• 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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• 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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• 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.

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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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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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• 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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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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Gartner research shows the shift is impossible to ignore. Roughly half of B2B buyers already use independent generative AI tools such as ChatGPT, Gemini and Claude to gather information about potential suppliers early in the buying journey. If your brand does not show up in those answers, you are not just ranking lower. You are being removed from consideration altogether.

This is why answer engine optimization, or AEO, has moved from an experimental tactic to a core CMO mandate.

AI Answer Engines Are the New Front Door

B2B buyers ask AI answer engines questions that are broader and more complex than traditional search queries. They want to understand capabilities, pricing ranges, deployment requirements and industry fit in a conversational interface. Answer engines respond by synthesizing information from brand content, social platforms, forums and third-party sites. The result is an answer box that effectively prequalifies vendors before sales ever hears from the account.

The opportunity is obvious. AI-powered answer engines can become a powerful new channel for brand visibility and demand generation. The risk is equally stark. When brands do not provide clear, structured and current information, AI systems fill in the gaps. That often means hallucinated pricing, outdated capabilities or incomplete explanations that misalign buying groups before the first sales call.

In this environment, traditional SEO alone is insufficient. CMOs must retool content strategies around how AI systems discover, interpret and present information.

AEO Requires a Different Content Mindset

One of the most important implications of answer engines is that AEO is not just SEO with a new label. These systems favor content that is explicit, well structured and directly responsive to buyer questions. They also place heavy weight on authoritative brand sources.

This changes how content teams should operate. Instead of optimizing pages around keywords, marketers must optimize around questions. What are buyers asking at the earliest stages of their journey? What language do they use when they talk to sales, website chatbots or peers in online communities? Those questions should become the backbone of FAQs, product pages and thought leadership.

Equally important is how those answers are delivered. Structured data, particularly JSON-LD markup, plays a critical role in making content understandable to AI systems. Without proper tagging, even the best answers may never surface in an AI response.

Productive Calls to Action are Risk Mitigation Tools

A common fear among B2B marketers is that AI answers will reduce direct engagement with brands. The reality is more nuanced.

Most B2B buyers still want to validate AI generated information with a human sales representative. Buyers whose first interaction with a supplier is to learn more about offerings or capabilities are more likely to report a high quality deal. The implication is clear. AI visibility can drive better leads if marketers guide buyers toward follow up.

That is where productive calls to action come in. Embedding reminders and recommended next steps directly into content can make a measurable difference. Examples include encouraging buyers to confirm specifications with a sales representative or to use a pricing calculator for a tailored estimate. These calls to action reduce the risk of an AI answer engine surfacing misinformation while nudging buyers into a direct relationship with the brand.

Pricing Transparency is No Longer Optional

Few shifts will make CMOs more uncomfortable than the push for greater pricing transparency. Many B2B organizations have historically avoided publishing pricing, relying instead on sales conversations to manage complexity.

Answer engines do not respect that boundary. When pricing information is absent, AI systems attempt to infer it from other sources. The result is often inaccurate ranges that set unrealistic expectations and derail deals. Publishing ranges for common configurations or offering visible price calculators gives AI systems something accurate to reference. It also ensures that buying groups approach sales with more realistic assumptions.

For CMOs, this requires close collaboration with sales, finance and product leaders. It is a business change driven by external buyer behavior, not a marketing preference.

Marketing Cannot win AEO Alone

Building answer engine friendly content requires deep insight into buyer personas, product capabilities and real world use cases.

Creating a centralized AEO focused team led by marketing but supported by sales, service and product can help address this gap. This group should audit existing content, remove outdated material, identify gaps and ensure consistent structured markup across the site. It should also monitor how answer engines represent the brand and flag inaccuracies quickly.

New success metrics are required as well. Beyond traffic and rankings, CMOs should track answer engine citation rates, brand mentions, sales reported lead quality and engagement with configuration or pricing tools. These signals provide early feedback on whether AEO investments are improving buyer conversion.

The CMO Mandate for 2026

AI powered answer engines are already shaping how B2B buyers learn, compare and decide. Nearly half of buyers are relying on these tools across multiple stages of the journey. That makes AEO a board level risk, not a niche optimization tactic.

CMOs who act now can turn that risk into an advantage. By rebuilding content around buyer questions, embracing structured data, improving pricing transparency and aligning tightly with sales and product, marketing leaders can win AI answer engines  and the buyer’s business.

Those who wait will discover that in the age of AI answers, invisibility is the most expensive outcome of all.

Nicholas Mortensen and Martin DeWitt are experts in the Gartner Marketing Practice, specializing in B2B marketing and digital marketing performance.  Learn more about AEO strategies at the Gartner Marketing Symposium/Xpo, June 8-10, 2026 in Denver, Colo.

The post Win the AI Answer Engine or Lose the Buyer: Why CMOs Must Rebuild for AEO Now appeared first on Demand Gen Report.

• The Pipeline Insights Dashboard connects multi-channel engagement signals to pipeline advancement in real-time, enabling data-driven decisions.
• It shifts focus from engagement reporting to pipeline intelligence, helping marketers achieve accelerated and predictable pipeline growth.

Madison Logic launched its Pipeline Insights Dashboard today, which officials tout as a game-changing feature designed to help marketers visualize and measure campaign impact.

With the new dashboard, B2B marketers can now connect content syndication, programmatic and social engagement signals directly to stage-by-stage pipeline advancement in real time, gaining clear visibility into how multi-channel campaigns influence opportunity movement across the buying journey.

By bridging the gap between engagement reporting and retroactive attribution, the new Pipeline Insights Dashboard empowers teams to make data-driven decisions that transform engagement into measurable pipeline acceleration.

What Madison Logic’s New Product Accomplishes

Justin Hoskins, Vice President of Product at Madison Logic, said for far too often marketers accountable for delivering measurable pipeline growth lack a clear, unified view of performance across the buying journey,

“As performance marketing becomes central to B2B growth strategies, brands need intelligence that connects signal to revenue with precision,” said Hoskins. “By providing this level of visibility, they can now not only identify optimization opportunities earlier but can achieve more accelerated and predictable pipeline outcomes.”

What the Pipeline Insights Dashboard Offers

Madison Logic’s research shows 62% of B2B leaders say the future of advertising will be defined by performance-driven strategies, while 84% report shifting away from impression-based marketing toward intelligence-led strategies. Meeting that mandate requires real-time pipeline intelligence that reveals what’s truly driving account progression and revenue impact.

The Pipeline Insights Dashboard addresses this challenge by connecting channel exposure directly to stage movement within the CRM. Instead of reporting on activity or retroactive attribution alone, the dashboard shows whether media programs are contributing to forward momentum.

Marketers can now analyze which campaigns are advancing, stalling, or regressing opportunities, understand the level of multi-channel engagement required to move an account to the next stage, and identify where pipeline movement is breaking down.

Madison Logic’s CEO Keith Turco

The new offering is part of B2B marketers moving away from impressions and eyeballs and into action and insights, said Madison Logic CEO Keith Turco. It gives data driven insights into how their marketing activity is impacting their pipeline, making it measurable so companies can quicken their pipeline and shorten their sales cycle.

“This launch marks a fundamental shift from engagement reporting to pipeline intelligence,” said Turco. “By giving marketers, a unified, progression-based view of campaign impact, we’re empowering them to move from measuring activity to intentionally engineering predictable pipeline growth. It’s about understanding how pipeline is built, not just how it’s reported.”

“It helps them visualize who they targeted, what account lists, what titles, who came into their pipeline, how they are moving through the pipeline, and what stages they’re at. We believe this provides a great component to help our clients efficiently spend and optimize their marketing dollars, as well as accelerate their sales cycle.”

The Pipeline Insights Dashboard is now available to all Madison Logic clients. Visit www.madisonlogic.com to learn more.

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• Outcomes Rocket’s Saul Marquez detailed the importance of aligning sales and marketing teams with shared KPIs and clear ownership improves GTM success.
• The State of Account-Based Marketing report promoted operationalizing AI around real revenue problems enhances GTM strategies.

Building a predictable revenue engine requires sales and marketing teams to pull in the same direction.

Outcomes Rocket’s State of Account-Based Marketing report sheds light on exactly what works and what falls flat in B2B growth strategies. This report offers a clear look at how top-performing companies structure their go-to-market systems to cut through crowded markets and engage the right buyers.

To help us make sense of these findings, we sat down with Saul Marquez from Outcomes Rocket. In our interview, Marquez explored the core challenges causing friction between departments, shares solutions to close the sales and marketing alignment gap, and how B2B marketers can start operationalizing AI to solve revenue problems.

Demand Gen Report (DGR): Saul, thanks for making time for us. What are the top challenges in aligning sales and marketing teams for GTM success? How can organizations close the 30% alignment gap between sales and marketing teams?

Saul Marquez: Great to be here. The alignment gap is a definition and accountability issue. Our data shows only 37% of respondents clearly understand GTM as an integrated, cross-functional revenue framework, and about 21% of companies either lack a formal GTM strategy or do not have clearly defined ownership. On top of that, 28.7% identify sales-marketing alignment and friction as one of the top GTM challenges they face today. When teams are working from different definitions of GTM, misalignment is the natural result.

In a B2B environment with longer cycles and multiple stakeholders, that gap shows up in very practical ways such as inconsistent messaging, delayed handoffs, conflicting performance metrics, etc. It directly affects win rate, pipeline velocity, and revenue reliability.

The way to close that gap is not complicated, but it does require discipline. I would start with governance before tactics. One clear GTM owner matters. That could sit under a CRO, a CMO, or a tightly defined cross-functional leadership structure, but somebody has to own the operating model. Second, sales and marketing need shared KPIs and a shared view of what success actually looks like. When one team is chasing lead volume and the other is chasing close quality, the system is misaligned by design.

DGR: How can B2B marketers operationalize AI to enhance GTM strategies? What are the best practices for accelerating AI integration into GTM strategies?

Marquez: A lot of companies are still in the early innings here. The excitement around AI is real, and so is the potential, but most teams are still using it in ways that are useful without being truly transformative. Right now, the most common applications in the data are:

• Content creation and repurposing: 42.9%
• Email personalization and sequencing: 32.5%
• Targeting and segmentation: 28.7%
• Predictive lead scoring: 27.8%
• Churn prediction: 26.9%

The market clearly believes AI is going to matter more and more. Nearly 58.4% of respondents expect AI-first GTM models to outperform traditional approaches over the next 12 to 24 months. But AI is not going to fix a GTM system that is already messy.

So the best practice is to operationalize AI around real revenue problems. Use it where the pain is already obvious. I also think companies need to respect the order of operations. Build the foundation first, then layer in AI. The report shows that a lot of teams are still operating with fairly basic GTM infrastructure and maturity. If you want AI to make GTM smarter, your data and segmentation have to be strong enough to support it.

Recommendation for Measuring ROI

DGR: How can organizations measure the ROI of their GTM strategies effectively?

Marquez: The “2026 State of B2B Go-to-Market (GTM) Strategy” shows 29.1% of respondents have limited confidence that their GTM efforts translate into measurable business impact, and on average about 24% of GTM budgets are going to initiatives without traceable commercial outcomes. Many companies still measure GTM activity instead of GTM contribution.

Organizations need to anchor the system around the outcomes leadership actually cares about. In our data, the top internal GTM metrics are revenue growth at 61.5%, customer retention at 42.0%, and win rate at 38.6%. After that come pipeline volume at 25.9%, lead-to-opportunity conversion at 24.7%, CAC at 22.4%, expansion revenue at 20.5%, sales cycle length at 18.3%, and pipeline velocity at 16.8%. That ordering tells us ROI should be measured first through business outcomes, then through the operational drivers that influence those outcomes.

My recommendation is a three-layer measurement model. First, track outcome metrics such as revenue, retention, win rate, and expansion. Second, track efficiency metrics like conversion rate, CAC, cycle length, and velocity. Third, track leading indicators tied to the actual GTM motions, such as segment engagement, meeting creation, opportunity creation, and progression quality. That structure keeps teams from overreacting to vanity activity while still giving them enough signal to optimize earlier in the funnel. The report’s conclusions point in the same direction by calling for cleaner attribution and closed-loop reporting.

DGR: What role do channel partners play in driving pipeline growth for B2B companies?

Marquez: Channel partners help companies expand reach and build trust faster than they could on their own. Our report shows partnerships are one of the more effective channels for driving pipeline, but not in isolation. A strong pipeline usually comes from a mix of relationship-driven channels and scalable digital programs.

Partners are most valuable when the market is crowded and buyers are more cautious. In those situations, a good partner can shorten the trust curve and create warmer entry points into the market. That can make a real difference when pipeline quality is harder to maintain and sales cycles are slower.

DGR: What are the most effective ways to leverage data analytics for GTM optimization?

Marquez: Of those responding, 40.7% said they are investing in data analytics and forecasting tools to strengthen GTM. The best use of data analytics is to make GTM decisions sharper.

From my perspective, analytics should do three things well. First, it should show what is actually driving commercial outcomes. Second, it should help teams prioritize better by showing which segments, accounts, and motions are producing the strongest results. Third, it should make optimization easier by highlighting where conversion slows down and where teams should adjust.

What Metrics are Key to Focus on for GTM teams 

DGR: How can B2B marketers balance foundational investments with forward-looking innovations?

Marquez: Start by protecting the core of the GTM system before expanding into newer capabilities. My advice is to make sure the basics are solid first, then invest in forward-looking tools only where they improve speed, precision, or decision-making in a measurable way.

I would not spread the budget evenly across both. I would sequence it. First, fix what affects visibility and execution, especially analytics, segmentation, and cross-functional alignment. After that, layer in innovations like AI, intent data, or ABM where they can strengthen targeting, forecasting, personalization, or pipeline efficiency. That approach gives innovation a real job to do instead of turning it into another disconnected experiment.

DGR: What metrics should be prioritized to measure GTM success beyond revenue growth?

Marquez: Beyond revenue growth, I would prioritize the metrics that tell you whether the GTM engine is actually getting more efficient. In our data, the most important metrics after revenue were customer retention at 42.0% and win rate at 38.6%, followed by pipeline volume, lead-to-opportunity conversion rate, CAC, expansion revenue, sales cycle length, and pipeline velocity. Revenue can go up for a quarter and still hide problems underneath. Therefore, that mix matters because it gives you a fuller picture of performance.

The question should be “How much of that growth can we actually explain, repeat, and scale?”.

DGR: How can B2B marketers address competition and market saturation in their GTM strategies?

Marquez: The first thing I would say is that when the market feels crowded, the answer is to do sharper marketing.

Competition and market saturation is the top GTM challenge right now. A lot of teams are struggling because too many companies sound alike and target too broadly. So the real opportunity is differentiation through clarity. That starts with advanced segmentation which combines firmographic, behavioral, and intent data.

I also think this is where channel strategy matters. The report shows that the strongest pipeline channels are in–person events, customer marketing and referrals, email automation, organic content and SEO, and partnerships. When a market is saturated, buyers look for credibility. So marketers need to get tighter on their ICP and lean harder into channels that build trust.

Trends to Watch for This Year and Beyond 

DGR: What are the key takeaways for long-term strategic planning in B2B GTM?

Marquez: GTM has to be treated like a business system. One of the clearest signals I mentioned above is that only 37% of respondents show a clear understanding of GTM as a cross-functional revenue discipline. That is a long-term planning problem. If ownership is unclear, strategy gets fragmented. And when strategy is fragmented, execution usually follows.

The second takeaway is that alignment has to become operational. Nearly 70% say their teams are mostly or fully aligned, which is good. But the remaining 30% still report partial or poor alignment, and that gap creates real execution risk.

And finally, long-term planning has to balance near-term performance with future growth. A lot of teams are trying to balance brand and performance, which I think is the right instinct. But that only works if measurement improves.

DGR: What are the emerging trends in GTM strategies that B2B marketers should prepare for?

Marquez: AI is obviously part of that story. The most common uses of AI today are content creation, email personalization, and targeting or segmentation. That tells me the next phase of GTM will be more about better prioritization. Another trend is that precision is becoming more important than scale alone. Personalization is improving, but for many companies it is still fairly surface-level, and advanced segmentation remains rare. There is still a big gap between the teams that are sending more messages and the teams that are actually getting smarter about who they are speaking to and why.

And maybe the most important trend underneath all of this is that GTM is being held to a higher standard. Leaders are being asked to prove contribution and that changes the conversation inside the business. GTM is being judged by whether it improves win rates, supports retention, increases pipeline quality, and creates revenue that can actually be traced back to a strategy.

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