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

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

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

How this is a First Step

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

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

How Integration Works

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

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

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

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

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

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

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

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

AI is Influencing Buyers Shortlists

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

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

How B2B Buyers are Relying on AI Chatbots

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

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

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

How Many Hours a Week Are AI Chatbots Being Used

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

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

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

Click here for the full report.

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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.

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

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

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

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

Why AgentStack Is Needed

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

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

What B2B Marketing Teams Can Accomplish

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

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

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

Who is Using AgentStack

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

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

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

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

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

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

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

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

How Salesforce is Rethinking AI

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

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

When Will Agentforce Operations Be Available

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

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

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

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

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

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

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

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

A Shift to Autonomous Revenue Orchestration

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

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

What Now Assist is Designed For

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

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

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

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

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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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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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• 44% of B2B SaaS companies score below 50 in AI visibility, with significant gaps in mention frequency.
• Companies with perfect sentiment scores need better distribution to improve AI-assisted buyer recognition.

As B2B marketers work to be recognized in a world of artificial intelligence (AI), a new study shows that less than half are visible to buyers using AI.

DerivateX, a B2B SaaS SEO and Generative Engine Optimization agency, has published The State of AI Visibility in B2B SaaS: 2026 Benchmark Report, where it analyzed 50 B2B SaaS companies across ChatGPT, Perplexity, Claude, and Gemini, running 1,400 buyer-intent prompts in total and scoring each company on a 0 to 100 composite scale.

The average AI Presence Score across the B2B SaaS companies in the study is 56.9 out of 100, with 44 percent of companies scoring below 50. The gap between the highest scorer (Clio at 89) and the lowest (LeadSquared at 2) is 87 points, despite both operating in established software categories with active marketing teams.

What is the Most Selective Platform

The report found that Claude is the most selective AI platform, mentioning 88 percent of tested brands, compared to 100 percent for ChatGPT and Gemini. Sentiment is nearly uniform: 44 of 50 companies score 19 or 20 out of 20 on sentiment, meaning the visibility gap is driven entirely by mention frequency and platform breadth, not brand perception, according to DerivateX officials.

“What the data shows clearly is that platform breadth alone does not determine the score,” said Apoorv Sharma, Co-Founder of DerivateX. “Make is present on all four platforms and still scores 40, while Zapier is absent from Claude and scores 63. The difference is how often and how prominently each brand appears when AI systems are asked category questions by actual buyers.

“Mention rate and position carry 60 of the 100 available points in the scoring framework. That is where the optimization work happens.”

Which Companies Have Perfect Sentiment Scores

Within competitive categories, the report found the gaps between direct rivals substantial. In field service management, ServiceTitan scores 68 and Jobber 41. In payments, Stripe scores 65 and Razorpay scores 39.  In SEO analytics, Ahrefs scores 83 and Semrush scores 68. In workflow automation, Zapier scores 63 and Make scores 40, despite Make appearing on all four platforms and Zapier being absent from Claude entirely.

The study identifies a highest-opportunity group: ten companies with perfect sentiment scores of 20 out of 20 but mention rates of 8 out of 30 or lower, inlcuded Close, WebEngage, Kissflow, CleverTap, Freshworks, Razorpay, BrightEdge, Mindbody, Mangools, and Toast. When AI platforms mention these brands, the framing is uniformly positive. The gap is frequency, not perception, said Sharma.

“Ten of the fifty companies we scored are being described positively every time AI mentions them, but that is happening in fewer than 9 of 28 tested prompts,” he said. “That is not a brand problem. That is a distribution problem. Getting cited more often in third-party content that AI platforms index is what moves those companies from occasionally mentioned to consistently recommended.”

To ready the full 2026 AI Visibility Benchmark Report, click here.

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• Demandbase AI centralizes go-to-market execution by using proprietary Context Intelligence to filter out market noise and align account signals with pipeline goals.
• The launch features new tools like a conversational Site Customization Agent and an open-standard Model Context Protocol to seamlessly connect with major AI assistants like ChatGPT and Claude.

At its annual customer conference April 14, Demandbase touted the debut of Demandbase AI as setting the new standard for the artificial intelligence (AI) GTM era to help enterprises scale strategy into measurable pipeline.

The debut at GO London of this new AI-first experience presents a simplified, conversational interface for orchestrating go-to-market execution across the platform— and is anchored by several key innovations, including a Site Customization Agent, LLM integrations including ChatGPT and Claude, and new capabilities for proving Pipeline Influence.

With marketers drowning in GTM signals and struggling to turn insights into outcomes. Demandbase AI uses Context Intelligence— a proprietary layer that applies each company’s unique GTM context— to analyze account signals and patterns against pipeline goals, identifying the opportunities most likely to drive results.

What Demandbase AI Does

Instead of leaving teams to activate the strategy across every channel, Demandbase AI removes the overwhelm by coordinating programs and plays across marketing, sales, and advertising to drive pipeline, said Gabe Rogol, CEO of Demandbase.

“In the rush to adopt AI, the industry is seeing that more data and activity don’t lead to better outcomes,” said Rogol in a statement. “AI without context creates noise— it requires more oversight and misses what actually matters. Demandbase AI is moving the industry beyond insights and point solutions to a unified system that activates teams, focuses them on what matters, and helps them drive revenue more predictably.”

‘Delivering on AI’s Promise’

Demandbase AI brings together data, teams, and workflows across native and ecosystem integrations to form a continuous system that turns goals into outcomes, transforms signals into actionable insights, coordinates cross-channel activations and continuously adapts.

To bring this system into how teams work everyday, the company is introducing new capabilities that enable harmonious workflows across the entire go-to-market. From agent interoperability to a robust ecosystem that enables data and tool integrations, Demandbase is extending AI-driven intelligence directly into the solutions teams rely on:

• LLM Workflow Integration: Demandbase now delivers deep company, contact, technographic, and intent data through Model Context Protocol (MCP), an open standard that enables seamless data interoperability between Demandbase AI and major AI assistants like ChatGPT, Claude, CoPilot, and Gemini.
• Site Customization Agent: A conversational interface that enables marketers to quickly refine campaign-matched landing pages. By “reading” page and audience context, it will reduce production time from days to minutes while improving conversion and pipeline outcomes, with every recommendation grounded in account and buying group signals.
• Pipeline Influence measurement: Through Demandbase AI Chat—a chat-based interface that enables prompt-based insights—Pipeline Influence easily moves teams beyond fragmented metrics to show how programs are driving pipeline across the GTM, helping teams scale what’s working.

“Our teams are under incredible pressure to both adopt AI and deliver real pipeline results,” said Ryan Oliver, Director of Enterprise Demand Generation Marketing, SAP Concur. “Demandbase is the first platform we’ve used that actually connects those two. It creates an AI-driven experience that works across our teams, keeps everyone aligned, and measures success based on the pipeline it generates. We’re reducing wasted spend and seeing better outcomes. Demandbase is truly delivering on the promise of AI and driving real business impact.”

To help the industry keep pace with the speed of AI innovation, Demandbase also launched a new AI GTM Certification program that will empower teams with the strategic framework and technical skills needed to master the AI GTM era.

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