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

It’s the data underneath it.

The Problem Enterprise Marketing Teams Have

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

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

The Real Cost of Bad Data

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

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

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

What AI Has Changed

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

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

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

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

The Formula for AI Value

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

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

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

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

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

Why All Roads Lead Back to The Data

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

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

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

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

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

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

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

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.

The post Marketers Need to Treat Data as a Product appeared first on Demand Gen Report.

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 problem is that too many marketing teams treat this data as something to review after the push is over. By delaying, they leave potential sales and new customers on the table.

Black Friday and Cyber Monday (BFCM) highlight what’s at stake when teams fail to act on live data. It’s the biggest weekend of the year in retail, and more first-party data flows in during this one weekend than almost any other time of the year. Every customer touchpoint— click paths, email opens, QR scans, regional performance spikes and conversions— can be tracked across multiple channels by the minute.

The Importance of QR Codes

This data is so valuable because it reveals what shoppers are actually doing in the moment. It shows what messages make them click, what offers drive action, which channels are pulling the most attention and where friction is costing sales. It’s a live feedback loop that tells marketers what’s performing and why. When used in real-time, that information allows them to optimize while the buying window is still open.

The challenge is that much of that potential goes untapped, particularly when it comes to QR code scan data. According to Uniqode’s 2025 BCFM QR Code Marketing Report, one in four marketers failed to act on 2024 Black Friday scan data in time to improve their campaign performance on Cyber Monday. By contrast, marketers using QR codes were less likely to miss the optimization window (31% for non-users vs. 20% for users) and more likely to rate their BFCM campaigns as very effective (45% vs. 26%). The takeaway here is that activating first-party signals is more effective while a campaign is live instead of after it ends.

Capture Consumer Intent

QR code scans might seem like a simple action, but they capture real consumer intent in real time. Someone sees an offer, a product or a piece of content and chooses to engage. That behavior creates an immediate data signal that shows what’s working, where interest is highest and what might need attention. When a contact clicks on a specific offer in an email, marketers have automated follow-ups built in. The opportunity is treating a QR code scan in the same fashion— as a high-intent action that triggers immediate, relevant next steps.

Almost 60% of marketers said they used QR codes in their 2024 campaigns. Adoption has become mainstream as a way to connect physical and digital touchpoints across print ads, packaging, in-store displays and social media. Roughly 60% of shoppers now scan QR codes on a weekly basis, and nearly 75% of shoppers said they were likely to scan a QR code during BFCM in 2025. The reasons for scanning varied, with 41% seeking discounts or coupons, 13% for faster checkout and 12% for product information. More than half expected an immediate benefit, like a promotional offer or an easy path to purchase.

QR codes are a case study in what’s possible when real-time, first-party data is treated as a live signal rather than a static report. Acting on those signals during the campaign can lead to more effective spend, stronger engagement and better overall outcomes.

What Marketers Can Do

The reality is that most marketing teams aren’t built to act on this data fast enough. BFCM shines a light on that gap as data floods in across devices, regions and platforms. Yet, campaign decisions still move through slow processes that effectively turn off that live feedback loop.

Part of the reason for this is timing. Half of marketers started preparing their BFCM campaigns only a week or two before the event. That tight planning window leaves little room for testing, learning or adjusting. Even teams with strong analytics find themselves using data in hindsight.

Changing this requires a shift in how marketers think about measurement itself. During peak sales moments like BFCM, data should be seen as a live signal. Teams should be monitoring scan rates, traffic patterns and engagement levels and have the authority to act when something changes. For example, if a creative asset starts outperforming, they boost it. If conversions drop on mobile, they immediately troubleshoot. If users are scanning QR codes specific to a product or offer, respond to that interest signal immediately— retarget those high-intent scanners across digital channels while their interest is still fresh. These small, responsive moves compound into big gains when made at the right time.

Speed Winds the Day

Speed is important. Customers move quickly, and they expect that brands will meet them in the moment. When a shopper engages, they’re signaling intent. Marketers who can respond to that signal within hours (not days) create personal and relevant experiences.

The hidden brand advantage in this responsiveness is simple: It proves you’re paying attention. A customer scans a code, finds a smoother path and trust builds. Repeat that pattern, and it becomes your brand promise— a company that listens, adapts and delivers when it matters.

Justine BaMaung is a seasoned B2B marketing leader with a focus in full-funnel strategy, digital performance, and growth acceleration. She is Vice President of Marketing at Uniqode, leading integrated marketing initiatives that drive brand impact, customer acquisition, and revenue performance. Justine’s career spans over a decade of marketing leadership across high-growth SaaS and enterprise environments. At ActiveCampaign, she held multiple roles, overseeing promotions strategy, lifecycle marketing, and the growth sales organization. Her work was instrumental in scaling the company’s reach and operational sophistication during rapid expansion. Earlier in her career at Grainger, Justine built out the company’s digital optimization program, leading to industry-recognized advancements in website personalization. Her team’s work was featured at Adobe Summit, spotlighting Grainger as a leader in digital commerce transformation.

The post Stop Measuring. Start Moving: Closing the Execution Gap in Peak Sales Moments appeared first on Demand Gen Report.

Buyers are not just researching differently; they are evaluating and shortlisting differently and increasingly deciding before sales reps ever enter the conversation. Sales teams are losing early-stage influence. Pricing power is shifting. Vendor differentiation is happening algorithmically, and the top of the funnel is tightening rapidly.

The implications for Chief Revenue Officers (CRO) and Chief Marketing Officers (CMO) are profound. A recent multi-industry survey confirms what many commercial leaders have sensed anecdotally: traditional sales motions are being displaced by an AI-accelerated, increasingly self-service buying process.

The Rise of AI Usage

In fact, 60% of buyers use AI moderately or extensively when researching potential solutions and 43% of buyers say AI has saved 30% or more of their time in discovery and qualification.

CROs and CMOs that adapt quickly will shape buying journeys in their favor; those that do not risk being excluded before conversations ever begin. Following are the four commercial imperatives that demand immediate action and what CMOs and CROs need to do to meet them.

Engineer Your Digital Footprint for AI Discovery

 If AI can’t interpret you clearly, it won’t recommend you. The test executives can use to determine how they are faring with this is to look at when AI summarizes your category, does your perspective shape the answer?

 Here’s what CMOs and CROs need to do:

• Restructure websites, case studies, pricing pages, and technical documentation so AI systems can easily ingest, analyze, and synthesize them – including proprietary research, named frameworks, and proof points.
• Make positioning explicit and declarative. Remove ambiguity in how you describe your category, differentiation, and ideal customer profile.
• Strengthen authority signals through consistent thought leadership, backlinks, and AEO driven formatting to increase AI citation likelihood.
• Conduct quarterly AI mystery shopping to assess how generative engines describe you versus competitors and close narrative gaps immediately.

Win the First Five Minutes

In an AI-accelerated buying cycle, speed and substance determine inclusion. The new standard means that the first touch must advance the buyer’s thinking.

 Here’s what CMOs and CROs need to do:

• Redesign lead management to achieve best-in-class response times (five minutes or less) – with real-time measurement and accountability.
• Ensure the first human interaction adds insight, not friction. AI-sourced leads expect expertise, not qualification scripts.
• Equip SDRs with AI tools to instantly contextualize the buyer, personalize outreach, pre-qualify intelligently, and route precisely.
• Elevate frontline technical fluency through training and revised coverage models; introduce SMEs earlier where it materially accelerates trust and deal velocity.

Remove Friction from the Buying Experience

 If buyers can research faster, they expect to purchase faster. The commercial reality is that speed and simplicity are increasingly competitive advantages.

 Here’s what CMOs and CROs need to do:

• Compress internal decision cycles – streamline pricing approvals, legal review, and contract negotiation.
• Simplify packaging, terms, and onboarding to reduce perceived risk and time-to-value.
• Deploy interactive ROI models, configurators, and AI-driven demo walkthroughs – now table stakes in competitive categories.
• Audit every stage of the buying journey for latency, redundancy, and unnecessary internal complexity.

Make Pricing AI-Resilient and Strategically Defensible

AI is increasingly interpreting and comparing your pricing model before a rep ever engages.  The question executives should ask themselves to determine how they are faring with this is if AI is reinforcing your premium (or undermining it) when it explains your pricing to a buyer.

 Here’s what CMOs and CROs need to do:

• Clarify competitive advantage and differentiation in ways that are explicit, structured, and machine interpretable.
• Simplify pricing architecture to ensure it is benchmark-aligned, value-backed, and easy to explain – both by sellers and by AI systems.
•  Evaluate outcome-based or hybrid pricing structures where they reinforce strategic positioning.
• Align pricing tightly to your core value drivers; ambiguity will be exposed and commoditized.

AI has abruptly and fundamentally reshaped the B2B buying journey. Buyers research more, shortlist differently, evaluate faster, expect transparency, and require higher-value interactions from reps. Sales organizations that respond proactively, redesigning content, tools, pricing, and capabilities, will thrive. Those that do not will increasingly lose deals before conversations ever begin.

Michael Smith is the Senior Managing Director – Technology, Media & Telecom Practice Leader at Blue Ridge Partners. Michael has over 35 years of experience helping companies accelerate revenue growth and develop winning sales strategies. Previously, Michael worked at McKinsey & Company and in multiple corporate executive operating roles running businesses and sales teams. Michael received his MBA from Stanford University and lives in the Boston, Massachusetts area.

The post The 4 Actions CMOs & CROs Must Take to Catch Up to the AI-Augmented Buyer appeared first on Demand Gen Report.

What used to be considered the final stage of the funnel— advocacy— has quietly become one of the primary drivers of discovery, trust, and pipeline growth.

Why Advocacy Matters Now

Two structural shifts are making advocacy central in B2B marketing:

Hidden buyers wield decisive influence. Buying committees increasingly include various stakeholders, including finance, legal, procurement, and operations, who rarely meet sales reps but influence vendor selection.

Many stakeholders say thought leadership is more persuasive than product sheets, and over 79% are more likely to champion a vendor with consistent, high-quality ideas. What’s more, over 40% of deals stall due to internal misalignment, which is often driven by these silent influencers.

Discovery is shifting toward AI + third-party proof. AI-driven search is changing how B2B buyers research and shortlist vendors. According to G2’s 2025 Buyer Behavior Report, leads that originate through AI-powered search convert approximately 40% better than those from traditional search engines, primarily because buyers encounter credible, third-party content earlier in the journey.

This evolution highlights a larger truth: advocacy must exist where research actually happens. The most persuasive proof points are increasingly discovered on neutral ground. Buyers are looking at review platforms, community discussions, and practitioner-authored content long before they reach a brand’s website.

Three Advocacy Engines B2B Organizations Can Scale

1. Customer Communities That Deliver Value (and Reduce Cost)

Communities should not be treated as engagement tools alone. When structured effectively, they generate long-term ROI across support, adoption, upsell, and advocacy.

Cisco’s partnership with Khoros is a good example: over their first year, engineers published 47% more content internally, community interactions drove over 1 million annual views, and the program delivered approximately $54.2 million in case deflection savings. Mature community programs are shifting focus from vanity metrics (posts, users) to business outcomes (deflection, retention, engagement).

Tactical moves:

• Stimulate “how-we-fixed-it” threads contributed by customers and internal experts
• Surface accepted answers, highlight best practices, and integrate these into onboarding, product documentation, and training
• Track deflection rates, time-to-first-answer, usage lift, and expansion signals

2. Peer Proof on Platforms Buyers Trust

Trust increasingly forms outside of brand-owned channels. Decision-makers rely on independent, experience-based sources. Review platforms, practitioner communities, and peer content often guide vendor selection.

Demand Gen Report’s 2024 B2B Buyer’s Survey highlights that discerning buyers increasingly rely on peer reviews and in-depth research as trusted guidance in purchase decisions. These findings point to a consistent pattern: credibility is earned on neutral ground. Buyers place greater weight on what peers and practitioners say about a solution than on what the vendor claims about itself.

Tactical moves:

• Treat reviews as a structured, ongoing program rather than one-time requests.
• Keep third-party profiles current with recent customer quotes, screenshots, and implementation details.
• Reuse authentic peer feedback in enablement content—always linking back to its verified, external source.

3. Employee & SME Advocacy That Reaches Hidden Buyers

Thought leadership remains one of the few credible routes into parts of an organization where sales lacks access. Even small adjustments to employee-shared content can boost reach dramatically.

Tactical moves:

• Issue a monthly advocacy brief with three credible themes (customer story, data, contrarian insight)
• Provide lightweight framing rather than scripts, and encourage personalization
• Track reach, engagement, and account-level influence

Building an Advocacy System, Not Just Tactics

• Design mutual value exchange.
  • For customers: offer visibility, early access, roadmap influence, expert forums
  • For employees: offer recognition, guardrails, support, and training
• Make advocacy easy. Toolkit components might include business-case one-pagers, compliance/security FAQs, comparison visuals, and internal champion scripts.
• Break down silos. Let best community content feed into knowledge bases, reviews, case studies, and SME insights. Enable CSMs and product leaders to nominate strong customer stories.
• Measure what matters. Go beyond engagement metrics. Tie advocacy to account coverage (which ICP accounts have an engaged advocate?), toolkits downloaded, review velocity, and SME reach by role. Emphasize outcomes over activity counts.

What to Report (and Benchmark)

Case deflection & cost savings (modeled or actual)

Engaged-account coverage: percent of target accounts with at least one advocate touchpoint

Champion enablement metrics: downloads/shares of toolkits, internal referrals

Review velocity & depth: number of reviews per quarter, recency, qualitative depth

SME influence on hidden buyers: impressions, engagements, internal advocacy contributions

A 60-Day Advocacy Launch Plan

Final Thought

As buying journeys become more distributed and AI surfaces more third-party insight, the companies that grow fastest will be those that build credible ecosystems of proof. Advocacy is no longer simply about retention or customer satisfaction. It is a scalable demand engine that influences discovery, accelerates consensus inside buying groups, and reinforces trust at every stage of the decision process.

Cynthia Ortiz is a Marketing Program Coordinator for Televerde, a global revenue creation partner supporting marketing, sales, and customer success for B2B businesses around the world. A purpose-built company, Televerde believes in second-chance employment and strives to help disempowered people find their voice and reach their human potential.

The post The New Demand Engine: Why Peer Proof Is Reshaping the B2B Buying Journey appeared first on Demand Gen Report.

For B2B marketers just getting started with buying groups, the first step is to identify the different stakeholder roles, from champion to procurement to technical implementation lead. Each stakeholder profile will then map to specific titles and roles at target companies, producing audience segments. But, before B2B marketers check the box and move on to “step two”— it’s important to note that the first step isn’t really a step at all, but an ongoing process that needs to be continuously managed and updated.

The reason is that audience segments can quickly get outdated. And when targeting a buying group, the network effect can amplify outdated audience segments quickly. Buying groups also mean different things in different companies, and B2B marketers need to pick up on those nuances and fine tune their approach to become more effective with each target account.

The Ever-Changing Buying Group

Companies are like organisms. There’s M&A, restructuring, and shifts in strategy that can affect buying groups.  At the individual level, people change due to promotions, internal transfers, job changes, maternity leave, reorgs, layoffs, contractors etc. We see consistent and meaningful contact and role changes occurring within months, not quarters or years, in active segments. On average the data degrades 2–5% per month. It doesn’t fail all at once, rather it erodes in layers with role relevance and buying group alignment deteriorating long before emails stop working—making continuous validation and refreshing essential.

Volatility is especially prevalent in high growth industries like tech, marketing and ops. 25-40% of buying group members can change within 6 months which leads to champions disappearing, buyers getting promoted or leaving. Decision-making authority starts shifting silently, so continuing to target the former decision maker leaves a gap in the current role owner.

Job title and employer change most frequently, and those shifts cascade into persona accuracy, buying group integrity, and activation performance—making continuous validation more important than record volume. Understanding that these job-related attributes change even when a contact stays at the same company is vital. Tracking internal movement is almost more important than company changes. As responsibilities shift, buying and decision making changes.

Managing the Buying Group System

Static buying group assumptions drive waste— assuming 6–12 month stability leads to mistargeted spend, missed influencers, and stalled deals. Roles persist, but people, influence, and intent change continuously.

To succeed, buying groups should be treated as living systems. Marketers should continuously monitor data and insights to understand patterns that can ensure they are updating and improving buying groups at the proper cadence.

• Buying group volatility: How frequently does the typical buying group composition change? (e.g., every 30, 60, or 90 days)
• Role stability: How often do individual titles/roles shift within buying groups?
• Core buying group composition: What are the most common titles consistently involved? What are the surprise titles?
• Industry variations: How does buying group size differ across industries (manufacturing vs. tech vs. finance, etc.)?
• Geographic patterns: Do buying group sizes vary by country/region?

Keeping tabs on these patterns can help marketers create a plan to update and refresh buying groups often enough to get ahead of changes.

Marketers should also continuously improve the accuracy of buying groups for top target accounts, because each organization is different. At one company, the CFO’s office holds the final decisions, while at another, a separate procurement group makes the final call. Each nuance about buying groups at a specific organization makes marketing and sales more accurate and more relevant.

Supporting The Buying Group Reality

Maintaining an accurate picture of each stakeholder requires access to high quality data that’s frequently updated, and the more types of data marketers have access to, the more accurate their understanding.

Even with accurate demographic and firmographic data, there can still be change within the buying group that affects marketing as a purchase process progresses. The influence paths for the role owner can shift mid-cycle due to budget owners changing, legal/procurement getting pulled in late, security suddenly becoming a blocker, or the exec sponsor disengaging. Buying groups expand and contract as deals mature.

This is where added insights such as intent data can be valuable. Intent is dynamic, not durable, so interest spikes and shifts by persona and topic need to be continually monitored as signals from last-quarter may be unreliable.

Focusing on buying groups can deliver great results, so putting a system in place is well worth the effort. Getting to know the nuances of a target account not only produces better marketing results, it enhances the connections that the sales team can make, creating stronger ties and shorter buying cycles.

Karie Burt, Chief Data and Privacy Officer, is a seasoned expert with over 20 years in B2B growth using data-driven solutions. Specializing in global data privacy, GDPR, and digital marketing strategies, particularly in Asia, she optimizes international data procurement and compliance. Karie advises on European Data Protection laws and cultural nuances for non-U.S. markets. Recognized by GRC World as a leading Woman in Privacy (2022), she champions responsible B2B marketing while respecting privacy rights. A member of the IAPP, her leadership extends to M&A activities. Karie enjoys international travel and her two unruly rescue dogs.

The post Ready to Target Buying Groups? Take a Systematic Approach appeared first on Demand Gen Report.

And yet, in my experience, there’s still a real struggle to clearly explain what retail media actually is, how its pieces fit together, or whether investments are working. That gap between excitement and clarity is worth talking about honestly; because it’s where budgets and opportunities can quietly slip away.

I came to this category recently, stepping into my first dedicated retail media role after more than a decade in broader retail marketing technology, working with brands like Nike, CVS Health, and Best Buy through my time at companies like Lexer, Cordial, and Monetate. I arrived with a lot of curiosity and a healthy skepticism toward industry jargon, which turned out to be surprisingly useful.

A Fresh Set of Eyes on a Crowded Category

What I’ve found is that retail media is both genuinely powerful and genuinely confusing. And most of the confusion isn’t because retailers made bad choices; it’s because the space grew really fast. Most retailers are running 15 to 20 tools stitched together that were never designed to work as a whole. That’s not a failure of vision. It’s just the reality of what happens when a category scales that quickly.

At its core, retail media is advertising powered by a retailer’s first-party shopper data. That can run onsite— on the retailer’s website, app, in-store screens, or email— or offsite, using that same data to reach shoppers across the open web, social platforms, and connected TV. The common thread is the data: a retailer has customers, those customers generate behavioral signals through shopping, and brands want access to those signals to show up closer to the point of purchase. Amazon figured this out first and built a multi-billion dollar business around it. Everyone else has spent the last several years trying to catch up, which is a big part of why the landscape feels so fragmented.

The Technology Problem Nobody Talks About Clearly

The complexity that tends to trip retailers up most often isn’t strategic, it’s operational. Managing a retail media network means coordinating across ad serving, data clean rooms, measurement providers, demand-side platforms, and more. The result is a lot of manual work, inconsistent reporting, and an inability to see the full picture of what’s actually happening.

This is where some technology vendors haven’t done retailers any favors. There’s been a lot of pitch language bandied about that leads with hyperbole and analogy instead of actual use cases and proof points. ‘Our AI-powered optimization engine delivers unprecedented ROAS’ sounds impressive until you ask what the methodology is and realize there’s no clean answer.

What Good Measurement Actually Looks Like

Measurement is the other place where the conversation tends to break down, and it’s worth slowing down here because this is where many teams struggle most. Retail media measurement is genuinely fragmented; different networks use different attribution windows, methodologies, and definitions of what counts as a conversion. What gets sold as robust measurement and what actually constitutes robust measurement don’t always line up.

The right questions to ask are whether the measurement is incremental, whether it’s been independently verified, and whether it reflects the metrics your organization actually cares about.

The Questions Worth Asking Before You Scale

Truly understanding retail media requires a clear framework and the willingness to ask straightforward questions before committing budget.

What does success look like for this investment, and how will we know if we achieved it? What does our current tech stack actually enable and where are the gaps? Who owns the measurement methodology, and what are its limitations?

These aren’t complicated questions, but consistently asking them is what separates retailers making meaningful progress from those who are simply adding to the stack.

The Customer at the Center

Here’s the thing that gets lost in all the talk about data and attribution and stack consolidation: the $69 billion in projected retail media spend exists because real people are shopping. That first-party data isn’t just an asset to monetize, it’s a signal of trust. Shoppers gave you their attention, their time, and their purchase behavior because they came to you for something.

Retail media only works, sustainably and at scale, if the shopping experience gets better, not worse. The retailers I admire most in this space are the ones who hold that tension honestly. Yes, they’re building media businesses, but they’re doing it in a way that makes their customers feel seen rather than targeted. That’s not just a values argument. It’s a business one.

Retail media is going to keep growing, and the pressure on retailers to operate it well is only going to increase. The good news is that clarity doesn’t require mastering every technical nuance of a rapidly evolving landscape. It starts with asking better questions and being willing to act on the answers, even when they push back on assumptions you’ve already made.

Abby Borden is Vice President of Marketing at Vantage, a retail media orchestration platform. She brings more than a decade of experience in marketing technology and go-to-market strategy, having built and led marketing functions at high-growth companies, including Lexer and Cordial, before stepping into her first dedicated retail media role at Vantage.

The post Retail Media is a $69B Opportunity. So Why Is It Still So Hard to Get Right? appeared first on Demand Gen Report.

Now, enterprises are entering into the era of agentic AI, a transformative leap that redefines enterprise operations. Unlike traditional AI, agentic AI goes beyond passive analysis to deliver autonomous, intelligent action.

These agents are digital executive partners. Agentic AI agents plan, adapt and execute sophisticated tasks in real time, unlocking new business models and driving productivity at scale. These agents anticipate needs, adapt to a shifting environment and support leaders in making timely, informed decisions.

Shift to Agentic AI

For instance, in the insurance world, agentic AI is streamlining claims processing. In the U.S., close to 62% of an insurer’s expenses are related to claims. Insurance processors are often buried in data, trying to verify claims and work with customers. Rather than being the bottleneck, agentic AI streamlines this data review, cutting down the processing time and providing processors with the information needed and recommendations for next steps. This case is duplicated across multiple industries, from manufacturing to healthcare to transportation.

Yet, the shift to agentic AI is not a simple incremental shift. Rather, it is a fundamental change in how work gets done across the enterprise. It empowers businesses to break down complex challenges into manageable actions without requiring constant human guidance and intervention. For example, in the banking sector, where analysts once spent hours evaluating loan requests, agentic AI can quickly assess market trends and applicant financials, recommend adjustments and accelerate decision cycles. The result is streamlined operations and enhanced business agility.

Looking Back to Know How to Move Ahead

To understand how enterprises got to this stage, we need to reflect on the journey of AI in the enterprise, beginning with when it was introduced to businesses in the 1980s. There is a clear evolution. Early AI implementations were rigid, rule-based systems that were effective for narrowly defined tasks but lacked the flexibility to adapt to dynamic business needs.

As technology advanced, enterprises began leveraging AI for more sophisticated applications, including personalizing interactions and offering contextual analysis. Then the emergence of LLMs opened up new possibilities, enabling AI to interpret nuanced infrastructures and respond to diverse scenarios. While these models deliver significant value, their insights are inherently limited to the scope of the training data, underscoring the need for solutions that combine intelligence with real-time adaptability.

Agentic AI answers this need. It fuses autonomous action with deep intelligence. The hybrid approach empowers enterprises to orchestrate real-time task execution with confidence. This marks a decisive step forward in enabling connected, intelligent operations that are both scalable and sustainable. Not surprisingly, agentic AI agents are expanding across enterprises, from customer service to product development, supply chain and more. A recent survey from BCG, found that 58% of companies are using AI agents while another 35% are exploring the adoption of agentic AI.

This is not just a simple technology upgrade. It is a corporate, strategic priority, as it impacts business outcomes and competitiveness. Agentic AI empowers enterprise teams with real-time decision-making capabilities that unlock new values and innovations. As an extension of the human workforce, agentic AI unleashes greater productivity.

Clarifying AI’s Role in the Workforce

While agentic AI offers greater intelligence and proactive operations, it is important to clarify its role in the workforce. The deployment of multiple agents for specific tasks amplifies efficiency, but it does not replace the human element. Rather, agentic AI liberates employees from repetitive, time-intensive responsibilities, allowing them to focus on strategic initiatives that drive business growth, profitability and enhance customer experience.

Human oversight remains critical. Team members ensure that AI solutions are deployed responsibly, aligned with organizational policies and compliant with regulatory and ethical standards. Through active governance and real-time visibility, businesses can harness the full potential of agentic AI while safeguarding trust and accountability.

The majority of employees want to use AI in their workflows. According to a recent McKinsey report, approximately 94% of employees are familiar with AI, with close to three times more employees using AI than leaders believe. While the knowledge base is there, enterprises are challenged with how to implement agentic AI in a manner that maximizes the benefits while minimizing risks related to biased outputs and security.

A platform-based approach allows enterprises to manage and orchestrate multiple AI agents, ensuring efficient data use, control, governance and security. A scalable agentic AI platform provides a simple solution to what could have been a complex business initiative.

Ultimately, agentic AI should be embraced as a strategic partner— one that extends the reach and impact of every team member. Enterprises that adopt a platform-first, integrated approach will unlock new levels of connectivity, intelligence and value creation. The future of business is being shaped by agentic AI through empowering organizations to lead with clarity, agility and purpose. Agentic AI is a transformative force reshaping every industry.

Sateesh Seetharamiah is the CEO of Edge Platforms, EdgeVerve Systems Limited (An Infosys Company), and a board member and Whole-time Director at EdgeVerve. Sateesh is an industry veteran with three decades of rich experience in entrepreneurship, management consulting, IT leadership, and supply chain. Sateesh believes in AI and Automation’s immense potential in transforming future enterprises. With deep-rooted experience in the supply chain, he pioneered the Internet of Things (IoT) in its early days. Sateesh is one of the founding members of EdgeVerve and comes with rich experience in the product and platforms domain. Being a passionate technologist, Sateesh has been instrumental in establishing many foundational technology capabilities that drive today’s EdgeVerve strategy. In addition, he has been on the board of various start-up firms in the IoT and pervasive computing space.

The post Scaling Enterprise Intelligence with Agentic AI Platforms appeared first on Demand Gen Report.

Edelman’s 2025 Trust Barometer underscores this shift, showing that confidence in business communication remains fragile. Credibility must be earned continuously rather than assumed.

As a result, early-stage interest now forms through verifiable proof: reviews, practitioner insights, transparent product information, and third-party validation that buyers and AI systems can cross-check before ever visiting a vendor’s site.

What’s Disrupting Interest and Consideration

Buyers prefer self-serve—and avoid irrelevant outreach. Gartner’s 2025 B2B Buyer Study found that 61% of B2B buyers now prefer a rep-free experience, while 73% actively avoid suppliers who send irrelevant outreach (gartner.com).

This signals a fundamental shift: aggressive outbound tactics no longer create curiosity; they repel it. The brands winning attention are those offering self-directed, transparent, and useful discovery paths.

Reviews Shape Ahortlists (And Buyers Triangulate Sources)

BrightLocal’s 2025 Local Consumer Review Survey shows that 74% of buyers check at least two review sites before making a decision, while only 4% say they “never” read reviews. The same pattern is now visible in B2B. Peer reviews, demos, and user feedback have overtaken analyst reports as the most trusted signals. Buyers no longer rely on a single source. Instead, they cross-verify experiences before shortlisting vendors.

Google’s reversal of its third-party cookie phase-out confirms what most marketers already know: the privacy tide has turned. With more than a dozen U.S. states now enforcing data-privacy laws, organizations need to lean on consented first-party and zero-party data, with explicit value exchange, not silent tracking.

At the interest stage, that means personalization must feel helpful, not invasive. It should be contextual, opt-in, and transparent.

AI is “Always Included” in Early Research

According to G2’s 2025 Buyer Behavior Report, AI now participates in every stage of the buying journey—helping buyers identify vendors, summarize comparisons, and evaluate risks long before a sales conversation.

For brands, that means early-stage credibility depends on whether their content is machine-readable, factually sound, and structured so AI systems can find and cite it accurately.

What Interest Looks Like Now

Interest is no longer a soft “maybe.” It’s the moment buyers internalize, “This could work for people like me.”

That belief forms when three signals align early:

• Independent proof: authentic reviews, real customer quotes, usage metrics, screenshots
• Transparent guidance: visible pricing ranges, clear pros and cons, comparative tools
• Low-risk evaluation: live demos, hands-on trials, calculators or templates that show impact

When these appear early, and without a rep gate, buyers keep leaning in. Gartner highlights interactive tools and validation assets as top contributors to pipeline progress in digital-first journeys.

Case Snapshots

GitLab: Radical Transparency as a Trust Engine. GitLab’s public handbook—detailing everything from structure to strategy—has become a benchmark for operational transparency. For skeptical buyers, this visibility transforms curiosity into confidence by showing, not telling, how the company works.

G2-Verified Social Proof in B2B. Across SaaS categories, vendors increasingly embed G2 Trust Badges and verified review excerpts into their early-stage pages. These badges offer recognizable, third-party validation that reduces friction when buyers prefer to self-serve and compare before speaking to sales.

Interest-Stage Playbook: Five Moves That Win Trust

1. Build a “trust layer” on early pages

• Add verifiable social proof to category explainers and feature overviews.
• Publish pricing ranges or tier guidance. Opacity sends buyers elsewhere.
• Embed third-party badges that link directly to verified sources.

2. Enable self-serve evaluation

• Offer ungated tours, interactive demos, or sandboxes.
• Add ROI calculators and comparison tools to help buyers self-diagnose fit.
• Publish a transparent implementation roadmap (people, time, data).

3. Treat reviews as a motion, not a moment

• Encourage ongoing reviews across multiple platforms.
• Showcase recency, depth, and authenticity in customer feedback.
• Respond to reviews publicly; the response itself builds credibility.

4. Personalize without overstepping

• Shift from third-party tracking to permission-based personalization.
• Use progressive profiling to gather small insights over time.
• Always reflect back value (“Because you’re in finance ops, here’s what peers measure”).

5. Calibrate human help to preference

• Offer “expert chat on demand” instead of forced meetings.

A Quick Narrative to Bring It Together

A Director of Operations, tasked with replacing a legacy system, ignores the sales outreach in her inbox. She starts with peer reviews, cross-checks on multiple sites, and lands on a vendor’s page featuring recent customer stories, an ungated tour, and a transparent “what implementation takes” guide. She joins an optional office hour for a quick question, then starts a trial.

She wasn’t convinced—she was equipped. That’s interest in the Future Funnel: credible proof, friction-light evaluation, and respect for how B2B buyers actually decide.

The Takeaway

In the modern buying journey, interest and consideration are shaped less by persuasive messaging and more by visible evidence of credibility. Buyers, and the AI systems helping them research, look for consistent signals across reviews, customer stories, pricing guidance, and transparent documentation.

The organizations that generate sustained interest are those that design early buying experiences around verification rather than persuasion. When proof is accessible, consistent, and easy to explore independently, curiosity turns into momentum, and skeptical buyers move forward with confidence.

Erica Flynn is a Marketing Generalist for Televerde, a global revenue creation partner supporting marketing, sales, and customer success for B2B businesses around the world. A purpose-built company, Televerde believes in second-chance employment and strives to help disempowered people find their voice and reach their human potential.

The post The Demand Gen Engine: Why Buyer Interest Now Starts With Proof appeared first on Demand Gen Report.