• 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.”

The post Adthena Launches Industry-First Tool for ChatGPT Ads Campaigns appeared first on Demand Gen Report.

• ABM professionals have a unique opportunity to influence and set industry benchmarks by participating in the Demand Gen Report’s annual survey. 
• The survey emphasizes the power of shared knowledge within the ABM community, addressing critical topics like AI integration, ROI measurement challenges, and predictive analytics, which are pivotal for advancing ABM strategies.

As an ABM professional, you’re not just part of the industry—you’re shaping it. Your expertise drives innovation, and now you have the opportunity to make an even greater impact. The Demand Gen Report’s annual survey is your chance to contribute to the benchmarks that guide our field.

This survey gathers insights from professionals like you to create a comprehensive report on the state of ABM. By participating, you’ll help ensure that the benchmarks reflect the real-world challenges and successes of today’s marketers.

Why Your Voice Matters

The ABM community is built on collaboration and shared knowledge. By participating in the survey, you’ll join a network of professionals dedicated to advancing the craft of ABM. Your input will help identify key trends, challenges, and opportunities in the industry. This year’s survey covers critical topics like:

• The integration of AI in ABM strategies.
• Challenges in measuring ROI and proving attribution.
• The role of predictive analytics in identifying high-value accounts.

Your insights are invaluable. By taking just a few minutes to complete the survey, you’ll help shape the future of ABM and gain access to exclusive insights that can refine your strategies. Take the survey now and be part of the change.

The post Final Week: Take the ABM Survey & Set Industry Benchmarks appeared first on Demand Gen Report.

• 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.

The post Half of B2B Software Buyers Now Start Their Research with AI Chatbots: G2 appeared first on Demand Gen Report.

So companies are pouring money into AI, but most of it isn’t working—and the reason isn’t AI.

It’s the data underneath it.

The Problem Enterprise Marketing Teams Have

Here’s where enterprise marketing teams have a problem most vendors aren’t talking about.

At scale, your marketing stack isn’t one system. It’s 12. Leads flow in from paid campaigns, content syndication, webinars, online forms, tradeshows and telemarketing— and every one of those sources feeds into a MAP that connects to multiple CRM instances, a unified data warehouse, analytics platforms, consent management systems and increasingly, AI models sitting on top of all of it making real-time decisions.

The Real Cost of Bad Data

The moment a bad record enters that stack, it doesn’t land in one place. It propagates. It hits the MAP and gets segmented. It moves to the CRM and gets routed. It flows into the data warehouse and gets stored. It surfaces in the analytics layer and gets reported on. The AI scoring model reads it and generates a recommendation. By the time anyone notices the record was garbage, it’s already inside every downstream system simultaneously—distorting segments, skewing scores, inflating pipeline forecasts and poisoning the training data for the next model run.

This is the real cost of bad data at enterprise scale. It’s not the cost of a single bad lead. It’s the cost of a bad lead at rest inside a 10-to-15 system stack, compounding silently across every tool that touches it.

The math on data quality was already damning before AI entered the picture. B2B contact data decays roughly 30% per year. One study tracking 1,200+ business contacts found 70% experienced at least one data change within 12 months (e.g., job title changes, phone numbers, email addresses, company moves), and 94% of organizations suspect their customer and prospect data is inaccurate. The average enterprise CRM carries a 25% critical error rate on contact records.

What AI Has Changed

The Sirius Decisions “1-10-100 rule” has been cited for years: $1 to verify a record at entry, $10 to clean it later, $100 if you ignore it. But that framework was built for a world where bad data landed in a CRM and stayed there. In a modern enterprise stack where a single record syncs in real time across a MAP, two CRM instances, a unified data store, an analytics platform and a consent layer, the multiplier isn’t 100x. It’s 100x per system.

Bad data costs the average organization $12.9 million annually, per Gartner. MIT Sloan puts the revenue impact at 15–25%. Those figures predate the era when every one of those corrupted records also feeds an AI model making autonomous decisions.

AI changes the stakes in a specific way that enterprise demand gen teams need to understand.

When a bad record sits in your CRM, a human sales rep might catch it. They call the number, it’s wrong, they update it. Slow and frustrating, but self-correcting at some level. When a bad record feeds an AI lead scoring model, there’s no human in the loop to catch the error. The model scores it, routes it and acts on it—confidently, at speed and at scale. The AI doesn’t know the contact changed jobs eight months ago. It doesn’t know the email domain bounced. It reads what’s there and optimizes accordingly.

The Formula for AI Value

This is the core problem. AI doesn’t correct for bad data. It amplifies it.

Forrester put it directly in 2024: “Data quality is now the primary factor limiting B2B GenAI adoption.” Not the models. Not the compute. Not the talent. The data. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. And 59% of organizations don’t even measure data quality, so they can’t assess the foundation they’re building on.

A Sales Hacker survey of 250 Sales Operations Managers found 41% of predictive lead scoring initiatives failed. In most cases the algorithm wasn’t the problem. The CRM data was.

The investment pattern makes this worse. US B2B marketing data spending growth is tracking at 0.5% (eMarketer)—essentially flat—while AI tool spending is growing at 36% year over year. Enterprise marketing teams are wiring increasingly sophisticated AI into increasingly unreliable data infrastructure and wondering why the ROI projections don’t materialize.

BCG’s 10-20-70 framework is instructive here: successful AI transformation allocates 10% of resources to algorithms, 20% to technology and 70% to people and processes—which includes data governance, data quality and data readiness. The companies actually extracting value from AI spend 50–70% of their implementation budget on data preparation before a model ever runs. Most enterprise teams have this ratio inverted.

Why All Roads Lead Back to The Data

There’s a structural fix, and the best enterprise marketing teams are already doing it: validate data at the point of entry before it touches anything downstream.

The logic is simple. If a bad record never enters the stack, it can’t propagate through it. It can’t corrupt the MAP segments, the CRM routing, the analytics reports, the AI training data or the consent records. The cost stays at $1 instead of compounding to $100 per system. The validation gate isn’t a nice-to-have layer. At enterprise scale, it’s load-bearing infrastructure for everything downstream that depends on clean signals to function.

The question enterprise demand gen and marketing ops leaders need to ask isn’t “which AI vendor should we buy?” It’s “what’s the state of the data every system in our stack is reading from?”

Only 37% of organizations say they’ve been able to improve data quality even as AI investment surges, per Wavestone’s 2024 Data and AI Leadership Survey. The teams that close that gap—that treat data infrastructure as a prerequisite rather than a cleanup task—are the ones that will actually get the ROI everyone else is still projecting on slide 14 of the QBR.

The AI isn’t broken. The plumbing is. And at enterprise scale, fixing it later costs a lot more than fixing it first.

Jason Gladu, COO of Convertr, is a lead generation and demand gen expert with a track record of scaling B2B businesses and building innovative intent model.

The post Your AI Stack Has a Data Problem. And It’s Bigger Than One Bad Lead. appeared first on Demand Gen Report.

• 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.”

The post Conductor Introduces AgentStack to Scale AI Search Visibility appeared first on Demand Gen Report.

• 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.

The post Salesforce Launches Agentforce Operations to Eliminate Back-Office Bottlenecks appeared first on Demand Gen Report.

• Signal stacking reveals hidden intent.
• It’s time to redefine account coverage metrics to role-based participation and stage-fit tracking.

You just finished an incredible demo. Your primary contact loved the presentation, shared your deck internally, and your pipeline looks perfectly healthy. Then, out of nowhere, the deal stalls. The problem is not your product or your pitch. Instead, the problem is that your strategy relied on a single champion.

At the B2B Marketing Exchange (B2BMX) 2026 powered by Advertising Week, industry leaders tackled this exact scenario in a session titled Champion to Consensus: Practical Buying Group Coverage That Improves Conversion. The panel, moderated by Demand.com’s Rick Robinson that featured his colleague Terry Arnold, Gorilla Logic’s Whitney Goldstein and Delinea’s John Johansen, dismantled traditional, single-lead approaches that create massive pipeline friction.

The four experts explained how B2B decisions are increasingly made not by individuals but, rather, by committees. To help teams adapt, the panel examined three persistent myths about buying group engagement and offered practical prescriptions to fix them.

Does One Engaged Contact Equal Account Momentum?

Although many sales and marketing teams falsely believe that a highly engaged champion means the entire account is ready to buy, the reality is quite different. Two-thirds of B2B buying committees now consist of six or more stakeholders.

The prescription for mitigating this risk is proactive engagement. Teams must define required roles early in the process and run targeted plays to handle objections before they become deal-killing vetoes. Marketing and sales must work together to build narratives that resonate across the entire organization — from executive leadership down to daily tactical users.

Arnold highlighted how marketing teams can structure this approach effectively by grouping stakeholders and mapping content to these clusters. By doing so, teams can ensure no critical voice is left in the dark.

“We create role-based clusters, which basically start to look at specific levels,” he explained. “We break that out so that, in marketing, we are engaging each of those different roles in a way which is relevant to the issues that they’re trying to solve.”

Does High Intent Equal Account Readiness?

Intent data has become a staple for revenue teams, leading to the assumption that high intent automatically equals account readiness. However, intent is almost always persona-specific, the panel members observed. Just because one mid-level manager is researching a problem does not mean the broader buying group acknowledges the issue or has budget allocated to fix it.

To combat this, teams must validate intent with role breadth and stage fit. A single signal is just a starting point. More important is for marketers to look for signal stacking — evidence that multiple people within the same account are researching related topics. Furthermore, as search behaviors shift, teams must optimize their content to leverage Large Language Models (LLMs) and Answer Engine Optimization (AEO) to identify hidden intent.

Goldstein stressed the importance of questioning initial data and demanding verifiable proof of momentum, stating, “I look at intent as a clue, not necessarily the conclusion. What I’m trying to do is make our pipeline predictable. And so, we really do need to distinguish what is the signal versus what is the noise.” Signal stacking, she said, is a great strategy for doing so.

How Should Teams Measure Account Coverage?

For years, marketing success was graded on lead volume. And many teams still believe that account coverage can be measured with Marketing Qualified Leads (MQLs), clicks, or a handful of hot prospects. But here’s the reality: Coverage is actually about role participation, depth of engagement and stage fit. A high volume of leads means nothing if they all sit in the same department and ignore the financial or operational stakeholders.

The prescription is a fundamental shift in tracking. Revenue teams must track exactly who is engaged, identify which key roles are missing, and define the precise next action to bridge that gap. An operations narrative that hasn’t been communicated represents a massive structural risk to the deal.

Measuring coverage means looking at the balance of engagement across the entire account. Johansen shared a practical way that his team enforces this mindset through systemic tracking.

“The answer for me is how many people you can get attached to the opportunity in your CRM,” Johansen said. “Starting to work on that behavior and being able to say who are you engaged with [and] add them here. And it also gives us that signal to ask, ‘OK, well, where are we lacking coverage?”

How Can Revenue Teams Shift from Champion to Consensus?

The transition from champion to consensus requires a complete shift in how revenue teams operate. Arnold pointed out that building structural coverage demands tailored narratives for every layer of the business. “If teams fail to communicate value to the tactical, operational, and executive levels simultaneously, deals will inevitably stall at the finish line,” he said.

Goldstein reinforced that marketing must build a foundation of credibility and proof. Relying on anonymous website traffic or isolated clicks is no longer enough to forecast accurately.

“Teams must demand verified engagement across multiple first-party touchpoints, ensuring that content drives measurable outcomes and answers the diverse questions of the entire buying collective,” she offered.

Johansen summarized the cultural shift required between sales and marketing, saying, “Both departments must share ownership of the pipeline and stop treating committee penetration as solely a sales responsibility.”

But the most important takeaway from the session, which the entire panel echoed, serves as a warning for modern revenue teams: Don’t confuse activity with coverage. True pipeline health only exists when the entire buying group moves forward together.

The post B2B Buying Decisions Demand Consensus, Not Champions: Lessons From B2BMX 2026 appeared first on Demand Gen Report.

• 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.

The post Xactly and ServiceNow Unveil AI Integration for RevOps to Boost Sales Productivity appeared first on Demand Gen Report.

• AI allows marketers to repurpose single live events into extensive libraries of targeted content, significantly extending the lifespan and reach of event messaging.
• By connecting event engagement data with multi-channel ABM campaigns, B2B marketers can turn trade shows into measurable drivers of pipeline creation and revenue.

Personalized and Efficient. That is the needle B2B marketers look to thread in the age of artificial intelligence (AI). AI has become a strategic initiative for those hosting both in-person and digital event as interactive event technologies are drivers to marketers who attend and participate as trade shows are a $15 billion market where 52% of business leaders say is their highest-ROI marketing channel.

This is happening as our 2026 B2B Trends Research Report found 89% of marketers report that evolving buyer behaviors and preferences are a top challenge, forcing a departure from traditional engagement models. Buyers now expect highly personalized, seamless, and digitally-native experiences. This shift requires marketers to invest in deeper data analysis and more sophisticated personalization tools to meet customers where they are. The days of one-size-fits-all campaigns are definitively over, replaced by a need for nuanced, account-specific strategies.

The value of face-to-face meetings compared with other business initiatives is remarkably strong— more than 70 percent say that events are either somewhat or significantly more valuable than other such initiatives, according to according to Northstar Meetings Group. Interactive event technologies is seen as a key to capitalizing on these meetings.

How AI Is Impacting Interactive Events

AI transforms conferences from passive learning experiences into personalized, highly efficient events for B2B marketers by utilizing predictive analytics for attendee targeting, generative AI for content creation, and AI concierges for real-time logistics.

“Events are shifting from one-time, isolated moments to integrated, always-on experiences within a broader go-to-market strategy,” said Keith Turco, Madison Logic CEO. “Marketers are under increased pressure to prove impact beyond attendance, which is driving a shift toward measurable pipeline contribution and buying group engagement.

“At the same time, there’s a growing recognition that a single attendee doesn’t represent a deal. The most successful event strategies are focused on engaging multiple stakeholders across the buying group before, during, and after the event.”

Detailing AI Use

Events a pivotal role in B2B growth strategies, with in-person and virtual gatherings, like hybrid roundtables and small-group meetings, gaining popularity. The events are being utilized to collect valuable first-party data, which is essential in today’s privacy-first marketing environment. American Express Global Business Travel’s 2026 Global Meetings and Events Forecast found AI is being used for event planning, attendee communications, during events, and for post-event evaluation. Top uses of AI include:

• Delivering AI-powered event apps (40%)
• Sponsor and attendee matchmaking (35%)
• Generating creative concepts and themes (34%)
• Content creation with tools like AI writing assistants (31%)
• Tracking attendee engagement (31%).

“AI will soon power much of what happens behind the curtain, acting as a critical assistant that helps event organizers reduce planning friction and enable more personalized face-to-face moments at scale,” said McNeel Keenan, vice president of product development at Cvent, told AMEX. “But even as technology advances, events will continue to be decidedly human. In fact, in an AI and tech-driven world, authentic connections will likely stand out even more.”

Current AI Uses at Conferences

The human touch was amplified by a Hilton survey that found 84 percent of attendees prefer in-person work events. While attendees seek authentic human connection, technology serves as a vital support system that enhances the overall experience. Today’s events deploy AI-driven tools that streamline logistics— from frictionless check-ins and personalized schedules to real-time communication via an events app.

Notably, 67 percent found AI helps personalize their experience by delivering tailored content recommendations, connecting them with like-minded professionals, and suggesting optimized networking opportunities. Two in three attendees report that AI-powered networking tools help them make meaningful connections more easily, while 65 percent express interest in AI assistants to help them prepare for meetings and navigate complex event agendas.

But beyond the content and personalization it provides, interactive event technologies impact the bottom line. Research from the Center for Exhibition Industry Research showed that interactive exhibits are 52% more likely attract attendees compared to traditional static displays. Booths incorporating gamified elements see 40% higher foot traffic with a three-minute interactive experience converts to lead capture at roughly 85%, compared to approximately 10% for a brief booth conversation.

How Does B2B Marketers Connect Event Engagement to Revenue?  

While event success previously was measured by surface-level metrics like registrations or attendance, AI enables a much deeper understanding of who engaged, how they engaged, and what that activity signals in terms of pipeline progression and deal readiness.

“With AI-powered insights, marketers can identify which accounts and buying group members are most likely to attend and convert, while also delivering more personalized engagement before and after the event,” said Turco.

Additionally, AI allows for automation to enable coordinated, behavior-driven outreach across channels without relying on manual follow-up— orchestrating a multi-channel ABM campaigns around events for example.

“Rather than relying on manual follow-up, marketers can trigger coordinated engagement across display, social, email, and content based on attendee behavior,” added Turco. “The result is a more connected view of ROI where events aren’t just a lead source, but a measurable driver of pipeline creation, acceleration, and revenue impact.”

Detailing Content Opportunities

AI integration is enhancing event experiences by automating insights from keynote speeches and repurposing them into content such as blogs and videos. At the same time, conversational AI and chatbots are revolutionizing customer engagement by addressing complex B2B queries.

The subject of his session at B2BMX 2026 and in their The 2026 Digital Engagement Benchmarks Report, On24’s Mark Bornstein VP, Marketing and Chief Evangelist detailed how marketing professionals consume content in shifting in a large way. Audiences are showing up in record numbers, but they demand personalization, interactivity, and always-on access. AI, automation, and content hubs are focused on buyers and delivering measurable ROI.

In 2025, the average webinar attracted 239 attendees, marking an 11% increase from the previous year with attendees stayed engaged for an average of 49 minutes. Total engagement per webinar jumped by 18%, generating over 400 unique interactions per session.

Repurposing Event Content

“You can capture this level of attention by integrating live polls, Q&A features, and downloadable resources directly into your event console,” said Bornstein. For example, the average webinar now sees 150 poll responses and 14 questions per session. These interactions provide valuable first-party data. as sales team can use these precise buying signals to tailor their follow-up and close deals faster.

Marketers are heavily adopting AI to repurpose their event content, having the ability to turn a single live event into a vast library of targeted assets, instantly transcribing webinars, summarizing key takeaways, and generating promotional clips.

The creation of all derivative content grew by 37% year over year— production of key moment video clips skyrocketed by 2.5X, while AI-generated blog posts and e-books increased by 2.4X, said Bornstein.

“AI and automation allow you to scale your output without sacrificing quality,” he said. “This automated workflow ensures your best ideas reach buyers across paid, earned, and owned media channels long after the live broadcast ends.”

How to Overcome the Challenges of Virtual Events

What started as a temporary workaround during the pandemic, hybrid and virtual event formats have evolved into a strategic advantage. These events give marketers the ability to scale reach, engage broader buying groups, and extend the lifespan of event content well beyond a single moment in time.

But, as Turco notes, the introduce new challenges— fragmented attention and the difficulty of sustaining meaningful engagement in digital environments. The event itself can no longer carry the full weight of engagement.

“That’s where a multi-channel ABM approach becomes essential,” he said. “Marketers can build awareness and drive registration through targeted pre-event campaigns, reinforce key messages during the event through complementary channels, and continue the conversation afterward by re-engaging both attendees and non-attendees with personalized follow-up.”

Another advantage is hybrid formats can unlock new opportunities to engage buying groups more holistically. Different stakeholders can interact in different ways with some attending in person, others virtually, and others through post-event content. “When orchestrated effectively, this expands internal alignment within target accounts and increases the likelihood of moving deals forward,” said Turco.

Why Personalization is a Priority

B2B marketers are consistently on the search to make their content more personalized. According to On24 officials, webinars featuring personalized offers saw a 118% increase in clicks and organizations using personalization generated four times as many meeting bookings per webinar.

To maximize ROI, Bornstein urged organization to implement interactive tools that capture first-party data during every live session. That data then can be fed into their MarTech to deliver hyper-relevant content hubs.

“Stop treating your webinars as isolated activities,” said Bornstein. “Start viewing them as the foundation of an intelligent, always-on campaign.”

The Data-ABM Connection That Conferences Provide

To achieve results, data needs to be treated as the foundation of modern event strategy. The most effective marketers are using data not just to report on events, but to design them by shaping everything from who gets invited to how each interaction is experienced.

“By leveraging account-level and buying group insights, marketers can prioritize the right accounts, tailor messaging to specific roles within the buying group, and personalize event experiences based on known interests and past engagement,” said Turco. “This ensures that every touchpoint feels relevant and intentional, rather than one-size-fits-all.”

Data becomes even more powerful when paired with a multi-channel ABM approach. For example, if only one stakeholder from a target account attends an event, that engagement doesn’t have to exist in isolation. Data can inform how to extend personalized follow-up to the rest of the buying group, effectively amplifying the impact of that single interaction across the account.

“Ultimately, data-driven decision-making enables marketers to move beyond generic event experiences and toward highly relevant, account-centric engagement strategies that drive stronger pipeline outcomes,” said Madison Logic’s CEO.

What is Working on Conference Floors

Walk on any trade show floor and you will see booths incorporating virtual reality games, motion simulators, and branded interactive installations that can be deployed at scale. Interactive booth games are the primary mechanism through which exhibitors capture prospect data. The average cost per lead at a trade show is $112, compared to $259 for a traditional field sales call.

This matters because 81% of trade show attendees arrive with buying authority. Two-thirds are entirely new prospects that exhibitors have not previously reached. When trade show booth games convert those attendees into registered leads at rates approaching 95% for the most engaging formats like racing simulators, the channel starts to look less like marketing and more like a sales pipeline with better conversion rates than most digital campaigns.

The results have encouraged a wider market to follow— 61% of exhibitors now prioritize personalization in their booth experiences, and 57% use digital business cards or QR codes to streamline contact exchange-both indicators that the trade show floor has moved decisively toward technology-driven engagement.

Driving Stronger Pipeline Outcomes

Turco stressed that first-party event data is one of the most valuable assets marketers have, but its true impact depends on how effectively it’s activated after the event. The most successful marketers treat event data not as a static output, but as a catalyst for ongoing engagement. Importantly, even limited engagement can be expanded into broader influence across the buying group.

“By mapping attendee behavior back to accounts and buying groups, marketers gain a more complete view of deal context and can tailor follow-up based on engagement levels, interests, and stage in the buyer journey,” said Turco. “This allows for more relevant, personalized outreach that continues the conversation rather than restarting it.”

Using those signals to guide outreach to additional decision-makers helps amplify the event’s impact within the account. Over time, these insights inform future strategies—. understanding which accounts engaged, what content resonated, and how buying groups interacted enables marketers to continuously refine their approach. Interactive event technologies tuns events into an ongoing source of intelligence that drives stronger pipeline outcomes.

The post Why AI is the Ultimate Tool for B2B Event Marketers appeared first on Demand Gen Report.

The AI system launched on time. The models were sophisticated. The dashboard looked impressive. And six weeks later, the marketing team stopped using it because nobody trusted the recommendations.

This scenario plays out repeatedly across organizations racing to deploy AI for marketing, operations, and decision-making. The hard truth: AI strategies fail not because of weak models, but because of weak data foundations. And when a system fails once, it fails for good. That bad taste lingers, and teams retreat to manual processes “until AI gets better.” Adoption becomes nearly impossible.

The problem is rarely the AI itself. The problem is treating data as a byproduct of disconnected systems rather than a strategic product that requires active management, clear governance, and intentional design for accessibility.

The Three-Question Test for Data Readiness

Before investing in sophisticated AI models, organizations should answer three fundamental questions about their data foundation:

How quickly can you access the data? If someone asks your head of investments, How much did we spend on a major publisher’s streaming and linear TV properties in the past year? How long does it take to get an answer? If it requires days of aggregation across multiple systems rather than minutes, your data foundation isn’t ready to support intelligent automation.

How accessible is the data? Can non-technical people find what they need, or does every business question require a data engineer to write queries? When only specialized teams can access critical information, you’ve created a bottleneck that undermines the entire promise of AI-driven insights.

Is the outcome consistent? Here’s the real test: Ask your head of investments, your finance lead, and your data team the same question about Disney spend. Do they all get the same answer? Are they pulling from the same root data asset? If different departments produce different answers to identical questions, your data isn’t ready— even if each team delivers results quickly.

These three criteria— speed, accessibility, and consistency— determine whether your organization has a data foundation that can support trustworthy AI or merely a collection of fragmented systems that will undermine even the most sophisticated models.

The Stale Data Problem Nobody Discusses

Data goes stale faster than most organizations recognize. And staleness matters far more for AI than it did for traditional analytics. Outside of specific use cases like Olympic advertising cycles, where historical comparisons across four-year intervals make sense, most marketing data becomes irrelevant within months.

Consider a quick-service restaurant brand with a loyalty program. If your point-of-sale system and your customer database don’t sync daily, your loyalty team might send a “we miss you” offer to someone who just bought lunch yesterday.

If you’re a clothing retailer marketing to families, what happened two years ago tells you almost nothing about what parents need today. You want to understand current seasonal behavior— back-to-school shopping patterns, holiday purchasing— not outdated assumptions about customer intent.

The same staleness problem affects B2B contexts. Customer behavior evolves. Market conditions shift. Organizational priorities change. When models train on datasets that no longer reflect current reality, they produce technically functional outputs that nobody can use for actual decision-making.

Siloed Tech Stacks and the Consistency Crisis

The siloed tech stack problem cuts across brands, agencies, and publishers equally. Large organizations frequently run multiple CRM systems: one for sales, another for the CMO’s team, and a separate platform for loyalty programs. These silos emerge because there’s no one-size-fits-all solution; different functions have legitimate requirements for specialized tools.

The critical failure happens when backend systems don’t communicate in real time or even on consistent cadences. Your sales data updates daily, but marketing receives weekly feeds. Your product catalog refreshes hourly, but your ad platform syncs monthly. These timing mismatches create the frustrating experiences customers know too well: retargeted ads for jackets they purchased two weeks ago, promotional emails offering discounts on items already in their cart, loyalty offers that ignore recent purchases.

More than an AI misstep, these issues are clear data synchronization failures that make your marketing team think the AI program is incompetent.

From Byproduct to Product: The Management Shift

Treating data as a product rather than a byproduct requires fundamental organizational changes. Just like any other product your company builds and maintains, a quality data product has clear ownership, documented governance, defined quality standards, and a complete lifecycle management framework.

Clear ownership means assigning accountability for data quality in each domain. Marketing owns customer data quality, not just IT managing the database infrastructure. Finance owns transaction data accuracy. Product teams own catalog integrity. Ownership sits with business stakeholders who understand context and can make informed decisions about data lifecycle and permissible use.

Strong governance goes beyond compliance documentation to establish actual standards for freshness, accessibility, and quality. This includes data lineage documentation, clear processes for resolving data disputes, and explicit rules about when data becomes too stale for specific use cases. Governance determines permissible use, which data can drive activation and targeting versus research and insights only, protecting organizations from regulatory jeopardy while enabling legitimate business applications.

Why Synchronizing Updates are Key

Unified data layers create consistent views of key entities, customers, campaigns, and products without requiring data engineering intervention for every business question.

This doesn’t mean forcing every team onto a single platform or eliminating specialized tools. Large organizations will always have multiple MarTech and AdTech systems serving different functions. The solution is a consumable data layer that reads from these distributed systems on regular cadences, ensuring marketing, finance, and sales teams all work from the same truth, even while using different operational tools.

Consistent refresh cycles build maintenance into systems rather than treating it as an afterthought. Different use cases require different refresh rates, real-time for ad targeting, daily for campaign pacing, and weekly for strategic planning. The key is synchronizing updates so systems don’t drift out of alignment, creating the consistency problems that erode trust in AI-driven outputs.

The Readiness Distinction That Matters

Organizations frequently confuse digital maturity with AI readiness. Having extensive technology infrastructure,  sophisticated platforms, advanced analytics tools, and cloud architecture doesn’t mean your data foundation can support intelligent automation.

Digital maturity means you’ve invested in technology. AI readiness means your data is trustworthy, accessible, and well-governed.

The distinction matters because AI amplifies whatever you feed it. Train models on inconsistent data, get inconsistent outputs. Build systems on stale information, produce outdated recommendations. Deploy AI across siloed systems, create fragmented experiences that destroy customer confidence.

Unlike purely technical failures that can be fixed with better code or more computing power, trust failures are extraordinarily difficult to recover from. As I mentioned earlier, when early disappointments cause teams to lose confidence in AI-driven insights , they become permanently skeptical, refusing to trust AI recommendations. The opportunity cost extends far beyond the failed pilot project, it impacts all the strategic advantages organizations that properly implement AI can offer.

Where to Begin?

For organizations recognizing their data foundations aren’t AI-ready, the path forward begins with an honest assessment rather than new technology purchases. Run the three-question test across your organization. Identify where data ownership is ambiguous or absent. Document the gaps where information is stale, siloed, or inconsistent.

Start with one high-value use case rather than attempting comprehensive transformation. Prove the model works with a focused workflow that demonstrates measurable business impact. Quick wins include establishing standard definitions for key metrics, creating data catalogs that document what exists and where, implementing automated quality checks, and instituting regular data review meetings with business stakeholders.

Avoid the temptation to buy more technology before fixing foundational issues. Technology won’t solve organizational problems around ownership, governance, and data lifecycle management. These are cultural and operational challenges that require business leadership, not just IT implementation.

Success Starts With Foundations

Five years from now, the companies that derived the greatest advantages from AI won’t be the ones that deployed the most sophisticated algorithms. They’ll be the ones who built trustworthy, accessible, well-managed data foundations first, treating their data as a strategic product that requires ongoing investment, clear accountability, and disciplined governance.

Before your next AI initiative, ask: Can we answer basic business questions quickly and reliably? Do we have clear data ownership across all critical domains? Are our governance policies more than documentation? Does the same question produce the same answer regardless of who asks?

Your AI-implemented strategies will only be as good as your data. Treat data as a strategic product, and AI becomes the competitive advantage everyone promises it will be.

Laura McElhinney is the Chief Data Officer at MadConnect, where she oversees the company’s data strategy, architecture, and governance for its Intelligent Connectivity Layer (ICL). With more than two decades of experience in enterprise data management, Laura helps organizations make sense of their fragmented tech stacks and unlock the full value of their data, securely and at scale. Before joining MadConnect, Laura led data transformation efforts across AdTech, MarTech, and enterprise companies, always focusing on turning complexity into practical and scalable solutions. She’s especially passionate about helping teams reduce data debt, modernize infrastructure, and get AI-ready, without needing to rip and replace what’s already in place. At MadConnect, she plays a central role in helping customers connect platforms like Salesforce, Snowflake, and The Trade Desk, enabling them to move faster and make smarter decisions with the data they already have.

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