commercetools announced on October 22, 2026, that it had been positioned as a Leader in the 2026 Gartner Magic Quadrant for Digital Commerce Platforms, marking its seventh consecutive year in the category. Gartner published the report on October 19, assessing 14 vendors in a market increasingly shaped by agentic AI, architectural flexibility and the ability to support business-to-business (B2B) and business-to-consumer (B2C) commerce.

The recognition arrives as commerce platforms evolve beyond powering websites and transaction systems. Retailers and enterprise sellers are exploring how AI assistants can discover products, retrieve catalog information, support purchasing decisions and interact with commerce infrastructure. That shift creates new requirements for platforms: they must support traditional storefronts while enabling controlled access for AI agents.

Gartner’s research identifies agentic AI readiness, architectural agility and scalability across unified B2B and B2C channels as priorities for digital commerce buyers. Its Magic Quadrant evaluates vendors using two broad criteria: Ability to Execute and Completeness of Vision. Gartner also cautions that its reports represent analyst opinions and do not endorse individual vendors.

commercetools builds its AI commerce portfolio

The company is using its latest product developments to address different stages of the AI-enabled commerce lifecycle, from development and infrastructure to agent governance and product data preparation.

Sphere brings together commercetools’ AI-oriented infrastructure and agent governance approach, including Commerce MCP. The company positions it as a foundation for enterprises that want to build, operate and sell through AI-enabled commerce experiences.

commercetools for Builders is designed to accelerate the development and extension of enterprise commerce applications using AI. The objective is to help development teams create and modify commerce capabilities more quickly.

AgentSphere focuses on building, managing and governing AI agents for commerce operations and agentic shopping. Its purpose is to give enterprises oversight of agents and their actions, an important consideration as AI systems gain access to product, cart and order functions.

The company is also promoting modular commerce offerings that allow enterprises to adopt selected capabilities and expand over time, rather than replacing an entire commerce platform in a single transformation. Its Commerce Integration Layer is intended to centralize integration logic so storefronts, mobile applications and AI agents can connect through a common layer. Smart Data Modeler uses AI to accelerate product data modeling, helping teams establish the product information structures required for commerce operations.

These offerings address a practical challenge: AI shopping experiences still depend on reliable product information, pricing, inventory, cart and order services. A conversational interface alone cannot complete a dependable transaction without access to those underlying systems.

Commerce MCP connects AI assistants to commerce operations

A key technical element is Commerce MCP, which enables AI tools to interact with commercetools projects through a standardized interface. According to the company’s documentation, supported operations can include retrieving product information and working with carts, orders and customer data. Access can be configured with permissions ranging from read-only to read-and-write, allowing organizations to control what an agent can do.

This creates a route for AI agents to move beyond recommending products toward taking permitted actions. But it also introduces governance requirements: enterprises need to define which agents can access sensitive information, which operations require approval, and how actions are monitored and audited.

commercetools says more than 500 global brands, retailers and enterprise companies rely on its platform, processing more than $100 billion in annualized gross merchandise value (GMV). These are company-reported figures that indicate the scale of its existing commerce operations.

Recognition is a milestone, not a deployment strategy

The Gartner positioning gives commercetools a market-recognition milestone, but it does not establish that the platform will be the right fit for every enterprise. Buyers still need to assess integration complexity, total cost of ownership, operational requirements, B2B and B2C needs, and the maturity of their data and AI governance.

The next test for commercetools will be whether its expanding AI portfolio helps customers deliver measurable improvements in development speed, operational efficiency and conversion across both conventional and agent-led shopping journeys. For commerce leaders, the broader lesson is clear: preparing for AI-driven commerce requires more than adding a chatbot. It requires infrastructure that connects product data and transaction services with governed, measurable AI workflows.

Market Landscape

Digital commerce platforms are competing across three increasingly connected areas:

• Composable architecture: Modular services that let enterprises adapt their commerce stack without replacing every component.

• Agentic commerce: AI agents that can discover products, support shopping and interact with transaction systems.

• Enterprise governance: Controls over permissions, data access, monitoring and the actions AI agents can take.

Gartner’s 2026 report includes vendors such as Adobe, Salesforce, SAP, Shopify, VTEX, Spryker and commercetools, illustrating the breadth of competition across enterprise and retail commerce platforms.

Strategic Outlook

commercetools is positioning its platform for a future in which purchases may originate from conventional storefronts, mobile apps or AI assistants. Its investments in MCP connectivity, agent management and modular infrastructure are intended to make those experiences possible without abandoning core commerce services.

For enterprise buyers, the decision should rest on practical readiness rather than analyst placement alone. Product data quality, integration effort, security controls and the ability to measure business outcomes will determine whether agentic commerce can scale beyond pilots.

Top Insights

1. commercetools has been named a Gartner Magic Quadrant Leader for the seventh consecutive year, amid growing emphasis on agentic AI and architectural flexibility.

2. Sphere, AgentSphere and Commerce MCP target the infrastructure, connectivity and governance needed to connect AI agents with enterprise commerce systems.

3. MCP can enable AI tools to retrieve product information and interact with carts and orders, subject to configured permissions.

4. Modular commerce offerings allow enterprises to adopt selected capabilities incrementally rather than undertake a full platform transformation at once.

5. Gartner recognition provides market context, but buyers should independently assess integration requirements, governance, cost and business outcomes before selecting a platform.

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Adobe’s partnership with BWT Alpine Formula One Team brings together AI-powered content production, real-time audience insights, AI search visibility and fan-facing creative tools. The collaboration signals how sports organizations are extending marketing technology beyond sponsorships and race-day campaigns toward more personalized digital experiences—and preparing for a future in which fans discover teams through AI assistants as well as traditional search and social channels.

Adobe connects four AI capabilities across the fan journey

The partnership, announced on October 22, 2026, will begin enhancing fan experiences from the 2027 Formula One season. Rather than focusing on a single AI application, it combines content production, audience data, AI search visibility and fan-generated creativity into a broader customer experience strategy.

The four technology components address different parts of the fan journey.

• Adobe GenStudio: The team will use AI-powered content production to create tailored versions of marketing material for different markets and audience preferences. This could help Alpine adapt campaigns more efficiently across channels, although the announcement does not specify planned content volumes or production savings.

• Adobe Experience Platform: The platform will provide a more comprehensive view of fan engagement, supporting audience analysis and more relevant communications across channels. The potential value depends on how effectively the team unifies its data and activates insights.

• Adobe Brand Visibility: Alpine plans to monitor how it appears across AI-powered discovery and research platforms. This brings generative engine optimization (GEO) into the team’s marketing strategy, extending brand visibility work beyond conventional search engine results.

• Adobe Express: Fans will gain access to creative tools with generative AI capabilities, enabling them to make images and other content expressing their enthusiasm for the team and the sport.

Together, these initiatives span both sides of the relationship: helping Alpine produce and distribute marketing content while giving fans more opportunities to create and share their own.

AI search visibility becomes part of sports marketing

One of the partnership’s more notable elements is Adobe Brand Visibility. As consumers use conversational AI tools to research teams, athletes, events and brands, marketers have a growing reason to monitor how their organizations are represented in AI-generated answers.

Adobe describes Brand Visibility as a generative AI optimization solution that helps businesses monitor brand presence, identify content gaps and prioritize improvements across AI-driven discovery environments.

For Alpine, the goal is to understand how the team appears across leading AI platforms and adjust its content strategy accordingly. This could become relevant when prospective fans research drivers, race schedules, team histories or merchandise through AI assistants.

However, visibility is not the same as guaranteed placement. AI-generated responses can vary by prompt, platform and context, and no tool can ensure that a brand will be recommended in every answer. The practical test will be whether monitoring and optimization improve Alpine’s discoverability and translate into meaningful audience engagement.

From sponsorship visibility to customer experience orchestration

The deal also illustrates how technology partnerships are evolving. Traditional sports sponsorships have often centered on logo placement, branded campaigns and promotional activations. Adobe and Alpine are positioning this agreement around the systems used to create, personalize and measure interactions with fans.

Adobe says more than 20,000 brands rely on its solutions for customer experience orchestration (CXO). In this context, CXO means coordinating customer data, content and interactions to deliver more relevant experiences.

For Alpine, the commercial opportunity is to connect interactions across digital channels and race-related moments rather than treating every campaign as a separate activity. Adobe, in turn, gains a high-profile sporting environment in which to demonstrate how its enterprise AI and marketing technologies can support a global brand.

The partnership’s success will depend on execution. Relevant measures could include content production efficiency, engagement rates, fan retention, referral traffic from AI discovery platforms and participation in fan-created campaigns. Neither company has announced specific performance targets or quantified expected returns.

The wider takeaway for marketing leaders is that AI-powered fan engagement is becoming a connected operating model—not simply a content-generation experiment. Alpine’s planned rollout from 2027 will provide a practical test of whether these tools can strengthen the relationship between a global sports team and its audience.

Market Landscape

Adobe’s partnership with Alpine reflects three developments in MarTech:

• AI-powered content supply chains: Generative AI can help teams produce and adapt campaign assets for multiple audiences and channels.

• Customer experience orchestration: Unified audience data can support more coordinated communications across digital touchpoints.

• Generative engine optimization: Brands are extending discoverability strategies to AI assistants and LLM-powered search experiences.

The differentiator is how well these capabilities work together. Producing more content is of limited value if the content is irrelevant, audience signals are fragmented or AI discovery does not lead to engagement.

Strategic Outlook

The partnership could give Alpine a more integrated way to manage fan relationships before, during and after race weekends. It also offers Adobe a visible example of how creative AI, customer data and AI search optimization can support one experience strategy.

The rollout will be worth watching for measurable improvements in personalization, engagement and discoverability. Marketers should look beyond the AI label and assess whether the systems create a coherent fan journey, preserve brand consistency and generate measurable commercial value.

Top Insights

1. Adobe becomes Alpine’s Official AI Fan Experience Partner, with planned fan-facing initiatives starting from the 2027 Formula One season.

2. GenStudio will support personalized content production, while Adobe Experience Platform will help Alpine understand fan engagement across digital touchpoints.

3. Adobe Brand Visibility brings generative engine optimization into sports marketing by helping the team monitor its presence across AI-driven discovery platforms.

4. Adobe Express will enable fan creativity through generative AI design tools, extending the partnership beyond team-produced marketing campaigns.

5. The companies have not disclosed performance targets, so content efficiency, fan engagement and AI-driven discoverability will be important indicators of success.

Get in touch with our MarTech Experts.

Sprinklr has made its Voice of the Customer (VOC) Agent Suite available in the Gemini Enterprise Agent Marketplace, bringing specialized customer intelligence capabilities into Google’s enterprise AI environment. Announced on October 22, 2026, the integration uses Agent-to-Agent (A2A) connectivity to help organizations investigate customer feedback, monitor brand health, benchmark competitors and identify emerging issues within existing Gemini workflows. The suite is available now to existing Sprinklr customers through authenticated Sprinklr environments and customer data.

Sprinklr embeds customer intelligence into enterprise AI workflows

Customer feedback is distributed across surveys, online reviews, social media conversations, support cases and news coverage. For marketing, customer experience (CX) and insights teams, the challenge is not simply collecting these signals but connecting them quickly enough to guide decisions.

Sprinklr’s new integration aims to reduce the distance between asking a business question and retrieving evidence-backed customer intelligence. Rather than requiring users to begin each investigation in a separate listening or analytics interface, Gemini Enterprise users can discover and interact with specialized Sprinklr AI agents through A2A connectivity.

The launch reflects a broader shift in enterprise AI adoption: organizations are increasingly looking to bring specialized capabilities into the environments where employees already work, rather than relying exclusively on standalone AI applications.

Google Cloud describes A2A as an open protocol that enables agents built on different platforms to discover one another, collaborate and securely delegate tasks. This provides a standardized mechanism for connecting external agents with Gemini Enterprise, although the quality and scope of each workflow depend on the agent’s implementation and permissions.

Eight agents target distinct marketing and CX needs

The Sprinklr VOC Agent Suite is organized around eight business use cases, moving beyond a general-purpose conversational assistant toward more specialized customer intelligence workflows.

• Brand health monitoring: Examines brand performance, sentiment drivers and potential actions.

• Crisis monitoring: Helps identify emerging issues, assess their spread and locate influential amplifiers.

• Competitive benchmarking: Compares brands across share of voice, sentiment and engagement.

• Trend forecasting: Identifies developing conversations and estimates their future momentum.

• Influencer and content strategy: Surfaces influential voices and explores which content resonates with audiences.

• Campaign performance: Evaluates launches, campaigns and sponsorships.

• Customer experience analysis: Highlights recurring customer pain points and positive feedback across channels.

• General listening: Answers broader questions about customer conversations and market signals.

Together, these capabilities could help marketing and CX teams investigate questions such as what is driving a sentiment decline, how a competitor’s campaign is being received or which customer complaints are appearing across multiple channels.

Sprinklr says its agents retrieve relevant information and return conclusions with supporting evidence and context. That distinction matters for enterprise users who need more than a fluent AI-generated answer: they need to understand the signals behind a recommendation and assess whether the evidence supports action. The company has not, however, disclosed independent performance benchmarks for the new suite in this announcement.

A distribution strategy as much as a product launch

The marketplace availability also has strategic implications for Sprinklr. Its Unified Customer Experience Management platform brings together capabilities spanning social media management, marketing, advertising, customer feedback and contact center operations. Embedding its VOC agents in Gemini Enterprise gives the company another route to deliver those capabilities within customers’ established AI workflows.

Sprinklr says more than 1,600 enterprises use its platform, including Microsoft, Procter & Gamble and Samsung, and that 59% of Fortune 100 companies are customers. These are company-reported figures, but they indicate the scale of the enterprise customer base the vendor can potentially serve through this integration.

The move also puts pressure on customer intelligence and social listening providers to make their insights accessible through more than their own dashboards. As AI assistants become work interfaces, vendors may increasingly compete on the quality of their underlying data, domain-specific analysis and integration capabilities—not just on the interface customers use.

Access, governance and measurable value remain important

The integration is available now, but Sprinklr specifies that existing customers access the full suite through authenticated Sprinklr environments and their customer data. The announcement does not provide detailed pricing, deployment timelines, service-level commitments or comparative accuracy results.

For enterprise buyers, the practical questions are therefore operational as well as analytical. Teams should assess how permissions flow between Gemini Enterprise and Sprinklr, how sensitive customer data is handled, whether conclusions can be traced to source evidence and how the agents perform across different languages, channels and markets.

Organizations should also define success metrics before broad deployment. Potential measures include the time required to investigate a customer issue, the speed of crisis detection, the frequency of actionable insights and whether those insights lead to measurable improvements in campaign decisions or customer experience.

Sprinklr’s announcement positions specialized AI agents as a way to make customer intelligence more accessible. The business case will ultimately depend on whether that accessibility translates into faster, more reliable decisions without weakening data governance or analytical rigor.

Market Landscape

The launch sits at the intersection of three enterprise technology trends:

• Agentic AI: Moving from question answering toward specialized workflows that retrieve information and support multi-step business tasks.

• Unified customer intelligence: Connecting solicited feedback, social conversations, reviews and service interactions to build a more complete picture of customer experience.

• AI ecosystem distribution: Making third-party capabilities available within enterprise AI environments instead of requiring users to switch between separate applications.

Google’s A2A framework supports interoperability between agents, while Sprinklr contributes customer feedback data and domain-focused analysis. The combination illustrates how enterprise AI platforms and specialist SaaS vendors can complement one another, even as they compete for ownership of the user experience.

Strategic Outlook

Sprinklr’s integration could be particularly relevant to large organizations whose marketing, brand, insights and CX teams need to investigate customer signals quickly. The immediate opportunity is to reduce workflow friction; the longer-term opportunity is to make customer intelligence a reusable capability across enterprise operations.

Three factors will shape adoption: the relevance and freshness of the underlying feedback, the reliability of agent-generated conclusions, and the ease with which organizations can govern access to customer data. A marketplace listing alone does not guarantee those outcomes.

For MarTech buyers, the key consideration is whether agent-based access complements existing analytics and listening processes or creates another layer of tools to manage. Evidence of faster investigations and better business decisions will matter more than the novelty of the AI interface.

Top Insights

1. Sprinklr is bringing eight specialized voice-of-customer agents into Gemini Enterprise, spanning brand health, crisis monitoring, competitor analysis, campaigns and customer experience.

2. Agent-to-Agent connectivity lets Gemini Enterprise users access external AI agents, supporting a shift toward interoperable enterprise AI workflows.

3. Existing Sprinklr customers can access the suite through authenticated environments, making data access and permission management important deployment considerations.

4. The launch could reduce friction between customer feedback and decision-making, but independent accuracy benchmarks and quantified business outcomes have not been disclosed.

5. Customer intelligence vendors may increasingly compete on data quality, domain expertise and integration into enterprise AI ecosystems, not just standalone dashboards.

Get in touch with our MarTech Experts.

RingCentral has expanded its decade-long partnership with the Golden State Warriors, becoming the NBA organisation’s official Agentic AI Powered Voice platform for fan engagement. The companies say the collaboration will explore AI-powered voice experiences for ticket inquiries, game-day questions and ongoing fan interactions. The agreement builds on RingCentral’s existing role in connecting Warriors staff and locations, extending the relationship from business communications infrastructure towards AI-assisted customer engagement.

RingCentral and Golden State Warriors Extend Partnership Into Voice AI

RingCentral and the Golden State Warriors are expanding a decade-long technology partnership to explore how agentic voice AI can support fan engagement across the sports organisation. Announced on October 22, 2026, the agreement names RingCentral the team’s official Agentic AI Powered Voice platform for fan engagement.

The planned use cases include ticket inquiries, game-day questions and ongoing interactions with fans. The announcement does not specify a deployment timetable, the exact capabilities fans will initially access or performance targets for the new experience.

The partnership reflects a broader shift in customer engagement technology: businesses are exploring AI agents that can do more than answer common questions, potentially helping users complete tasks and move through service workflows. RingCentral has been expanding its agentic AI portfolio through products such as AIR Pro, which the company describes as a platform for voice-first and omnichannel AI agents that can interpret intent and execute multistep actions.

A decade of communications infrastructure

The new agreement builds on RingCentral’s role as the Warriors’ communications provider. According to the companies, the team has expanded from a single Oakland office to operations in San Francisco and Santa Cruz, alongside facilities managed through Golden State Sports Academy. Its staff has grown fivefold during that period.

RingCentral says its cloud phone system and mobile applications helped maintain business communications when eligible Warriors employees shifted to remote work in March 2020. Staff continued using their business numbers to communicate with season ticket members, corporate partners, community organisations and fans.

That history provides the foundation for the expanded relationship. The companies are now looking beyond internal communications to explore how AI could help the Warriors respond to fan needs and make information easier to access.

From answering calls to completing tasks

Agentic voice AI is intended to combine conversational interfaces with the ability to take actions across connected systems. Depending on how it is configured, an AI agent may be able to interpret a caller’s request, retrieve relevant information, guide the person through a process or pass the interaction to a human employee.

For a sports organisation, potential applications include helping fans find ticket information, answering routine questions about game-day arrangements and directing more complex requests to the appropriate team. These are plausible use cases for the partnership, not confirmed descriptions of the Warriors’ initial deployment.

RingCentral’s wider customer engagement strategy includes AI agents embedded in contact centre workflows, automated outreach and intelligent handoffs to human agents with relevant interaction context. Those capabilities could be relevant to future fan-service workflows, although the Warriors announcement does not confirm which specific RingCentral products or features will be deployed.

Sports organisations compete on service as well as spectacle

Fan engagement extends beyond what happens during a game. Ticketing, event information and ongoing communication all contribute to how supporters experience a sports brand. High-demand events can also create peaks in incoming inquiries, making the ability to respond consistently an operational concern.

AI-powered voice interfaces may help organisations handle routine requests more efficiently, but the experience will depend on answer accuracy, integration with ticketing and service systems, and the ability to escalate unusual or sensitive requests to staff. Clear controls are particularly important when a conversation involves purchases, account information or changes to a booking.

The wider sports technology market is also seeing investment in AI-assisted customer service. In May 2026, Zendesk announced a separate partnership naming it the official AI-powered customer service provider for the Warriors, the Golden State Valkyries and Chase Center. That agreement illustrates how sports organisations are exploring AI across multiple aspects of the fan journey, while RingCentral’s newly expanded agreement focuses on voice-platform capabilities.

For RingCentral, the Warriors relationship offers an opportunity to demonstrate how agentic voice technology can support a high-profile, consumer-facing organisation. For the team, the potential benefit is a more accessible and responsive service experience. Whether the partnership delivers measurable improvements will depend on implementation details and outcomes that have yet to be announced.

Market Landscape

The partnership sits at the intersection of conversational AI, contact centre technology and sports fan engagement. Vendors are moving beyond basic virtual receptionists and scripted chatbots towards agents that can interpret requests, connect to business systems and carry out multistep tasks.

RingCentral is developing this model through its AIR Pro and RingCX portfolio. Meanwhile, Zendesk's separate Warriors and Chase Center agreement demonstrates that AI-powered customer service is also becoming part of the broader technology strategy for sports venues and teams.

The competitive differentiator will be execution: accurate answers, reliable integrations, smooth escalation to human staff and a consistent experience across phone and digital channels.

Strategic Outlook

RingCentral's expanded partnership gives it a prominent setting in which to explore agentic voice AI beyond conventional business communications. Three factors will determine the significance of the initiative:

• Fan experience: AI must make ticket and game-day information easier to access without introducing frustrating or inaccurate interactions.

• Operational integration: The value of voice agents will depend on how effectively they connect with relevant information and service workflows.

• Trust and accountability: Human escalation, privacy safeguards and clear handling of requests will remain important, particularly when interactions involve tickets or customer accounts.

The next meaningful milestone will be evidence of deployment, including which use cases go live and whether they improve response times, inquiry resolution or fan satisfaction.

Top Insights

1. RingCentral's decade-long relationship with the Warriors is expanding from communications infrastructure into agentic voice AI for fan-facing interactions.

2. Initial use cases identified by the companies include ticket inquiries, game-day questions and ongoing engagement, although deployment details have not yet been disclosed.

3. RingCentral's broader AI portfolio supports multistep workflows and human handoffs, but the Warriors announcement does not confirm which specific capabilities will be implemented.

4. A separate Zendesk partnership shows that AI-powered service is becoming another area of technology investment across the Warriors and Chase Center fan experience.

5. The partnership's impact will depend on accurate answers, effective system integrations and measurable improvements in fan service—not simply the introduction of AI.

Get in touch with our MarTech Experts.

Reputation has introduced AI Readiness and AI-optimized Location Pages to help multi-location businesses monitor and improve how AI-powered search tools describe individual branches. The additions to its AI Search solution combine location-level visibility tracking, technical and content audits, prioritized recommendations, and automatically updated web pages linked to business listings. The launch comes as AI becomes a more common route to local discovery: BrightLocal's 2026 research found that 45% of consumers had used AI tools for local business recommendations, up from 6% in its previous year's comparison.

Reputation Targets Location-Level AI Search Visibility

Reputation is expanding its reputation management platform beyond monitoring customer feedback and business listings, introducing tools designed to help multi-location brands understand and improve how AI assistants represent each branch.

The company's new AI Readiness and AI-optimized Location Pages products are part of its AI Search solution. They address a challenge for businesses operating across many locations: a strong brand-level reputation does not guarantee that AI-powered search tools will accurately describe every store, clinic, dealership, hotel or office.

The shift matters as consumers increasingly use generative AI to research businesses. McKinsey's 2025 AI Discovery Survey found that 44% of AI-powered search users considered it their primary and preferred source of insight, ahead of traditional search at 31%.

For local businesses, the implications extend beyond rankings to whether AI systems surface the right location, provide accurate details and describe services correctly.

AI Readiness monitors every location

Reputation's AI Readiness gives brands a separate view of individual locations rather than relying on one overall score. Businesses create brand and location profiles describing their mission, values, services and key messages. The product then compares this information with responses from AI engines, including ChatGPT and Gemini, and reports listing accuracy and AI sentiment.

The system also audits location pages and websites across five areas: Discovery, Schema, Content, Technical and Server-Side Rendering. Each location receives an AI Readiness score, with portfolio-level views helping teams identify weaker sites and track progress. Recommendations include ranked actions, explanations of business impact and separate flags for critical issues.

Reputation says AI responses are monitored continuously for changes and discrepancies against brand profiles, while readiness scores update monthly. The goal is to help marketing teams spot inaccurate descriptions or missing information before they undermine customer discovery.

AI-optimized pages connect to business listings

The second product, Reputation Location Pages, creates structured web pages for individual locations using the listings, reviews and social data businesses already manage in the platform.

The pages support structured data formats and standards including LocalBusiness, Review and FAQPage schema, JSON-LD and llms.txt. They also incorporate live review content, FAQs and practical conversion features such as directions, click-to-call and online booking. Templates can be deployed across multiple locations, and pages republish automatically when connected listing information changes.

That connection to a shared data source is central to the proposition. If a location's address, phone number or opening hours changes, businesses can update the underlying listing rather than manually correcting disconnected pages. Reputation offers vertical starter packs for healthcare, automotive, retail, hospitality and real estate.

Local discovery requires more than technical SEO

The products reflect a broader change in local search optimisation. AI systems can draw on business websites, directories, reviews and other third-party sources when assembling recommendations. BrightLocal's 2026 survey found that AI tools had become a substantially more common source of local business recommendations, although consumers may still verify suggestions before making a decision.

For multi-location organisations, this creates a coordination challenge: central marketing teams need consistent brand messaging, while each branch requires accurate local information. Technical fixes alone may not resolve gaps caused by incomplete reviews, weak location content or conflicting third-party listings.

Reputation's combined approach is intended to connect monitoring with remediation—showing teams where AI descriptions diverge from brand information and giving them a way to improve the underlying pages and data. However, structured pages and readiness scores cannot guarantee inclusion in AI answers, which can vary by platform, query and location.

The commercial test will be whether the products help businesses improve accurate local discovery, increase qualified calls and bookings, and reduce the work required to maintain consistent information across large location portfolios.

Market Landscape

AI-powered local discovery is becoming another layer of local SEO, alongside traditional search rankings, business listings and customer reviews. BrightLocal's 2026 research reported that 45% of consumers had used AI tools for local business recommendations, compared with 6% in the previous year's comparison.

Reputation's positioning combines two related functions: monitoring how AI systems describe individual locations and improving the underlying information those systems may use. This places the product at the intersection of reputation management, local SEO, structured data and generative engine optimization (GEO).

The competitive challenge is that AI visibility is not controlled by a single ranking factor. Accurate location pages can help provide reliable information, but third-party coverage, reviews, platform differences and changing AI responses also influence what users see.

Strategic Outlook

Reputation's launch reflects a shift from measuring digital presence to managing the information that shapes AI-generated local recommendations.

Three factors will determine the products' practical value:

• Location-level accountability: Regional and central teams need to identify which branches have inaccurate information or weaker AI visibility without losing sight of portfolio-wide performance.

• Reliable source data: Automatically synchronised location pages may reduce inconsistencies, but businesses still need to maintain accurate listings, reviews and local content.

• Measurable outcomes: Improvements in readiness scores should ultimately be evaluated against business outcomes such as qualified calls, appointments, bookings and visits.

AI readiness should be treated as an ongoing optimisation process, not a guarantee that a business will appear in a particular AI-generated answer.

Top Insights

1. Reputation is bringing AI search monitoring and location-page management together, giving multi-location brands a workflow for identifying and addressing location-specific information gaps.

2. AI Readiness compares brand-approved messaging with AI-generated descriptions, then prioritises technical, schema, content and discovery improvements across individual locations.

3. Reputation Location Pages connect directly to business listings, allowing key location details to update automatically when the underlying source information changes.

4. Growing consumer use of AI for local recommendations increases the importance of accurate business information, although AI visibility remains variable across platforms and queries.

5. The business case will depend on measurable gains in discovery, customer engagement and operational efficiency—not simply higher AI readiness scores.

Get in touch with our MarTech Experts.

Blackbaud is expanding its AI-powered fundraising and marketing portfolio for social impact organisations in Australia and New Zealand, introducing a new data-management agent, widening access to its fundraising agent and announcing an AI-assisted multichannel marketing platform. The announcements at bbcon Sydney build on the company’s broader Platform for Good strategy, with a fundraising-focused language model, Lantern, planned to begin powering selected experiences in 2027.

Blackbaud is bringing more AI capabilities into the fundraising and supporter-engagement workflows used by nonprofits, educational institutions and other social impact organisations. Announced at bbcon Sydney on October 23, the updates span data quality, donor outreach, digital marketing and core fundraising software, reflecting a shift from standalone AI features towards connected systems that help teams act on supporter information.

The announcements extend Blackbaud’s Platform for Good, a unified technology foundation intended to connect data, workflows, AI and partner solutions across its product portfolio. The company says this shared foundation will carry context between interactions, helping organisations coordinate work and create more consistent supporter journeys.

AI agents target fundraising capacity and data quality

Blackbaud is expanding the early-access programme for its Development Agent to Raiser's Edge NXT customers in Australia and New Zealand, alongside existing programmes in the UK and Canada. The agent is designed to support personalised, brand-aligned donor outreach, with interactions recorded in Raiser's Edge NXT and fundraising teams retaining control.

The company also introduced the Data Health Agent, which is designed to identify data-quality issues, recommend corrections and route them through review-and-approve workflows. Initially entering early access in the United States, the agent is intended to reduce the manual work involved in maintaining fundraising records. Blackbaud plans international expansion after incorporating lessons from the early-access programme.

Reliable data is a foundational requirement for AI-assisted fundraising. Incomplete or inconsistent constituent records can undermine segmentation, personalisation and reporting, limiting the value of automation. By treating data maintenance as a continuous workflow, rather than an occasional cleanup exercise, Blackbaud is positioning data quality as part of its wider AI strategy.

Blackbaud Marketing connects campaigns with fundraising data

Another major announcement is Blackbaud Marketing, a new AI-assisted multichannel digital marketing platform built within Raiser's Edge NXT. The platform is designed to bring fundraising and marketing teams closer together by connecting supporter engagement with constituent information used by fundraisers.

AI will assist with marketing planning, content creation and optimisation. Blackbaud anticipates releasing the platform by the second quarter of 2027, so its capabilities and commercial availability remain future milestones rather than currently available features.

The product addresses a familiar operational challenge for nonprofits: marketing activity and fundraising records can sit in disconnected workflows. Linking campaign engagement to constituent information could help teams coordinate communications and understand supporter relationships more holistically. The practical value will depend on how well the platform supports channel execution, measurement, integrations and the consent requirements associated with donor data.

Lantern brings fundraising-specific AI into the roadmap

Blackbaud also outlined plans for Lantern, a domain-specific language model developed through a strategic collaboration with Databricks. Rather than positioning it as a general-purpose model, the company says Lantern is being built around fundraising language, workflows and philanthropic context.

Selected experiences within Platform for Good are expected to begin using Lantern in 2027. Its performance, deployment scope and measurable impact on fundraising outcomes remain to be demonstrated as implementation progresses.

The broader opportunity is significant, but adoption alone does not guarantee results. Blackbaud cited research suggesting that 85% of social impact professionals use AI in their daily work, while only around 10% of organisations see significant returns from their investment. That gap puts the focus on workflow integration, trusted data and measurable outcomes—not simply adding more AI features.

Modernising the fundraising technology stack

Blackbaud is also enhancing Raiser's Edge NXT and Blackbaud Enterprise Fundraising CRM. Updates include a more unified web experience, Blackbaud AI Chat for conversational access to fundraising data and content support, and Intelligent Tags to help fundraisers track important major-gift actions. Enterprise CRM development includes a refreshed interface and planned AI Chat capabilities.

The strategic test for Blackbaud will be whether these connected tools save staff time while preserving human oversight, data governance and donor trust. For organisations with limited resources, a coherent fundraising technology stack could reduce fragmented processes—but implementation, staff adoption and evidence of improved fundraising performance will determine the return.

Market Landscape

Nonprofits are increasingly dependent on digital channels to engage supporters, but many still face resource constraints and fragmented technology. Salesforce’s 2025 Nonprofit Trends Report found that 89% of surveyed Australian nonprofits used digital fundraising channels, while 48% identified managing workload as a challenge. The Australian snapshot was based on 103 respondents, so the figures should be interpreted as survey findings rather than a census of the sector.

Blackbaud’s announcements place it in a competitive market for nonprofit CRM, fundraising automation and supporter engagement. Its strategy combines core fundraising records with AI agents, marketing workflows and a domain-specific model. Competitors and adjacent platforms can offer parts of this stack, making integration quality, usability, data controls and demonstrable fundraising outcomes important differentiators.

Strategic Outlook

Blackbaud is pursuing an integrated model in which AI works inside fundraising workflows rather than as a separate destination. The approach could reduce handoffs between marketing, development and data teams, but the commercial impact will depend on adoption and execution.

Three milestones deserve attention:

• Product delivery: Blackbaud Marketing is anticipated in Q2 2027, while Lantern is expected to begin powering selected experiences in 2027.

• Responsible automation: Review-and-approve workflows and human oversight will be important for donor outreach, data corrections and AI-generated content.

• Measurable results: Customers will need evidence of time saved, stronger supporter engagement, improved data quality and fundraising outcomes to assess return on investment.

Top Insights

1. Blackbaud is connecting fundraising CRM, AI agents and digital marketing in a broader platform strategy designed to reduce fragmented workflows across social impact organisations.

2. The Data Health Agent makes data quality a continuous operational task, addressing a foundational challenge for personalisation, reporting and dependable AI-assisted fundraising.

3. Blackbaud Marketing is planned for Q2 2027 and could connect campaign activity more closely with constituent records inside Raiser's Edge NXT.

4. Lantern introduces a fundraising-specific model to Blackbaud's roadmap, but its value will depend on real-world performance and measurable outcomes after deployment.

5. The announcements retain human oversight in important workflows, reflecting the need to balance automation with donor trust, data governance and organisational accountability.

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Peec AI has launched Peec Agent and expanded its Actions capabilities, aiming to help marketing teams turn AI search visibility data into prioritized work across technical SEO, content, and third-party brand mentions. The updates target a growing challenge for brands appearing in AI-generated answers from platforms such as ChatGPT, Gemini, and Perplexity: identifying why a brand is included, omitted, or inaccurately described, then coordinating changes across marketing, editorial, and engineering teams. Peec AI says its recommendations include supporting evidence, while its agent can investigate findings and prepare content briefs, page edits, or tasks in connected tools.

Peec AI Adds Agentic Workflows to Its AI Search Visibility Platform

AI search analytics is moving beyond tracking how often a brand appears in generated answers. With its new Peec Agent and expanded Actions features, Peec AI is seeking to connect visibility monitoring with the investigation, prioritization, and execution work required to improve a brand's representation across AI-powered search experiences.

The company says its platform is used by more than 3,500 businesses. Its latest release addresses a practical limitation of AI search visibility tools: identifying a change in brand mentions or citations is only useful if teams can determine what caused it and decide what to do next.

Peec AI's expanded Actions feature organizes recommendations across four areas: technical website issues, improvements to owned content, opportunities for third-party coverage, and inaccurate claims appearing in AI answers. Each recommendation includes a five-level relative impact rating and supporting evidence drawn from AI-generated answers, cited sources, or website analysis. Users can filter actions by AI model, topic, country, and action type, as well as add their own tasks.

This evidence layer is important because a brand's visibility in AI answers can depend on more than its website rankings. AI systems may draw on company pages, publisher coverage, review sites, and community discussions. A brand could therefore be missing from a relevant answer because its own content lacks useful information, because external sources do not mention it, or because technical problems limit access to its website.

Peec Agent is designed to investigate those possibilities. Users can ask questions about charts, visibility changes, competitors, and cited sources, with the agent using the relevant project context and selected filters. The company says its library includes 30 skills for recurring tasks, including diagnosing visibility changes and preparing projects for new markets. Teams can also create custom skills for repeatable workflows.

The agent connects with more than 25 tools across content management, marketing, CRM, project management, SEO, and analytics. Listed integrations include Webflow, Contentful, HubSpot, Klaviyo, Notion, Linear, Moz, SISTRIX, Cloudflare, and PostHog. These connections are intended to help teams move from an AI search finding to a content brief, an engineering task, or work in their existing systems without manually reconstructing the investigation.

For content recommendations, Peec AI can prepare briefs or suggest edits to eligible pages through its content editor. Teams retain responsibility for reviewing and publishing those changes. The company also says the agent requests approval before making changes by default, with configurable approval settings and connections limited to the projects and tools users authorize.

The product reflects a broader shift in generative engine optimization (GEO), the emerging practice of improving how brands are surfaced and represented in AI-generated responses. Traditional SEO tools primarily help marketers understand search rankings, traffic, and technical performance. AI visibility platforms add another layer: monitoring brand mentions, answer citations, and how AI systems describe products or companies.

However, AI-generated answers can vary by model, prompt, location, and time. A recommendation that appears plausible in one analysis may not translate into consistent visibility gains across platforms. Evidence-backed actions can help teams investigate potential causes, but their business impact still needs to be measured.

Peec AI's approach places the agent in a supporting role rather than fully automating marketing decisions. That distinction may matter to teams concerned about inaccurate edits, unsupported brand claims, or AI systems making changes without oversight. The product's value will ultimately depend on whether its recommendations lead to reliable improvements in discoverability and reduce the time required to coordinate work across marketing, content, and technical teams.

Market Landscape

AI search visibility is emerging as a new layer of digital marketing measurement, but it does not replace conventional SEO. Google says established SEO fundamentals—including crawlability, useful content, internal linking, and accurate structured data—remain relevant to its AI search features.

The audience opportunity is substantial: Google reported that AI Overviews had more than 2.5 billion monthly active users in 2026. At the same time, Gartner found that 53% of surveyed consumers distrusted or lacked confidence in the reliability and impartiality of AI-powered search results in 2025. These findings make both discoverability and brand accuracy important considerations for marketers.

Peec AI is competing in a developing category that includes AI visibility monitoring, generative engine optimization, citation analysis, and workflow automation. Its differentiation is the effort to connect monitoring and recommendations with execution across existing business tools. Whether this integrated approach produces better outcomes than using separate analytics, SEO, and task-management platforms will depend on recommendation quality, workflow adoption, and measurable results.

Strategic Outlook

Peec Agent reflects a broader move from AI tools that report marketing performance toward agents that investigate problems and prepare work for human review. That could reduce the time required to diagnose visibility changes and coordinate action between SEO specialists, content teams, public relations, and engineering.

Three factors will determine the product's practical value:

• Evidence quality: Recommendations need to distinguish verified technical issues and source-backed inaccuracies from hypotheses about why an AI model produced a particular answer.

• Workflow and access controls: Integrations can speed up execution, but teams need clear permissions, approval processes, and review before changes reach live websites or external systems.

• Outcome measurement: Marketers need to track whether completed actions correlate with changes in AI citations, brand mentions, referral traffic, qualified leads, and conversions.

Google's guidance also cautions against treating GEO as a substitute for SEO or assuming that special optimization techniques guarantee inclusion in AI answers.

Peec AI's opportunity is to help teams operationalize the fundamentals while giving them a clearer way to investigate how AI search platforms represent their brands.

Top Insights

• Peec Agent connects analysis to execution: The new agent investigates AI search performance and helps turn findings into content briefs, page edits, and tasks in connected tools.

• Actions prioritizes evidence-backed recommendations: The feature covers technical website issues, owned content gaps, third-party mentions, and inaccurate brand claims.

• Integrations target cross-functional marketing work: More than 25 connectors link Peec Agent with publishing, CRM, project management, SEO, analytics, and infrastructure tools.

• Human approval remains part of the workflow: Users can review suggested content changes, and the agent requests approval before making changes by default.

• AI visibility requires more than traditional rank tracking: Teams must assess brand mentions, citations, technical accessibility, and external coverage alongside conventional SEO performance.

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Impartner has been named a Leader in G2's Fall 2026 Enterprise Grid for Partner Relationship Management (PRM) and ranked No. 1 in Mid-Market Through-Channel Marketing in both the Summer and Fall 2026 reports. The rankings provide a snapshot of customer feedback on the partner management platform as businesses look to coordinate indirect sales, automate channel marketing, and improve visibility across partner ecosystems. Impartner also reported a 4.5-out-of-5 rating across 554 G2 reviews, although customer ratings alone do not establish whether a platform delivers better commercial results than competing products.

Impartner Earns G2 Recognition as Partner Management Software Competition Grows

Impartner has received multiple placements in G2's 2026 software rankings, including Leader status in the Fall 2026 Enterprise Grid for Partner Relationship Management and the top position in Mid-Market Through-Channel Marketing for two consecutive reporting periods.

The results place the company in a market where partner relationship management software is becoming an increasingly important part of B2B go-to-market infrastructure. Companies selling through resellers, distributors, managed service providers, and technology alliances need systems to manage partner onboarding, deal registration, channel campaigns, performance tracking, and revenue attribution.

Impartner's reported G2 rating stands at 4.5 out of 5 across 554 reviews. The company also cited a 91% likelihood to recommend, 90% satisfaction for ease of doing business, and 89% for support quality. Among its top-rated features were deal management at 91%, cross-system integration at 90%, and workflow automation and notifications at 89%.

These figures offer prospective buyers insight into how reviewers assess the product's usability, support, and operational features. G2 rankings and satisfaction scores, however, reflect the platform's standing within the review methodology and available customer feedback. They should not be treated as direct evidence of market share, revenue growth, or superior business outcomes.

The distinction matters because PRM software sits at the intersection of sales operations, marketing automation, and partner enablement. A platform may provide strong deal registration and workflow capabilities, but its value to an enterprise also depends on integration with CRM systems, partner data quality, incentive structures, and the ability to measure partner-sourced and partner-influenced revenue.

Impartner is positioning its platform around partner revenue orchestration, connecting partner management with channel marketing, deal execution, automation, and analytics. Its latest technology investments include Aimi AI, the company's AI engine, and Orchestration Studio, which supports AI plugin capabilities. HyperscalerGTM is designed to support go-to-market initiatives involving cloud marketplaces and hyperscaler ecosystems.

That combination addresses a broader industry shift. Partner programs are no longer limited to recruiting resellers and distributing marketing materials. Enterprise technology companies increasingly need to coordinate joint selling, marketplace listings, co-marketing, and partner-led demand generation across complex ecosystems.

AI could help automate repetitive partner operations and surface useful information for channel managers, but practical value depends on how these capabilities work within existing workflows. Buyers will need to evaluate whether AI features reduce administrative effort, improve partner responsiveness, or make campaign and pipeline decisions more effective.

Impartner's G2 recognition provides a useful customer-feedback signal as it develops those capabilities. Its enterprise placement may appeal to organizations evaluating PRM software for larger partner networks, while its mid-market through-channel marketing ranking speaks to businesses seeking structured campaign execution across indirect sales channels.

The next test is whether the platform can translate favorable reviews into measurable improvements in partner activation, deal velocity, campaign participation, and ecosystem revenue. Those outcomes will be more consequential for enterprise buyers than rankings alone.

Market Landscape

Partner relationship management (PRM) software is evolving into broader partner ecosystem management, connecting partner onboarding, deal registration, co-selling, channel marketing, and revenue analytics. Gartner's September 2025 Market Guide describes how AI-enhanced partner and ecosystem relationship management applications can help organizations scale partner efforts and coordinate collaborative revenue models.

Impartner operates in a competitive landscape that includes Salesforce Partner Cloud, ZINFI, impact.com, Channelscaler, and WorkSpan. These providers address overlapping but distinct needs, from partner portals and channel sales to affiliate management and ecosystem collaboration. G2's enterprise PRM category tracks 86 products, illustrating the range of options buyers may encounter.

Impartner's Enterprise Grid recognition and mid-market through-channel marketing rankings give buyers useful review-based signals, but they do not establish that the platform outperforms every competitor across all use cases. Selection should depend on partner program complexity, integration requirements, marketing capabilities, and measurable business outcomes.

Strategic Outlook

The next phase of PRM competition will likely focus on how effectively platforms combine AI, automation, and partner data to support revenue operations. Impartner's Aimi AI, Orchestration Studio, and HyperscalerGTM reflect its effort to connect partner management with AI-assisted workflows and cloud marketplace go-to-market activity.

For enterprise buyers, the central question is whether these capabilities make partners more productive and improve the visibility of partner-sourced revenue. G2 reviews can help identify user perceptions, but organizations should also evaluate integration depth, governance, AI reliability, and the ability to measure results across their partner ecosystem.

Top Insights

• Enterprise recognition strengthens Impartner's market position: G2 named the company a Leader in its Fall 2026 Enterprise PRM Grid, providing buyers with an external customer-feedback signal.

• Mid-market channel marketing remains a core strength: Impartner ranked No. 1 in G2's Summer and Fall 2026 Through-Channel Marketing reports, highlighting its position in partner campaign management.

• Customer ratings indicate generally positive feedback: Impartner reported 4.5 stars across 554 reviews and a 91% likelihood to recommend, though ratings do not prove superior financial outcomes.

• AI is becoming part of partner operations: Aimi AI and Orchestration Studio support Impartner's effort to bring AI capabilities into partner workflows and marketing processes.

• Integration and measurable revenue remain decisive: Enterprise buyers should assess CRM connectivity, partner adoption, deal execution, and attribution alongside third-party review rankings.

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