• Canva’s acquisition of Simtheory and Ortto enhances its AI capabilities, enabling end-to-end marketing lifecycle management.
• The move positions Canva as a leader in AI-driven marketing automation, integrating design, collaboration, and customer data tools.

Canva has acquired Simtheory, an artificial intelligence (AI) workspace and collaboration platform for building custom agents, and Ortto, a customer data and marketing automation company, as part of its continued investment in powerful AI and marketing automation.

Cliff Obrecht, Co-Founder and Chief Operations Officer at Canva, said Simtheory accelerates their evolution from a design platform with AI tools, to an AI platform with design and productivity tools at its core. Ortto is seen as strengthening their ability to power the entire marketing and content lifecycle through Canva Grow, from planning and creating to publishing and optimising across every channel.

Why the Simtheory and Ortto Purchase

Both companies were founded by Chris and Mike Sharkey, previously founders of Stayz, which was acquired by Fairfax Media. They will join Canva in leadership roles across the company’s AI and marketing technology teams.

“[Simtheory and Ortto] built exceptional teams and technology, and this acquisition marks an important step toward evolving Canva from a design tool into the system where work happens end-to-end, whether it’s a quick idea or a full campaign,” said Obrecht in a statement.

AI Platform


With AI making it easier than ever to get started, generating something is only one step in a much bigger process. For most workers, bringing an idea to life is still too complex and disconnected, spread across too many tools, tabs, and workflows.

Known for its work in AI-powered collaboration and multi-model systems, Simtheory allows teams to apply the latest models across a wide range of use cases, and easily set up agentic systems that meet their needs. Since launching, it has quickly gained traction among teams exploring how AI can move beyond generation into execution.

Ortto combines a customer data platform with powerful marketing automation, enabling teams to design and orchestrate journeys across email, SMS, push notifications, in-app messaging, forms, and surveys, all within a single system. With an event-driven architecture and no-code integrations, Ortto makes it easy to connect and activate customer data in real time, helping teams deliver more personalized and effective campaigns at scale. The platform has more than 11,000 customers across 190 countries.

Canva’s Growth Plan

The acquisition strengthens Canva Grow and builds on Canva’s recent additions of MagicBrief, MangoAI, and Doohly, accelerating its ability to power the full marketing and content lifecycle, from ideation and creation through to deployment, measurement, and optimisation.

The Ortto team will continue to support and maintain the Ortto product while bringing their expertise to Canva Grow.

“We’re thrilled to be joining Canva. The opportunity to bring our technology to the quarter of a billion people using Canva every month and to help more people make the most of AI in their everyday work is incredibly exciting to us. It’s rare to find such strong alignment in mission and values,” said Mike Sharkey, Co-Founder and Chief Executive Officer of Ortto and Simtheory. “From day one, we’ve been working to make complex things simple with both Simtheory and Ortto, and we can’t wait to continue doing that on an even larger scale as part of Canva.”

The post Canva Acquisitions Expands Marketing AI Capabilities appeared first on Demand Gen Report.

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

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

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

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

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

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

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

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

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

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

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

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

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

Recommendation for Measuring ROI

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

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

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

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

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

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

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

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

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

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

What Metrics are Key to Focus on for GTM teams 

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

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

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

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

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

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

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

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

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

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

Trends to Watch for This Year and Beyond 

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

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

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

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

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

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

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

The post How to Align Sales, Marketing for Better ROI: The DGR Interview with Outcomes Rocket’s Saul Marquez appeared first on Demand Gen Report.

• 96% of B2B companies are invisible in AI-driven buyer discovery, appearing only in late-stage queries.
• AI visibility requires structured data, authority signals, and proactive management of AI crawlers and community sentiment.

2X, a subscription-based go-to-market services partner for B2B organizations,  2026 2X AI Visibility Index reveals that most companies are effectively invisible during the earliest stages of artificial intelligence (AI)-driven buyer discovery.

Currently, only 4.3% of companies maintain a healthy discovery funnel where their brands appear in early-stage buyer questions, according ot the index. The remaining 95.7% appear primarily in queries where buyers already know the company name— meaning they are largely absent from the AI-generated answers increasingly shaping vendor shortlists.

The research was conducted by the 2X AI Innovation Lab, a research and development initiative focused on operationalizing AI across go-to-market (GTM) functions. The inaugural index analyzed 70 B2B companies to understand how brands appear across generative AI environments used by buyers to research vendors and solutions.

The Necessity of AI Visibility

As generative AI becomes a primary research tool for business buyers, companies that fail to appear in AI-generated answers risk losing influence before a sales conversation ever begins, said Lisa Cole, Chief Marketing, Product & AI Officer at 2X.

“CMOs are waking up to a hard truth: you can’t manage what you don’t show up for,” said Cole in a statement. “AI is increasingly shaping perception, trust, and vendor shortlists. If your brand isn’t present in those conversations, you’re effectively invisible to a growing portion of the market.”

Most Brands Are Invisible in Discovery 

The 2X AI Visibility Index benchmarks how often B2B brands appear in generative AI responses across the buyer journey, from early discovery questions to purchase validation queries. The index measures both a company’s technical readiness for AI discovery and the authority signals that influence whether AI systems recommend a brand in response to buyer prompts.

Data from the 2X AI Visibility Index shows that most organizations now operate with what researchers describe as an “inverted discovery funnel.” Instead of appearing early when buyers are exploring solutions, most companies surface only in later-stage queries where the buyer already knows the brand or category.

This pattern suggests that many organizations are losing influence during the most important stages of the buying journey when needs are being defined, categories are being explored, and vendor shortlists are being formed.

Structural Blind Spots Undermining AI Visibility

“Artificial intelligence is fundamentally reshaping how B2B buyers discover solutions,” said Will Waugh, Executive Director of the 2X AI Innovation Lab. “General brand awareness is no longer enough. If AI systems don’t recognize your brand as an authoritative answer to buyer questions, you risk losing opportunities before sales teams even know the deal exists.”

The research identified technical and authority gaps that suppress AI visibility, including missing or incomplete structured data, blocked or unmanaged AI crawlers, weak third-party review ecosystems, limited independent citations across the open web, and unmanaged community sentiment on platforms such as Reddit.

The 2X AI Visibility Index segments companies into four stages of AI visibility maturity based on readiness, authority, and discoverability:

• Authority Leaders — companies with strong technical readiness and deep authority signals that consistently appear in AI-generated answers.
• Strong Contenders — organizations with strong visibility and readiness but less comprehensive authority signals.
• Paradoxical & Niche Players — companies with fragmented brand authority or strong visibility within narrow domains.
• Emerging & Lagging — organizations with significant gaps in both readiness and discoverability.

Study Conclusion

Together, the findings point to a new strategic reality for B2B marketing leaders: the buyer journey is increasingly shaped by AI-generated answers long before a human interaction occurs.

“AI models don’t care about org charts or market caps,” Waugh added. “They respond to clarity, consistency, and corroboration across the open web. Companies that don’t adapt their digital presence for AI risk handing narrative control to competitors and sometimes to their critics.”

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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.

• Global IT spending is projected to reach $4.96 trillion in 2026, with enterprise investments making up nearly all of it
• AI-driven advancements are fueling growth in IT hardware investments, particularly in servers, components, and data center infrastructure

As artificial intelligence (AI) reshapes enterprise priorities, global IT spending is set to reach $4.96 trillion in 2026, according to the HG Insights 2026 Global IT Spend Report. The forecast includes $4.5 trillion in enterprise investment and $460.5 billion in SMB spending, signaling broad-based acceleration in technology investment.

The expansion of global AI infrastructure is driving significant growth in IT hardware investment, according to the report. This surge in spending is primarily focused on servers, components, and data center infrastructure, reflecting the critical role of hardware in supporting AI advancements.

This year’s report incorporates buyer intelligence from TrustRadius, acquired by HG Insights in June 2025. By integrating verified reviews and first-party intent data, the report connects spending forecasts with real-world buyer behavior, offering insight not only into where organizations are investing, but how they research, evaluate, and adopt technology solutions.

‘Volatile Technology Environments’

Rohini Kasturi, CEO of HG Insights noted the report comes out as GTM leaders are operating in one of the “most volatile technology environments we’ve seen in decades.”

“AI is accelerating investment in some areas, while increasing scrutiny in others,” said Kasturi in a statement. “The organizations that win will be those that understand precisely where budgets are shifting and why. Our [report] provides that clarity.”

Spend by Region

The report found worldwide IT is projected across 16.3 million organizations on hardware, software, services, and communications. Enterprise organizations dominate this expenditure, contributing $4.5 trillion to the total. Meanwhile, SMBs are expected to invest $460.5 billion, a significant milestone as it marks the first time HG Insights has released comprehensive data on SMB spending. By region, the spending trends is projected to be:

• North, Central, and South America is projected to reach $1.98 trillion.
• Asia-Pacific is forecasted at $1.42 trillion.
• Europe, Middle East, and Africa is expected to total $1.1 trillion.

Software and IT services continue to dominate enterprise IT spending, accounting for 74% of the total. Enterprise software spending alone is projected to reach $1.39 trillion, while enterprise cloud services, the largest segment within IT services, are forecasted at $716.7 billion. Additionally, communications spending is expected to total $295.5 billion, driven primarily by mobile and fixed data services.

To access more detailed insights and analysis, including buyer behavior, real user sentiment, and customer spotlights, read the full IT Spend Report: The Ultimate B2B Market Forecast for 2026.

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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.

• 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.

The post Demandbase AI Now Available for Modern GTM Teams appeared first on Demand Gen Report.

• Tofu’s platform consolidates marketing tools for scalable, personalized campaigns across multiple channels.
• The $12M funding will expand product capabilities and integrations for GTM teams.

Tofu has completed a Series A funding round of $12 million to further their mission of building a unified artificial intelligence (AI) platform for GTM teams.

As budgets have poured into AI-driven initiatives, marketing teams found themselves with solutions that often overlapped in functionality and delivered little measurable impact on revenue.

Instead of relying on multiple disconnected marketing tools, Tofu officials said that have created a unified platform that supports a comprehensive range of GTM use cases, enabling marketing teams to build cohesive, on-brand, omni-channel campaigns at scale while personalizing assets for every customer segment.

EJ Cho on the Shift to Comprehensive Marketing Platforms

The need for tools like Tofu is growing as budgets tighten and marketing teams face greater scrutiny over software spend, said EJ Cho, Co-founder & CEO of Tofu.

“Marketing leaders are becoming more intentional about their purchases and are turning to comprehensive platforms like Tofu over point solutions to scale campaigns without the burden of managing numerous tools,” Cho wrote in a blog post announcing the fundraising round.

How New Platform Performs

Building a platform that replaces fragmented tools is complex, as marketing teams need support for a wide range of use cases, whether it’s demand generation, content marketing, lifecycle marketing, event marketing, digital marketing, or outbound SDR campaigns. Cho said his company is launching a new campaign platform that gives teams the flexibility to build custom workflows that connect different actions, making it easy to configure repeatable campaigns for every use case.

“Our approach to building a unified platform for GTM teams has resonated strongly with our customers as companies,” said Cho. “Over the past year, we’ve seen explosive growth, increasing our revenue by 12x and the amount of content generated by 36x.”

Tofu has been adopted by marketing teams of all sizes, from fast growing startups to enterprise organizations like Check Point, RingCentral and Bluecore where hundreds of marketers use Tofu every day. With this adoption, their platform has matured to support a growing set of GTM use cases.

List of Investors

Tofu personalize landing pages and emails for specific personas or industries and push these assets to your CMS and marketing automation tools. It can repurpose webinars and whitepapers into derivative assets such as social posts, blog articles, and emails while maintaining brand guidelines and templates.

For 1:1 ABM campaigns, the company can ingest private data from CRM and combine it with real-time public data from the web to generate hyper-personalized ads and emails.

The funding round was led by SignalFire with participation from HubSpot Ventures, Tau Ventures, Correlation Ventures, Alumni Ventures, SH Fund, Riverside Ventures, Eudemian Ventures, and Han Bridge Capital. Previous existing investors included Index Ventures, Stage 2 Capital and Liquid 2 Ventures.

‍” We’re continually expanding our integrations and content types to support even more marketing use cases,” said Cho.“Our fresh round of capital allows us to accelerate our mission by scaling our team, expanding our product capabilities, and increasing value to our customers.”

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• Demand Gen Report’s 2026 Campaign Optimization Series focused on marketing strategy that build trust across external channels, as B2B buyers now conduct extensive independent research well before contacting vendors.
• Speakers discussed getting on buyers shortlists, why company websites are often not the first stop in the buying funnel and what an Agent Qualified Lead is

B2B marketing professionals continuously seek proven strategies to drive revenue generation and optimize their campaign life cycles.

During Demand Gen Report’s 2026 Campaign Optimization Series (#COSeries), industry leaders from Demandbase, NetLine, and Docket shared their expertise on navigating shifting buyer behaviors. Across three comprehensive webinars, these experts detailed how marketers can build dynamic campaigns, capture true intent, and leverage artificial intelligence to foster meaningful connections.

Here are five essential lessons learned from the series.

Influence Starts Long Before the Shortlist

B2B buyers now conduct extensive independent research well before they ever contact a vendor. They review peer insights, explore trusted third-party content, and define their exact requirements in private channels. This independent approach means marketing teams can no longer wait for buyers to raise their hands.

Instead, marketing professionals must focus heavily on shaping demand during these early research phases. Building influence requires a consistent presence across the broader digital landscape, delivering credible perspectives that help buyers validate their thinking. By the time a buyer forms a shortlist, their core decisions are largely finalized.

Josh Baez from NetLine highlighted this massive shift in buying behavior and the urgent need for marketers to adapt.

“The most important buying decisions are happening well before vendors even know that a deal exists,” said Baez. “Buyers are doing more research on their own. They’re involving more stakeholders, and they are delaying vendor interaction until much later in the process. In fact, 83% of buyers define their requirements before ever speaking to sales.”

To succeed, marketing content must do more than answer basic questions. Content must actively shape the questions buyers ask themselves, positioning your brand as a trusted authority. This proactive influence secures your place on the shortlist before the buyer even realizes they are ready to purchase.

Aligning Campaign Spend with Sales Priorities

Resource allocation remains a critical challenge for marketing teams striving to maximize their return on investment. Treating all target accounts equally drains budgets and dilutes the impact of personalized messaging. Marketers need a structured methodology to determine which prospects deserve the highest level of investment.

Hannah Jordan from Demandbase stressed that implementing a rigorous account scoring model provides the necessary framework to categorize accounts into specific tiers. This tiering system allows marketing operations to dictate exactly how much budget and effort goes toward engaging each prospect. High-value accounts receive premium, highly personalized outreach, while lower-tier accounts receive more automated, scalable communications.

Jordan emphasized the importance of using these tiers to synchronize marketing and sales efforts, stating “the tier is really going to determine the spend per account…we want to align our spend and our resources and make sure that we’re prioritizing the accounts that sales is also prioritizing.”

This deliberate alignment prevents wasted spend and ensures both departments work toward shared revenue goals. When marketing invests heavily in the exact accounts that sales wants to close, the entire campaign life cycle accelerates, resulting in stronger pipeline quality and demonstrable revenue growth.

Shifting to a Coverage-Centric Strategy

Relying solely on driving inbound traffic to a corporate website is an outdated approach to demand generation. Buyers spend the majority of their time consuming information on external platforms, industry publications, and peer networks. Marketers must expand their focus beyond their own digital properties to capture attention effectively.

A coverage-centric strategy solves this problem by ensuring your brand maintains a consistent presence wherever your buyers naturally gather. This means showing up across various channels, environments, and buying groups to build familiarity and trust over time. Coverage focuses on visibility in the places where actual buying decisions form.

Baez explained the fundamental difference between these two marketing philosophies during his session. “We move from a website centric model to a coverage centric instead of asking, how do we get buyers to come to us, we ask, how do we show up where buyers already are?” Baez said.

By prioritizing market coverage, brands integrate themselves into the buyer’s daily research habits. This consistent, multi-channel exposure ensures that when buyers finally decide to visit your website, they already view your company as a credible and familiar solution provider.

The Demise of the Traditional MQL

For over a decade, the Marketing Qualified Lead served as the ultimate metric for measuring campaign success and pipeline health. However, static forms and basic website click data fail to capture true buyer intent accurately. A massive gap now exists between raw website traffic and genuinely qualified sales pipeline.

Modern buyers utilize sophisticated tools, including large language models, to gather information before they ever navigate to a vendor’s site. Because they gather basic facts elsewhere, their visits to your website serve a much different purpose than they did years ago. Traditional lead scoring models simply cannot interpret this new pattern of behavior.

Lauren McHugh from Docket pointed out that marketing teams must abandon outdated measurement frameworks. McHugh explained, “The erosion of our traditional conversion signals like the MQL that metric has really been defined for B2B marketing over the past decade, and it really is no longer built for how buyers are behaving today.”

Marketers must rethink how they measure website conversion altogether. Instead of tracking passive clicks or document downloads, the focus must shift to capturing substantive engagement and measuring the depth of a prospect’s actual questions.

The Rise of Agent Qualified Leads

Because buyers do their preliminary research elsewhere, the corporate website now functions as the endpoint of the buyer journey rather than the starting line. When buyers finally arrive at your site, they want immediate, specific answers to complex questions. Forcing these highly educated buyers to fill out static forms creates unnecessary friction.

Artificial intelligence provides a powerful solution through conversational engagement. AI-powered agents can conduct real-time discovery, answer sensitive questions, and provide personalized support without requiring human intervention. This interactive approach captures a much deeper level of intent than any traditional web form.

McHugh highlighted how this technological shift fundamentally changes the quality of leads passed to sales teams. Agent Qualified Leads (AQL) supply sales representatives with rich, contextual information about the buyer’s specific challenges and requirements. This intelligence allows sales teams to bypass generic introductory calls, moving directly into meaningful, solution-oriented discussions that drive faster revenue growth.

“AQL or an agent qualified lead really is substantive. In the context behind that conversation, the AQLs are moving through the pipeline faster and actually converting at higher rates because they’re arriving to those sales people,” McHugh said.

The post 5 Key Insights from the 2026 Campaign Optimization Series (#COSeries) appeared first on Demand Gen Report.

Alongside the RGI Platform, HG Insights is introducing RGI Agent Builder, its agentic infrastructure for enterprise GTM teams, now available in early preview. HG’s agentic infrastructure turns fragmented GTM data and signals into an integrated system that feeds every copilot and agent with shared context, powers agentic workflows across the stack, and gives GTM leaders reliability and control over how AI helps drive pipeline and revenue outcomes.

Built on HG Insights’ RGI Fabric, the RGI Platform delivers advanced copilots and agentic workflows to optimize GTM execution and efficiency.

Focused on GTM Use

Rohini Kasturi, CEO of HG Insights, stated GTM teams are being tasked to grow revenue with fewer resources than ever before, but the answer can’t be just spending on more tools, data, and dashboards.

“AI in GTM simply amplifies whatever data you feed it. If it is shallow or fragmented, you just scale noise and mistakes,” said Kasturi in statement. “The RGI Platform, Fabric, and our agentic infrastructure were built to flip that script. For the first time, GTM teams have a unified Revenue Growth Intelligence Platform where deep, connected intelligence doesn’t just inform decisions, it drives them into precise, scalable execution.”

Three AI Copilots. Expansive GTM Application.

GTM is undergoing a structural shift: from insights to execution, from static dashboards to guided action, and from manual workflows to agentic ecosystems. Most teams still operate on fragmented data sets and workflows that are error-prone and difficult to scale. Despite mounting pressure to do more with less budget and resources, prioritize smarter, and prove ROI, many organizations still lack the intelligence and use-case coverage their GTM teams need to execute consistently.

HG Insights’ RGI Platform is built for this shift, providing cohesive GTM intelligence, AI copilots, and composable workflows in a single system to turn insights into scalable action. The RGI Platform’s three AI Copilots deliver advanced functions across a broad range of GTM use cases, consistently turning the right insight into action.

• The Market Analyzer helps organizations identify and size their top market opportunities, giving CMOs, RevOps, and strategy leaders actionable insight into which segments and accounts to prioritize, where competitors are gaining or losing ground, and where to invest. From TAM/SAM/SOM modeling and ICP analysis to territory optimization and whitespace analysis, Market Analyzer brings clarity to market complexity.
• The Data Studio lets marketing and RevOps teams score and prioritize leads and accounts by combining internal data with HG Insights’ Fabric data, and then applying fully explainable predictive models. The result is coordinated GTM execution, higher-propensity targeting, sharper ABM performance, and more effective engagement and conversion.
• The Sales Copilot puts the right intelligence in front of sellers at the right moment. By connecting and contextualizing disparate signals with HG Insights Fabric data to automate account research, trigger sales plays, and shorten sales cycles, Sales Copilot ensures sellers spend more time building and closing pipeline.

HG Insights Platform and Fabric lets customers integrate their own first- and third-party data with HG Insights intelligence, enabling copilots to analyze a richer, combined dataset to generate even more precise insights and actions.

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