As Meta increasingly uses creative signals to influence automated ad delivery, Tinuiti is introducing a dashboard designed to translate creative fatigue and diversification data into media-spend and performance decisions.

Creative fatigue has long been visible to performance marketers through declining engagement and rising acquisition costs. What has been harder is determining how much media investment is exposed to the problem and when additional creative production can materially change the outcome.

Tinuiti is targeting that gap with a new capability inside its Bliss Point Marketing Operating System. The agency says its Meta Creative Health Dashboard is the first solution to translate Meta's newly available Creative Fatigue and Creative Diversification signals into financial and operational guidance.

The dashboard combines Meta's creative-health signals with spend, performance, format and placement data. Rather than treating creative performance as an isolated media metric, the system is designed to connect creative conditions with the amount of advertising investment potentially affected.

One of the dashboard's primary functions is estimating media spend exposed to fatigued or declining ad sets. Bliss Point also calculates the associated ROAS gap against what Tinuiti describes as healthy performance baselines.

That approach changes the conversation around creative refreshes. Instead of asking whether a campaign needs more assets based primarily on media-buying experience, marketing teams can use the dashboard to identify affected ad sets, estimate the financial exposure and determine where additional creative variation may be warranted.

The platform also evaluates creative diversification across formats and placements. This matters as automated media-buying systems increasingly determine where and when advertisements are delivered. A campaign that relies too heavily on a narrow group of assets can potentially limit the options available to an automated delivery system.

Tinuiti says API-based alerts can surface Meta's fatigue signals and direct media buyers and creative teams toward the ad sets requiring attention. The objective is earlier intervention, rather than waiting for performance deterioration to become obvious in campaign reporting.

The development reflects a broader change in paid media. Automation has reduced the number of manual decisions marketers make around bidding, targeting and delivery, increasing the importance of the inputs that automated systems use to optimize campaigns.

Creative is becoming one of those inputs.

Meta has increasingly emphasized creative diversification as advertisers move toward more automated campaign structures. That makes creative operations less of a downstream production function and more closely connected to media performance.

For marketers, however, a platform signal is not necessarily a business recommendation. The value of Tinuiti's approach lies in connecting the signal to spend and performance data, potentially giving marketing and finance leaders a common framework for evaluating creative investment.

Tinuiti says the dashboard is designed to provide that financial context by showing where fatigue exists, how much spending may be affected and where a creative refresh could have the greatest potential impact. These are Tinuiti's product capabilities and positioning rather than independently verified performance results.

The launch also fits into Tinuiti's broader expansion as a full-funnel media agency. The company reports that its average deal size has increased 66% over the past two years and that it has secured nearly 50 integrated, multichannel or agency-of-record wins since January 2024. Those figures are company-reported.

The more important industry development is the increasing convergence of creative intelligence, media measurement and automated advertising. As platforms make more delivery decisions algorithmically, marketers need better visibility into which creative variables are influencing those systems and what happens financially when creative performance deteriorates.

Market Landscape

Meta's advertising ecosystem has become increasingly automated, shifting more campaign optimization decisions toward machine-learning systems. That puts greater emphasis on the quality and diversity of creative inputs available to those systems.

For advertisers, this creates a measurement challenge. Traditional creative reporting often focuses on individual asset metrics, while media reporting concentrates on spend, conversions and ROAS. Connecting those datasets can provide a clearer view of whether creative is contributing to media efficiency or becoming a constraint.

Tinuiti's dashboard sits within that intersection, combining platform-generated creative signals with broader campaign and investment data.

Strategic Outlook

The role of the creative team in performance marketing is changing as automated media buying becomes more sophisticated.

Creative volume alone is unlikely to solve fatigue. Marketers also need to understand where creative concentration exists, which formats and placements are underrepresented, and whether new assets produce measurable improvements after deployment.

That makes creative intelligence increasingly relevant to both CMOs and media buyers. The next stage of paid-media optimization could involve treating creative as a measurable component of media infrastructure rather than simply an input into campaigns.

The critical question will be whether platforms, agencies and advertisers can establish reliable links between creative-health signals and incremental business outcomes.

Top Insights

• Tinuiti's Meta Creative Health Dashboard connects Creative Fatigue and Creative Diversification signals with spend, performance, formats and placements.
• The dashboard estimates media investment exposed to fatigued ad sets and calculates potential ROAS gaps against Tinuiti's performance baselines.
• Creative diversification is becoming more important as automated advertising systems rely increasingly on creative signals to influence campaign delivery.
• API-based alerts are designed to help media buyers and creative teams identify deteriorating ad sets before performance problems become more costly.
• The launch illustrates a broader shift toward treating creative as measurable media infrastructure rather than a separate production function.

Cloudflare is giving website owners a more granular way to manage AI crawler access without forcing them to choose between AI training and traditional search visibility. The company has launched Disallow AI Training, alongside new controls for search, AI training and AI agents.

For years, website owners could largely treat search crawling as a straightforward exchange: crawlers accessed content, and search engines potentially returned visitors to the site. The growth of generative AI has complicated that relationship by introducing new uses for the same web content, including model training, AI-generated summaries and agentic browsing.

Cloudflare is now attempting to separate those uses.

On September 15, the company launched Disallow AI Training, allowing publishers and businesses to prohibit AI training while continuing to permit search crawling. Cloudflare is also introducing an Accountable designation for crawlers that provide controls and transparency around how website content is used.

The change addresses a technical problem created by mixed-use crawlers. A single crawler can sometimes collect content for both search and AI-related purposes, making it difficult for a publisher to block training without also affecting search discovery.

Cloudflare says mixed-use crawlers represented 36.6% of verified crawler traffic on its network, making them the largest crawler category in its data. The company also reports that fewer than 1% of website owners block search crawlers, compared with 17% restricting AI training. These are Cloudflare network measurements rather than industry-wide statistics.

The company's new framework establishes four criteria for its Accountable designation. Crawler operators must provide a mechanism for opting out of AI training, offer controls for AI-generated search summaries, provide URL-level visibility into content usage, and publicly confirm that opting out of training does not affect traditional search rankings.

Cloudflare identifies Apple, Google and Microsoft as Accountable under the new framework, based on its stated criteria and commitments. Google's existing documentation independently confirms that publishers can use the Google-Extended robots.txt token to control whether content is used to train future Gemini models, and that this control does not affect inclusion in Google Search or act as a Google Search ranking signal.

Cloudflare is also replacing its previous single Block AI Bots approach with three separate controls for Search, Training and Agent behavior. Its documentation says that, beginning September 15, new Cloudflare domains have updated defaults in which Training and Agent crawlers are blocked on pages displaying ads while Search remains allowed.

That distinction matters for publishers whose business depends on organic discovery. Blocking AI training no longer necessarily has to mean blocking the crawler responsible for search discovery, provided the operator supports the required controls.

Cloudflare is also introducing Bot Preference Sync, designed to let customers establish crawling preferences once and apply them across supported crawlers. The company says it intends to add more granular controls for AI-generated summaries, allowing publishers to determine how much content can be included rather than relying only on an all-or-nothing setting.

The development comes as the web moves toward more formal mechanisms for expressing AI usage preferences. The IETF's AI Preferences Working Group is developing standards for communicating how digital content may be collected and processed by AI systems. Current drafts address both a vocabulary for AI usage preferences and mechanisms for associating those preferences with content through HTTP and the Robots Exclusion Protocol.

For publishers and SEO teams, the emerging model changes the question from simply asking whether a crawler should be allowed to asking what the crawler is allowed to do.

Market Landscape

The web's crawler ecosystem is becoming more complicated as search engines, AI platforms and autonomous agents access the same publisher infrastructure for different purposes.

Traditional SEO has largely focused on crawlability, indexation and search rankings. AI introduces additional considerations: whether content can be used for model training, whether it can appear in generated answers, and whether agents can retrieve or act on information in real time.

Cloudflare's approach separates these behaviors into distinct categories. Google similarly provides Google-Extended as a control for Gemini-related training while maintaining Google Search access.

The emerging IETF work suggests that the industry is also moving toward standardized ways of expressing these preferences rather than relying solely on vendor-specific controls.

Strategic Outlook

The distinction between search optimization and AI visibility is becoming increasingly important for publishers.

A website may want maximum search discovery while limiting training use. It may allow AI systems to summarize content while restricting autonomous agents. Another publisher may choose to permit all three because its commercial model depends on broad distribution.

That creates a more granular version of technical SEO governance. Robots.txt, crawler identification and access controls are becoming part of a wider content-rights and AI-discovery strategy.

For marketing and publishing teams, the practical priority will be understanding which crawlers access their content, what those crawlers are permitted to do and whether those preferences affect organic discovery.

Cloudflare's announcement also indicates that crawler accountability may become an increasingly important layer between publishers and AI platforms as the web develops standards for AI-era content access.

Top Insights

• Cloudflare's Disallow AI Training setting separates AI model training controls from search visibility, addressing a key concern for publishers dependent on organic discovery.
• Mixed-use crawlers create a technical challenge because one crawler can support both search and AI purposes, making granular access controls increasingly important.
• Cloudflare's Accountable designation establishes requirements around AI training opt-outs, AI summaries, URL-level visibility and search-ranking assurances.
• Google independently confirms that Google-Extended can restrict Gemini training without affecting a website's inclusion in Google Search or its ranking.
• The IETF's AI Preferences work indicates that standardized mechanisms for communicating AI content-use preferences are developing alongside vendor-specific controls.

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AI adoption has become nearly universal across marketing organizations, but proving that those investments generate measurable business value remains a significant challenge. New research from Alexander Group finds that 96% of surveyed marketing organizations now use AI, while only 41% can demonstrate its return on investment.

The findings from Alexander Group's 2026 Revenue-Ready Marketing Organization Research highlight a widening gap between AI adoption and operational accountability in B2B marketing. The research surveyed executives across more than 300 marketing organizations spanning 11 industries and found that AI has moved rapidly from experimentation into everyday marketing operations.

The problem is that adoption alone does not establish business value.

According to Alexander Group, only 31% of surveyed organizations have managed AI governance, while 41% can demonstrate ROI from their AI initiatives. The findings suggest that marketing organizations are increasingly deploying AI without consistently connecting individual use cases to revenue, pipeline contribution or measurable productivity improvements.

The issue extends beyond AI governance. Alexander Group reports that only 24% of organizations have a mature demand-generation engine, while just 26% generate more than 40% of their sales pipeline from marketing. Meanwhile, 56% use advanced attribution models, but only 30% trust the accuracy of those results across Marketing and Sales.

That measurement gap creates a particularly difficult environment for AI investments. A marketing team can quantify faster content production, automated analysis or reduced campaign-production time without necessarily proving that those improvements affected pipeline or revenue.

Independent research points to a similar challenge. Gartner reported in May 2026 that only 30% of surveyed CMOs considered their organizations mature or fully developed in AI readiness, despite allocating an average 15.3% of marketing budgets to AI initiatives. Gartner's survey covered 401 marketing leaders across North America, the United Kingdom and Europe.

Alexander Group identifies four areas where marketing organizations can move AI toward greater accountability: governance, broader AI application, demand generation and measurement confidence.

The research indicates that higher-performing organizations use AI across more marketing activities. Alexander Group reports that 63% of its identified top performers apply AI to at least five use cases, compared with 56% among the broader group. The emphasis is not simply on deploying more tools, but on connecting those applications to demonstrable productivity gains.

The buyer journey is adding further pressure. Alexander Group says customer acquisition costs have risen substantially since 2022 while buyers increasingly conduct research across multiple channels before directly engaging with vendors. That makes identifying high-value prospects, understanding buyer behavior and forecasting demand more complicated.

Marketing AI therefore has applications beyond content generation and campaign operations. Alexander Group points to prospect identification, customer segmentation, forecasting and buyer-experience optimization as areas where AI could influence revenue outcomes.

The shift is also visible in broader industry research. Gartner says high-performing CMOs increasingly measure AI through downstream business outcomes such as conversion, customer satisfaction and campaign impact rather than time savings alone.

The implication for marketing technology vendors is significant. As AI becomes embedded across CRM, marketing automation, analytics and advertising platforms, differentiation may increasingly depend on how effectively those systems connect AI activity to measurable commercial results.

For marketing leaders, the challenge is consequently changing. AI adoption is no longer the only milestone. The next question is whether organizations can build the governance, data quality, attribution and operating processes required to prove what AI is actually contributing.

Market Landscape

Marketing technology is moving from isolated AI features toward broader AI-enabled operating models. Gartner reports that marketing leaders expect the share of marketing work automated by AI to rise from 16% in 2026 to 36% by 2028.

That expansion increases the importance of measurement infrastructure. If more campaign creation, optimization, analysis and customer interaction becomes automated, organizations will need reliable methods for determining whether those activities improve commercial outcomes.

The challenge is particularly relevant for B2B organizations, where long buying cycles, multiple stakeholders and offline sales interactions can make direct attribution difficult.

Strategic Outlook

The next phase of AI adoption in marketing is likely to focus less on experimentation volume and more on operating discipline.

Governance will determine which AI applications can be deployed and under what conditions. Data quality will influence the reliability of AI recommendations. Attribution will determine whether teams can connect AI-enabled activity with pipeline and revenue. Human oversight will remain important where automated decisions affect customers, budgets or brand communications.

Alexander Group's research consequently reflects a broader transition: marketing organizations are moving from asking where AI can be used toward asking where AI can be measured and held accountable.

That distinction could shape how marketing budgets, technology stacks and operational responsibilities evolve as AI becomes a standard component of the B2B go-to-market organization.

Top Insights

• Alexander Group reports 96% AI adoption among surveyed marketing organizations, but only 41% can demonstrate ROI, exposing a significant measurement gap.
• Only 31% of surveyed organizations have managed AI governance, highlighting the growing need for oversight as marketing automation expands across workflows.
• Alexander Group reports that just 24% have mature demand-generation engines, limiting how effectively AI-driven activity can translate into measurable pipeline contribution.
• Advanced attribution is widespread, but only 30% of surveyed organizations trust its accuracy across Marketing and Sales, complicating AI ROI measurement.
• Gartner's 2026 research similarly finds that only 30% of surveyed CMOs report mature or fully developed AI readiness capabilities.

Get in touch with our MarTech Experts.

Informa TechTarget is expanding its B2B intent-data offering with Buyer Intelligence, an AI-driven solution designed to turn person-level behavioral signals into recommendations for sales, marketing and account-based go-to-market teams.

B2B organizations have spent years collecting intent signals to determine which accounts may be researching a product or entering a buying cycle. The harder problem has been turning those signals into timely actions for sales and marketing teams. Informa TechTarget is targeting that gap with its new Buyer Intelligence solution, which combines person-level intent data with AI-powered analysis and recommendations.

The platform is built on Informa TechTarget's proprietary audience data, generated from engagement with editorial content and webinars across its B2B media properties. The company says its permissioned audience produces millions of intent and behavioral signals each day.

Rather than treating intent as a simple account-level indicator, Buyer Intelligence is designed to provide additional context around what individual members of a buying group are researching and the business challenges behind that activity.

Three AI capabilities form the initial focus.

Pain Points analyzes the content that buyers consume, including articles, videos and webinars, to identify the problems and challenges being researched. The resulting insights can be used to prepare sales teams for conversations and develop more specific messaging.

Pitch Assist turns account-level intelligence into messaging guidance, providing sales teams with suggested angles tied to the business problems identified within an account.

Persona and ICP Intelligence uses active buying signals to identify customer segments that are currently showing interest and can highlight potential changes to an organization's ideal customer profile.

Together, the capabilities point toward a broader change in how intent data is being incorporated into B2B revenue technology. Instead of sending another signal into a CRM or marketing automation platform, AI can potentially interpret the signal and connect it to a recommended next step.

That distinction becomes more important as organizations add AI functionality to their go-to-market systems. Generative AI can produce sales emails, campaign concepts and account summaries quickly, but those outputs remain dependent on the quality and specificity of the underlying data.

Informa TechTarget CEO Gary Nugent argued that AI-enabled GTM systems require high-quality inputs, positioning the company's person-level behavioral data as the foundation for those workflows. The company says Buyer Intelligence can support use cases spanning buying-group identification, sales personalization, ABM, new-customer discovery, campaign amplification and content planning.

The solution is available through Informa TechTarget's Portal and can also connect with marketing automation, CRM and ABM platforms, as well as AI-ready data clouds and modern data-stack environments.

Informa TechTarget also cites analyst recognition of its intent-data business. The company says it was named a Leader and Customer Favorite in the Forrester Wave: Intent Data Providers For B2B, Q1 2025, while also receiving Leader recognition in reports from Frost & Sullivan and Research in Action. Those are analyst assessments rather than independent measurements of Buyer Intelligence's performance.

The strategic question for the market is whether AI can make intent data operational rather than simply informative. If systems can connect observed buyer behavior with persona context, messaging and recommended actions, intent intelligence could become more deeply embedded in the daily workflows of B2B revenue teams.

Market Landscape

Intent data has become an important component of account-based marketing, sales intelligence and revenue orchestration platforms. Providers increasingly compete not only on the volume of signals they collect but also on data freshness, identity resolution, buyer-level context and integrations.

The addition of AI changes that competitive equation. Sales and marketing teams increasingly want systems that interpret signals rather than requiring users to manually analyze dashboards and determine what to do next.

For vendors, this puts greater emphasis on proprietary data quality, explainability and activation infrastructure. AI-generated recommendations are only as useful as the behavioral evidence supporting them.

Strategic Outlook

Buyer Intelligence reflects the convergence of three areas that have historically operated somewhat separately: intent intelligence, generative AI and GTM execution.

For enterprise marketing organizations, the opportunity is to shorten the path from detecting active research to delivering a relevant sales or marketing response. The challenge will be maintaining accuracy, transparency and appropriate human oversight as AI becomes more involved in account and persona decisions.

The next stage of B2B intent technology is therefore likely to focus less on producing more signals and more on making existing signals understandable, contextual and actionable within the systems revenue teams already use.

Top Insights

• Informa TechTarget is combining person-level intent data with AI to connect observed buyer research with sales messaging, ABM and GTM actions.
• Buyer Intelligence uses content engagement signals to identify researched business problems, giving sales teams additional context for account conversations.
• Pitch Assist moves intent data toward execution by generating account-specific messaging guidance based on the problems buyers are researching.
• Persona and ICP Intelligence uses live buying signals to identify active customer segments and potential opportunities to reassess ideal customer profiles.
• The launch highlights a wider B2B technology shift from collecting intent signals toward AI systems that interpret signals and recommend revenue actions.

Get in touch with our MarTech Experts.

Ecommerce optimization is moving beyond dashboards and recommendations as Noibu launches six AI agents designed to identify problems, propose changes, execute approved updates and measure their impact inside online stores.

Noibu, an ecommerce operations platform, has introduced six AI agents covering conversion optimization, experimentation, accessibility, technical errors, site performance and advertising efficiency. The launch represents a broader shift in ecommerce technology from systems that identify opportunities to platforms designed to participate directly in the optimization process.

The six agents include a CRO Agent, A/B Testing Agent, ADA Compliance Agent, Bug Resolution Agent, Performance Agent and ROAS Agent. According to Noibu, they operate against store-specific behavioral, commercial and technical data, including customer journeys, click activity, Core Web Vitals, errors and checkout events.

The distinction is important because ecommerce teams have traditionally had to move recommendations through separate workflows. An analytics platform might identify a conversion problem, while developers implement a fix, marketers establish an experiment and analysts later determine whether the change produced an improvement.

Noibu is attempting to connect those stages into one operating loop: detect, diagnose, recommend, act and validate.

Human approval remains part of the process. Changes are not automatically pushed to a live storefront without an individual approving the deployment, while technical fixes can be generated as pull requests for review.

The model also reflects a growing emphasis on AI agents in enterprise software. Rather than simply generating text or answering questions, agentic systems are increasingly being positioned as software that can execute defined tasks across connected workflows.

For ecommerce operators, the potential value is particularly tied to the large number of small changes that can influence conversion and revenue. Product data errors, checkout issues, page performance problems, accessibility gaps and ineffective landing experiences may each represent relatively narrow problems, but collectively they can affect customer acquisition and sales.

Noibu says early deployments demonstrate that model. At Totally Bamboo, the company reports that an agent identified a product-data error occurring roughly 40 times per day and helped produce a fix that went live the following morning. Noibu also reports a collection-page test that produced a 24% conversion improvement and a subsequent 7% increase in conversion over a 30-day comparison period. These figures are company-reported results rather than independently verified performance benchmarks.

The platform is designed to work across several commerce environments, including Shopify, commercetools, Adobe Commerce and BigCommerce, as well as headless implementations. That cross-platform data layer is central to Noibu's proposition because agentic optimization requires access to both the problem and the outcome.

The more significant development may therefore be less about individual AI agents and more about connecting commerce analytics, experimentation, development and measurement into a continuous workflow.

Market Landscape

The ecommerce technology market already contains specialized platforms for experimentation, analytics, personalization, performance monitoring and advertising optimization. Noibu's approach attempts to bring several of those operational functions closer together through AI agents.

The competitive question will increasingly shift from whether AI can identify an optimization opportunity to whether it can safely execute changes and establish measurable causal evidence that the change worked.

That creates a critical role for governance. Ecommerce changes can affect revenue, accessibility, search visibility and customer experience simultaneously. Human approval, controlled experiments, release tracking and reliable measurement therefore become important safeguards as more operational decisions move into AI-assisted workflows.

Strategic Outlook

Noibu's launch illustrates how AI could change the role of ecommerce operations teams. Instead of spending most of their time finding problems and coordinating individual fixes, teams could increasingly supervise a pipeline of machine-generated opportunities and decide which changes deserve deployment.

The effectiveness of this model will depend on the quality of underlying data, platform integrations and experimentation methodology. Agentic automation can accelerate execution, but faster execution does not automatically produce better commercial outcomes.

For ecommerce businesses, the emerging advantage may come from creating a repeatable system in which every approved change generates measurable evidence that informs the next optimization cycle.

Top Insights

• Noibu's six AI agents move ecommerce optimization from recommendations toward execution across conversion, testing, accessibility, performance, bugs and advertising.
• The platform connects behavioral, technical and commercial data so teams can evaluate changes against the storefront conditions and experiments where they occurred.
• Human approval remains part of deployment, providing a governance layer before AI-generated changes reach production ecommerce environments.
• Noibu's approach reflects the wider move toward agentic enterprise software that performs operational tasks rather than simply generating analysis or recommendations.
• The competitive differentiator may increasingly be measurement infrastructure that connects AI-generated actions with verified business outcomes.

Get in touch with our MarTech Experts.

Newell Brands is putting design closer to the center of its product and marketing operations with a new Atlanta Design Center intended to connect consumer research, creative development, prototyping and innovation in a single operating environment.

The consumer goods company, whose portfolio includes Rubbermaid, Sharpie, Coleman, Graco and Yankee Candle, has opened nearly 37,000 square feet of design facilities across two Atlanta locations. The investment is aimed at shortening the distance between consumer insights and the products, packaging and brand experiences that ultimately reach the market.

The new Design Center combines two facilities: the Queen Tower at Concourse Office Park near Newell's Atlanta headquarters and a photography and creative studio at the company's Morgan Falls location.

Rather than functioning solely as a conventional creative studio, the center brings together industrial and brand design, digital visualization, physical prototyping and AI-enabled capabilities. The broader objective is to allow teams to test ideas earlier, visualize concepts faster and make product and commercial decisions with more direct consumer input.

That model reflects a broader change in how consumer companies approach design. Design teams are increasingly expected to contribute beyond visual identity, working alongside product development, research, marketing and innovation teams from the beginning of the product lifecycle.

Newell is also investing in dedicated user experience research environments, including a Kitchen UX Lab and Kids UX Lab. These spaces are designed to simulate real-world environments where researchers can observe consumers interacting with products.

The Kitchen UX Lab, for example, recreates a household kitchen for consumer research, while the Kids UX Lab provides an environment for parents, caregivers and children to interact with products. Such facilities can provide product teams with behavioral observations that conventional surveys or digital analytics may not capture.

The center's use of AI and digital visualization also points to another change in product development. Generative and visualization technologies can make it possible to explore more design variations before committing resources to physical prototypes. That does not eliminate the need for physical testing, but it can change when and how teams decide which concepts deserve further investment.

For a company managing a large portfolio of consumer brands, the operational advantage is potentially significant. Centralizing design expertise can create shared processes and capabilities while allowing individual brands to retain their own identities.

Newell's move also fits into a wider enterprise trend toward bringing creative, customer experience and technology functions closer together. Adobe, Microsoft and other enterprise technology providers have increasingly positioned generative AI and digital collaboration tools as components of creative and marketing workflows. The differentiator for consumer companies is increasingly how those technologies are integrated with real-world product research and commercialization.

The Atlanta location is also intended to support recruitment. Newell points to Atlanta's technology, creative and university ecosystem as a source of design talent. Locating the center near the company's global headquarters also puts designers closer to business leaders and cross-functional teams.

That proximity could matter more than the physical space itself. Design organizations often influence outcomes most effectively when they participate before product specifications, packaging decisions and marketing strategies have already been finalized.

Market Landscape

Consumer goods companies are facing shorter product cycles, changing consumer expectations and growing pressure to differentiate products across increasingly crowded categories.

As a result, design is becoming more closely connected to consumer research, digital experiences, product innovation and brand strategy. The traditional separation between a company's design studio and its commercial organization is gradually becoming less practical.

Newell's model combines physical research environments with digital visualization, prototyping and AI-enabled capabilities. That gives its designers access to both qualitative consumer observation and technology-assisted development.

For enterprise marketing teams, the broader lesson is that customer experience increasingly begins before a campaign is created. Product design, packaging, brand identity and consumer research can all influence how effectively marketing organizations communicate value once a product reaches the market.

Strategic Outlook

The Atlanta Design Center represents an organizational investment as much as a physical one. Its success will depend on whether the company can translate closer collaboration between designers, researchers, marketers and product teams into faster development and stronger consumer outcomes.

The growing use of AI may accelerate that process by making visualization, iteration and concept exploration less resource-intensive. But physical product testing and direct consumer observation remain important, particularly for household, children's and other tangible consumer products.

Newell's approach suggests that the next generation of consumer brand innovation will increasingly combine consumer research, design systems, AI-assisted creativity and physical prototyping rather than treating them as separate stages.

Top Insights

• Newell's Atlanta Design Center combines consumer research, industrial design, prototyping and AI-enabled tools to bring product and brand development closer together.
• Dedicated Kitchen and Kids UX Labs allow teams to observe consumers interacting with products in simulated real-world environments before commercial launches.
• AI-powered visualization can help designers explore more concepts earlier, potentially reducing iteration time before teams commit to physical prototypes.
• Locating design capabilities near Newell's headquarters creates closer connections between designers, marketers, R&D teams, executives and innovation partners.
• The investment reflects a broader shift toward treating design and consumer experience as strategic enterprise capabilities rather than downstream creative functions.

Get in touch with our MarTech Experts.

Lokalization is becoming a broader enterprise content challenge as brands expand campaigns, websites and customer communications across markets. Lokalise is responding with Vantage, a no-code workspace designed to help marketing, legal and support teams localize long-form content without relying on technical teams for every request.

Lokalise says Vantage will become available to customers at the end of September 2026. The product extends the company's localization platform beyond software and design workflows into campaign collateral, websites, creative assets, legal documents and support content.

Vantage is designed around a self-service workflow in which non-technical users can submit, manage and review localization projects. That changes the role of localization from a specialized production process into something marketing teams can initiate directly.

The technology's main differentiator is how it handles long-form material. Rather than treating every sentence or content fragment as an isolated translation unit, Lokalise says Vantage uses document parsing and segmentation to understand the structure of an entire document. Its language intelligence then evaluates translations and routes content requiring additional review to human linguists.

That hybrid model reflects a wider change in enterprise localization. Generative AI has made basic translation considerably faster and cheaper, but producing language that fits a company's brand, terminology and market context remains a more complex problem. Lokalise describes this goal as Language Market Fit: content should feel appropriate to the target market rather than simply represent a translated version of the source.

Vantage connects that process to Lokalise's existing language assets, including translation memory, glossaries and analytics. This creates a common terminology layer between marketing and engineering teams, potentially reducing a familiar enterprise problem in which product language and campaign language drift apart across markets.

The platform also incorporates governance features that become increasingly important as localization moves into self-service workflows. Localization managers can establish permissions, quality requirements, approval rules, budgets and audit logs centrally, with those controls applied across requests.

Integration is another part of the strategy. Lokalise says Vantage can work with Contentful, Webflow, Braze, Strapi, Google Docs and SendGrid, while supporting formats including DOCX, PPTX, HTML, Markdown, IDML and XLSX. The objective is to place localization closer to the systems where content is created rather than requiring teams to export files and move them through separate translation processes.

The approach puts Lokalise into competition with traditional translation-management systems, specialist localization providers and increasingly AI-enabled translation tools. Companies such as Smartling, Phrase and TransPerfect already address different parts of enterprise localization, while cloud and AI providers are making machine translation and generative language capabilities increasingly accessible.

The competitive question is therefore shifting. Translation accuracy remains important, but enterprise buyers also need workflow integration, terminology consistency, human review and governance. A platform that handles those elements together can potentially reduce the operational friction associated with launching content across multiple markets.

Lokalise cites customer-reported results including market launches falling from four weeks to less than two days, more than 92% acceptance among expert linguists and approximately 80% average savings compared with human translation. These figures are company-reported and should be evaluated against independent customer and workflow data.

For enterprise marketing teams, Vantage could be particularly relevant as campaign volumes increase. Localization managers can retain control over quality and governance while marketers gain a self-service workspace for launching multilingual content.

Market Landscape

AI is changing localization from a translation-only function into a broader content operations discipline. The emergence of large language models has lowered the cost and time required to produce multilingual drafts, but enterprises still need mechanisms for brand voice, terminology, regulatory review and market-specific adaptation.

Lokalise's strategy is to combine AI-assisted translation with centralized language assets and workflow governance. That puts Vantage closer to a localization operations platform than a standalone machine-translation tool.

For global marketing organizations, this distinction matters because multilingual content increasingly spans websites, lifecycle campaigns, creative assets, documentation and customer support. Managing those channels independently can create inconsistent messaging and duplicated translation work.

Strategic Outlook

The next stage of localization technology is likely to focus less on translating individual documents and more on maintaining a consistent brand language across markets and content systems.

Vantage's no-code approach could expand access to localization beyond technical teams, while integrations with content and marketing platforms could make multilingual publishing a more continuous process.

The larger challenge will be maintaining human oversight where cultural nuance, legal requirements or brand risk make automated decisions unsuitable. AI can accelerate the workflow, but market relevance still depends on context, terminology and judgment.

Top Insights

• Lokalise Vantage gives non-technical marketing teams a no-code workspace for managing multilingual campaigns, websites, creative assets and long-form documents.
• AI-assisted translation is moving beyond speed toward market relevance, with brand voice, terminology and contextual adaptation becoming critical enterprise requirements.
• Shared translation memory and glossaries can help marketing and engineering teams maintain consistent terminology across products, campaigns and customer experiences.
• Integrations with Contentful, Webflow, Braze and other content platforms position localization closer to continuous marketing operations rather than separate translation projects.
• Governance features allow localization leaders to establish permissions, quality controls, budgets and review workflows while expanding self-service access across organizations.

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The race to expand AI infrastructure is increasingly being constrained by something more fundamental than GPUs: electricity. Hammerhead AI and TD SYNNEX are targeting that bottleneck with a new distribution partnership that brings Hammerhead's Orchestrated RL Control Agents (ORCA) software to TD SYNNEX's global reseller ecosystem.

Announced September 15, 2026, the collaboration makes TD SYNNEX a distributor for ORCA and introduces a standardized offering intended to help data center operators convert underutilized power into additional AI-ready capacity. TD SYNNEX says its network spans more than 150,000 resellers worldwide.

ORCA uses reinforcement learning agents to coordinate power, cooling and compute in data centers in real time. Hammerhead says the software is designed to increase utilization of power that has already been allocated to a facility, potentially allowing operators to support additional AI workloads without immediately securing new grid capacity.

That proposition addresses a growing infrastructure problem. Gartner forecasts worldwide data center electricity consumption will reach 565 TWh in 2026, a 26% increase from 2025. The research firm also expects AI-optimized servers to account for 31% of data center power consumption this year.

The implication for AI infrastructure providers is significant: adding accelerators is only useful when sufficient power, cooling and supporting infrastructure are available to run them. New grid connections can involve lengthy planning and equipment lead times, creating pressure to extract more capacity from existing facilities.

Federal regulators are also responding to the scale of the issue. In June 2026, the Federal Energy Regulatory Commission addressed large-load interconnection issues, including concerns about speculative projects occupying interconnection queues and complicating transmission planning.

Hammerhead and TD SYNNEX are therefore positioning ORCA around brownfield as well as greenfield data centers. The companies plan to offer a standardized MW ORCA SKU that resellers can quote and deploy alongside other infrastructure rather than requiring every customer to assemble a bespoke power-optimization stack.

The timing also corresponds with a changing AI workload mix. Gartner forecasts that worldwide spending on AI-optimized infrastructure-as-a-service will grow 96% in 2026, while inference spending is expected to reach $23.3 billion, surpassing the $19 billion forecast for AI training.

Inference creates a different infrastructure profile from model training. Production AI applications can generate persistent and unpredictable demand, particularly as agentic applications and retrieval-augmented generation increase the number of model calls required for individual tasks. In this environment, improving tokens processed per unit of available power becomes an infrastructure-efficiency issue as much as a compute issue.

For colocation providers, AI cloud service providers, neoclouds and enterprises, ORCA is consequently positioned as a software layer between available physical infrastructure and expanding AI workloads. Hammerhead claims its platform can unlock at least 30% additional capacity from existing infrastructure, although that figure is a company claim rather than an independently established industry benchmark.

The TD SYNNEX relationship changes the commercial distribution model around that technology. Instead of selling power orchestration only through direct infrastructure engagements, Hammerhead can reach system integrators and resellers already assembling AI infrastructure stacks. TD SYNNEX will distribute ORCA through its Destination AI program, alongside other AI infrastructure technologies.

For enterprise technology teams, the broader significance is that AI capacity planning is becoming a cross-layer problem. GPUs, networking and cloud services remain critical, but power availability, cooling efficiency and infrastructure utilization increasingly determine how quickly new workloads can reach production.

Market Landscape

The AI infrastructure market is expanding beyond accelerator procurement into software designed to optimize the physical resources surrounding those chips. Companies are addressing workload scheduling, cooling, energy management, data center operations and infrastructure utilization from different angles.

Hammerhead's approach is differentiated by combining reinforcement learning with real-time coordination of power, cooling and compute. Its partnership with TD SYNNEX also gives the technology a channel-led route into colocation, cloud and enterprise infrastructure projects.

For buyers, the important comparison is not simply whether a platform can optimize energy. The practical questions include how it integrates with existing data center controls, what operational safeguards are available, how performance is measured and whether additional usable capacity can be demonstrated under real workloads.

Strategic Outlook

The expansion of AI infrastructure is increasingly turning electricity into a strategic technology resource. Gartner's forecasts suggest that the growth of AI workloads will continue increasing data center power requirements, while the shift toward inference creates demand for infrastructure that can operate continuously and efficiently.

That creates an opening for software that optimizes infrastructure already deployed rather than waiting exclusively for new capacity. ORCA's commercial success will ultimately depend on measurable improvements in usable compute capacity, operational reliability and economics across different data center environments.

The TD SYNNEX partnership gives Hammerhead a broader distribution mechanism, but the technology will still need to demonstrate that power orchestration can translate into dependable AI workload capacity in production environments.

Top Insights

• ORCA coordinates power, cooling and compute with reinforcement learning, targeting data centers where existing electrical capacity limits additional AI workloads.
• TD SYNNEX adds a large reseller channel to Hammerhead's go-to-market strategy, potentially making power optimization part of standardized AI infrastructure deployments.
• Gartner expects global data center electricity consumption to reach 565 TWh in 2026, highlighting the infrastructure pressure created by AI workloads.
• AI inference spending is forecast to surpass training spending in 2026, increasing demand for infrastructure capable of supporting continuous production workloads.
• Enterprises and colocation providers may increasingly evaluate AI infrastructure by usable capacity and power efficiency rather than accelerator availability alone.

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