcement provides no pricing details, customer adoption figures or performance benchmarks showing how much Mavic improves conversion rates. It also does not specify the depth of integration between the two platforms or whether marketing performance can be directly tied to GlobalTix booking data.

Those details will matter to operators evaluating the offering. The strongest value proposition would be a measurable connection between marketing activity and commercial outcomes, including ticket sales, booking conversions and customer acquisition costs—not simply a higher volume of published content.

The broader market is moving toward integrated AI capabilities across commerce, customer engagement and marketing automation. Established platforms from companies such as Salesforce and Adobe demonstrate the importance of connecting customer data with campaign execution, although their enterprise-oriented ecosystems serve a different market segment from many independent attraction operators.

For GlobalTix and Mavic, the opportunity is to make coordinated marketing more accessible to businesses that lack large teams or complex technology stacks. Whether the partnership becomes a meaningful growth engine will depend on ease of adoption, the quality of AI-generated content and evidence that automated marketing translates into incremental traveller demand.

Top Insights

• GlobalTix expands beyond booking infrastructure: The partnership adds AI-powered marketing capabilities to its attractions ecosystem, helping operators address demand generation before customers make reservations.

• Mavic automates the marketing workflow: Its platform combines planning, content creation, social publishing and performance optimization, potentially reducing routine work for resource-constrained tourism businesses.

• Independent attractions are a key audience: Operators without dedicated marketing teams could gain access to more structured digital marketing workflows without building an in-house department.

• Marketing performance needs commercial measurement: The partnership's value will depend on whether automated campaigns improve bookings, customer acquisition costs and revenue rather than simply increase content output.

• Travel discovery is increasingly digital: Expedia Group's 2025 research found that 71% of surveyed travellers said video influenced their travel decisions, reinforcing the importance of compelling visual content in travel marketing

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Artificial intelligence is raising expectations for marketing teams, but headcount is not keeping pace. New research from Demandbase finds that 72% of surveyed B2B marketing teams are the same size or smaller than they were two years ago, while 70% of marketers face shorter delivery timelines. The findings, unveiled at the company’s GO conference in New York City, point to a widening operational challenge: marketing departments must deliver faster while preserving the human judgment, creativity and strategic thinking needed to stand out in an increasingly AI-driven market.

Demandbase Research Highlights the Human Cost of Faster AI Marketing

The findings suggest that AI adoption is changing expectations faster than it is resolving the organizational constraints that slow marketing execution. According to Demandbase, company leadership is the biggest source of pressure to accelerate delivery, while limited team growth leaves marketers with less room to absorb additional work.

The skills marketers consider most valuable are also shifting. Some 56% of respondents said human capabilities—including strategy, creativity, judgment, communication and leadership—matter more than they did two years ago. By comparison, 38% said AI proficiency, prompting, tool fluency and data literacy had become more important.

The results challenge the assumption that AI-driven marketing will primarily reward technical tool expertise. Generative AI can accelerate content production, research and campaign preparation, but marketers still need to determine whether the output is accurate, differentiated and appropriate for the audience. Only 21% of respondents said they rarely or never need to correct AI-generated output.

Half of the marketers surveyed expect their jobs to resemble those of creative directors more closely in the future. Meanwhile, 58% said producing work that stands out now requires more creativity, and just 16% believe creativity matters less because AI handles more execution.

Agentic Marketing Moves Into the Productivity Debate

Demandbase is positioning agentic marketing as one response to the capacity problem. Its Mojo B2B marketing agent is designed to turn strategy briefs into cross-channel campaigns, define audiences, develop briefs, launch and manage campaigns, monitor performance and use results to inform future execution.

The approach moves beyond conventional generative AI, which typically produces content or recommendations in response to prompts. An agentic system can coordinate multiple steps in a workflow, although the degree of autonomy depends on the product’s capabilities, integrations and approval controls.

For enterprise marketing teams, the potential benefit is less manual coordination across campaign planning and execution. The challenge is ensuring that automation removes unnecessary work rather than adding another layer of software, review and governance.

Demandbase’s research points to workflow design as a larger constraint than technology alone. Fifty-six percent of respondents identified processes and cross-functional workflows as their biggest organizational challenge, compared with 21% who cited the technology stack, data and tools. That gap suggests that marketing, sales, product and legal teams may need to redesign handoffs before another AI platform can deliver its full value.

AI Search Adds Another Marketing Channel to Manage

The research also highlights a measurement gap as buyers increasingly use AI-powered search and answer engines during product discovery. Only 35% of respondents said they systematically track how their brands appear in AI search, even though 62% have changed their search strategies and 64% have attempted to influence how AI systems describe their brands.

Owned websites remain relevant. Forty-four percent reported increased website traffic over the past year, compared with 15% who reported a decline. Another 25% said traffic was flat, while 15% could not assess the change.

For B2B marketers, the implication is not to abandon conventional search engine optimization. It is to extend visibility measurement to AI-generated answers while continuing to invest in owned channels that support credibility, product education and conversion.

The workforce picture is more nuanced than a simple story of AI replacing junior employees. Nearly half of respondents, 49%, said entry-level and junior headcount had remained unchanged over the past two years. Half also reported outsourcing less or bringing more work in-house, suggesting that AI may be changing how work is allocated between agencies and internal teams.

The pace of change is unlikely to ease soon: 74% expect their jobs to change more over the next two years than they did over the previous two. The findings are based on Demandbase’s own research, and the announcement does not provide the sample size or full methodology needed to assess how representative the results are across the wider B2B marketing industry.

Market Landscape

Demandbase’s findings arrive as B2B marketing platforms compete to embed AI throughout campaign planning, account-based marketing and revenue operations.

• Demandbase: Focuses on account-based marketing, go-to-market intelligence and agentic campaign execution through Mojo.

• Salesforce: Connects customer data, marketing automation and AI agents across its CRM ecosystem.

• Adobe: Applies AI to content production, customer experience orchestration and marketing campaign management.

• HubSpot: Integrates AI into marketing, sales and customer relationship management workflows.

• 6sense: Combines account intelligence, predictive analytics and AI-assisted revenue generation.

The competitive question is shifting from which platform can generate content fastest to which can coordinate complex workflows reliably. Demandbase’s research reinforces that distinction: if cross-functional processes are the biggest bottleneck, AI investments must address coordination, approvals and execution rather than simply add more tools.

Strategic Outlook

The next phase of AI marketing will likely depend on combining automation with human oversight. Agentic platforms can help teams coordinate repetitive tasks, but campaign strategy, brand differentiation, factual accuracy and customer trust remain human responsibilities.

Marketing leaders should evaluate AI investments against measurable outcomes, including campaign cycle times, conversion rates, cost per qualified opportunity and the amount of manual work eliminated. They should also establish a clear process for monitoring AI search visibility and correcting inaccurate brand representations.

Demandbase’s findings suggest that productivity gains will depend as much on organizational redesign as on AI capabilities. Teams that streamline workflows and establish clear decision rights may be better positioned to scale automation without sacrificing creative quality.

Top Insights

• Marketing teams face a capacity squeeze: Demandbase reports that 72% of surveyed teams have not grown or have shrunk, while 70% face shorter timelines, increasing pressure to automate execution.

• Human judgment remains essential: With only 21% rarely or never correcting AI output, marketing leaders must preserve editorial oversight, strategic thinking and brand consistency as automation expands.

• Workflow friction outweighs tool shortages: Fifty-six percent identify cross-functional processes as their biggest challenge, suggesting that AI investments must eliminate handoffs rather than add another disconnected platform.

• AI search creates a measurement gap: Although 62% have changed search strategies, only 35% systematically track brand visibility in AI search, leaving marketers with incomplete discovery data.

• Agentic marketing targets execution bottlenecks: Demandbase’s Mojo aims to coordinate campaign workflows, potentially freeing marketers to focus more on strategy and creative differentiation.

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