• Adthena’s ChatGPT AdBridge simplifies migration by creating artificial intelligence (AI)-enriched keyword lists and negative keywords.
• The tool offers a free, practical entry point for advertisers to prepare campaigns for the ChatGPT Ads platform.

The launch of Adthena ChatGPT AdBridge is being touted as an industry-first, migration product designed to help agencies and brands prepare for ChatGPT Ads.

This free tool analyzes an advertiser’s Google Ads campaigns to create keyword lists and negative keywords ready for immediate upload directly into the new ChatGPT Ads platform. With ChatGPT Ads trial surpassing 600 advertisers and OpenAI expected to open the platform to all markets, Adthena is giving advertisers a practical head start, according to company officials.

How this is a First Step

Phillip Thune, CEO of Adthena, noted that ChatGPT Ads is the most significant new paid search channel in a generation, and the time to act is now.

“ChatGPT AdBridge turns our search intelligence into a free, practical tool that gets campaigns ready in minutes,” said Thune in a statement. “And for those who want to go further, our full ChatGPT Ads solution…will bring the same whole-market visibility that has made Adthena essential for Google paid search, applied to the world’s fastest growing new ad platform.”

How Integration Works

AdBridge connects to an advertiser’s Google Ads account via a simple integration that supplies both campaign structure and negative lists ready for immediate upload to ChatGPT Ads. The steps are:

• Connect to Google Ads account.
• Build ChatGPT Ads setup, as AI enriches campaigns, expands keyword coverage, condenses negatives, and maps intent categories.
• Download and export. Users will have two CSVs land in their inbox hat can be imported directly into ChatGPT Ads.
• Launch and go live. Download ready-to-submit keyword lists and paste directly into ChatGPT Ads.

Adthena will release its full ChatGPT Ads solution for trialists and early adopters, delivering a prompt market view alongside immediate optimization actions and search intelligence.

“You cannot ignore this new channel. We need to get ready, and this expansion will likely be funded from existing paid search budgets,” said Matthew Crawley at 7Stars, who recently joined Adthena’s Customer Advisory Board. “That makes AI search intelligence tools like Adthena’s critical, you need to know where to reallocate and where to invest. AdBridge gives our teams a fast, practical route to getting clients into ChatGPT Ads with confidence.”

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• G2 found 51% of B2B software buyers now start their research with AI chatbots, reshaping vendor selection and decision-making.
• AI chatbots are influencing buyer shortlists, with 83% of buyers feeling more confident in their final choice.

A new report from G2 highlights the impact that artificial intelligence (AI) is having with B2B software buyers.

G2’s The Answer Economy: How AI Search Is Rewiring B2B Software Buying report found that 51% of B2B software buyers now begin their purchasing process in an AI chatbot rather than a traditional search engine— and buyers increasingly return to AI chatbots at every stage of their journey.

The shift to AI chatbots as the starting point for software buying increases since G2’s last report, influencing vendor selection, and accelerating decisions— 69% of buyers indicated they chose a different software vendor than they initially planned based on AI chatbot guidance, and one-third purchased from a vendor they had never heard of before.

AI is Influencing Buyers Shortlists

G2 first began tracking this shift to AI search in its April 2025 Buyer Behavior Report. Now the trend shows no signs of plateauing as 83% report feeling more confident in their final choice with AI chatbots the top source influencing which vendors make buyer shortlists.

“We’re watching the third compression era of the buyer journey unfold in real time,” said Tim Sanders, Chief Innovation Officer at G2, in a statement. “The Yellow Pages compressed the market into the big book. Google compressed it into the first page of results. Now, AI chatbots are compressing it into a single answer.”

How B2B Buyers are Relying on AI Chatbots

The report, based on a March 2026 survey of 1,076 B2B software buyers and decision-makers, highlights how AI chatbots have accelerated software research, helping buyers achieve stronger outcomes. Nearly three in four (71%) B2B software buyers rely on AI chatbots for software research, compared to 60% previously. And 53% of B2B software buyers feel research done with an AI chatbot is more productive than traditional search, up from 36%.

In the answer economy, buyers have moved from reference to inference, leveraging AI to synthesize the research process and return a shortlist.

A third of respondents purchased from a vendor they weren’t familiar with. Eighty-five percent of buyers think more highly of a software vendor when an AI chatbot mentions them in a recommendation while four out of five buyers say AI chatbots accelerated their purchasing decision.

How Many Hours a Week Are AI Chatbots Being Used

G2 officials noted today’s software buyer is sophisticated as they increase their time with AI chatbots at work, running head-to-head comparisons, creating deep research reports, and using thinking mode for high-stakes evaluations. Other key finding of the report incldue:

• Nearly two-thirds of buyers now spend six or more hours per week using AI chatbots for work, and over 40% self-identify as power users who leverage them daily.
• Comparing vendor strengths and weaknesses is the top use case for AI chatbots in software research (41%) — ahead of basic product research, vendor identification, and use case validation.
• Forty-one percent of buyers use Deep Research tools regularly for software evaluations.
• ChatGPT remains the dominant AI chatbot for B2B software research (63%), but the competitive landscape is shifting fast.

“Buyers have moved from reference to inference. Instead of gathering sources and synthesizing the data themselves, they trust AI chatbots to return the shortlist in a single prompt. That disrupts how software vendors need to think about their presence,” said Sanders.

Click here for the full report.

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• Conductor’s AgentStack enables brands to scale AI-driven search visibility with turnkey agents and custom workflows.
• The platform supports Answer Engine Optimization (AEO) to ensure brands are present in AI-generated answers.

Conductor is now offering a new enterprise suite of native Large Language Models (LLM) apps, developer infrastructure, and turnkey agents, designed to help brands build, manage, and scale their visibility across AI-driven search experiences.

What used to require multiple searches, pages, and touchpoints now increasingly happens in a single interaction on LLM platforms like ChatGPT and Claude. The shift to artificial intelligence (AI) search has created a new discipline for brands: Answer Engine Optimization (AEO), the practice of ensuring your company is present, cited, and trusted in AI-generated answers. Brands are rapidly reallocating budget and resources toward AEO to secure visibility. Those who fail to invest risk becoming invisible.

Conductor’s AgentStack allows B2B marketers to deploy agentic workflows for AEO, at enterprise scale. Through Conductor’s APIs, MCP server, and LLM apps in ChatGPT, Claude, and Copilot, enterprises and partners can power agents and applications.

Why AgentStack Is Needed

Conductor’s new suite is being offered as AI is reinventing how marketing technology is built and delivered. A new model is emerging: AI-powered agents that can be customized and deployed on demand. Instead of adapting workflows to pre-built software, teams and partners can build agentic systems and custom applications tailored to their exact needs.

“Once you connect Conductor’s MCP to your AI platform or deploy our native connectors, you can build a custom version of our application in a day,” said Seth Besmertnik, Co-Founder and CEO of Conductor, in a statement. “[This reduces] reporting time by 90% while improving output, 100x your ability to produce AI search-optimized content across emails, blog posts, and product pages. You’re only limited by your imagination. And even there, we help with use case libraries and skills guides.”

What B2B Marketing Teams Can Accomplish

Conductor has spent the last four years building the foundation for this new reality. Its unified data engine brings together the intent, content, and technical signals that determine discoverability across both AI systems and traditional search, all fully connected within a single software stack.

“Agents are only as powerful as the intelligence behind them,” said Wei Zheng, Chief Product Officer at Conductor. “Conductor brings together the signals that determine discoverability across AI systems and search engines. That intelligence is what enables agents to automate meaningful marketing work.”

B2B marketing teams now have the ability to generate automated board-ready presentations grounded in real AI visibility data, track brand sentiment across AI platforms in real time, and identify which topics competitors are winning in AI-driven answers. From there, they can optimize content to close those gaps in minutes. Technical teams can monitor whether AI crawlers can access key pages to ensure discoverability and resolve issues before performance is impacted.

Who is Using AgentStack

In addition to enterprise brands, leading agencies and technology providers, including Optimizely, Razorfish, Havas, and IBM, are already building on AgentStack for their clients.

“As AI agents become central to how enterprise marketing gets done, access to reliable, unified intelligence becomes essential,” said Alexis Zamkow, Global Offering Lead, Marketing Transformation at IBM. “Conductor’s agent infrastructure provides the data foundation needed to build systems that adapt in real time across AI-driven experiences.”

Additionally, AgentStack introduces Conductor’s first turnkey agents that combine proprietary AI search and content intelligence with a zero-configuration workflow, taking content teams from insight to published, optimized content in under three minutes, no prompt engineering or technical expertise required. Unlike other agent tools that force teams into complex interfaces, Conductors’ agents are the only turnkey solution built exclusively for AEO and content teams, delivered through a guided, point-and-click experience that makes enterprise AEO automation accessible from day one.

“We’re moving from a world where marketers optimize pages to one where systems optimize presence across AI experiences,” said Besmertnik. “Companies need an intelligence layer to power those systems. That’s the role Conductor is stepping into.”

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• Salesforce’s Agentforce Operations reduces back-office bottlenecks with AI, cutting cycle times by up to 70% and manual errors by 80%.
• The platform introduces features like Intelligent Operations and Instant Blueprints, enabling faster, more accurate workflows and seamless integrations.

Salesforce introduced Agentforce Operations, a new solution designed to transform outdated, manual back-office processes into streamlined tasks managed by artificial intelligence (AI).

Many organizations deliver fast, personalized experiences for customers and employees, only to hit delays when those modern front-end interfaces connect to legacy back-office systems. Employees frequently switch between platforms, manually transfer data, and search for updates in email. These delays slow productivity and increases operational costs— Agentforce Operations bridges this gap by extending intelligent automation deep into the core systems that run the business.

By deploying specialized artificial intelligence agents to handle process coordination, data verification, compliance checks, and approvals, the new platform reduces cycle times by up to 70 percent and decreases manual data entry errors by 80 percent, according to company officials.

How Salesforce is Rethinking AI

As companies accelerate AI adoption to become agentic anterprises, Aman Naimat, SVP and GM of Agentforce Operations, has observed that most are still burdened by an underlying layer of fragmented, manual processes across supply chain, procurement, finance, and the broader back office.

“This quietly slows operations, increases costs, and limits growth,” said Naimat. “With Agentforce Operations, we’re not just digitizing those processes but rethinking them for the AI-first world, optimizing how agents and humans work together.”

When Will Agentforce Operations Be Available

Unlike legacy workflow platforms that simply route tasks, Agentforce Operations completes complex work autonomously while maintaining a complete audit trail. The solution features Intelligent Operations, where specialized agents extract data and run computations in minutes instead of hours. Personnel can interact with the system using existing tools like email, with Slack and Microsoft Teams integrations launching in June.

Additionally, the software includes Instant Blueprints, a feature that converts unstructured documents or whiteboard diagrams into working digital workflows in minutes. Business leaders can adapt these processes without developer support by using simple plain-language email updates. An integrated proactive engine flags potential delays and suggests immediate fixes before they impact the client experience.

“Salesforce’s introduction of Agentforce Operations marks an important step forward in bringing AI-driven automation to the back office,” said Ian Kahn, Principal, Commercial & Service Excellence Platform Leader, PwC US. “Together, we’re helping clients use Agentforce to transform manual operations into intelligent workflows— from AI-powered contact centers to automated onboarding and compliance— driving faster execution, greater accuracy, and more seamless experiences.”

Agentforce Operations is generally available today. Ecosystem integration features, including the ability to automatically synchronize data and trigger actions with Salesforce Flows, will enter Beta testing in May.

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• Xactly and ServiceNow introduce the Dispute Management AI Agent to streamline compensation inquiries and workflows.
• The integration focuses on autonomous revenue orchestration, reducing friction and enhancing productivity for sales teams.

 Xactly has rolled out the first innovation from its AI-driven collaboration with ServiceNow: the Dispute Management AI Agent.

Powered by the Model Context Protocol (MCP) and integrated with ServiceNow Now Assist, the solution enables secure, real-time coordination between Xactly’s artificial intelligence (AI)-powered revenue platform and ServiceNow’s conversational AI to automate compensation inquiries and dispute workflows end-to-end .

The first application of this framework, the Dispute Management AI Agent, is designed to reduce the manual work of commission investigations for both sellers and administrators. By transforming compensation from a back-office ‘black box’ into a conversational, in-workflow experience, the framework allows the agent to proactively manage the investigation and resolution process, as opposed to just surfacing the data, according to company officials.

A Shift to Autonomous Revenue Orchestration

This shift is focused on reducing friction by minimizing unnecessary dispute submissions, and ensure that when questions do arise, they are resolved within a single, AI-guided interaction. The result is a more transparent, frictionless environment that allows both sellers and administrators to stay focused on driving revenue, said Chris Li, Chief Product Officer at Xactly.

“This solution marks a shift beyond just AI to intelligent, autonomous revenue orchestration,” said Li in a statement. “By enabling our AI to securely collaborate with ServiceNow’s Now Assist, we eliminate the friction tax for sales teams and turn complex compensation data into an instant, conversational asset that drives measurable productivity.”

What Now Assist is Designed For

This release is the first of a fleet of agents to be powered by the Xactly and ServiceNow agentic framework, with a focus on efficiency through autonomous automation to move from static integrations to intelligent automation across revenue workflows.

Now Assist is designed to bring contextual, AI-driven insights directly into the workflows where work gets done, said Anandan Jayaraman, Vice President, Product, Sales CRM at ServiceNow.

“Xactly and ServiceNow’s Dispute Management Agent demonstrates how agent-to-agent orchestration can eliminate manual processes and accelerate resolution cycles for revenue teams,” said Jayaraman. “Together, we’re helping customers unlock what’s only possible when data, AI, and workflow converge – autonomous resolution at enterprise scale.”

To learn more about Xactly’s agentic capabilities within the ServiceNow ecosystem, visit our website.

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• Pinterest’s high-intent audiences can now be targeted on Connected TV through tvScientific’s platform.
• Early testing shows a 73% increase in unique household reach and a 24% lift in new customer acquisition.

tvScientific by Pinterest announced on April 27 that advertisers can now reach Pinterest’s exclusive high-intent audiences off of Pinterest and on the biggest screen in the house, TV.

This launch, the first step in a broader vision for a unified cross-screen advertising experience, marks the first major milestone since Pinterest completed its acquisition of tvScientific in February 2026, bringing together Pinterest’s first-party audiences and tvScientific’s performance advertising platform.

As streaming platforms have become a primary way consumers watch TV, movies, and other content, advertisers are increasingly seeking more channels to reach high-intent audiences and drive measurable performance. Connected TV (CTV), internet-connected devices like smart TVs, streaming sticks, and gaming consoles that deliver streaming content directly to the big screen, delivers the reach of traditional TV with the precision of digital advertising.

How Large is the CTV Market

Last year, CTV ad spend reached $32.45 billion in the United States and is expected to surpass linear TV ad spending, to more than $45 billion by 2028.

“Pinterest brings a distinct opportunity, with more than 600 million monthly active users and growing,” said Lee Brown, Chief Business Officer of Pinterest in a statement. “Because of how users search and shop on the platform, we can give advertisers new ways to reach people at every stage of the shopping journey, from discovery to buying, both on and off Pinterest.”

Why this is an Opportunity for Marketers

tvScientific by Pinterest is the first ad platform to offer direct access to the social media platform’s high-intent audiences for CTV campaigns. Advertisers can use Pinterest’s commercial intent signals to reach audiences across premium inventory on the biggest screen in the house, while leveraging tvScientific’s artificial intelligence (AI)-powered optimization technology.

As a result, brands can reach their target audience at scale and drive measurable performance. On average, when tvScientific AI is enriched with Pinterest’s high-intent signals, it leads to a +27% increase in outcomes driven per $100 in spend, with purchases up +65%.

“The real opportunity for advertisers is turning consumer intent into action, and Pinterest’s signals are unique because they reflect what people are planning, not just what they’ve already done,” said Jason Fairchild, CEO of tvScientific by Pinterest.

LG Success on CTV

Early testing from advertisers demonstrates tvScientific by Pinterest’s scale and performance— LG expanded its existing audience approach and delivered a 73% increase in unique households reached with access to Pinterest audiences. This additional reach drove a 24% lift in net new customer.

According to Fairchild, this shows that Pinterest intent can help advertisers find more of the right viewers on CTV and turn incremental reach into measurable business results, according to company officials.

“Every month, there are more than 80 billion monthly searches on Pinterest, and by bringing those signals into tvScientific, advertisers can run more precise CTV campaigns and optimize toward the business outcomes that matter most,” said Fairchild.

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• StackAdapt’s MCP Server integrates campaign intelligence into AI tools, enabling real-time performance insights and optimization.
• The server supports conversational access to campaign data, replacing manual reporting and fragmented workflows.

StackAdapt on April 21 announced the general availability of its Model Context Protocol (MCP) Server, a new integration that makes campaign intelligence directly accessible within artificial intelligence (AI) tools such as Claude.

The MCP Server extends the capabilities of Ivy, StackAdapt’s AI marketing assistant, beyond the platform, allowing advertisers to monitor performance, audit creative, and analyze campaign data in real time without needing to log into the platform directly.

By connecting StackAdapt to the AI tools teams already use, including large language models, agents, and workflow automation systems, the integration enables conversational access to campaign intelligence— replacing manual reporting, spreadsheets, and fragmented workflows with a single prompt.

Explaining StackAdapt Integration

Designed for both business leaders and technical practitioners, the MCP Server gives decision-makers across B2B marketing and strategy direct visibility into campaign performance while enabling teams to integrate that data into internal systems, dashboards, and custom AI workflows.

“AI is reshaping how teams work, yet most platforms still require users to operate within their own environments,” said Yang Han, Co-founder and CTO at StackAdapt, in a statement. “The MCP Server brings StackAdapt’s intelligence into the AI workflows where decisions are already being made, giving customers direct, real-time access to their campaign data without added complexity.”

How Does MCP Server Help Marketers

The new server is designed to be completed in minutes with no engineering resources, API integrations, or additional cost required. Once connected, users can ask natural language questions about their campaigns and retrieve performance insights, including pacing, audience-level results, and creative status.

At launch, the MCP Server provides access to campaign configuration, performance metrics, and creative assets across all supported channels, including connected TV (CTV), display, native, audio, digital out-of-home (DOOH), and programmatic linear TV.

Beyond conversational access, the MCP Server lays the foundation for more advanced, agent-assisted workflows, according to company officials. AI systems can continuously monitor campaign performance, surface insights, and facilitate automated triggers based on user defined guardrails across tools and teams. By making programmatic intelligence accessible in real time, StackAdapt enables advertisers to augment manual analysis with always-on optimization powered by AI.

While many platforms are embedding AI within their own environments, StackAdapt is making its intelligence accessible across the broader AI ecosystem, said Han. Built for the open web, the MCP Server spans all channels and supply sources without tying advertisers to a single platform or inventory ecosystem, bringing domain-specific programmatic intelligence directly into AI workflows, rather than relying on generalized outputs.

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• Adobe introduces CX Enterprise, an AI-powered system to streamline customer lifecycle management.
• CX Enterprise integrates with leading AI platforms to deliver personalized, scalable customer experiences.

At the Adobe Summit last week, unveiled Adobe CX Enterprise, a new end-to-end agentic artificial intelligence (AI) system offered to simplify how businesses manage their entire customer lifecycle, from acquiring and engaging prospects to driving conversion and lasting loyalty.

Customer Experience Orchestration (CXO) is changing in the era of agentic AI as agents become critical resources that can help accelerate complex workflows, from on-brand content production to one-to-one personalization. With the introduction of CX Enterprise, Adobe is solving CXO with a new class of intelligent systems.

Grounded in decades of domain expertise in data, content and customer journeys, the CX Enterprise is an anchor for AI agents that are reliable, auditable and can understand context. CX Enterprise powers agents that can help scale impactful, personalized customer experiences so that interactions consistently reflect brand guidelines.

How Adobe is Enabling the Scaling of AI Agents

Anil Chakravarthy, President, Customer Experience Orchestration Business, Adobe, commented that CX Enterprise is powered by the new Adobe Brand Intelligence, a continuously learning reasoning engine that can capture evolving brand signals, and Adobe Engagement Intelligence, a decisioning engine optimized for customer lifetime value, to deliver personalization at scale.

“Adobe CX Enterprise enables businesses to scale agentic AI with a fully customizable solution that is tailored to the needs of their organization, moving teams beyond AI experiments to tangible business outcomes,” said Chakravarthy in a statement. “This end-to-end solution fits naturally into any environment, built to work alongside tools across leading AI platforms with seamless interoperability.”

A List of Adobe’s AI Partners

CX Enterprise is being offered with multiple access points and forward-deployed engineers that can tailor solutions to real workflows. A composable architecture that works with various technology stacks provides businesses flexibility and control, including extending agentic skills and workflows across Adobe and partner solutions from Amazon Web Services, Anthropic, Google Cloud, IBM, Microsoft, NVIDIA and OpenAI.

The new agents streamline time-intensive tasks and help businesses to meet customer retention and growth objectives. Powered by Adobe Experience Platform Agent Orchestrator— which enables teams to build, manage and coordinate agents across Adobe and third-party ecosystems— the agents are natively integrated into new and existing Adobe solutions across areas such as customer engagement, content supply chain and brand visibility.

By managing the quality of output and governance, the Adobe is extending these capabilities by bringing the agents into surfaces including Amazon Quick, Anthropic Claude Enterprise, ChatGPT Enterprise, Gemini Enterprise, IBM watsonx Orchestrate and Microsoft 365 Copilot.

A Look At CX Enterprise’s Key Benefits

Other offerings in CX Enterprise will include:

• Agent skills catalog: This set of agent skills make it easy for businesses to create custom workflows by packaging reusable instructions, such as reviewing performance metrics to drive content creation or journey orchestration workflows.
• Developer tools: CX Enterprise will provide access to Adobe’s agentic skills, MCP servers and the infrastructure needed to build customizable use cases. It will enable developers to incorporate  agentic capabilities directly into tools from Anthropic, Google Cloud, Microsoft, and OpenAI among others, with a composable platform that makes it straightforward to bring functionality directly into the tools used daily by marketing and creative teams.
• Adobe CX Enterprise Coworker: Businesses have the flexibility and control needed to bring AI agents into daily workflows with confidence, increasing levels of automation that will help teams drive business outcomes, while maintaining human oversight and control. The goal is to improve the capability, choice and automation that transforms how brands engage customers.

Anjul Bhambhri, SVP of engineering, Customer Experience Orchestration, Adobe, further detailed that the CX Enterprise Coworker is designed to execute tasks based on defined business goals, coordinating work across multiple agents by translating clear objectives into multi-step actions, said

“Coworker will be the next step for businesses that are retooling marketing workflows to help teams take advantage of agentic AI technology,” said Bhambhri. “By synthesizing intelligence from across Adobe applications, enterprise systems, and leading AI platforms, we are closing the gap between insight and action, enabling brands to deliver one-to-one personalized experience at scale.

“This is what it means to put the full power of agentic intelligence to work for marketing teams.”

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The growing importance of explainable artificial intelligence (XAI) will drive large language model (LLM) observability investments to 50% of GenAI deployments by 2028, up from 15% currently, according to a recent report from Gartner.

Gartner defines XAI as a set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior and identifies any potential biases. LLM observability solutions monitor, analyze and provide actionable insights into the behavior and performance of LLMs. They go beyond standard IT measurements, such as response times to look at specific LLM metrics such as hallucinations, bias and token utilization.

These tools are used by teams that develop and operationalize AI systems, and increasingly by IT operations and SREs responsible for the performance and resilience of these systems in production.

The Importance of XAI

Pankaj Prasad, Senior Principal Analyst at Gartner, stated that as enterprises scale GenAI, the trust requirement grows faster than the technology itself.

“XAI provides visibility into why a model responded a certain way, while LLM observability validates how that response was generated and whether it can be relied on,” said Prasad in a statement. “Without robust XAI and observability foundations, GenAI initiatives will be restricted to low risk, internal, or noncritical tasks where output verification is easily managed or inconsequential, severely limiting the potential return on investment.”

Why the Growing Need for XAI and LLM Observability

Gartner forecasts the global GenAI models market will exceed $25 billion in 2026 and reach $75 billion by 2029, driven by rapid adoption across industries. As usage increases, so does the need for mechanisms that verify AI-generated content and protect against hallucinations, factual inaccuracies and biased reasoning.

“Traditional observability is focused on speed and cost, but the priority is now moving toward deeper quality measures such as factual accuracy, logical correctness and sycophancy,” said Prasad. “This shift requires new governance-focused metrics and evaluation methods, such as human-in-the-loop validation of the generated content’s narrative and citation accuracy.”

To improve the reliability, transparency and business value of GenAI use cases, Gartner advises organizations to prioritize the following steps:

• XAI Tracing for High Impact Use Cases: Mandate verifiable XAI tracing for all high impact GenAI use cases to document the model’s reasoning steps and the source data behind each output.
• Multidimensional LLM Observability: Prioritize observability platforms that monitor latency, drift, token usage and cost, error rates, and output‑quality metrics to ensure reliable GenAI performance.
• Continuous LLM Evaluation in CI/CD Pipelines: Integrate LLM evaluation metrics, including factual‑accuracy benchmarks and safety checks, into continuous integration (CI)/continuous delivery (CD) pipelines for continuous validation before deployment.
• Stakeholder Education on Explainability Requirements: Educate legal, compliance, and other key stakeholders on explainability requirements to ensure alignment on risk, governance expectations, and implementation challenges.

“Explainability turns a GenAI output into a defensible, auditable insight. LLM observability ensures the model behaves as expected over time,” said Prasad. “Without both, GenAI cannot mature beyond controlled lab environments.”

AI-Driven Sales Enablement Will Deliver 40% Faster Sales

By 2029, sales organizations with AI-driven enablement functions will achieve 40% faster sales stage velocity than those using traditional enablement approaches, according to Gartner. 

Findings from a Gartner survey of 227 chief sales officers (CSOs) underscore why this shift is becoming urgent. Sales organizations completed an average of four transformations in the past 12 months, making the ability to drive performance through continuous change a core requirement for CSO success.

The survey additionally found that sales organizations that collaborate on enablement content with other functions, such as marketing and service, are 2.4 times more likely to achieve strong commercial growth than those that do not.

How to Keep Up

To keep pace with constant transformation and rising revenue pressure, sales leaders must move beyond static content and training to deliver in‑workflow, data‑driven guidance; align enablement across sales, marketing and service to drive consistent revenue execution; and leverage AI and automation to scale performance through continuous transformation.

“Traditional enablement was built as a reactive support function, not as a system engineered to drive measurable seller performance,” said Shayne Jackson, VP Analyst in the Gartner Sales Practice. “As CSOs face ongoing transformation and heightened revenue pressure, enablement must become an AI‑driven function that orchestrates seller behavior in real time. Organizations that fail to make this shift will struggle to improve deal velocity and sustain growth.”

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• The marketing technology market is projected to grow at a CAGR of 20.1% from 2026 to 2033, reaching $2.4 trillion.
• Social media tools and AI-powered solutions dominate the market, with North America leading in revenue share.

The global marketing technology market size is expected to reach $2.4 trillion over the next eight years, registering a CAGR of 20.1% from 2026 to 2033, according to a new report by Grand View Research.

The internet, mobile devices, social media, and customer relationship management programs are key for business growth and success,  transforming the B2B marketing landscape and the type of communications strategy businesses to conduct online research, connect with others, and conduct business.

As B2B digital marketing is central to modern marketing strategies, digital tools like tablets, smartphones, and personal computers are key. Game consoles and connected televisions are increasingly used to interact with stakeholders. The report found that North America held the largest share of the global marketing technology (MarTech) market in 2025, capturing over 33.8% of revenue.

Why Digital Marketing Maters

Due to its superior ability to target and track a variety of factors, including Return on Investment (ROI), more precisely than other conventional marketing channels, digital marketing is a key pillar for B2B marketers.

The most harmful factor in the shift of power from marketers to consumers has been the development of the internet, accoridng to the report. B2B buyers now have access to a vast amount of global data that is timeless, important, and verifiable thanks to the internet. Consumers may get useful information about businesses and their goods with just one click. This allows users to  look for lower costs, compare products, read reviews, and converse with other users regarding the caliber of the product and customer satisfaction.

Which is the Largest MarTech Market

Additionally, internet users are not just consuming content; they are producing it and sharing it with others,. With the rise of social media platforms like Facebook, Twitter, Instagram, Reddit, TikTok and others, customers can access immediate feedback from friends and family on products, both good and poor. A blog post or video made by a disgruntled consumer these days can become viral in a matter of hours.

Among the key finding of the report includes:

• The social media tools segment dominated the MarTech market, accounting for a revenue share of 23.6% in 2025, driven by the accelerating importance of social platforms in digital marketing strategies.
• In 2025, digital marketing dominated the marketing technology market, capturing the largest revenue share. Businesses are increasingly investing in AI-powered tools, automation, and analytics to enhance campaign effectiveness.
• The healthcare segment accounted for the largest revenue share of the marketing technology industry in 2025, driven by the growing emphasis on patient-centric marketing and personalized engagement strategies.

Click here for the full Marketing Technology Market Report

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