The pipeline construct in B2B marketing has a dual nature. On the one hand, it is a true reflection of how buying groups move through purchasing hardware, services, and software. Concretely, it is true that companies, departments inside companies, and individual decisionmakers must be first made aware of a problem; then understand that a category of solutions to that problem exist; become aware of the vendors offering these solutions; at some point engage with the marketing and sales resources of one or more of those companies; and finally transact. Once they are a customer, they continue to update their experience of the company, perhaps adding services based on other perceived needs.

However, in most cases, the buyers and influencers who make up the customer buying group are indifferent to or unaware of whether they are interacting with a vendor’s “marketing,” “sales,” or “customer success” teams. To them, there is a brand, and that brand either meets or exceeds expectations, or does not. They simply want the best product and service at the best price, with the lowest risk (no one got fired for buying Company A) and do not want to jump through hoops to do so.

The reflection of this customer-centric pipeline inside the typical vendor is distorted but still relevant. For a typical B2B vendor—think Microsoft, Caterpillar, Oracle, Goldman Sachs, GE, etc.—the pipeline is divided into discrete stages, each made of either leads or opportunities, with different values and forecasted close dates. Typically, a “lead” is marketing’s responsibility, and an “opportunity” is owned by sales—but it’s critical to note that to a customer, these categories are irrelevant. This “lead / opportunity” split is a legacy of how B2B marketing and sales has typically functioned: Marketing “generates demand” and sales “closes deals.” The best way to think about “generated demand” in a software system is as a “hand raiser”—someone who has poked their head above water and can now be pursued. That hand raiser “becomes” an opportunity when they have been nurtured and developed, and at that point, the opportunity will gain momentum and hopefully turn into real revenue. Of course, leads and opportunities are both abstractions and simplifications of what is really going on.

We all want to measure marketing ROI

ROI (return on investment) continues to be a hot topic for B2B marketing and sellers, for obvious reasons. An accurate ROI (one that is non-duplicative, counterfactual, and based on a financial outcome) is extremely useful, because it allows all investments to be traded off against one another, particularly at the marginal or “last dollar” basis. If my marginal ROI for paid social is 1.1, and my marginal ROI for events is 0.9, then I should increase my paid social budget and decrease my events budget. Critically, ROI as an outcome metric allows marketing to be traded off against any other investment—at least in theory.

B2C companies are arguably closer to an ROI view of the marketing world. For large consumer brands like Coke, marketing mix models (MMMs) are constantly updated to provide ROAS (return on advertising spend) for various channels. The curves these models output are then used to remix dollars up, down, and across the funnel to maximize some objective—usually total revenue. However, MMMs are slow and prone to omitted variable bias—meaning that lurking, unknown variables, if left out of the model, can drive unrealistically rosy assessments of marketing’s performance.

B2B companies can’t generally use MMMs to measure marketing’s effectiveness (some try, and they “sort of” work, but that’s a topic for another day.) The same structural dynamics that lead to a pipeline view of the world make MMMs—which depend on large volumes of frequent time series data, including daily sales and marketing spend by region—ill-suited for B2B, namely:

• Long sales cycles (months to years, typically)
• Large transaction sizes, few transactions (chunkiness)
• Complex buyer groups
• Poor data quality when humans are involved (events, field sales, partner channels)

The pipeline, rooted in a database view of the world, is both a cause of and a solution to measuring ROI for B2B firms. It is the cause of the problem when it is taken too literally—that is, that the “lead” is a real thing that someone “generated.”

At some point in the foggy history of corporate marketing, “marketing attributed sales” became a commonly used term. This probably happened when someone in sales asked someone in marketing what value they were providing, which, by corollary, meant how many leads were being handed off.

Now, we commonly speak of “marketing attributed sales” as those opportunities that started with a marketing-generated lead. This means, concretely, that some individual at a buying group filled out a form, and was then “nurtured” until ready for handoff to sales as a “sales qualified lead.” In some cases, sales has to accept the lead for it to “count”—as a “sales accepted lead.”

There are three problems with this way of looking at marketing value. First, it assumes that marketing generated all of the “value” of the lead that it generated. This overstates marketing’s impact. However, this isn’t the biggest problem: All of the other value that marketing creates “under the water” is missed, because it’s not a part of the marketing software / CRM software that has largely come to define the B2B marketing organization. Finally, once a lead is “handed off,” marketing’s role is cut off, leading to both double-counting (marketing and sales both want credit for the deal), and a somewhat toxic “what have you done for me lately” adversarial stance between marketing and sales.

These dysfunctions have real negative impacts. Marketing’s insistence on taking full credit for leads—understandable given its typical fight to show value—drives a bias towards lower funnel behaviors that might not optimize long-run growth. The inability or unwillingness to understand how marketing drives value for all opportunities—known or unknown—makes assessing true ROI impossible. Finally, the “hand-off” concept itself creates an us-them duality that is nonsensical to a customer, and, once again, does not accurately capture marketing’s role in driving value.

Conclusion

Marketing and sales have a common goal: to drive revenue. Yet the most common marketing, sales and CRM tools today pit marketing and sales against one another to claim holistic credit for each sale. True B2B marketing ROI is achievable with the right measurement approach.

Stay tuned for Part 2: How to measure B2B marketing ROI and subscribe to our monthly Consulting newsletter so you don’t miss an insight.

The post The problem with B2B measurement appeared first on Marketbridge.

Economic predictions for a mixed year of nominal growth largely held true, supported by injections of tariff uncertainty and massive AI investments, leading to an unbalanced market that’s basically the magnificent seven vs. the rest of the economy, 2025 was a wild ride. Overall, AI dominated the GTM landscape, broadening the market understanding beyond generative AI to agentic integration across the GTM stack.

What we failed to anticipate was the stark, almost painful, misalignment between the pace of tech innovation and the inertia of the enterprise. Providers delivered tools at lightning speed, but customers couldn’t keep up. Much like the economy in general, AI predictions at least fared okay, and in most cases, pretty good!

Making the grade

To assess the veracity and reality of these shifts, I looked at consensus predictions against in-market performance and commentary from 2025, assigning grade based on proximity to how close predicted outcomes came to reality. To simplify, I bucketed the analysis on three dimensions that determined revenue success:

• GTM model alignment: Traditional GTM models will be replaced by blended Hybrid GTM approaches
• Data-driven profitability: GTM must transition to data-driven, Intelligent Pricing strategies
• AI workflow challenge: AI will successfully automate seller administrative tasks at scale

GTM model alignment

Blended GTM models were not fully adopted, but the economic climate necessitated the collapse of GTM silos and the adoption of more agile growth models, driven by predictions focused on unity and efficiency (Forrester, McKinsey). The core prediction here, the transition to Hybrid GTM Models, was a strategic success of the year, while implementation struggled.

Blending GTM models earned an A, signifying market adoption, but the underlying goal of Organizational Alignment (under the RevOps umbrella) fell short with a C+.

GTM successfully moved toward a hybrid operating model but underestimated the difficulty of achieving true organizational unity and structural alignment required to execute it efficiently.

Data-driven profitability

Maximizing margin and improving sales economics were paramount, requiring innovative intelligence-based GTM levers (Bain & Company). The single greatest failure of 2025: the inability to capitalize on advanced profit levers due to data deficiencies.

The most ambitious prediction, Intelligent and Dynamic Pricing, fell short with a C grade, directly contrasting the success of the foundational prediction: Data and RevOps as the Foundation, which earned an A+.

The ambitious revenue-driving predictions were entirely contingent on the fundamental work of RevOps and data quality, reinforcing that basic technical integrity is the prerequisite for innovation. While the assumptions are correct and the direction clear, much like organizational adoption, Data has a long way to go to achieve its profitability promises.

AI workflow challenge

The most compelling prediction for 2025 was the transformative impact of AI (Deloitte, Gartner). The critical question was whether organizations could translate this promise into measurable, scaled success. The results here were split: AI Co-Pilots earned a resounding A, while the mandate to Scale AI Across the Enterprise lagged with a C.

2025 proved AI’s effectiveness as an augmentation tool (Sales Support), but it revealed significant bottlenecks in process management and change adoption necessary for enterprise-wide transformation, especially as it comes up against entrenched teams, processes and vendors.

Final evaluation

A clear narrative coming at the end of 2025 is that while investors and technology providers move forward with AI-abandon, and talks of a bubble have dissipated, GTM leaders are taking more cautious approaches and investing strategically. The year demonstrated that while AI and market shifts are accelerating, successful transformation is ultimately limited by an organization’s willingness to address difficult, systemic, and people-centric challenges (Scale, Pricing, CX).

GTM organizations are entering 2026 leaner and smarter, having successfully prioritized operational efficiency and technology consolidation. However, the clarity gained from 2025 confirmed that the biggest blockers aren’t technological advancements—they are systemic and people-centric.

The success for your 2026 growth roadmap hinges on closing the adoption gap, turning C grades into A grades. This means tackling the fundamental human challenges. As you navigate this next phase of GTM transformation, we’d love to connect to help bridge the gap between technology potential and revenue reality.

The post Report card: Grading 2025 GTM predictions appeared first on Marketbridge.

The way people discover information has forever changed.

Large Language Models like ChatGPT, Gemini, DeepSeek and Claude have gone from what was initially considered a novelty experience to a core part of the internet. According to a new study by Similarweb, Generative AI (GenAI) systems have progressed beyond just influencing how users start their journeys; they are now a core destination.

As we wrap up 2025, we’re seeing that it’s no longer a niche group of users leveraging AI; it’s a substantial share of netizens.

_{Image source: Similarweb}

AI adoption is increasing at a significant pace

So, how much has it grown? The 2025 Generative AI Landscape study shows growth across major engagement channels. Some key highlights include:

• A 76% increase in monthly visits to GenAI platforms year over year
• A 319% increase in LLM app downloads across the category
• Older audiences (45+) are the biggest drivers of this growth, growing 14% when other age groups have remained stable

_{Image source: Similarweb}

Another key insight: Traffic going to LLMs is rivaling social media numbers, with ChatGPT becoming the fifth most popular destination on the internet in the United States.

_{Image source: Similarweb}

B2B marketers see the benefits of AI

And those adoption numbers reinforce the need for brands to ensure they are represented in Gen AI.

Norwest recently partnered with Marketbridge to conduct their 3rd annual 2025 B2B Sales & Marketing Benchmark Report. Findings confirmed that marketers investing in AI Optimization say it is having a huge influence on performance. When we asked which AI-enabled use case had the most impact on their efforts, AI Search Optimization ranked second, with content and copy generation topping the list.

_{Image source: Norwest}

And this makes sense, you go to where your audience is.

The emerging risks

While the benefits are promising, there are also practical considerations to consider:

• Uncertain ROI: AI referral traffic is growing, but performance varies. Not every content type benefits equally.
• Operational overhead: Creating structured, machine-friendly content requires development time, quality assurance and continued monitoring.
• Crawl volatility: AI tools are aggressive crawlers. This can increase server load and create unpredictable logs if not monitored.
• Evolving standards: The AI ecosystem is still shifting. What works today may need adjustment within months.

These risks do not outweigh the opportunity, but they should be factored into infrastructure planning.

What does this mean for B2B brands and their content?

For B2B organizations looking to maintain visibility in this evolving landscape, understanding current LLM performance provides the best foundation for strategic action. When trying to improve performance in an LLM environment, auditing how and where your content currently appears in AI-generated responses gives brands actionable insights. Here are some key considerations:

For content owners

GenAI’s ability to discover new content hasn’t grown as quickly as its adoption. In fact, when LLMs search for sources, they use relatively simple technology and can miss significant parts of a brand’s message.

AI platforms prefer content that is well-structured and backed by clean code, so elements like schema markup, semantic HTML, and consistent authorship information matter when trying to gain GenAI visibility.

For social media

LLMs often have preferred social media sources, so you may see an over-index on Reddit, YouTube or LinkedIn when analyzing their citations. If your brand maintains a social presence, leverage all available optimization options on each respective platform to improve visibility, including strategic use of hashtags, descriptive titles and detailed descriptions.

The more structured and contextually rich your social content, the more likely it is to be surfaced by GenAI.

For PR

Authoritative spaces like news sites and well-known publications are often included as referenced sources for many LLMs, though the weight can vary by industry and platform. Having content published about your brand in these prioritized sources not only creates credible touchpoints for users but can also influence LLM responses.

Strategic media placement is now serving a dual purpose: reaching human audiences and training AI systems on your brand narrative.

Taking action

GenAI has become a structural part of how users access information. Organizations that take steps now to make themselves more AI-friendly will be better positioned as buyers continue to adopt usage.

While the exact impact is still evolving, the opportunity is significant and justifies investment. Brands that treat AI optimization as a strategic priority will maintain visibility in a landscape where Search and Social are no longer the only gateways to information.

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Under Shomaker’s leadership, Marketbridge strengthened its market position by acquiring industry-leading capabilities, unifying multiple companies under a single operating model, and establishing a modernized Marketbridge brand built for enterprise growth leaders. The company now enters its next phase, expanding its role as a new type of go-to-market operating partner, one that integrates strategy, insight, data, orchestration, and activation, with emerging AI-enabled intelligence layers.

“Marketbridge is now firmly established as a global growth consultancy and marketing services firm,” said Tony Brindisi, Board Member, co-founder, and Managing Partner at RTC Partners. “With Bob’s leadership and the addition of key talent, we will accelerate our next chapter and deliver truly differentiated, outcome-focused growth solutions for enterprise clients worldwide.”

Ray, a veteran agency CEO, previously led Merkle B2B within the Dentsu Group, helping build one of the most recognized digital and data-driven growth agencies in the world. He has held leadership roles across global marketing firms, serving clients including Cisco, Oracle, AWS, and GE.

“Brand and growth leaders today don’t need another agency, consultancy, or tech vendor,” said Ray. “They need one partner who unifies strategy, insight, data, activation, and media, who designs, operates, and continuously optimizes the end-to-end go-to-market engine. That’s the category Marketbridge is building.”

With nearly 400 employees across North America, London, and remote hubs worldwide, Marketbridge continues to invest in AI-powered decision intelligence, connected analytics, and integrated activation capabilities that remove fragmentation between strategy and execution, offering a strategic alternative to traditional holding-company or consultancy models, and a platform for measurable, end-to-end growth.

About Marketbridge
Marketbridge is the Go-to-Market® growth firm. Part strategic consultancy, part scaled marketing services platform, Marketbridge helps enterprise organizations design, operationalize and activate their go-to-market models—powered by data, analytics, decisioning layers and measurable activation. Marketbridge partners with over 200 clients, including AWS, AMD, Elevance Health, ServiceNow and Intel. In the 2025 B2B Agencies Benchmarking Report, Marketbridge ranked #1 in both Brand and Demand Agency categories, and #2 overall fastest-growing U.S. B2B agency.

The post Marketbridge appoints Bob Ray as CEO to lead expansion as a unified go-to-market operating partner for enterprise organizations appeared first on Marketbridge.

Quality can differentiate brands, drive loyalty and increase revenue. We’ve been talking about this for a while on LinkedIn, on our blog, and at events.

Last week we co-hosted with the Association of National Advertisers a one-day conference that gathered marketing leaders and practitioners to discuss why quality matters and how to move toward quality marketing and analytics. Below are 5 key takeaways for marketing leaders to evaluate and discuss internally.

Build audiences offline to better control who you’re targeting

“One of our biggest dangers is thinking about people as datasets,” said Chief Analytics Officer Andy Hasselwander during his quality marketing analytics session, but multiple sessions discussed why thinking about your target audience as big numbers (and not individuals) is a problem.

Multiple sources say 252,000 websites are created daily, and the number of viewable impressions and IP addresses vastly outnumber human beings on earth. According to Truth{set}, any two given data providers agree on what IP address matches a postal address at most only 14% of the time. Privacy is not an excuse for bad data, but marketers are getting duped thinking they’re targeting one person but reaching another. Marketing needs to start policing itself on quality—potentially by bringing the identity spine into the open rather than relying on blackbox, outsourced providers.

If you want better results, leverage data and PII to build offline audiences and “stay in the PII as long as possible,” according to Mark Pilipczuk from The Industrial Arts. Segment your ICP within your own PII data, and leverage a vendor’s database to build lookalike models for your target audience, hashed audience is uploaded to the publisher or ad platform (audiences drops due to match rates), and then the files are delivered with partner cookies.

Image credit: Mark Pilipczuk / The Industrial Arts LLC, © 2025 — used with permission.

This offline, more targeted audience almost certainly will outperform the third-party interest and intent categories available in DSPs and ad platforms for customer lifetime value (CLV) and return on ad spend (ROAS).

One lever advertisers can pull to improve impression quality is to ask their DSPs and ad platforms to provide data on refresh rates (higher is worse), sites with multiple advertisers in the same consumer view, and sites with high ad-to-content ratios.

Marketing and analytics should embrace uncertainty

Understanding what we know and what’s still uncertain supports good decision making. Yet many in both marketing and analytics are hesitant to state when we don’t have a definitive answer.

In statistics and modeling, error bars show the variability of data. When MMM reports out a cost per acquisition (CPA) or ROAS, typically only the mean or median value is reported. But that’s where you get into trouble.

In the image below, the estimate for TikTok’s CPA is €41. If Instagram Reels’ estimate CPA is €60, the marketing team may decide to shift budget to TikTok. But then in the next readout (and with more data), TikTok’s CPA is €80. Now marketing’s mad and doesn’t trust the MMM. But in reality, TikTok’s CPA is still within the confidence interval—error bars would’ve helped marketing make a more informed decision.

Analytics teams must help educate their stakeholders about error bars and confidence intervals so the organization can make better decisions.

Delivering ROAS or CPA without error bars doesn’t breed confidence, it breeds distrust.

Cultivate curiosity and come with a solution mindset

Humans are wired to collect data, but creating knowledge, driving insight and providing wisdom don’t happen automatically. So what makes a great analyst? Curiosity, clarity and capability are the core skills of a great analyst, according to Sravanthi Konduri from Navy Federal Credit Union. Often we equate degrees with skill, but building experience and knowledge is needed to drive insight (and eventually wisdom).

Cultivating curiosity within the organization is another matter. Organizations with a growth mindset support an analytical environment and aren’t scared of data and learning. Analytics teams can fail because of analysis paralysis and wanting to have the perfect answer, rather than collaborating with internal stakeholders.

A solution mindset is key for marketing analytics teams to partner internally and offer alternative approaches and solutions. Analysts should think like the GM of a business unit—understand the problems, where the question fits in and who would care, and delivering an answer in the context of why it matters.

How to combat too high and too low ad frequency

Often marketers worry about capping frequency for individual views to prevent waste. This has long been an issue, especially if you’re running a campaign across multiple channels and platforms. David Riva from The Trade Desk pushes for unified frequency control—rather than capping each individual placement, DSPs should support capping frequency across channels.

Another area of waste, according to Ray Van Iterson from the United States Postal Service, is the large group of people who see 1, 2 or 3 fewer impressions than needed to achieve the goal.

And the key question is: do you even know who those people are? Can you identify and target them differently or with additional inventory?

Understanding consumer journeys is essential and MTA isn’t dead

The announcement of cookie deprecation was overblown and yet many organizations stopped trying to understand individual consumer journeys. But reporting focused on campaigns, channels or business units is inherently biased.

Marketers should know what combinations of channels and which sequences lead to the best outcomes. This can be done for known, trackable touches as well as likely touches using probabilistic mapping. Adding up the small probability of seeing an ad on a given day in a specific DMA across an entire campaign can give a better picture of how channels work together.

That individual journey data also can identify if someone is less engaged than we expect. Marketers can then deploy a higher impact channel to achieve the goal.

Understanding the consumer journey at the most granular level of data possible arms marketers with an understanding of the impact of particular platforms or partners within a channel. Identifying the high engagement or high attention platforms and partners can provide optimization opportunities and drive better outcomes.

Want more insights?

If focusing on quality in marketing and analytics feels like a challenge, let’s talk. We’d love to hear about the roadblocks, share best practices and brainstorm solutions.

Complete the form below and we’ll connect to schedule time.

The post 5 key takeaways on quality in go-to-market appeared first on Marketbridge.

“Influ2 is so unique—it lets Marketing meet Sales where they need to be, with the right message, to the right buyer, at the right time.”

—Amy Grucela, SVP, Demand Strategy

Smaller teams. Tighter budgets. Higher stakes. Today’s GTM leaders are being asked to do more with less—and Marketbridge proves you don’t have to choose between precision and scale—or between data-driven results and meaningful human connection.

With Influ2, we built a smarter contact-level advertising framework—one that delivers real buyer signals, relevant ad journeys, and meaningful marketing moments that actually move pipeline for our clients.

The Challenge

We work across industries, verticals, and complex buying groups—but the challenge is always the same: smarter targeting in a world full of noise. Our goal is to help clients connect with the right people, show real impact, and make Sales’ job easier.

Traditional ABM tools were falling short. We needed something more precise to:

• Target specific stakeholders across client accounts
• Deliver contact-level engagement signals that sellers could act on
• Launch campaigns built to engage complex buying groups
• Seamlessly integrate with each client’s existing tech stack

“We needed to solve one of our clients’ biggest challenges—how to surgically target the right people,” says Amy Grucela, SVP, Demand Strategy at Marketbridge. “With Influ2, we know exactly who we’re reaching and who’s engaging.”

The Solution

A scalable framework that flexes and scales, we used Influ2 to build a repeatable, multi-layered approach to precision targeting—one that enabled us to meet each client’s needs without sacrificing efficiency or control.

Step 1: Strategy on Autopilot—(Almost)

With Influ2, we transform strategy into action—from persona mapping to real-time sales signals—enabling us to move fast without losing focus.

With Influ2 we can:

• Map buying committees and intent signals
• Sync dynamic contact lists into Influ2
• Launch persona and stage-based ad journeys
• Send real-time engagement data to Sales

Step 2: Content that Actually Feels Relevant

Forget the one-size-fits-none approach. We use Influ2 to deliver content that feels relevant—tailored to the buyer’s role, their stage in the journey, and how they engage over time.

We follow a clear structure to meet each persona with the right message at the right moment:

• Awareness: Problem-led messaging to drive familiarity
• Consideration: Role-specific product and solution content
• Decision: ROI-focused assets, case studies, and customer proof

“We design campaigns around specific personas and adapt the ad journey based on how each person engages over time,” says Bailey Creeden, Director, Media at Marketbridge. “It’s just like a nurture stream—but with ads.”

Step 3: Real-Time Sales Signals

When a CMO clicks three times in a week, Sales isn’t left guessing—they’re already reaching out. They get the full story in Salesforce, Slack, or HubSpot, so sellers always know who is active, what they clicked, and how to follow up.

“Sales gets notified instantly and knows exactly who to reach out to, what resonated, and how to steer the conversation,” says Maggie Forbush, Senior Specialist, Media at Marketbridge. “No other platform has given us that level of clarity.”

Step 4: Insights That Sharpen Your Strategy

With every campaign we run, Influ2 provides clear insights that clients actually want (yes, really). With the Influ2 Dashboard, we can analyze performance by account, campaign, and creative—and turn those insights into immediate strategy upgrades.

“The Influ2 dashboard is incredibly intuitive,” says Maggie Forbush, Senior Specialist, Media at Marketbridge. “It gives us clear contact, account, and campaign data our clients actually want—making it easy to show what’s working and where to go next.”

Key Takeaways

Today’s GTM teams don’t have time (or budget) to play the “maybe” game. You need to know who to talk to, when they’re ready, and what actually matters.

We cracked the code—turning strategy into action and giving Marketing and Sales the green light to move faster, work smarter, and stay perfectly in sync.

Here’s how we do it:

• Start with strategy → Define your ICP and target buyers
• Contact-level ad targeting → Reach real people, not just accounts
• Lead with relevance → Match content to persona and stage
• Enable Sales in real time → Deliver signals they can act on instantly
• Optimize continuously → Use insights to scale what works

“Influ2 acts as the bridge between our campaigns and our clients’ revenue teams,” says Amy Grucela, SVP, Demand Strategy at Marketbridge. “It connects marketing efforts directly to what Sales needs to move deals forward.”

The Results

For one standout client campaign, we set out to prove that contact-level advertising could do more than just reach the right people—it could drive real momentum through the funnel.

With Influ2, we achieved powerful results:

• 100% of late-stage deals had at least one engaged contact
• 41% faster deal velocity when contacts engaged with Influ2
• 6x more accounts entered active pipeline
• 19 engaged contacts per deal on average
• 85% more accounts moved into active engagement
• 10% of all target accounts converted to pipeline

For us, this campaign validated our approach: when you know exactly who you’re reaching and how they’re engaging, Sales can follow up with confidence and timing that actually drives results.

See Influ2’s full Marketbridge campaign spotlight here

“With Influ2, we’re not just running ads—we’re tracking impact and connecting the dots from click to close,” says Amy Grucela, SVP, Demand Strategy at Marketbridge. “It’s a complete view of how marketing drives revenue.”

The Conclusion

We elevated our go-to-market strategy with Influ2, replacing generic ABM tactics with a contact-level framework that delivered precision targeting, real-time buyer signals, and relevant ad journeys. By bridging the gap between marketing and sales, we scaled campaign impact without sacrificing personalization—leading to 6x more accounts in pipeline, a 41% increase in deal velocity, and a 94% boost in site engagement. With Influ2 powering our ad strategy, we turned insight into action and made contact-level advertising a growth engine for our clients.

The post Scaling contact-level ads with Influ2—Without losing the human touch appeared first on Marketbridge.

At the center of the issue is a recognition that, in theory, AI has potential to reshape every aspect of the go-to-market (GTM) organization ― from prospecting and pipeline management to customer support and pricing. In practice, however, leaders confront pressing realities: budgets for technology and transformation are finite, and teams already face limits on how much change they can absorb.

Instead of spreading resources thinly across the latest AI trends, success demands a disciplined focus on the highest impact opportunities, and constant attention to downstream organizational implications to turn AI investments into measurable results.

From possibility to impact: The critical importance of focus

For sales leaders, translating AI’s wide-ranging potential into practical tangible outcomes starts with identifying the right problem to solve. The key is not in asking, “where could we apply AI?” but rather, “where should we apply it first?”

The answer lies in framing AI opportunities through a clear set of guiding questions that connect business priorities, process pain points, and organizational readiness such as:

1. What are the strategic GTM priorities over the next year (new logo, cross-sell, churn reduction, etc.)?
2. Where in the sales process do reps or managers lose the most time?
3. Which parts of our sales model create the most drag on performance today?
4. Where would enhanced insight or foresight most change seller and buyer behavior?
5. Do we have the data and organizational readiness to act here?

High-performing teams use these questions to cut through the noise and target a handful of use cases where AI can truly change the game. Instead of scattering bets across pilots, they invest in focused applications that drive measurable business value.

Consider five proven examples:

• Dynamic lead scoring: Equipping teams to identify and act on accounts most likely to convert, streamlining prospecting for greater efficiency and increasing qualified pipeline coverage.
• On-demand sales intelligence: Providing real-time access to relevant product, technical, industry, and client information, enabling sellers to navigate even the most complex conversations without pulling in additional specialist resources.
• AI-enabled sales coaching: Leveraging analytics platforms and conversational intelligence to provide real-time, personalized coaching to reps—guiding call strategies, recommending best practices, and helping sales managers tailor development to each team member’s strengths and opportunities.
• AI agents for inside sales: Deploying conversational AI avatars to qualify leads, book appointments, and handle routine inquiries before seamlessly passing high-potential prospects to human reps.
• Pricing optimization: Adapting pricing in real time based on client behavior and market conditions, helping teams close deals faster and at better margins.

New capabilities, new operating models

Done correctly, successful AI deployment should not simply tweak workflows; rather, it should help to inform the future shape of the sales organization itself. As automation handles more data analysis and tactical decisions, the burden of manual, repetitive tasks shrink. Account executives shift toward relationship-building and strategic thinking. Operations and enablement teams shift from report builders and content archivers to stewards of data quality and insight. In aggregate, these shifts enable GTM organizations to deploy fully empowered teams designed for agility and impact.

For example:

• AI-enabled account executives: At a global SaaS company, account executives use AI assistants embedded in their CRM. Rather than depending on a separate team of product specialists, they instantly access up-to-date case studies, technical specs, and dynamic pricing proposals ― strengthening credibility and accelerating sales cycles.
• Operations as a strategic center of excellence: An industrial manufacturer consolidates its sales operations and analytics into a single “insights” team. This group goes well beyond reporting; they continually curate and upgrade the data that AI models rely on, so field reps always act on the clearest possible view of client needs.
• AI agents for inside sales: A technology firm deploys conversational AI avatars to manage the initial stages of prospecting ― qualifying leads, booking appointments, and handling routine inquiries before seamlessly passing high-potential prospects to a human touch. This reallocation of effort allows business development reps to focus on high-value client engagement and strategic nurturing, while machines efficiently scale outreach and qualification.

These shifts let people do what machines can’t: listen, collaborate, and build trust ― faster and with more precision than ever before.

Common pitfalls

Even well-intentioned AI programs stumble when the basics aren’t in place. Two pitfalls in particular tend to limit momentum before value is ever realized.

1. Underestimating the data lift
   AI doesn’t run on hope ― it runs on clean, connected data. Too many sales teams launch pilots only to discover their CRM is riddled with duplicates, gaps, and outdated records. Without sustained investment in data quality, governance, and integration, even the most advanced AI deployments stall.
   
   Key imperatives:
   • Treat data stewardship as a core enablement function, not an afterthought.
   • Establish clear ownership for data quality across sales, marketing, and operations.
   • Start with one or two critical data domains (e.g., accounts, opportunities) before scaling.
     
2. Treating technology as the strategy
   AI can sharpen decisions and automate repetitive tasks, but it cannot replace judgment, creativity, or trust-building. Leaders who treat AI as a silver bullet risk weakening customer relationships and demotivating teams. Technology should enable—not dictate—the sales strategy.
   
   Key imperatives:
   • Position AI as a strategic enabler providing guidance and augmentation, not replacement.
   • Reinforce the uniquely human strengths GTM teams bring: teamwork, empathy, negotiation, creativity.
   • Set adoption expectations early and broadcast success stories throughout the change management cycle.

Principles for sales organizations in 2025

• Prioritize ruthlessly: Anchor every initiative in business value, rather than novelty or hype.
• Redesign deliberately: Let structure follow strategy, adapting roles to maximize new capabilities.
• Invest in data: Treat data quality and integration as non-negotiables.
• Retain a human core: Encourage teams to use AI as a catalyst for insight and creativity, not a substitute for them.

The future of B2B sales will be shaped by leaders prepared to invest with discipline, reimagine their structures, and blend technological horsepower with human-led strategy and ingenuity.

If you want help evaluating if your organization is ready for AI or which use cases to implement first, get in touch.

The post Sharpening the edge: How focused AI integration is transforming B2B sales organizations appeared first on Marketbridge.